Datasets:

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acmc

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beamit-annotated_full_texts_dataset

like 0

[ "Adalimumab", "Crohn Disease", "Gastrointestinal Agents", "Humans", "Infliximab", "Rectal Fistula", "Retrospective Studies", "Treatment Outcome", "Tumor Necrosis Factor-alpha" ]

[ "INTRODUCTION", "Study design and patient population", "Demographic data", "Current management", "Primary and secondary outcomes", "Statistical analysis", "RESULTS", "Association between fistula healing and closure with infliximab trough levels", "Association between fistula healing and closure with adalimumab trough levels", "DISCUSSION", "CONCLUSION" ]

[ "Perianal fistulising disease is a common manifestation occurring in up to 30% of patients with Crohn’s disease (CD). The development of abnormal tracts between the bowel and perineum can cause perianal drainage, pain, bleeding, abscess formation, sepsis and faecal incontinence[1,2]. Perianal CD is associated with significant morbidity and decreased quality of life, negatively impacting physical, emotional, sexual and social wellbeing[1-3] and is an independent predictor for decreased productivity in patients with CD[4,5]. Given that the incidence of perianal fistulising CD is highest in the third and fourth decades of life, this places significant burden on patients, society, the economy and the health care system[6].\nTreatment for perianal fistulising CD requires a multidisciplinary approach involving medical management with immunosuppressants and antibiotics, as well as surgical management with sepsis control, seton insertion and sometimes diversion or resection. Anti-tumor necrosis factor (anti-TNF) alpha agents, including infliximab[7,8] and adalimumab[9,10], are the most effective medical therapies available for inducing and maintaining remission of fistulas. Unfortunately, up to 60% of patients treated with maintenance infliximab lose response within one year[7,8]. Accumulating evidence suggests that this loss of response is partly due to subtherapeutic anti-TNF trough levels. Retrospective studies and post-hoc analyses of prospective data have identified that higher infliximab trough levels are associated with fistula healing and closure compared to what is observed for mucosal healing in luminal disease, with emerging data suggesting similar results for adalimumab[11-14]. Quantitative assays for therapeutic drug monitoring (TDM) permit individualisation of infliximab and adalimumab dosing[15,16], however there are very few studies on perianal fistulising CD and the optimal target levels for perianal fistulising CD remain unclear. Our study aims to assess the association between serum trough infliximab and adalimumab levels and perianal fistula healing and closure and identify optimal target levels.", "This was a multicentre retrospective cross-sectional study of patients with perianal fistulising CD at four tertiary inflammatory bowel disease centres across Australia between January 2014 and June 2020. All patients qualified for infliximab or adalimumab under the Australian Pharmaceutical Benefits Scheme criteria[17] which constitutes the following: (1) A confirmed diagnosis of CD using clinical, radiological, histological and/or endoscopic criteria; and (2) At least one active externally draining complex perianal fistula. We included patients on maintenance infliximab or adalimumab with a documented perianal examination who had a serum infliximab or adalimumab trough level collected within 12 wk before or after their most recent clinical assessment. Infliximab and adalimumab trough levels as well as antibodies to infliximab and adalimumab were measured using a drug sensitive enzyme-linked immunosorbent assay (Grifols Promonitor for adalimumab; LISA-Tracker and Grifols Promonitor for infliximab). Infliximab and adalimumab trough levels were measured both in a proactive manner and reactive manner in patients failing treatment across the study sites. Patients who had been changed from infliximab to adalimumab or vice versa and had relevant data were included in both the infliximab and adalimumab groups.\nAll patients had received standard infliximab or adalimumab induction dosing (infliximab 5 mg/kg intravenously at weeks 0, 2, and 6; adalimumab subcutaneously 160 mg at week 0, 80 mg at week 2) followed by maintenance therapy. The current dose of anti-TNF therapy was recorded and patients with or without dose-escalated maintenance therapy were included. Patients who had a diversion ostomy, rectovaginal fistula or no documented perianal examination were excluded.", "Data was retrospectively collected from a clinical database that was updated prospectively during routine clinical practice. Patient demographics collected included age, gender, weight, body mass index, smoking status and CD phenotype classified according to the Montreal Classification[18]. The location of CD was identified as ileal, ileocolonic, colonic, upper gastrointestinal involvement or no luminal disease. The presence or absence of fistulising and stricturing disease was noted, in particular the presence of anal strictures. Biochemical markers of disease activity including C-reactive protein (CRP) and albumin were also recorded.", "Prior history of surgical management of perianal disease or fistula was recorded and categorised as examination under anaesthesia and curettage, examination under anaesthesia and seton insertion or fistulotomy. The duration from the last surgical procedure to the follow up visit was recorded. Concomitant medical therapy at the time of follow up was assessed, including corticosteroid use, 5-aminosalicylates and immunomodulators. The doses of infliximab and adalimumab were recorded and stratified according to dose and interval between doses. For patients on dose-escalated anti-TNF therapy, the duration between last dose escalation and follow up was recorded.", "The primary outcome was fistula healing, which was defined as cessation of fistula drainage, with or without a seton in situ[7]. The secondary outcome was fistula closure, which was defined as healing and closure of all external fistula openings[7].", "Statistical review of this study was performed by a biostatistician from the Ingham Institute for Applied Medical Research. Descriptive statistics were used to assess the baseline characteristics of both the infliximab and adalimumab cohorts. Categorical variables were expressed as percentages and compared using the chi-square test. Continuous variables were expressed using mean ± SD for normally distributed variables and median and interquartile range (IQR) for non-normally distributed variables. The means were compared using the t test for normally distributed variables and the mean ranks compared using the Mann-Whitney U test for non-normally distributed variables. A receiver operating characteristic (ROC) curve analysis was used to assess the sensitivity and specificity of infliximab and adalimumab levels at different cut-off points for predicting fistula healing. All reported P values were 2-sided, with P < 0.05 considered statistically significant. Multivariate analysis using logistic regression with forwards selection was used to analyse variables that predicted fistula healing. Variables which were statistically significant in the univariate analysis were included in the multivariate analysis model. Ethics approval was obtained from the South Western Sydney Local Health District (Human Research Ethics Committee LNR/18/LPOOL/404; Local Project Number: HE18/261).", "Out of 454 patients screened, 114 patients (66 infliximab, 48 adalimumab) on maintenance infliximab or adalimumab for perianal CD had a trough level collected within 12 wk of clinical assessment. Five patients had been changed from infliximab to adalimumab or vice versa and were included in both the infliximab and adalimumab groups. Seventy-five (66%) patients were on combination therapy (43 azathioprine, 16 6-mercaptopurine, 16 methotrexate). Nineteen patients (28.8%) on maintenance infliximab were on dose escalated infliximab therapy (5, 7.5, 10, 15 or 20 mg/kg every 6 or 8 wk). For these patients, the median duration between last infliximab dose adjustment and follow up was 60.0 wk (IQR = 44.5-81.0). Eleven (22.9%) patients on maintenance adalimumab were on dose escalated adalimumab therapy (40 mg weekly). For these patients, the median duration between last adalimumab dose adjustment and follow up was 39.0 wk (IQR = 24.0-86.0). Fifty-nine (89.3%) patients on infliximab had prior surgical management of their fistula, with a median duration of 93.0 wk (IQR = 45.5-284.5) between their last surgical procedure and their most recent follow up visit. Thirty-seven (77.1%) patients on adalimumab had prior surgical management of their fistula, with a median duration of 83.0 wk (IQR = 28.75-223.0) between their last surgical procedure and their most recent follow up visit. Patient demographics and disease characteristics of the population are summarised in Table 1.\nPatient demographics and disease characteristics\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\n Association between fistula healing and closure with infliximab trough levels Forty-eight (72.7%) patients on maintenance infliximab achieved fistula healing. Table 2 summarises the differences between patients on infliximab with and without fistula healing. Patients who achieved fistula healing had higher infliximab trough levels [6.4 (3.8-9.5) vs 3.0 (0.3-6.2) mg/L, P = 0.003], lower rates of detectable anti-infliximab antibodies (4.3% vs 33.3%, P = 0.004) and a younger age (33.0 vs 43.5 years old; P = 0.003) compared to patients who did not achieve fistula healing. The presence of detectable anti-infliximab antibodies was associated with lower infliximab trough levels (P = 0.02). The CRP and albumin levels were not significantly different between patients with and without fistula healing. The rates of combination therapy with an immunomodulator were not significantly different between patients who achieved fistula healing and those who did not (P = 0.522).\nDifferences between patients on infliximab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nROC curve analysis identified a positive correlation between infliximab trough levels and healing [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.60-0.88, P = 0.003; Figure 1A] with an infliximab trough level of 6.10 mg/L that maximised the sensitivity and specificity of predicting fistula healing [sensitivity 58%, specificity 78%, odds ratio (OR) = 4.9, P = 0.013]. Upon tertile analysis, higher tertiles of infliximab levels were associated with a higher proportion of patients achieving fistula healing with 54.5% healing rate for tertile 1 compared to 90.1% for tertile 3 (Figure 2A; P = 0.026). Out of the patients who achieved fistula healing on infliximab, 90% and 95% of the patients who achieved fistula healing were healed with an infliximab trough level of 12.7 and 14.4 mg/L respectively. Given that a drug-sensitive infliximab assay was used where anti-infliximab antibody titres were only performed if infliximab concentrations were < 2.0 mg/L, anti-infliximab antibodies were not included in the multivariate analysis. On multivariate logistic regression analysis, age was associated with healing (P = 0.026) but adequate infliximab levels ≥ 6.10 mg/L were not (P = 0.097). Within our cohort, 18 (27.3%) of patients on infliximab achieved fistula closure. The infliximab trough level for patients with and without fistula closure was not significantly different [6.9 (4.3-10.2) vs 5.5 (2.5-8.3) mg/L, P = 0.105].\n\nCorrelation between serum trough level of infliximab, adalimumab and fistula healing. A: Infliximab; B: Adalimumab.\n\nTertile analysis of infliximab and adalimumab trough levels for patients with fistula healing and fistula closure. A: Infliximab; B: Adalimumab.\nForty-eight (72.7%) patients on maintenance infliximab achieved fistula healing. Table 2 summarises the differences between patients on infliximab with and without fistula healing. Patients who achieved fistula healing had higher infliximab trough levels [6.4 (3.8-9.5) vs 3.0 (0.3-6.2) mg/L, P = 0.003], lower rates of detectable anti-infliximab antibodies (4.3% vs 33.3%, P = 0.004) and a younger age (33.0 vs 43.5 years old; P = 0.003) compared to patients who did not achieve fistula healing. The presence of detectable anti-infliximab antibodies was associated with lower infliximab trough levels (P = 0.02). The CRP and albumin levels were not significantly different between patients with and without fistula healing. The rates of combination therapy with an immunomodulator were not significantly different between patients who achieved fistula healing and those who did not (P = 0.522).\nDifferences between patients on infliximab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nROC curve analysis identified a positive correlation between infliximab trough levels and healing [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.60-0.88, P = 0.003; Figure 1A] with an infliximab trough level of 6.10 mg/L that maximised the sensitivity and specificity of predicting fistula healing [sensitivity 58%, specificity 78%, odds ratio (OR) = 4.9, P = 0.013]. Upon tertile analysis, higher tertiles of infliximab levels were associated with a higher proportion of patients achieving fistula healing with 54.5% healing rate for tertile 1 compared to 90.1% for tertile 3 (Figure 2A; P = 0.026). Out of the patients who achieved fistula healing on infliximab, 90% and 95% of the patients who achieved fistula healing were healed with an infliximab trough level of 12.7 and 14.4 mg/L respectively. Given that a drug-sensitive infliximab assay was used where anti-infliximab antibody titres were only performed if infliximab concentrations were < 2.0 mg/L, anti-infliximab antibodies were not included in the multivariate analysis. On multivariate logistic regression analysis, age was associated with healing (P = 0.026) but adequate infliximab levels ≥ 6.10 mg/L were not (P = 0.097). Within our cohort, 18 (27.3%) of patients on infliximab achieved fistula closure. The infliximab trough level for patients with and without fistula closure was not significantly different [6.9 (4.3-10.2) vs 5.5 (2.5-8.3) mg/L, P = 0.105].\n\nCorrelation between serum trough level of infliximab, adalimumab and fistula healing. A: Infliximab; B: Adalimumab.\n\nTertile analysis of infliximab and adalimumab trough levels for patients with fistula healing and fistula closure. A: Infliximab; B: Adalimumab.\n Association between fistula healing and closure with adalimumab trough levels Thirty-seven (77%) patients on maintenance adalimumab achieved fistula healing. Table 3 summarises the differences in patients on adalimumab with and without fistula healing. Patients who achieved fistula healing had higher adalimumab trough levels compared to those who did not [9.2 (6.5-12.0) vs 5.4 (2.5-8.3) mg/L, P = 0.004]. Patients who achieved fistula healing had higher rates of combination therapy with an immunomodulator than those who did not (P = 0.048). The CRP and albumin levels were not significantly different in patients with and without fistula healing. ROC curve analysis identified a positive correlation between adalimumab trough levels and healing (AUC = 0.79, 95%CI: 0.66-0.93, P = 0.004) with an adalimumab trough level of 7.05 mg/L that maximised the sensitivity and specificity of infliximab levels in predicting fistula healing (sensitivity 70%; specificity 73%; OR = 6.3; P = 0.016; Figure 1B). Upon tertile analysis, higher tertiles of adalimumab levels were associated with a higher proportion of patients achieving fistula healing, with 62.5% healing rate for tertile 1 compared to 100% for tertile 3 (Figure 2B; P = 0.034). Out of the patients who achieved fistula healing on adalimumab, 90% and 95% of the patients who achieved fistula healing were healed with an adalimumab trough level of 12.0 and 18.0 mg/L respectively. On multivariate logistic regression analysis, adequate adalimumab trough levels ≥ 7.05 mg/L (P = 0.008) and concurrent immunomodulator therapy (P = 0.026) both remained associated with healing. Within our cohort, 17 (35.4%) of patients on adalimumab achieved fistula closure. The adalimumab trough level for patients with and without fistula closure was not significantly different [10.0 (6.6-12.0) vs 7.8 (4.2-10.0) mg/L, P = 0.083].\nDifferences in patients on adalimumab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nThirty-seven (77%) patients on maintenance adalimumab achieved fistula healing. Table 3 summarises the differences in patients on adalimumab with and without fistula healing. Patients who achieved fistula healing had higher adalimumab trough levels compared to those who did not [9.2 (6.5-12.0) vs 5.4 (2.5-8.3) mg/L, P = 0.004]. Patients who achieved fistula healing had higher rates of combination therapy with an immunomodulator than those who did not (P = 0.048). The CRP and albumin levels were not significantly different in patients with and without fistula healing. ROC curve analysis identified a positive correlation between adalimumab trough levels and healing (AUC = 0.79, 95%CI: 0.66-0.93, P = 0.004) with an adalimumab trough level of 7.05 mg/L that maximised the sensitivity and specificity of infliximab levels in predicting fistula healing (sensitivity 70%; specificity 73%; OR = 6.3; P = 0.016; Figure 1B). Upon tertile analysis, higher tertiles of adalimumab levels were associated with a higher proportion of patients achieving fistula healing, with 62.5% healing rate for tertile 1 compared to 100% for tertile 3 (Figure 2B; P = 0.034). Out of the patients who achieved fistula healing on adalimumab, 90% and 95% of the patients who achieved fistula healing were healed with an adalimumab trough level of 12.0 and 18.0 mg/L respectively. On multivariate logistic regression analysis, adequate adalimumab trough levels ≥ 7.05 mg/L (P = 0.008) and concurrent immunomodulator therapy (P = 0.026) both remained associated with healing. Within our cohort, 17 (35.4%) of patients on adalimumab achieved fistula closure. The adalimumab trough level for patients with and without fistula closure was not significantly different [10.0 (6.6-12.0) vs 7.8 (4.2-10.0) mg/L, P = 0.083].\nDifferences in patients on adalimumab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.", "Forty-eight (72.7%) patients on maintenance infliximab achieved fistula healing. Table 2 summarises the differences between patients on infliximab with and without fistula healing. Patients who achieved fistula healing had higher infliximab trough levels [6.4 (3.8-9.5) vs 3.0 (0.3-6.2) mg/L, P = 0.003], lower rates of detectable anti-infliximab antibodies (4.3% vs 33.3%, P = 0.004) and a younger age (33.0 vs 43.5 years old; P = 0.003) compared to patients who did not achieve fistula healing. The presence of detectable anti-infliximab antibodies was associated with lower infliximab trough levels (P = 0.02). The CRP and albumin levels were not significantly different between patients with and without fistula healing. The rates of combination therapy with an immunomodulator were not significantly different between patients who achieved fistula healing and those who did not (P = 0.522).\nDifferences between patients on infliximab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nROC curve analysis identified a positive correlation between infliximab trough levels and healing [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.60-0.88, P = 0.003; Figure 1A] with an infliximab trough level of 6.10 mg/L that maximised the sensitivity and specificity of predicting fistula healing [sensitivity 58%, specificity 78%, odds ratio (OR) = 4.9, P = 0.013]. Upon tertile analysis, higher tertiles of infliximab levels were associated with a higher proportion of patients achieving fistula healing with 54.5% healing rate for tertile 1 compared to 90.1% for tertile 3 (Figure 2A; P = 0.026). Out of the patients who achieved fistula healing on infliximab, 90% and 95% of the patients who achieved fistula healing were healed with an infliximab trough level of 12.7 and 14.4 mg/L respectively. Given that a drug-sensitive infliximab assay was used where anti-infliximab antibody titres were only performed if infliximab concentrations were < 2.0 mg/L, anti-infliximab antibodies were not included in the multivariate analysis. On multivariate logistic regression analysis, age was associated with healing (P = 0.026) but adequate infliximab levels ≥ 6.10 mg/L were not (P = 0.097). Within our cohort, 18 (27.3%) of patients on infliximab achieved fistula closure. The infliximab trough level for patients with and without fistula closure was not significantly different [6.9 (4.3-10.2) vs 5.5 (2.5-8.3) mg/L, P = 0.105].\n\nCorrelation between serum trough level of infliximab, adalimumab and fistula healing. A: Infliximab; B: Adalimumab.\n\nTertile analysis of infliximab and adalimumab trough levels for patients with fistula healing and fistula closure. A: Infliximab; B: Adalimumab.", "Thirty-seven (77%) patients on maintenance adalimumab achieved fistula healing. Table 3 summarises the differences in patients on adalimumab with and without fistula healing. Patients who achieved fistula healing had higher adalimumab trough levels compared to those who did not [9.2 (6.5-12.0) vs 5.4 (2.5-8.3) mg/L, P = 0.004]. Patients who achieved fistula healing had higher rates of combination therapy with an immunomodulator than those who did not (P = 0.048). The CRP and albumin levels were not significantly different in patients with and without fistula healing. ROC curve analysis identified a positive correlation between adalimumab trough levels and healing (AUC = 0.79, 95%CI: 0.66-0.93, P = 0.004) with an adalimumab trough level of 7.05 mg/L that maximised the sensitivity and specificity of infliximab levels in predicting fistula healing (sensitivity 70%; specificity 73%; OR = 6.3; P = 0.016; Figure 1B). Upon tertile analysis, higher tertiles of adalimumab levels were associated with a higher proportion of patients achieving fistula healing, with 62.5% healing rate for tertile 1 compared to 100% for tertile 3 (Figure 2B; P = 0.034). Out of the patients who achieved fistula healing on adalimumab, 90% and 95% of the patients who achieved fistula healing were healed with an adalimumab trough level of 12.0 and 18.0 mg/L respectively. On multivariate logistic regression analysis, adequate adalimumab trough levels ≥ 7.05 mg/L (P = 0.008) and concurrent immunomodulator therapy (P = 0.026) both remained associated with healing. Within our cohort, 17 (35.4%) of patients on adalimumab achieved fistula closure. The adalimumab trough level for patients with and without fistula closure was not significantly different [10.0 (6.6-12.0) vs 7.8 (4.2-10.0) mg/L, P = 0.083].\nDifferences in patients on adalimumab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.", "Fistulising perianal CD is a highly morbid condition for which treatment outcomes remain suboptimal in many patients. While there is limited data on the role of newer biologic agents such as ustekinumab in perianal CD[19], anti-TNF agents remain the treatment of choice. Our study showed a significant association between both infliximab and adalimumab trough levels and fistula healing, with higher levels associated with increased healing rates. We demonstrated that higher tertiles of both infliximab and adalimumab levels were associated with a higher proportion of patients achieving fistula healing. Notably, when plotting the cumulative percentage of healed patients against infliximab level, we found that 50% of the patients who achieve healing will heal with a level of 6.4 mg/L, 90% of the patients who achieve healing will heal with a level of 12.7 mg/L and 95% of the patients who achieve healing will heal with a level of 14.4 mg/L. Similarly, for patients on adalimumab, 50% of the patients who achieve healing will heal with a level of 9.2 mg/L, and 90% and 95% of patients who achieved fistula healing were healed with levels of 12.0 and 18.0 mg/L respectively. Our results support dose-escalation of both infliximab and adalimumab in non-responders, targeting higher levels to achieve fistula healing prior to changing biologic therapy. Importantly, this study is the largest study to date assessing the relationship between adalimumab trough levels and clinical fistula healing. This data adds to the growing body of evidence that fistula healing improves with higher anti-TNF trough levels, and that higher levels may be required for perianal fistula healing than for mucosal healing in luminal CD[12-14,20].\nThis study did not show an association between infliximab and adalimumab trough levels and fistula closure. Not all previous studies have assessed fistula closure, but some have found that patients with fistula closure had significantly higher maintenance infliximab and adalimumab trough levels[13,14]. Our results may have been limited by inadequate power due to relatively small numbers of patients who achieved fistula closure in our cohort. We had a high fistula healing rate in this study, with 72.7% and 77% of patients on maintenance infliximab and adalimumab achieving fistula healing respectively. This finding was possibly due to high rates of combination therapy with an immunomodulator (69.7% and 60.4% in the infliximab and adalimumab groups respectively).\nRandomised controlled trials have shown that infliximab is effective at both inducing and maintaining fistula healing[7,8]. Our study found that fistula healing was associated with higher infliximab trough levels. This finding is supported by a post-hoc analysis of ACCENT II which found that higher infliximab trough levels during induction were associated with a complete absence of draining fistulas at week 14[12], as well as similar findings in other studies assessing induction and maintenance infliximab therapy[11,13]. In the future, there may be a role for the infliximab biosimilar CT-P13 in order to achieve these high infliximab levels required for perianal fistula healing; with recent randomised controlled trials demonstrating higher trough levels from subcutaneous administration of CT-P13 compared to intravenous administration[21]. Interestingly, our study found that fistula healing was associated with younger age in both univariate and multivariate analyses. Whilst patient factors including albumin and body weight have previously been shown to affect infliximab trough levels[22], the influence of age is unclear. This finding may be due to the relatively younger age at diagnosis of CD for patients with fistula healing or longer duration of infliximab therapy. Five patients in this study had been changed from infliximab to adalimumab or vice versa and were included in both groups, however the anti-TNF level and anti-TNF antibody levels at the time of changing treatment were not collected. Reassuringly, previous studies have demonstrated that the presence of infliximab antibodies does not decrease future response rates to adalimumab and vice versa[23].\nAdalimumab has also been shown to be effective in both inducing[9] and maintaining fistula healing[24]. Our study found that fistula healing was associated with higher adalimumab trough levels. Whilst there is limited data on the association between adalimumab trough levels and fistula healing, our findings are consistent with two smaller retrospective studies that showed that patients with fistula healing had higher adalimumab trough levels compared to those without fistula healing[14,20]. On multivariate logistic regression analysis, adalimumab trough levels ≥ 7.05 mg/L and concurrent immunomodulator therapy both remained significantly associated with healing. This reflects how concomitant immunosuppressive therapy can be used to decrease the immunogenic response and therefore improve fistula healing rates[25].\nThis study has several limitations. Assessment of fistula healing was based on clinical assessment, which may not be as accurate as an objective assessment such as with magnetic resonance imaging of the pelvis. A recent study has demonstrated that higher anti-TNF trough levels are associated with improved rates of radiological healing in perianal fistulising CD[26]. However, the absence of drainage remains a clinically relevant endpoint that impacts on patient quality of life. In order to provide an objective marker of response, biochemical markers of disease activity including CRP and albumin were analysed and found not to correlate with fistula healing. Data was retrospectively collected, so in order to address this we only included patients with documented perianal exams and definitions for fistula healing and closure that were in line with previous randomised controlled trials[8]. We found that fistula healing is associated with higher infliximab and adalimumab trough levels, however further randomised controlled trials are required to assess whether dose escalation to higher levels improves healing and the optimal method for dose escalation. Whilst reactive TDM with dose escalation at the time of loss of response is effective, it remains unknown whether proactive TDM with subsequent dose modification improves outcomes. Notably, all previous studies on proactive TDM have focused on luminal disease with no prospective studies evaluating proactive TDM in perianal fistulising CD.", "Our study showed that higher infliximab and adalimumab trough levels are associated with perianal CD fistula healing, with higher rates of healing in higher tertiles of infliximab and adalimumab levels. However, no association with fistula closure was observed. Further prospective studies are required to confirm target infliximab and adalimumab trough levels and determine the optimal dose escalation method to achieve these target levels." ]

[ null, null, null, null, null, null, null, null, null, null, null ]

[ "INTRODUCTION", "MATERIALS AND METHODS", "Study design and patient population", "Demographic data", "Current management", "Primary and secondary outcomes", "Statistical analysis", "RESULTS", "Association between fistula healing and closure with infliximab trough levels", "Association between fistula healing and closure with adalimumab trough levels", "DISCUSSION", "CONCLUSION" ]

[ "Perianal fistulising disease is a common manifestation occurring in up to 30% of patients with Crohn’s disease (CD). The development of abnormal tracts between the bowel and perineum can cause perianal drainage, pain, bleeding, abscess formation, sepsis and faecal incontinence[1,2]. Perianal CD is associated with significant morbidity and decreased quality of life, negatively impacting physical, emotional, sexual and social wellbeing[1-3] and is an independent predictor for decreased productivity in patients with CD[4,5]. Given that the incidence of perianal fistulising CD is highest in the third and fourth decades of life, this places significant burden on patients, society, the economy and the health care system[6].\nTreatment for perianal fistulising CD requires a multidisciplinary approach involving medical management with immunosuppressants and antibiotics, as well as surgical management with sepsis control, seton insertion and sometimes diversion or resection. Anti-tumor necrosis factor (anti-TNF) alpha agents, including infliximab[7,8] and adalimumab[9,10], are the most effective medical therapies available for inducing and maintaining remission of fistulas. Unfortunately, up to 60% of patients treated with maintenance infliximab lose response within one year[7,8]. Accumulating evidence suggests that this loss of response is partly due to subtherapeutic anti-TNF trough levels. Retrospective studies and post-hoc analyses of prospective data have identified that higher infliximab trough levels are associated with fistula healing and closure compared to what is observed for mucosal healing in luminal disease, with emerging data suggesting similar results for adalimumab[11-14]. Quantitative assays for therapeutic drug monitoring (TDM) permit individualisation of infliximab and adalimumab dosing[15,16], however there are very few studies on perianal fistulising CD and the optimal target levels for perianal fistulising CD remain unclear. Our study aims to assess the association between serum trough infliximab and adalimumab levels and perianal fistula healing and closure and identify optimal target levels.", " Study design and patient population This was a multicentre retrospective cross-sectional study of patients with perianal fistulising CD at four tertiary inflammatory bowel disease centres across Australia between January 2014 and June 2020. All patients qualified for infliximab or adalimumab under the Australian Pharmaceutical Benefits Scheme criteria[17] which constitutes the following: (1) A confirmed diagnosis of CD using clinical, radiological, histological and/or endoscopic criteria; and (2) At least one active externally draining complex perianal fistula. We included patients on maintenance infliximab or adalimumab with a documented perianal examination who had a serum infliximab or adalimumab trough level collected within 12 wk before or after their most recent clinical assessment. Infliximab and adalimumab trough levels as well as antibodies to infliximab and adalimumab were measured using a drug sensitive enzyme-linked immunosorbent assay (Grifols Promonitor for adalimumab; LISA-Tracker and Grifols Promonitor for infliximab). Infliximab and adalimumab trough levels were measured both in a proactive manner and reactive manner in patients failing treatment across the study sites. Patients who had been changed from infliximab to adalimumab or vice versa and had relevant data were included in both the infliximab and adalimumab groups.\nAll patients had received standard infliximab or adalimumab induction dosing (infliximab 5 mg/kg intravenously at weeks 0, 2, and 6; adalimumab subcutaneously 160 mg at week 0, 80 mg at week 2) followed by maintenance therapy. The current dose of anti-TNF therapy was recorded and patients with or without dose-escalated maintenance therapy were included. Patients who had a diversion ostomy, rectovaginal fistula or no documented perianal examination were excluded.\nThis was a multicentre retrospective cross-sectional study of patients with perianal fistulising CD at four tertiary inflammatory bowel disease centres across Australia between January 2014 and June 2020. All patients qualified for infliximab or adalimumab under the Australian Pharmaceutical Benefits Scheme criteria[17] which constitutes the following: (1) A confirmed diagnosis of CD using clinical, radiological, histological and/or endoscopic criteria; and (2) At least one active externally draining complex perianal fistula. We included patients on maintenance infliximab or adalimumab with a documented perianal examination who had a serum infliximab or adalimumab trough level collected within 12 wk before or after their most recent clinical assessment. Infliximab and adalimumab trough levels as well as antibodies to infliximab and adalimumab were measured using a drug sensitive enzyme-linked immunosorbent assay (Grifols Promonitor for adalimumab; LISA-Tracker and Grifols Promonitor for infliximab). Infliximab and adalimumab trough levels were measured both in a proactive manner and reactive manner in patients failing treatment across the study sites. Patients who had been changed from infliximab to adalimumab or vice versa and had relevant data were included in both the infliximab and adalimumab groups.\nAll patients had received standard infliximab or adalimumab induction dosing (infliximab 5 mg/kg intravenously at weeks 0, 2, and 6; adalimumab subcutaneously 160 mg at week 0, 80 mg at week 2) followed by maintenance therapy. The current dose of anti-TNF therapy was recorded and patients with or without dose-escalated maintenance therapy were included. Patients who had a diversion ostomy, rectovaginal fistula or no documented perianal examination were excluded.\n Demographic data Data was retrospectively collected from a clinical database that was updated prospectively during routine clinical practice. Patient demographics collected included age, gender, weight, body mass index, smoking status and CD phenotype classified according to the Montreal Classification[18]. The location of CD was identified as ileal, ileocolonic, colonic, upper gastrointestinal involvement or no luminal disease. The presence or absence of fistulising and stricturing disease was noted, in particular the presence of anal strictures. Biochemical markers of disease activity including C-reactive protein (CRP) and albumin were also recorded.\nData was retrospectively collected from a clinical database that was updated prospectively during routine clinical practice. Patient demographics collected included age, gender, weight, body mass index, smoking status and CD phenotype classified according to the Montreal Classification[18]. The location of CD was identified as ileal, ileocolonic, colonic, upper gastrointestinal involvement or no luminal disease. The presence or absence of fistulising and stricturing disease was noted, in particular the presence of anal strictures. Biochemical markers of disease activity including C-reactive protein (CRP) and albumin were also recorded.\n Current management Prior history of surgical management of perianal disease or fistula was recorded and categorised as examination under anaesthesia and curettage, examination under anaesthesia and seton insertion or fistulotomy. The duration from the last surgical procedure to the follow up visit was recorded. Concomitant medical therapy at the time of follow up was assessed, including corticosteroid use, 5-aminosalicylates and immunomodulators. The doses of infliximab and adalimumab were recorded and stratified according to dose and interval between doses. For patients on dose-escalated anti-TNF therapy, the duration between last dose escalation and follow up was recorded.\nPrior history of surgical management of perianal disease or fistula was recorded and categorised as examination under anaesthesia and curettage, examination under anaesthesia and seton insertion or fistulotomy. The duration from the last surgical procedure to the follow up visit was recorded. Concomitant medical therapy at the time of follow up was assessed, including corticosteroid use, 5-aminosalicylates and immunomodulators. The doses of infliximab and adalimumab were recorded and stratified according to dose and interval between doses. For patients on dose-escalated anti-TNF therapy, the duration between last dose escalation and follow up was recorded.\n Primary and secondary outcomes The primary outcome was fistula healing, which was defined as cessation of fistula drainage, with or without a seton in situ[7]. The secondary outcome was fistula closure, which was defined as healing and closure of all external fistula openings[7].\nThe primary outcome was fistula healing, which was defined as cessation of fistula drainage, with or without a seton in situ[7]. The secondary outcome was fistula closure, which was defined as healing and closure of all external fistula openings[7].\n Statistical analysis Statistical review of this study was performed by a biostatistician from the Ingham Institute for Applied Medical Research. Descriptive statistics were used to assess the baseline characteristics of both the infliximab and adalimumab cohorts. Categorical variables were expressed as percentages and compared using the chi-square test. Continuous variables were expressed using mean ± SD for normally distributed variables and median and interquartile range (IQR) for non-normally distributed variables. The means were compared using the t test for normally distributed variables and the mean ranks compared using the Mann-Whitney U test for non-normally distributed variables. A receiver operating characteristic (ROC) curve analysis was used to assess the sensitivity and specificity of infliximab and adalimumab levels at different cut-off points for predicting fistula healing. All reported P values were 2-sided, with P < 0.05 considered statistically significant. Multivariate analysis using logistic regression with forwards selection was used to analyse variables that predicted fistula healing. Variables which were statistically significant in the univariate analysis were included in the multivariate analysis model. Ethics approval was obtained from the South Western Sydney Local Health District (Human Research Ethics Committee LNR/18/LPOOL/404; Local Project Number: HE18/261).\nStatistical review of this study was performed by a biostatistician from the Ingham Institute for Applied Medical Research. Descriptive statistics were used to assess the baseline characteristics of both the infliximab and adalimumab cohorts. Categorical variables were expressed as percentages and compared using the chi-square test. Continuous variables were expressed using mean ± SD for normally distributed variables and median and interquartile range (IQR) for non-normally distributed variables. The means were compared using the t test for normally distributed variables and the mean ranks compared using the Mann-Whitney U test for non-normally distributed variables. A receiver operating characteristic (ROC) curve analysis was used to assess the sensitivity and specificity of infliximab and adalimumab levels at different cut-off points for predicting fistula healing. All reported P values were 2-sided, with P < 0.05 considered statistically significant. Multivariate analysis using logistic regression with forwards selection was used to analyse variables that predicted fistula healing. Variables which were statistically significant in the univariate analysis were included in the multivariate analysis model. Ethics approval was obtained from the South Western Sydney Local Health District (Human Research Ethics Committee LNR/18/LPOOL/404; Local Project Number: HE18/261).", "This was a multicentre retrospective cross-sectional study of patients with perianal fistulising CD at four tertiary inflammatory bowel disease centres across Australia between January 2014 and June 2020. All patients qualified for infliximab or adalimumab under the Australian Pharmaceutical Benefits Scheme criteria[17] which constitutes the following: (1) A confirmed diagnosis of CD using clinical, radiological, histological and/or endoscopic criteria; and (2) At least one active externally draining complex perianal fistula. We included patients on maintenance infliximab or adalimumab with a documented perianal examination who had a serum infliximab or adalimumab trough level collected within 12 wk before or after their most recent clinical assessment. Infliximab and adalimumab trough levels as well as antibodies to infliximab and adalimumab were measured using a drug sensitive enzyme-linked immunosorbent assay (Grifols Promonitor for adalimumab; LISA-Tracker and Grifols Promonitor for infliximab). Infliximab and adalimumab trough levels were measured both in a proactive manner and reactive manner in patients failing treatment across the study sites. Patients who had been changed from infliximab to adalimumab or vice versa and had relevant data were included in both the infliximab and adalimumab groups.\nAll patients had received standard infliximab or adalimumab induction dosing (infliximab 5 mg/kg intravenously at weeks 0, 2, and 6; adalimumab subcutaneously 160 mg at week 0, 80 mg at week 2) followed by maintenance therapy. The current dose of anti-TNF therapy was recorded and patients with or without dose-escalated maintenance therapy were included. Patients who had a diversion ostomy, rectovaginal fistula or no documented perianal examination were excluded.", "Data was retrospectively collected from a clinical database that was updated prospectively during routine clinical practice. Patient demographics collected included age, gender, weight, body mass index, smoking status and CD phenotype classified according to the Montreal Classification[18]. The location of CD was identified as ileal, ileocolonic, colonic, upper gastrointestinal involvement or no luminal disease. The presence or absence of fistulising and stricturing disease was noted, in particular the presence of anal strictures. Biochemical markers of disease activity including C-reactive protein (CRP) and albumin were also recorded.", "Prior history of surgical management of perianal disease or fistula was recorded and categorised as examination under anaesthesia and curettage, examination under anaesthesia and seton insertion or fistulotomy. The duration from the last surgical procedure to the follow up visit was recorded. Concomitant medical therapy at the time of follow up was assessed, including corticosteroid use, 5-aminosalicylates and immunomodulators. The doses of infliximab and adalimumab were recorded and stratified according to dose and interval between doses. For patients on dose-escalated anti-TNF therapy, the duration between last dose escalation and follow up was recorded.", "The primary outcome was fistula healing, which was defined as cessation of fistula drainage, with or without a seton in situ[7]. The secondary outcome was fistula closure, which was defined as healing and closure of all external fistula openings[7].", "Statistical review of this study was performed by a biostatistician from the Ingham Institute for Applied Medical Research. Descriptive statistics were used to assess the baseline characteristics of both the infliximab and adalimumab cohorts. Categorical variables were expressed as percentages and compared using the chi-square test. Continuous variables were expressed using mean ± SD for normally distributed variables and median and interquartile range (IQR) for non-normally distributed variables. The means were compared using the t test for normally distributed variables and the mean ranks compared using the Mann-Whitney U test for non-normally distributed variables. A receiver operating characteristic (ROC) curve analysis was used to assess the sensitivity and specificity of infliximab and adalimumab levels at different cut-off points for predicting fistula healing. All reported P values were 2-sided, with P < 0.05 considered statistically significant. Multivariate analysis using logistic regression with forwards selection was used to analyse variables that predicted fistula healing. Variables which were statistically significant in the univariate analysis were included in the multivariate analysis model. Ethics approval was obtained from the South Western Sydney Local Health District (Human Research Ethics Committee LNR/18/LPOOL/404; Local Project Number: HE18/261).", "Out of 454 patients screened, 114 patients (66 infliximab, 48 adalimumab) on maintenance infliximab or adalimumab for perianal CD had a trough level collected within 12 wk of clinical assessment. Five patients had been changed from infliximab to adalimumab or vice versa and were included in both the infliximab and adalimumab groups. Seventy-five (66%) patients were on combination therapy (43 azathioprine, 16 6-mercaptopurine, 16 methotrexate). Nineteen patients (28.8%) on maintenance infliximab were on dose escalated infliximab therapy (5, 7.5, 10, 15 or 20 mg/kg every 6 or 8 wk). For these patients, the median duration between last infliximab dose adjustment and follow up was 60.0 wk (IQR = 44.5-81.0). Eleven (22.9%) patients on maintenance adalimumab were on dose escalated adalimumab therapy (40 mg weekly). For these patients, the median duration between last adalimumab dose adjustment and follow up was 39.0 wk (IQR = 24.0-86.0). Fifty-nine (89.3%) patients on infliximab had prior surgical management of their fistula, with a median duration of 93.0 wk (IQR = 45.5-284.5) between their last surgical procedure and their most recent follow up visit. Thirty-seven (77.1%) patients on adalimumab had prior surgical management of their fistula, with a median duration of 83.0 wk (IQR = 28.75-223.0) between their last surgical procedure and their most recent follow up visit. Patient demographics and disease characteristics of the population are summarised in Table 1.\nPatient demographics and disease characteristics\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\n Association between fistula healing and closure with infliximab trough levels Forty-eight (72.7%) patients on maintenance infliximab achieved fistula healing. Table 2 summarises the differences between patients on infliximab with and without fistula healing. Patients who achieved fistula healing had higher infliximab trough levels [6.4 (3.8-9.5) vs 3.0 (0.3-6.2) mg/L, P = 0.003], lower rates of detectable anti-infliximab antibodies (4.3% vs 33.3%, P = 0.004) and a younger age (33.0 vs 43.5 years old; P = 0.003) compared to patients who did not achieve fistula healing. The presence of detectable anti-infliximab antibodies was associated with lower infliximab trough levels (P = 0.02). The CRP and albumin levels were not significantly different between patients with and without fistula healing. The rates of combination therapy with an immunomodulator were not significantly different between patients who achieved fistula healing and those who did not (P = 0.522).\nDifferences between patients on infliximab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nROC curve analysis identified a positive correlation between infliximab trough levels and healing [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.60-0.88, P = 0.003; Figure 1A] with an infliximab trough level of 6.10 mg/L that maximised the sensitivity and specificity of predicting fistula healing [sensitivity 58%, specificity 78%, odds ratio (OR) = 4.9, P = 0.013]. Upon tertile analysis, higher tertiles of infliximab levels were associated with a higher proportion of patients achieving fistula healing with 54.5% healing rate for tertile 1 compared to 90.1% for tertile 3 (Figure 2A; P = 0.026). Out of the patients who achieved fistula healing on infliximab, 90% and 95% of the patients who achieved fistula healing were healed with an infliximab trough level of 12.7 and 14.4 mg/L respectively. Given that a drug-sensitive infliximab assay was used where anti-infliximab antibody titres were only performed if infliximab concentrations were < 2.0 mg/L, anti-infliximab antibodies were not included in the multivariate analysis. On multivariate logistic regression analysis, age was associated with healing (P = 0.026) but adequate infliximab levels ≥ 6.10 mg/L were not (P = 0.097). Within our cohort, 18 (27.3%) of patients on infliximab achieved fistula closure. The infliximab trough level for patients with and without fistula closure was not significantly different [6.9 (4.3-10.2) vs 5.5 (2.5-8.3) mg/L, P = 0.105].\n\nCorrelation between serum trough level of infliximab, adalimumab and fistula healing. A: Infliximab; B: Adalimumab.\n\nTertile analysis of infliximab and adalimumab trough levels for patients with fistula healing and fistula closure. A: Infliximab; B: Adalimumab.\nForty-eight (72.7%) patients on maintenance infliximab achieved fistula healing. Table 2 summarises the differences between patients on infliximab with and without fistula healing. Patients who achieved fistula healing had higher infliximab trough levels [6.4 (3.8-9.5) vs 3.0 (0.3-6.2) mg/L, P = 0.003], lower rates of detectable anti-infliximab antibodies (4.3% vs 33.3%, P = 0.004) and a younger age (33.0 vs 43.5 years old; P = 0.003) compared to patients who did not achieve fistula healing. The presence of detectable anti-infliximab antibodies was associated with lower infliximab trough levels (P = 0.02). The CRP and albumin levels were not significantly different between patients with and without fistula healing. The rates of combination therapy with an immunomodulator were not significantly different between patients who achieved fistula healing and those who did not (P = 0.522).\nDifferences between patients on infliximab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nROC curve analysis identified a positive correlation between infliximab trough levels and healing [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.60-0.88, P = 0.003; Figure 1A] with an infliximab trough level of 6.10 mg/L that maximised the sensitivity and specificity of predicting fistula healing [sensitivity 58%, specificity 78%, odds ratio (OR) = 4.9, P = 0.013]. Upon tertile analysis, higher tertiles of infliximab levels were associated with a higher proportion of patients achieving fistula healing with 54.5% healing rate for tertile 1 compared to 90.1% for tertile 3 (Figure 2A; P = 0.026). Out of the patients who achieved fistula healing on infliximab, 90% and 95% of the patients who achieved fistula healing were healed with an infliximab trough level of 12.7 and 14.4 mg/L respectively. Given that a drug-sensitive infliximab assay was used where anti-infliximab antibody titres were only performed if infliximab concentrations were < 2.0 mg/L, anti-infliximab antibodies were not included in the multivariate analysis. On multivariate logistic regression analysis, age was associated with healing (P = 0.026) but adequate infliximab levels ≥ 6.10 mg/L were not (P = 0.097). Within our cohort, 18 (27.3%) of patients on infliximab achieved fistula closure. The infliximab trough level for patients with and without fistula closure was not significantly different [6.9 (4.3-10.2) vs 5.5 (2.5-8.3) mg/L, P = 0.105].\n\nCorrelation between serum trough level of infliximab, adalimumab and fistula healing. A: Infliximab; B: Adalimumab.\n\nTertile analysis of infliximab and adalimumab trough levels for patients with fistula healing and fistula closure. A: Infliximab; B: Adalimumab.\n Association between fistula healing and closure with adalimumab trough levels Thirty-seven (77%) patients on maintenance adalimumab achieved fistula healing. Table 3 summarises the differences in patients on adalimumab with and without fistula healing. Patients who achieved fistula healing had higher adalimumab trough levels compared to those who did not [9.2 (6.5-12.0) vs 5.4 (2.5-8.3) mg/L, P = 0.004]. Patients who achieved fistula healing had higher rates of combination therapy with an immunomodulator than those who did not (P = 0.048). The CRP and albumin levels were not significantly different in patients with and without fistula healing. ROC curve analysis identified a positive correlation between adalimumab trough levels and healing (AUC = 0.79, 95%CI: 0.66-0.93, P = 0.004) with an adalimumab trough level of 7.05 mg/L that maximised the sensitivity and specificity of infliximab levels in predicting fistula healing (sensitivity 70%; specificity 73%; OR = 6.3; P = 0.016; Figure 1B). Upon tertile analysis, higher tertiles of adalimumab levels were associated with a higher proportion of patients achieving fistula healing, with 62.5% healing rate for tertile 1 compared to 100% for tertile 3 (Figure 2B; P = 0.034). Out of the patients who achieved fistula healing on adalimumab, 90% and 95% of the patients who achieved fistula healing were healed with an adalimumab trough level of 12.0 and 18.0 mg/L respectively. On multivariate logistic regression analysis, adequate adalimumab trough levels ≥ 7.05 mg/L (P = 0.008) and concurrent immunomodulator therapy (P = 0.026) both remained associated with healing. Within our cohort, 17 (35.4%) of patients on adalimumab achieved fistula closure. The adalimumab trough level for patients with and without fistula closure was not significantly different [10.0 (6.6-12.0) vs 7.8 (4.2-10.0) mg/L, P = 0.083].\nDifferences in patients on adalimumab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nThirty-seven (77%) patients on maintenance adalimumab achieved fistula healing. Table 3 summarises the differences in patients on adalimumab with and without fistula healing. Patients who achieved fistula healing had higher adalimumab trough levels compared to those who did not [9.2 (6.5-12.0) vs 5.4 (2.5-8.3) mg/L, P = 0.004]. Patients who achieved fistula healing had higher rates of combination therapy with an immunomodulator than those who did not (P = 0.048). The CRP and albumin levels were not significantly different in patients with and without fistula healing. ROC curve analysis identified a positive correlation between adalimumab trough levels and healing (AUC = 0.79, 95%CI: 0.66-0.93, P = 0.004) with an adalimumab trough level of 7.05 mg/L that maximised the sensitivity and specificity of infliximab levels in predicting fistula healing (sensitivity 70%; specificity 73%; OR = 6.3; P = 0.016; Figure 1B). Upon tertile analysis, higher tertiles of adalimumab levels were associated with a higher proportion of patients achieving fistula healing, with 62.5% healing rate for tertile 1 compared to 100% for tertile 3 (Figure 2B; P = 0.034). Out of the patients who achieved fistula healing on adalimumab, 90% and 95% of the patients who achieved fistula healing were healed with an adalimumab trough level of 12.0 and 18.0 mg/L respectively. On multivariate logistic regression analysis, adequate adalimumab trough levels ≥ 7.05 mg/L (P = 0.008) and concurrent immunomodulator therapy (P = 0.026) both remained associated with healing. Within our cohort, 17 (35.4%) of patients on adalimumab achieved fistula closure. The adalimumab trough level for patients with and without fistula closure was not significantly different [10.0 (6.6-12.0) vs 7.8 (4.2-10.0) mg/L, P = 0.083].\nDifferences in patients on adalimumab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.", "Forty-eight (72.7%) patients on maintenance infliximab achieved fistula healing. Table 2 summarises the differences between patients on infliximab with and without fistula healing. Patients who achieved fistula healing had higher infliximab trough levels [6.4 (3.8-9.5) vs 3.0 (0.3-6.2) mg/L, P = 0.003], lower rates of detectable anti-infliximab antibodies (4.3% vs 33.3%, P = 0.004) and a younger age (33.0 vs 43.5 years old; P = 0.003) compared to patients who did not achieve fistula healing. The presence of detectable anti-infliximab antibodies was associated with lower infliximab trough levels (P = 0.02). The CRP and albumin levels were not significantly different between patients with and without fistula healing. The rates of combination therapy with an immunomodulator were not significantly different between patients who achieved fistula healing and those who did not (P = 0.522).\nDifferences between patients on infliximab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.\nROC curve analysis identified a positive correlation between infliximab trough levels and healing [area under the curve (AUC) = 0.74, 95% confidence interval (CI): 0.60-0.88, P = 0.003; Figure 1A] with an infliximab trough level of 6.10 mg/L that maximised the sensitivity and specificity of predicting fistula healing [sensitivity 58%, specificity 78%, odds ratio (OR) = 4.9, P = 0.013]. Upon tertile analysis, higher tertiles of infliximab levels were associated with a higher proportion of patients achieving fistula healing with 54.5% healing rate for tertile 1 compared to 90.1% for tertile 3 (Figure 2A; P = 0.026). Out of the patients who achieved fistula healing on infliximab, 90% and 95% of the patients who achieved fistula healing were healed with an infliximab trough level of 12.7 and 14.4 mg/L respectively. Given that a drug-sensitive infliximab assay was used where anti-infliximab antibody titres were only performed if infliximab concentrations were < 2.0 mg/L, anti-infliximab antibodies were not included in the multivariate analysis. On multivariate logistic regression analysis, age was associated with healing (P = 0.026) but adequate infliximab levels ≥ 6.10 mg/L were not (P = 0.097). Within our cohort, 18 (27.3%) of patients on infliximab achieved fistula closure. The infliximab trough level for patients with and without fistula closure was not significantly different [6.9 (4.3-10.2) vs 5.5 (2.5-8.3) mg/L, P = 0.105].\n\nCorrelation between serum trough level of infliximab, adalimumab and fistula healing. A: Infliximab; B: Adalimumab.\n\nTertile analysis of infliximab and adalimumab trough levels for patients with fistula healing and fistula closure. A: Infliximab; B: Adalimumab.", "Thirty-seven (77%) patients on maintenance adalimumab achieved fistula healing. Table 3 summarises the differences in patients on adalimumab with and without fistula healing. Patients who achieved fistula healing had higher adalimumab trough levels compared to those who did not [9.2 (6.5-12.0) vs 5.4 (2.5-8.3) mg/L, P = 0.004]. Patients who achieved fistula healing had higher rates of combination therapy with an immunomodulator than those who did not (P = 0.048). The CRP and albumin levels were not significantly different in patients with and without fistula healing. ROC curve analysis identified a positive correlation between adalimumab trough levels and healing (AUC = 0.79, 95%CI: 0.66-0.93, P = 0.004) with an adalimumab trough level of 7.05 mg/L that maximised the sensitivity and specificity of infliximab levels in predicting fistula healing (sensitivity 70%; specificity 73%; OR = 6.3; P = 0.016; Figure 1B). Upon tertile analysis, higher tertiles of adalimumab levels were associated with a higher proportion of patients achieving fistula healing, with 62.5% healing rate for tertile 1 compared to 100% for tertile 3 (Figure 2B; P = 0.034). Out of the patients who achieved fistula healing on adalimumab, 90% and 95% of the patients who achieved fistula healing were healed with an adalimumab trough level of 12.0 and 18.0 mg/L respectively. On multivariate logistic regression analysis, adequate adalimumab trough levels ≥ 7.05 mg/L (P = 0.008) and concurrent immunomodulator therapy (P = 0.026) both remained associated with healing. Within our cohort, 17 (35.4%) of patients on adalimumab achieved fistula closure. The adalimumab trough level for patients with and without fistula closure was not significantly different [10.0 (6.6-12.0) vs 7.8 (4.2-10.0) mg/L, P = 0.083].\nDifferences in patients on adalimumab with and without fistula healing\nADA: Adalimumab; BMI: Body mass index; CRP: C-reactive protein; IFX: Infliximab; IQR: Interquartile range; SD: Standard deviation; TNF: Tumor necrosis factor.", "Fistulising perianal CD is a highly morbid condition for which treatment outcomes remain suboptimal in many patients. While there is limited data on the role of newer biologic agents such as ustekinumab in perianal CD[19], anti-TNF agents remain the treatment of choice. Our study showed a significant association between both infliximab and adalimumab trough levels and fistula healing, with higher levels associated with increased healing rates. We demonstrated that higher tertiles of both infliximab and adalimumab levels were associated with a higher proportion of patients achieving fistula healing. Notably, when plotting the cumulative percentage of healed patients against infliximab level, we found that 50% of the patients who achieve healing will heal with a level of 6.4 mg/L, 90% of the patients who achieve healing will heal with a level of 12.7 mg/L and 95% of the patients who achieve healing will heal with a level of 14.4 mg/L. Similarly, for patients on adalimumab, 50% of the patients who achieve healing will heal with a level of 9.2 mg/L, and 90% and 95% of patients who achieved fistula healing were healed with levels of 12.0 and 18.0 mg/L respectively. Our results support dose-escalation of both infliximab and adalimumab in non-responders, targeting higher levels to achieve fistula healing prior to changing biologic therapy. Importantly, this study is the largest study to date assessing the relationship between adalimumab trough levels and clinical fistula healing. This data adds to the growing body of evidence that fistula healing improves with higher anti-TNF trough levels, and that higher levels may be required for perianal fistula healing than for mucosal healing in luminal CD[12-14,20].\nThis study did not show an association between infliximab and adalimumab trough levels and fistula closure. Not all previous studies have assessed fistula closure, but some have found that patients with fistula closure had significantly higher maintenance infliximab and adalimumab trough levels[13,14]. Our results may have been limited by inadequate power due to relatively small numbers of patients who achieved fistula closure in our cohort. We had a high fistula healing rate in this study, with 72.7% and 77% of patients on maintenance infliximab and adalimumab achieving fistula healing respectively. This finding was possibly due to high rates of combination therapy with an immunomodulator (69.7% and 60.4% in the infliximab and adalimumab groups respectively).\nRandomised controlled trials have shown that infliximab is effective at both inducing and maintaining fistula healing[7,8]. Our study found that fistula healing was associated with higher infliximab trough levels. This finding is supported by a post-hoc analysis of ACCENT II which found that higher infliximab trough levels during induction were associated with a complete absence of draining fistulas at week 14[12], as well as similar findings in other studies assessing induction and maintenance infliximab therapy[11,13]. In the future, there may be a role for the infliximab biosimilar CT-P13 in order to achieve these high infliximab levels required for perianal fistula healing; with recent randomised controlled trials demonstrating higher trough levels from subcutaneous administration of CT-P13 compared to intravenous administration[21]. Interestingly, our study found that fistula healing was associated with younger age in both univariate and multivariate analyses. Whilst patient factors including albumin and body weight have previously been shown to affect infliximab trough levels[22], the influence of age is unclear. This finding may be due to the relatively younger age at diagnosis of CD for patients with fistula healing or longer duration of infliximab therapy. Five patients in this study had been changed from infliximab to adalimumab or vice versa and were included in both groups, however the anti-TNF level and anti-TNF antibody levels at the time of changing treatment were not collected. Reassuringly, previous studies have demonstrated that the presence of infliximab antibodies does not decrease future response rates to adalimumab and vice versa[23].\nAdalimumab has also been shown to be effective in both inducing[9] and maintaining fistula healing[24]. Our study found that fistula healing was associated with higher adalimumab trough levels. Whilst there is limited data on the association between adalimumab trough levels and fistula healing, our findings are consistent with two smaller retrospective studies that showed that patients with fistula healing had higher adalimumab trough levels compared to those without fistula healing[14,20]. On multivariate logistic regression analysis, adalimumab trough levels ≥ 7.05 mg/L and concurrent immunomodulator therapy both remained significantly associated with healing. This reflects how concomitant immunosuppressive therapy can be used to decrease the immunogenic response and therefore improve fistula healing rates[25].\nThis study has several limitations. Assessment of fistula healing was based on clinical assessment, which may not be as accurate as an objective assessment such as with magnetic resonance imaging of the pelvis. A recent study has demonstrated that higher anti-TNF trough levels are associated with improved rates of radiological healing in perianal fistulising CD[26]. However, the absence of drainage remains a clinically relevant endpoint that impacts on patient quality of life. In order to provide an objective marker of response, biochemical markers of disease activity including CRP and albumin were analysed and found not to correlate with fistula healing. Data was retrospectively collected, so in order to address this we only included patients with documented perianal exams and definitions for fistula healing and closure that were in line with previous randomised controlled trials[8]. We found that fistula healing is associated with higher infliximab and adalimumab trough levels, however further randomised controlled trials are required to assess whether dose escalation to higher levels improves healing and the optimal method for dose escalation. Whilst reactive TDM with dose escalation at the time of loss of response is effective, it remains unknown whether proactive TDM with subsequent dose modification improves outcomes. Notably, all previous studies on proactive TDM have focused on luminal disease with no prospective studies evaluating proactive TDM in perianal fistulising CD.", "Our study showed that higher infliximab and adalimumab trough levels are associated with perianal CD fistula healing, with higher rates of healing in higher tertiles of infliximab and adalimumab levels. However, no association with fistula closure was observed. Further prospective studies are required to confirm target infliximab and adalimumab trough levels and determine the optimal dose escalation method to achieve these target levels." ]

[ null, "methods", null, null, null, null, null, null, null, null, null, null ]

[ "Crohn’s disease", "Perianal disorders", "Biologics", "Inflammatory bowel disease" ]

[ 350, 294, 104, 108, 46, 222, 2313, 568, 406, 1090, 68 ]

[ "infliximab", "fistula", "healing", "adalimumab", "patients", "fistula healing", "levels", "trough", "infliximab adalimumab", "mg" ]

[ "levels perianal fistulising", "adalimumab perianal cd", "healing perianal fistulising", "management perianal disease", "perianal cd fistula" ]

[ "Brazil", "COVID-19", "Humans", "Kidney Failure, Chronic", "Pandemics", "Renal Dialysis", "Surveys and Questionnaires" ]

[ "Data collection", "Data analysis", "Calculations of estimates", "Estimated incidence, prevalence, and mortality rates", "Demographic and clinical characteristics", "Characteristics of dialysis treatment", "Characteristics of participating centers", "COVID-19" ]

[ "Dialysis clinics filled out an online questionnaire available on the BSN website. It contained questions about sociodemographic, clinical, and therapeutic variables of patients on chronic dialysis (Supplement). The questionnaire was available from August 2020 to January 2021. Participation in the survey was voluntary, and all dialysis centers registered at BSN were invited to participate by email and by BSN media. After the initial invitation, reminders were emailed monthly to centers that had not filled the questionnaire. During the survey period, the chairs of the regional sections of the BSN were asked to contact the dialysis centers in their states and reiterate the importance of their participation.", "Data for each center were grouped rather than analyzed individually. For the 2020 survey, 235 of 834 active centers responded to the questionnaire, a response rate of 28%.\nFor national estimates of total number of patients and prevalence rate, the sample was expanded. We assumed that the units that did not respond to the questionnaire had the same number of patients as participants (mean of 173.6 patients per unit). Because this extrapolation can be imprecise, we used a variation of ± 5% in the obtained mean (164.9 to 182.3 patients per unit) for the prevalence calculations. Likewise, the mean number of new patients per unit was applied to units that did not report incidence rates. All other sociodemographic data and patient characteristics refer to the sample studied. Annual mortality and annual incidence of patients on dialysis were estimated from events in July 2020. For calculating prevalence and incidence rates, national and regional population data were obtained from the Brazilian Institute of Geography and Statistics (IBGE) estimates for July 2020. According to IBGE, the Brazilian population at that date was 211.75 million inhabitants\n3\n. Most data were presented descriptively, and results were compared with data from previous years. Information on COVID-19, such as incidence, hospitalizations, and lethality, was considered for the period from February 26th (date of the first case reported in the country) to July 31st, 2020. The diagnosis of COVID-19 required confirmation by real-time polymerase chain reaction (RT-PCR) of nasal/oropharyngeal specimens or serologic testing.", "The main calculations and estimates are shown in Table 1.", "In July 2020, there were 834 active chronic dialysis centers registered at BSN, a number 3.6% higher than in 2019. However, the percentage of participating centers decreased compared to the previous year (from 39 to 28%), with a slight difference in percentage of participation among regions (North and Midwest: 25%; South: 28%; Northeast and Southeast: 29%). The lower adherence resulted in a 25% decrease in patients whose data were used for the annual report (54,488 to 40,795).\nThe estimated total number of patients in July 2020 was 144,779 (a variation of ± 5% = 137,527 to 152,038), 3.6% higher than July 2019. The trend observed in recent years toward an increase in number of dialysis patients continued in 2020 (Figure 1). The prevalence rate of dialysis patients also continued to increase, from 665 in 2019 to 684 per million population (pmp) in 2020, consistent with the trend observed in recent years. When stratified by region, a significant decrease in prevalence rate was observed only in the North region (Figure 2); the numbers increased slightly in the other regions. The estimated number of new dialysis patients in 2020 was 44,264, and the overall incidence rate was 209 pmp, lower than in 2019 when it reached 218 pmp. The incidence rate ranged from 75 pmp in the North region to 227 pmp in the South. The estimated number of deaths for the entire year was 35,413. The annual crude mortality rate has been between 18 and 20% since 2016, and is projected to increase to 24.5% in 2020 (Figure 3).\n\nFigure 1Estimated number of patients on chronic dialysis per year.\n\n\nFigure 2Estimated prevalence of patients on dialysis by geographic region in Brazil, per million population.\n\n\nFigure 3Estimated annual crude mortality rate of dialysis patients.\n", "The most prevalent age group was between 45 and 64 years, representing 42.5% (Figure 4). The distribution by sex, 58% men and 42% women, remained stable, as did the percentage of main underlying diseases. Systemic arterial hypertension and diabetes mellitus accounted each for almost one third of all cases (Figure 5). The percentage of patients with hepatitis C continued to decrease, while the percentage of patients with hepatitis B and HIV remained stable (Figure 6). Regarding vascular access, 25% of hemodialysis (HD) patients used a central venous catheter. A decrease in the use of short-term catheters and prostheses was observed, while the use of long-term catheters increased (11%) (Figure 7). The estimated number of dialysis patients on the kidney transplant waiting list in 2020 was 33,239 (23%), similar to the previous year.\n\nFigure 4Distribution of patients according to age group.\n\n\nFigure 5Distribution of dialysis patients according to chronic kidney disease etiology.\n\n\nFigure 6Prevalence of patients with positive serology for hepatitis B and C and HIV viruses.\n\n\nFigure 7Distribution of vascular accesses used for hemodialysis.\n", "The distribution of patients by dialysis modality and payment source is shown in Table 2. Hemodialysis (HD) continued to be the treatment for most patients (92.6%) and 7.4% were treated with peritoneal dialysis (PD). Treatment was financed by the public health system for 81.6% of patients and by private health insurance for 18.4% of patients in the participating units.", "Of the 235 participating dialysis units, 71% were privately owned, 18% were philanthropic, and 10% were public. Among private clinics, international corporations managed 14.4% of participating units. Most centers described themselves as satellite (54%), and 46% were in-patient units. Fifty-six percent of participating units reported having PD as a treatment option. The national average number of patients per nephrologist was 27 and ranged from 21 in the North to 30 in the South.", "In the 234 dialysis centers between March and July, there were 2791 reported cases of COVID-19. The incidence rate of confirmed COVID-19 between February 26th and July 31st, 2020 was 684/10,000 dialysis patients. Confirmation was performed by real time polymerase chain reaction (RT-PCR) in 68.4%, by serologic testing in 26.3%, and by both methods in 6%. Of the total number of infected patients, 95.7% were on HD and 4.3% on PD. Nearly 52% of confirmed cases were hospitalized and, of these, 57.6% required treatment in intensive care units. Seven hundred and eighteen COVID-19 patients died. Case-fatality rate was 25.7% and mortality rate reached 176/10,000 patients. The estimated crude annual mortality rate attributed to COVID-19 was 4.2%. As a strategy to isolate suspected or confirmed cases of COVID-19, 76.1% of participating centers reported treatment in a separate room or space. The remainder (23.9%) chose transfer to a specific shift (Figure 8). The total percentage of infection among health professionals working in clinics was 21.9%. The percentages in physicians, nurses, and nursing technicians were 25.1, 24.0, and 20.8%, respectively. Death by COVID-19 was only reported in nursing technicians (0.1%).\n\nFigure 8COVID-19 mortality rate, case-fatality rate and strategy adopted to isolate cases.\n" ]

[ null, null, null, null, null, null, null, null ]

[ "Introduction", "Methods", "Data collection", "Data analysis", "Calculations of estimates", "Results", "Estimated incidence, prevalence, and mortality rates", "Demographic and clinical characteristics", "Characteristics of dialysis treatment", "Characteristics of participating centers", "COVID-19", "Discussion" ]

[ "With the aim of obtaining and analyzing data on clinical and epidemiological aspects of patients undergoing chronic dialysis, in addition to information on dialysis therapy, the Brazilian Society of Nephrology (BSN) annually sponsors the Brazilian Dialysis Survey\n1,2\n. Since its initial implementation in 1999, the survey has been conducted nationwide and, in the last decade, it has been completed electronically by dialysis centers. The continuous survey is justified because it provides relevant information for the development of health policies and strategies aimed at improving care for individuals undergoing dialysis. In this study we are reporting data from the 2020 Brazilian Dialysis Survey, including information about the impact of the COVID-19 pandemic on dialysis clinic patients and staff.", "Data collection Dialysis clinics filled out an online questionnaire available on the BSN website. It contained questions about sociodemographic, clinical, and therapeutic variables of patients on chronic dialysis (Supplement). The questionnaire was available from August 2020 to January 2021. Participation in the survey was voluntary, and all dialysis centers registered at BSN were invited to participate by email and by BSN media. After the initial invitation, reminders were emailed monthly to centers that had not filled the questionnaire. During the survey period, the chairs of the regional sections of the BSN were asked to contact the dialysis centers in their states and reiterate the importance of their participation.\nDialysis clinics filled out an online questionnaire available on the BSN website. It contained questions about sociodemographic, clinical, and therapeutic variables of patients on chronic dialysis (Supplement). The questionnaire was available from August 2020 to January 2021. Participation in the survey was voluntary, and all dialysis centers registered at BSN were invited to participate by email and by BSN media. After the initial invitation, reminders were emailed monthly to centers that had not filled the questionnaire. During the survey period, the chairs of the regional sections of the BSN were asked to contact the dialysis centers in their states and reiterate the importance of their participation.\nData analysis Data for each center were grouped rather than analyzed individually. For the 2020 survey, 235 of 834 active centers responded to the questionnaire, a response rate of 28%.\nFor national estimates of total number of patients and prevalence rate, the sample was expanded. We assumed that the units that did not respond to the questionnaire had the same number of patients as participants (mean of 173.6 patients per unit). Because this extrapolation can be imprecise, we used a variation of ± 5% in the obtained mean (164.9 to 182.3 patients per unit) for the prevalence calculations. Likewise, the mean number of new patients per unit was applied to units that did not report incidence rates. All other sociodemographic data and patient characteristics refer to the sample studied. Annual mortality and annual incidence of patients on dialysis were estimated from events in July 2020. For calculating prevalence and incidence rates, national and regional population data were obtained from the Brazilian Institute of Geography and Statistics (IBGE) estimates for July 2020. According to IBGE, the Brazilian population at that date was 211.75 million inhabitants\n3\n. Most data were presented descriptively, and results were compared with data from previous years. Information on COVID-19, such as incidence, hospitalizations, and lethality, was considered for the period from February 26th (date of the first case reported in the country) to July 31st, 2020. The diagnosis of COVID-19 required confirmation by real-time polymerase chain reaction (RT-PCR) of nasal/oropharyngeal specimens or serologic testing.\nData for each center were grouped rather than analyzed individually. For the 2020 survey, 235 of 834 active centers responded to the questionnaire, a response rate of 28%.\nFor national estimates of total number of patients and prevalence rate, the sample was expanded. We assumed that the units that did not respond to the questionnaire had the same number of patients as participants (mean of 173.6 patients per unit). Because this extrapolation can be imprecise, we used a variation of ± 5% in the obtained mean (164.9 to 182.3 patients per unit) for the prevalence calculations. Likewise, the mean number of new patients per unit was applied to units that did not report incidence rates. All other sociodemographic data and patient characteristics refer to the sample studied. Annual mortality and annual incidence of patients on dialysis were estimated from events in July 2020. For calculating prevalence and incidence rates, national and regional population data were obtained from the Brazilian Institute of Geography and Statistics (IBGE) estimates for July 2020. According to IBGE, the Brazilian population at that date was 211.75 million inhabitants\n3\n. Most data were presented descriptively, and results were compared with data from previous years. Information on COVID-19, such as incidence, hospitalizations, and lethality, was considered for the period from February 26th (date of the first case reported in the country) to July 31st, 2020. The diagnosis of COVID-19 required confirmation by real-time polymerase chain reaction (RT-PCR) of nasal/oropharyngeal specimens or serologic testing.\nCalculations of estimates The main calculations and estimates are shown in Table 1.\nThe main calculations and estimates are shown in Table 1.", "Dialysis clinics filled out an online questionnaire available on the BSN website. It contained questions about sociodemographic, clinical, and therapeutic variables of patients on chronic dialysis (Supplement). The questionnaire was available from August 2020 to January 2021. Participation in the survey was voluntary, and all dialysis centers registered at BSN were invited to participate by email and by BSN media. After the initial invitation, reminders were emailed monthly to centers that had not filled the questionnaire. During the survey period, the chairs of the regional sections of the BSN were asked to contact the dialysis centers in their states and reiterate the importance of their participation.", "Data for each center were grouped rather than analyzed individually. For the 2020 survey, 235 of 834 active centers responded to the questionnaire, a response rate of 28%.\nFor national estimates of total number of patients and prevalence rate, the sample was expanded. We assumed that the units that did not respond to the questionnaire had the same number of patients as participants (mean of 173.6 patients per unit). Because this extrapolation can be imprecise, we used a variation of ± 5% in the obtained mean (164.9 to 182.3 patients per unit) for the prevalence calculations. Likewise, the mean number of new patients per unit was applied to units that did not report incidence rates. All other sociodemographic data and patient characteristics refer to the sample studied. Annual mortality and annual incidence of patients on dialysis were estimated from events in July 2020. For calculating prevalence and incidence rates, national and regional population data were obtained from the Brazilian Institute of Geography and Statistics (IBGE) estimates for July 2020. According to IBGE, the Brazilian population at that date was 211.75 million inhabitants\n3\n. Most data were presented descriptively, and results were compared with data from previous years. Information on COVID-19, such as incidence, hospitalizations, and lethality, was considered for the period from February 26th (date of the first case reported in the country) to July 31st, 2020. The diagnosis of COVID-19 required confirmation by real-time polymerase chain reaction (RT-PCR) of nasal/oropharyngeal specimens or serologic testing.", "The main calculations and estimates are shown in Table 1.", "Estimated incidence, prevalence, and mortality rates In July 2020, there were 834 active chronic dialysis centers registered at BSN, a number 3.6% higher than in 2019. However, the percentage of participating centers decreased compared to the previous year (from 39 to 28%), with a slight difference in percentage of participation among regions (North and Midwest: 25%; South: 28%; Northeast and Southeast: 29%). The lower adherence resulted in a 25% decrease in patients whose data were used for the annual report (54,488 to 40,795).\nThe estimated total number of patients in July 2020 was 144,779 (a variation of ± 5% = 137,527 to 152,038), 3.6% higher than July 2019. The trend observed in recent years toward an increase in number of dialysis patients continued in 2020 (Figure 1). The prevalence rate of dialysis patients also continued to increase, from 665 in 2019 to 684 per million population (pmp) in 2020, consistent with the trend observed in recent years. When stratified by region, a significant decrease in prevalence rate was observed only in the North region (Figure 2); the numbers increased slightly in the other regions. The estimated number of new dialysis patients in 2020 was 44,264, and the overall incidence rate was 209 pmp, lower than in 2019 when it reached 218 pmp. The incidence rate ranged from 75 pmp in the North region to 227 pmp in the South. The estimated number of deaths for the entire year was 35,413. The annual crude mortality rate has been between 18 and 20% since 2016, and is projected to increase to 24.5% in 2020 (Figure 3).\n\nFigure 1Estimated number of patients on chronic dialysis per year.\n\n\nFigure 2Estimated prevalence of patients on dialysis by geographic region in Brazil, per million population.\n\n\nFigure 3Estimated annual crude mortality rate of dialysis patients.\n\nIn July 2020, there were 834 active chronic dialysis centers registered at BSN, a number 3.6% higher than in 2019. However, the percentage of participating centers decreased compared to the previous year (from 39 to 28%), with a slight difference in percentage of participation among regions (North and Midwest: 25%; South: 28%; Northeast and Southeast: 29%). The lower adherence resulted in a 25% decrease in patients whose data were used for the annual report (54,488 to 40,795).\nThe estimated total number of patients in July 2020 was 144,779 (a variation of ± 5% = 137,527 to 152,038), 3.6% higher than July 2019. The trend observed in recent years toward an increase in number of dialysis patients continued in 2020 (Figure 1). The prevalence rate of dialysis patients also continued to increase, from 665 in 2019 to 684 per million population (pmp) in 2020, consistent with the trend observed in recent years. When stratified by region, a significant decrease in prevalence rate was observed only in the North region (Figure 2); the numbers increased slightly in the other regions. The estimated number of new dialysis patients in 2020 was 44,264, and the overall incidence rate was 209 pmp, lower than in 2019 when it reached 218 pmp. The incidence rate ranged from 75 pmp in the North region to 227 pmp in the South. The estimated number of deaths for the entire year was 35,413. The annual crude mortality rate has been between 18 and 20% since 2016, and is projected to increase to 24.5% in 2020 (Figure 3).\n\nFigure 1Estimated number of patients on chronic dialysis per year.\n\n\nFigure 2Estimated prevalence of patients on dialysis by geographic region in Brazil, per million population.\n\n\nFigure 3Estimated annual crude mortality rate of dialysis patients.\n\nDemographic and clinical characteristics The most prevalent age group was between 45 and 64 years, representing 42.5% (Figure 4). The distribution by sex, 58% men and 42% women, remained stable, as did the percentage of main underlying diseases. Systemic arterial hypertension and diabetes mellitus accounted each for almost one third of all cases (Figure 5). The percentage of patients with hepatitis C continued to decrease, while the percentage of patients with hepatitis B and HIV remained stable (Figure 6). Regarding vascular access, 25% of hemodialysis (HD) patients used a central venous catheter. A decrease in the use of short-term catheters and prostheses was observed, while the use of long-term catheters increased (11%) (Figure 7). The estimated number of dialysis patients on the kidney transplant waiting list in 2020 was 33,239 (23%), similar to the previous year.\n\nFigure 4Distribution of patients according to age group.\n\n\nFigure 5Distribution of dialysis patients according to chronic kidney disease etiology.\n\n\nFigure 6Prevalence of patients with positive serology for hepatitis B and C and HIV viruses.\n\n\nFigure 7Distribution of vascular accesses used for hemodialysis.\n\nThe most prevalent age group was between 45 and 64 years, representing 42.5% (Figure 4). The distribution by sex, 58% men and 42% women, remained stable, as did the percentage of main underlying diseases. Systemic arterial hypertension and diabetes mellitus accounted each for almost one third of all cases (Figure 5). The percentage of patients with hepatitis C continued to decrease, while the percentage of patients with hepatitis B and HIV remained stable (Figure 6). Regarding vascular access, 25% of hemodialysis (HD) patients used a central venous catheter. A decrease in the use of short-term catheters and prostheses was observed, while the use of long-term catheters increased (11%) (Figure 7). The estimated number of dialysis patients on the kidney transplant waiting list in 2020 was 33,239 (23%), similar to the previous year.\n\nFigure 4Distribution of patients according to age group.\n\n\nFigure 5Distribution of dialysis patients according to chronic kidney disease etiology.\n\n\nFigure 6Prevalence of patients with positive serology for hepatitis B and C and HIV viruses.\n\n\nFigure 7Distribution of vascular accesses used for hemodialysis.\n\nCharacteristics of dialysis treatment The distribution of patients by dialysis modality and payment source is shown in Table 2. Hemodialysis (HD) continued to be the treatment for most patients (92.6%) and 7.4% were treated with peritoneal dialysis (PD). Treatment was financed by the public health system for 81.6% of patients and by private health insurance for 18.4% of patients in the participating units.\nThe distribution of patients by dialysis modality and payment source is shown in Table 2. Hemodialysis (HD) continued to be the treatment for most patients (92.6%) and 7.4% were treated with peritoneal dialysis (PD). Treatment was financed by the public health system for 81.6% of patients and by private health insurance for 18.4% of patients in the participating units.\nCharacteristics of participating centers Of the 235 participating dialysis units, 71% were privately owned, 18% were philanthropic, and 10% were public. Among private clinics, international corporations managed 14.4% of participating units. Most centers described themselves as satellite (54%), and 46% were in-patient units. Fifty-six percent of participating units reported having PD as a treatment option. The national average number of patients per nephrologist was 27 and ranged from 21 in the North to 30 in the South.\nOf the 235 participating dialysis units, 71% were privately owned, 18% were philanthropic, and 10% were public. Among private clinics, international corporations managed 14.4% of participating units. Most centers described themselves as satellite (54%), and 46% were in-patient units. Fifty-six percent of participating units reported having PD as a treatment option. The national average number of patients per nephrologist was 27 and ranged from 21 in the North to 30 in the South.\nCOVID-19 In the 234 dialysis centers between March and July, there were 2791 reported cases of COVID-19. The incidence rate of confirmed COVID-19 between February 26th and July 31st, 2020 was 684/10,000 dialysis patients. Confirmation was performed by real time polymerase chain reaction (RT-PCR) in 68.4%, by serologic testing in 26.3%, and by both methods in 6%. Of the total number of infected patients, 95.7% were on HD and 4.3% on PD. Nearly 52% of confirmed cases were hospitalized and, of these, 57.6% required treatment in intensive care units. Seven hundred and eighteen COVID-19 patients died. Case-fatality rate was 25.7% and mortality rate reached 176/10,000 patients. The estimated crude annual mortality rate attributed to COVID-19 was 4.2%. As a strategy to isolate suspected or confirmed cases of COVID-19, 76.1% of participating centers reported treatment in a separate room or space. The remainder (23.9%) chose transfer to a specific shift (Figure 8). The total percentage of infection among health professionals working in clinics was 21.9%. The percentages in physicians, nurses, and nursing technicians were 25.1, 24.0, and 20.8%, respectively. Death by COVID-19 was only reported in nursing technicians (0.1%).\n\nFigure 8COVID-19 mortality rate, case-fatality rate and strategy adopted to isolate cases.\n\nIn the 234 dialysis centers between March and July, there were 2791 reported cases of COVID-19. The incidence rate of confirmed COVID-19 between February 26th and July 31st, 2020 was 684/10,000 dialysis patients. Confirmation was performed by real time polymerase chain reaction (RT-PCR) in 68.4%, by serologic testing in 26.3%, and by both methods in 6%. Of the total number of infected patients, 95.7% were on HD and 4.3% on PD. Nearly 52% of confirmed cases were hospitalized and, of these, 57.6% required treatment in intensive care units. Seven hundred and eighteen COVID-19 patients died. Case-fatality rate was 25.7% and mortality rate reached 176/10,000 patients. The estimated crude annual mortality rate attributed to COVID-19 was 4.2%. As a strategy to isolate suspected or confirmed cases of COVID-19, 76.1% of participating centers reported treatment in a separate room or space. The remainder (23.9%) chose transfer to a specific shift (Figure 8). The total percentage of infection among health professionals working in clinics was 21.9%. The percentages in physicians, nurses, and nursing technicians were 25.1, 24.0, and 20.8%, respectively. Death by COVID-19 was only reported in nursing technicians (0.1%).\n\nFigure 8COVID-19 mortality rate, case-fatality rate and strategy adopted to isolate cases.\n", "In July 2020, there were 834 active chronic dialysis centers registered at BSN, a number 3.6% higher than in 2019. However, the percentage of participating centers decreased compared to the previous year (from 39 to 28%), with a slight difference in percentage of participation among regions (North and Midwest: 25%; South: 28%; Northeast and Southeast: 29%). The lower adherence resulted in a 25% decrease in patients whose data were used for the annual report (54,488 to 40,795).\nThe estimated total number of patients in July 2020 was 144,779 (a variation of ± 5% = 137,527 to 152,038), 3.6% higher than July 2019. The trend observed in recent years toward an increase in number of dialysis patients continued in 2020 (Figure 1). The prevalence rate of dialysis patients also continued to increase, from 665 in 2019 to 684 per million population (pmp) in 2020, consistent with the trend observed in recent years. When stratified by region, a significant decrease in prevalence rate was observed only in the North region (Figure 2); the numbers increased slightly in the other regions. The estimated number of new dialysis patients in 2020 was 44,264, and the overall incidence rate was 209 pmp, lower than in 2019 when it reached 218 pmp. The incidence rate ranged from 75 pmp in the North region to 227 pmp in the South. The estimated number of deaths for the entire year was 35,413. The annual crude mortality rate has been between 18 and 20% since 2016, and is projected to increase to 24.5% in 2020 (Figure 3).\n\nFigure 1Estimated number of patients on chronic dialysis per year.\n\n\nFigure 2Estimated prevalence of patients on dialysis by geographic region in Brazil, per million population.\n\n\nFigure 3Estimated annual crude mortality rate of dialysis patients.\n", "The most prevalent age group was between 45 and 64 years, representing 42.5% (Figure 4). The distribution by sex, 58% men and 42% women, remained stable, as did the percentage of main underlying diseases. Systemic arterial hypertension and diabetes mellitus accounted each for almost one third of all cases (Figure 5). The percentage of patients with hepatitis C continued to decrease, while the percentage of patients with hepatitis B and HIV remained stable (Figure 6). Regarding vascular access, 25% of hemodialysis (HD) patients used a central venous catheter. A decrease in the use of short-term catheters and prostheses was observed, while the use of long-term catheters increased (11%) (Figure 7). The estimated number of dialysis patients on the kidney transplant waiting list in 2020 was 33,239 (23%), similar to the previous year.\n\nFigure 4Distribution of patients according to age group.\n\n\nFigure 5Distribution of dialysis patients according to chronic kidney disease etiology.\n\n\nFigure 6Prevalence of patients with positive serology for hepatitis B and C and HIV viruses.\n\n\nFigure 7Distribution of vascular accesses used for hemodialysis.\n", "The distribution of patients by dialysis modality and payment source is shown in Table 2. Hemodialysis (HD) continued to be the treatment for most patients (92.6%) and 7.4% were treated with peritoneal dialysis (PD). Treatment was financed by the public health system for 81.6% of patients and by private health insurance for 18.4% of patients in the participating units.", "Of the 235 participating dialysis units, 71% were privately owned, 18% were philanthropic, and 10% were public. Among private clinics, international corporations managed 14.4% of participating units. Most centers described themselves as satellite (54%), and 46% were in-patient units. Fifty-six percent of participating units reported having PD as a treatment option. The national average number of patients per nephrologist was 27 and ranged from 21 in the North to 30 in the South.", "In the 234 dialysis centers between March and July, there were 2791 reported cases of COVID-19. The incidence rate of confirmed COVID-19 between February 26th and July 31st, 2020 was 684/10,000 dialysis patients. Confirmation was performed by real time polymerase chain reaction (RT-PCR) in 68.4%, by serologic testing in 26.3%, and by both methods in 6%. Of the total number of infected patients, 95.7% were on HD and 4.3% on PD. Nearly 52% of confirmed cases were hospitalized and, of these, 57.6% required treatment in intensive care units. Seven hundred and eighteen COVID-19 patients died. Case-fatality rate was 25.7% and mortality rate reached 176/10,000 patients. The estimated crude annual mortality rate attributed to COVID-19 was 4.2%. As a strategy to isolate suspected or confirmed cases of COVID-19, 76.1% of participating centers reported treatment in a separate room or space. The remainder (23.9%) chose transfer to a specific shift (Figure 8). The total percentage of infection among health professionals working in clinics was 21.9%. The percentages in physicians, nurses, and nursing technicians were 25.1, 24.0, and 20.8%, respectively. Death by COVID-19 was only reported in nursing technicians (0.1%).\n\nFigure 8COVID-19 mortality rate, case-fatality rate and strategy adopted to isolate cases.\n", "In the last two decades, the Brazilian dialysis survey has highlighted the panorama of dialysis treatment, providing data and analyses that contribute to public policies and strategies to improve this therapy in our country. In general, the trends observed in recent years were maintained in 2020. As a novelty, we reported information on the impact of the COVID-19 pandemic on dialysis centers.\nThere was a decrease in the percentage of dialysis units participating in the 2020 survey compared to the previous year, from 39 to 28%. Although we did not investigate the causes, we believe that the difficulties of the COVID-19 pandemic faced by dialysis centers may have contributed to this lower voluntary participation. Despite that, participation was around 30% and was similar across the five macro-regions, increasing the chance of national representativeness of the sample.\nThe upward trend in total number of patients by 3.6% and in prevalence by 2.9% followed the pattern observed in recent years. This may be explained by the increase in population longevity\n3\n and by improvements in the quality of care and facilitation of dialysis access for patients with chronic kidney disease, although the latter should be confirmed by further investigations. Compared with the Latin American Society of Nephrology (SLAHN) 2018 registry\n4\n, our prevalence of people on dialysis (684 pmp) was slightly higher than the average of the included countries (617 pmp). Our numbers were substantially lower than those of the United States in 2017 (1538 pmp)\n5\n and higher than those of the European registry in 2018 (556 pmp)\n6\n.\nThe 2020 incidence rate (209 pmp) was slightly lower than the national estimate in 2019 (218 pmp), higher than that of Latin America (159 pmp)\n4\n and Europe (122 pmp) in 20186 and lower than that of the United States (370 pmp) in 20175.\nWe found a significant increase in crude mortality rate, which had been close to 20% in the last years, but reached 24.5% in 2020. The estimated annual number of deaths among dialysis patients jumped from 25,400 to 35,400. In addition to the increase in age and a greater burden of comorbidities in patients in recent years, the mortality associated with COVID-19 in dialysis\n7\n may partially account for the finding. According to our estimate, the annual crude mortality rate attributed to COVID-19 was 4.2%, corresponding to 17.3% of the overall annual crude mortality rate (estimated at 25.7%). It is important to highlight that July and August were the most critical months of 2020 for the COVID-19 pandemic in hemodialysis\n8\n. Therefore, it is conceivable that the extrapolation of July mortality to 12 months led to an overestimation of the annual crude mortality rate for 2020. Regarding the distribution by age group, the upward trend in the prevalence of patients over 45 years of age continues, with those over 75 years of age accounting for 11.8% of the total, slightly more than half of the prevalence observed in developed countries\n5,6,9\n. The prevalence of male dialysis patients has remained constant at 58%.\nA trend toward the stability of baseline diagnosis was observed, with hypertension and DM accounting for 32 and 31%, respectively. In the US in 2017, hypertension accounted for 30% and DM for 45% of baseline diagnoses of the dialysis population\n5\n.\nThe decrease in the percentage of patients with hepatitis C continued in 2020, falling below 3% for the first time. This result was a consequence of the implementation of preventive measures such as the reduction of blood transfusions and the prohibition of the reuse of dialyzers and lines for patients with positive serology, as well as the improved access to direct-acting antiviral agents, which allowed high cure rates\n10\n.\nAs in the previous year, it was found that approximately a quarter of HD patients (24.7%) used a central venous catheter as vascular access (7.6% short-term and 17.1% long-term). In the United States, the prevalence of central venous catheters has remained at one-fifth of all patients on HD therapy.\n5\n There was an increase in the use of long-term catheters, which was 62% in 2019 and 69% in 2020.\nAlthough more than half of the participating centers offer PD as treatment option (56%), only 7.4% of dialysis patients were treated by this modality of renal replacement therapy. The main reason seems to be the model proposed by our public health system, which is not economically viable for most clinics\n11\n. In some countries, strategies that have led to increased use of PD included the implementation of policies and incentives that favor this modality, allow the production and supply of materials at a low cost, and appropriate training for nephrology teams aimed increasing the use of therapy and continuously reducing the failure rates\n12\n.\nWe first reported that multinational companies operate about 15% of private dialysis centers. However, we emphasize that this share seems to be increasing in recent years and that the accuracy of our estimate may be limited due to sampling and the low response rate of these centers.\nCOVID-19 information limited to the period February 26-July 31, 2020, yielded an incidence rate of 684/10,000 patients. We emphasize that this rate was probably underestimated because it assumes a positive diagnostic test, which excludes many asymptomatic, untested cases. The lack of testing in the population, especially in the initial period of the pandemic, also influenced the result obtained. The high need for hospitalization for positive cases in intensive care units and the high lethality confirm the national and global results already reported\n7,13-16\n. Nevertheless, we believe that we found a high lethality rate because we included more severe patients in the sample and possibly because of the lack of experience in case management and the non-standardized treatment of patients in the first months of the pandemic.\nWe highlight as limitations the electronic data collection through voluntary completion, the aggregation of patient data by dialysis center, and the lack of response validation. Because only about 30% of active dialysis centers participated in the study, the methodology used in the national estimates of prevalence and incidence rates has limited accuracy, and caution should be used in interpreting the data. Our death rates estimates should also be interpreted with caution. The 2020 peak of COVID-19 cases in hemodialysis in July and August may have impacted our estimate of the overall annual crude mortality rate for 2020 because July figures were extrapolated to the year.\nIn conclusion, the 2020 survey confirmed a continuous increase in prevalence rate over the years and showed a slight decrease in incidence of dialysis patients in our country. The low prevalence of PD as dialysis therapy continues, although more than half of the centers offer this dialysis modality. The trend towards a progressive increase in the use of long-term central venous catheters was maintained. Also, the high lethality of COVID-19 on dialysis had an unfavorable impact on crude mortality rate in this population, and the influence of the COVID-19 pandemic in this population warrants continuous monitoring. Regular national assessment of patients on chronic dialysis is paramount for monitoring the quantitative and qualitative aspects of chronic kidney disease care in the country." ]

[ "intro", "methods", null, null, null, "results", null, null, null, null, null, "discussion" ]

[ "Renal Dialysis", "Peritoneal dialysis", "Peritoneal Dialysis", "Epidemiology", "COVID-19", "Diálise Renal", "Diálise Peritoneal", "Epidemiologia", "COVID-19" ]

[ 119, 292, 11, 355, 222, 72, 95, 256 ]

[ "patients", "dialysis", "rate", "2020", "figure", "19", "number", "centers", "covid 19", "covid" ]

[ "dialysis patients according", "dialysis survey highlighted", "pandemic dialysis clinic", "2020 brazilian dialysis", "decades brazilian dialysis" ]

[ "Humans", "Prognosis", "Gastrointestinal Stromal Tumors", "Nutrition Assessment", "Lymphocyte Count", "China" ]

[ "2.1. Search strategy", "2.2. Selection criteria", "2.3. Data extraction and quality assessment", "2.4. Statistical analysis", "3. Results", "3.2. Meta-analysis", "4. Publication bias", "6. Conclusion" ]

[ "Two independent researchers conducted a thorough literature search on the electronic databases PubMed, Embase, and the Cochrane Library from inception up to June 2022. Appropriate search terms included MeSH terms, as well as the free text words “gastrointestinal stromal tumors” or “GISTs,” and “prognostic nutritional index” or “PNI,” and “survival” or “prognosis” or “recurrence” or “clinical outcome.” References cited by the identified documents were screened carefully to avoid missing possible eligible articles. This review proceeded in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses[23] and was registered in the PROSPERO database, an international register of systematic reviews (register number CRD42022345440).", "Prespecified acceptance criteria for studies included in the present meta-analysis were discussed by the authors and the final decision was made by the senior author when encountering disagreements, and was then approved by all authors. The inclusion criteria were: studies concerned with GISTs confirmed by pathology; studies provided the prognostic value of the PNI prior to treatment; studies with available hazard ratios (HRs) and 95% confidence intervals (CIs) for survival data of GISTs; availability of the full text published in English. The exclusion criteria were: studies irrelevant to PNI for prognosis of GISTs; duplicated publications; case reports, comments, conference abstracts, and reviews.", "Data extraction was carried out from publications that met the requirements after full-text reading by 2 investigators independently, including the first name of authors, year of publication, region, study type, age, gender, sample size, tumor type, recurrence risk according to the National Institutes of Health (NIH) criteria, treatment, observation period, follow-up, endpoint, cutoff value of PNI, and quality score. The HRs and 95% CIs for endpoints were extracted directly from multivariate or univariate analyses, as feasible. Because all studies met the inclusion criteria reported just for the recurrence-free survival (RFS), here, we took this parameter as the only endpoint.[8,11–18] The Newcastle–Ottawa quality assessment scale was utilized to assess the quality of the included studies.[24]", "The original data were synthesized using Review Manager software (version 5.4, Cochrane Collaboration, Copenhagen, Denmark). I-square (I2) statistical testing and Cochrane (Q) tests were used to investigate the heterogeneity among the eligible studies, with a P value of < .1 taken to indicate significant heterogeneity.[25] This was classified into 3 degrees according to the I2 results as follows: low (I2 < 25%), moderate (I2, 25–75%) and high (I2 > 75%).[26] A random effect model was used if heterogeneity was observed. A two-sided P value of < .05 was considered statistically significant. Subgroup analysis was conducted to explore the source of heterogeneity subsequently based on the common layering characteristic of the publications. Moreover, sensitivity analysis was executed to further identify the source of heterogeneity and demonstrate the stability of the pooled results by omitting any single study. Results were visualized using Graphpad Prism software (version 7.0, San Diego, California). Finally, publication bias was shown as funnel plots and further validated by Egger’s test and Begg’s test[27,28] using Stata statistical software (version 12.0, College Station, TX).", "3.1. Search results The specific literature search process is shown in the flowchart (Fig. 1). The initial literature search yielded 27 articles, 11 of which were retained for rigorous screening through recognition of titles and abstracts. After reading the full text of these 11 articles, 2 that did not provide enough data and another 2 found to be published by the same center[11,14] were excluded so that finally 8 retrospective studies including 2627 patients with GISTs that fully met the requirements were adopted for the current meta-analysis.[8,11–13,15–18]\nSearch flow diagram for studies included in the meta-analysis.\nAll of these eligible studies published between 2019 and 2022 evaluated the predictive capacity of PNI for the RFS of GISTs patients from China. The main treatment for GISTs in these studies was surgical resection, with 4 of them reporting primary GISTs treated solely by surgery,[8,13,16,18] while the other 4 reported the predictive effect of PNI on the prognosis of GISTs treated with surgery and followed by imatinib.[11,12,15,17] HRs and 95% CIs were extracted directly from the articles, accompanied by the cutoff values of PNI ranging from 40.7 to 51.3. The characteristics of the accepted studies and patients are summarized in Table 1.\nBaseline characteristics of included studies.\nHR = hazard ratio, MV = multivariate, NOS = Newcastle-Ottawa quality assessment, NR = not reported, PNI = prognostic nutritional index, RFS = recurrence-free survival, UV = univariate.\nThe specific literature search process is shown in the flowchart (Fig. 1). The initial literature search yielded 27 articles, 11 of which were retained for rigorous screening through recognition of titles and abstracts. After reading the full text of these 11 articles, 2 that did not provide enough data and another 2 found to be published by the same center[11,14] were excluded so that finally 8 retrospective studies including 2627 patients with GISTs that fully met the requirements were adopted for the current meta-analysis.[8,11–13,15–18]\nSearch flow diagram for studies included in the meta-analysis.\nAll of these eligible studies published between 2019 and 2022 evaluated the predictive capacity of PNI for the RFS of GISTs patients from China. The main treatment for GISTs in these studies was surgical resection, with 4 of them reporting primary GISTs treated solely by surgery,[8,13,16,18] while the other 4 reported the predictive effect of PNI on the prognosis of GISTs treated with surgery and followed by imatinib.[11,12,15,17] HRs and 95% CIs were extracted directly from the articles, accompanied by the cutoff values of PNI ranging from 40.7 to 51.3. The characteristics of the accepted studies and patients are summarized in Table 1.\nBaseline characteristics of included studies.\nHR = hazard ratio, MV = multivariate, NOS = Newcastle-Ottawa quality assessment, NR = not reported, PNI = prognostic nutritional index, RFS = recurrence-free survival, UV = univariate.\n3.2. Meta-analysis Eight studies reported the prognostic value of PNI for RFS of 2627 patients, with a pooled HR of 0.52 (95% CI: 0.40–0.68), indicating that patients with an elevated PNI had a better RFS relative to those with a lower PNI. Heterogeneity analysis revealed an I2 value of 38%, indicating moderate heterogeneity among the included studies, but this was not statistically significant (P = .13, Fig. 2). A random-effects model was used and subgroup analysis was performed to explore the differences between groups with respect to the characteristics of the different studies. Subgroup analysis according to sample size (cutoff point, 300) and treatment (surgery only vs. surgery and imatinib) showed lack of heterogeneity in the subgroup “sample size >300” (I2 value = 0; HR: 0.46, 95% CI: 0.36–0.58) and in the subgroup “surgery only” (I2 value = 0; HR: 0.45, 95% CI: 0.33–0.60). In contrast, significant heterogeneity was observed among the subgroup “sample size < 300” (I2 value = 74%; HR: 0.67, 95% CI: 0.47-0.95) and the subgroup “surgery and imatinib” (I2 value = 61%; HR: 0.63, 95% CI: 0.39–1.02). However, these results failed to identify the source of the heterogeneity, as subgroup analysis based on the sample size and treatment could not eliminate or even reduce it (Table 2).\nSubgroup analyses of PNI for RFS in GISTs patients.\nCI = confidence interval, GISTs = gastrointestinal stromal tumors, HR = hazard ratio, PNI = prognostic nutritional index, RFS = recurrence-free survival.\nForest plot of prognostic nutritional index (PNI) in predicting recurrence-free survival (RFS) of gastrointestinal stromal tumors (GISTs) patients.\nTo identify the source of heterogeneity, as well as document the stability of our results, a sensitivity analysis was carried out by omitting one study at a time and recalculating the summarized HRs for the remaining studies. The results changed only when the study by Li et al[12] was excluded (I2 value = 0; HR: 0.48, 95% CI: 0.39–0.59). In contrast, including that study resulted in an I2 value ranging from 38% to 47%, with a similar range of HRs (0.51–0.53) and 95% CIs (Fig. 3). The results of this sensitivity analysis thus indicated that the study published by Li et al in 2020[12] was the sole source of heterogeneity in the current meta-analysis.\nSensitivity analyses of HRs for recurrence-free survival (RFS) in gastrointestinal stromal tumors (GISTs) patients.\nEight studies reported the prognostic value of PNI for RFS of 2627 patients, with a pooled HR of 0.52 (95% CI: 0.40–0.68), indicating that patients with an elevated PNI had a better RFS relative to those with a lower PNI. Heterogeneity analysis revealed an I2 value of 38%, indicating moderate heterogeneity among the included studies, but this was not statistically significant (P = .13, Fig. 2). A random-effects model was used and subgroup analysis was performed to explore the differences between groups with respect to the characteristics of the different studies. Subgroup analysis according to sample size (cutoff point, 300) and treatment (surgery only vs. surgery and imatinib) showed lack of heterogeneity in the subgroup “sample size >300” (I2 value = 0; HR: 0.46, 95% CI: 0.36–0.58) and in the subgroup “surgery only” (I2 value = 0; HR: 0.45, 95% CI: 0.33–0.60). In contrast, significant heterogeneity was observed among the subgroup “sample size < 300” (I2 value = 74%; HR: 0.67, 95% CI: 0.47-0.95) and the subgroup “surgery and imatinib” (I2 value = 61%; HR: 0.63, 95% CI: 0.39–1.02). However, these results failed to identify the source of the heterogeneity, as subgroup analysis based on the sample size and treatment could not eliminate or even reduce it (Table 2).\nSubgroup analyses of PNI for RFS in GISTs patients.\nCI = confidence interval, GISTs = gastrointestinal stromal tumors, HR = hazard ratio, PNI = prognostic nutritional index, RFS = recurrence-free survival.\nForest plot of prognostic nutritional index (PNI) in predicting recurrence-free survival (RFS) of gastrointestinal stromal tumors (GISTs) patients.\nTo identify the source of heterogeneity, as well as document the stability of our results, a sensitivity analysis was carried out by omitting one study at a time and recalculating the summarized HRs for the remaining studies. The results changed only when the study by Li et al[12] was excluded (I2 value = 0; HR: 0.48, 95% CI: 0.39–0.59). In contrast, including that study resulted in an I2 value ranging from 38% to 47%, with a similar range of HRs (0.51–0.53) and 95% CIs (Fig. 3). The results of this sensitivity analysis thus indicated that the study published by Li et al in 2020[12] was the sole source of heterogeneity in the current meta-analysis.\nSensitivity analyses of HRs for recurrence-free survival (RFS) in gastrointestinal stromal tumors (GISTs) patients.", "Eight studies reported the prognostic value of PNI for RFS of 2627 patients, with a pooled HR of 0.52 (95% CI: 0.40–0.68), indicating that patients with an elevated PNI had a better RFS relative to those with a lower PNI. Heterogeneity analysis revealed an I2 value of 38%, indicating moderate heterogeneity among the included studies, but this was not statistically significant (P = .13, Fig. 2). A random-effects model was used and subgroup analysis was performed to explore the differences between groups with respect to the characteristics of the different studies. Subgroup analysis according to sample size (cutoff point, 300) and treatment (surgery only vs. surgery and imatinib) showed lack of heterogeneity in the subgroup “sample size >300” (I2 value = 0; HR: 0.46, 95% CI: 0.36–0.58) and in the subgroup “surgery only” (I2 value = 0; HR: 0.45, 95% CI: 0.33–0.60). In contrast, significant heterogeneity was observed among the subgroup “sample size < 300” (I2 value = 74%; HR: 0.67, 95% CI: 0.47-0.95) and the subgroup “surgery and imatinib” (I2 value = 61%; HR: 0.63, 95% CI: 0.39–1.02). However, these results failed to identify the source of the heterogeneity, as subgroup analysis based on the sample size and treatment could not eliminate or even reduce it (Table 2).\nSubgroup analyses of PNI for RFS in GISTs patients.\nCI = confidence interval, GISTs = gastrointestinal stromal tumors, HR = hazard ratio, PNI = prognostic nutritional index, RFS = recurrence-free survival.\nForest plot of prognostic nutritional index (PNI) in predicting recurrence-free survival (RFS) of gastrointestinal stromal tumors (GISTs) patients.\nTo identify the source of heterogeneity, as well as document the stability of our results, a sensitivity analysis was carried out by omitting one study at a time and recalculating the summarized HRs for the remaining studies. The results changed only when the study by Li et al[12] was excluded (I2 value = 0; HR: 0.48, 95% CI: 0.39–0.59). In contrast, including that study resulted in an I2 value ranging from 38% to 47%, with a similar range of HRs (0.51–0.53) and 95% CIs (Fig. 3). The results of this sensitivity analysis thus indicated that the study published by Li et al in 2020[12] was the sole source of heterogeneity in the current meta-analysis.\nSensitivity analyses of HRs for recurrence-free survival (RFS) in gastrointestinal stromal tumors (GISTs) patients.", "The shape of the funnel plot showed symmetry of the whole dataset (Fig. 4) and indicated no apparent publication bias among the included studies. Further statistical analysis suggested that publication bias was not significant (Table 3, Begg’s test P = .711, Egger’s test P = .995). However, it should be noted that although there is no significant publication bias, there is clearly a geographical bias because all the studies were from China.\nAssessment for publication bias.\nPNI = prognostic nutritional index.\nFunnel plot.", "In conclusion, despite the above limitations, this meta-analysis is the first systematic review concerning the prognostic value of the PNI for GISTs. The pooled results demonstrate that an elevated PNI is associated with a favorable RFS in patients from China with GISTs. As a parameter of immunonutritional status, PNI may not only be helpful for pretreatment evaluation but also for guiding early nutritional and immunological intervention, although well-designed multi-regional studies not limited to China are required for confirmation of this hypothesis." ]

[ null, null, null, null, "results", null, null, null ]

[ "1. Introduction", "2. Materials and methods", "2.1. Search strategy", "2.2. Selection criteria", "2.3. Data extraction and quality assessment", "2.4. Statistical analysis", "3. Results", "3.1. Search results", "3.2. Meta-analysis", "4. Publication bias", "5. Discussion", "6. Conclusion" ]

[ "Gastrointestinal stromal tumors (GISTs) are the most common gastrointestinal (GI) mesenchymal tumors. They harbor activating somatic mutations involving the tyrosine kinase receptor c-kit (CD117) and platelet-derived growth factor-α, expressed as by the Interstitial Cells of Cajal which control GI track peristalsis.[1] Tyrosine kinase inhibitors (TKIs), as represented by imatinib, have achieved gratifying therapeutic benefit for the treatment of GISTs.[2] Complete excision is recommended for primary resectable GISTs without significant risk of recurrence, but otherwise, targeted therapy should be considered as the preferred treatment option based on risk assessment, as emphasized by the first guidelines separately and specifically for GISTs recently published by the National Comprehensive Cancer Network.[3] Tumor location, size, mitotic index and tumor rupture are convincingly incorporated into the assessment of potentially malignant biological behavior of GISTs,[4–6] but nevertheless, it is difficult to accurately assess the risk of recurrence without pathological assessment. Hence, for predicting the likelihood of recurrence, other easily accessible and effective indicators are needed.\nNutritional status is closely connected to both tumor progression and prognosis.[7] Malnutrition usually indicates a worse clinical outcome of most cancers, and several prognosis-related nutritional parameters have been identified and shown to represent effective predictors of prognosis, such as Nutritional Risk Screening 2002, Patient-Generated Subjective Global Assessment, and the prognostic nutritional index (PNI).[8–18] The PNI was originally proposed by Buzby in 1980 and applied by Onodera in 1984 to predict the surgical risk in GI malignancy. Due to its convenience and efficiency, the PNI was rapidly tested in other types of cancers as well, including GISTs.[8,11–22] The population sample size in reports on predicting the recurrence risk of GISTs based on the PNI was usually small, making it difficult to draw convincing conclusions. However, according to the National Comprehensive Cancer Network guidelines, preoperative effective assessment of postoperative recurrence risk is particularly important for GISTs, because it will determine the priority of surgical treatment or preoperative TKI treatment.[3]\nAlthough other meta-analyses have previously determined the prognostic value of PNI in most tumors,[21,22] these earlier studies had not clearly differentiated GISTs from GI epithelial cancers by pathology. In fact, unlike GI epithelial cancers, the existing risk assessment criteria and prognostic parameters for GISTs are self-contained and developing.[6,13] Accordingly, we conducted a meta-analysis to verify the prognostic significance of PNI in GISTs. This is the first meta-analysis in this field.", "The present study was carried out based on the published studies. Thus, the approval from an ethics committee or institutional review board was not required.\n2.1. Search strategy Two independent researchers conducted a thorough literature search on the electronic databases PubMed, Embase, and the Cochrane Library from inception up to June 2022. Appropriate search terms included MeSH terms, as well as the free text words “gastrointestinal stromal tumors” or “GISTs,” and “prognostic nutritional index” or “PNI,” and “survival” or “prognosis” or “recurrence” or “clinical outcome.” References cited by the identified documents were screened carefully to avoid missing possible eligible articles. This review proceeded in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses[23] and was registered in the PROSPERO database, an international register of systematic reviews (register number CRD42022345440).\nTwo independent researchers conducted a thorough literature search on the electronic databases PubMed, Embase, and the Cochrane Library from inception up to June 2022. Appropriate search terms included MeSH terms, as well as the free text words “gastrointestinal stromal tumors” or “GISTs,” and “prognostic nutritional index” or “PNI,” and “survival” or “prognosis” or “recurrence” or “clinical outcome.” References cited by the identified documents were screened carefully to avoid missing possible eligible articles. This review proceeded in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses[23] and was registered in the PROSPERO database, an international register of systematic reviews (register number CRD42022345440).\n2.2. Selection criteria Prespecified acceptance criteria for studies included in the present meta-analysis were discussed by the authors and the final decision was made by the senior author when encountering disagreements, and was then approved by all authors. The inclusion criteria were: studies concerned with GISTs confirmed by pathology; studies provided the prognostic value of the PNI prior to treatment; studies with available hazard ratios (HRs) and 95% confidence intervals (CIs) for survival data of GISTs; availability of the full text published in English. The exclusion criteria were: studies irrelevant to PNI for prognosis of GISTs; duplicated publications; case reports, comments, conference abstracts, and reviews.\nPrespecified acceptance criteria for studies included in the present meta-analysis were discussed by the authors and the final decision was made by the senior author when encountering disagreements, and was then approved by all authors. The inclusion criteria were: studies concerned with GISTs confirmed by pathology; studies provided the prognostic value of the PNI prior to treatment; studies with available hazard ratios (HRs) and 95% confidence intervals (CIs) for survival data of GISTs; availability of the full text published in English. The exclusion criteria were: studies irrelevant to PNI for prognosis of GISTs; duplicated publications; case reports, comments, conference abstracts, and reviews.\n2.3. Data extraction and quality assessment Data extraction was carried out from publications that met the requirements after full-text reading by 2 investigators independently, including the first name of authors, year of publication, region, study type, age, gender, sample size, tumor type, recurrence risk according to the National Institutes of Health (NIH) criteria, treatment, observation period, follow-up, endpoint, cutoff value of PNI, and quality score. The HRs and 95% CIs for endpoints were extracted directly from multivariate or univariate analyses, as feasible. Because all studies met the inclusion criteria reported just for the recurrence-free survival (RFS), here, we took this parameter as the only endpoint.[8,11–18] The Newcastle–Ottawa quality assessment scale was utilized to assess the quality of the included studies.[24]\nData extraction was carried out from publications that met the requirements after full-text reading by 2 investigators independently, including the first name of authors, year of publication, region, study type, age, gender, sample size, tumor type, recurrence risk according to the National Institutes of Health (NIH) criteria, treatment, observation period, follow-up, endpoint, cutoff value of PNI, and quality score. The HRs and 95% CIs for endpoints were extracted directly from multivariate or univariate analyses, as feasible. Because all studies met the inclusion criteria reported just for the recurrence-free survival (RFS), here, we took this parameter as the only endpoint.[8,11–18] The Newcastle–Ottawa quality assessment scale was utilized to assess the quality of the included studies.[24]\n2.4. Statistical analysis The original data were synthesized using Review Manager software (version 5.4, Cochrane Collaboration, Copenhagen, Denmark). I-square (I2) statistical testing and Cochrane (Q) tests were used to investigate the heterogeneity among the eligible studies, with a P value of < .1 taken to indicate significant heterogeneity.[25] This was classified into 3 degrees according to the I2 results as follows: low (I2 < 25%), moderate (I2, 25–75%) and high (I2 > 75%).[26] A random effect model was used if heterogeneity was observed. A two-sided P value of < .05 was considered statistically significant. Subgroup analysis was conducted to explore the source of heterogeneity subsequently based on the common layering characteristic of the publications. Moreover, sensitivity analysis was executed to further identify the source of heterogeneity and demonstrate the stability of the pooled results by omitting any single study. Results were visualized using Graphpad Prism software (version 7.0, San Diego, California). Finally, publication bias was shown as funnel plots and further validated by Egger’s test and Begg’s test[27,28] using Stata statistical software (version 12.0, College Station, TX).\nThe original data were synthesized using Review Manager software (version 5.4, Cochrane Collaboration, Copenhagen, Denmark). I-square (I2) statistical testing and Cochrane (Q) tests were used to investigate the heterogeneity among the eligible studies, with a P value of < .1 taken to indicate significant heterogeneity.[25] This was classified into 3 degrees according to the I2 results as follows: low (I2 < 25%), moderate (I2, 25–75%) and high (I2 > 75%).[26] A random effect model was used if heterogeneity was observed. A two-sided P value of < .05 was considered statistically significant. Subgroup analysis was conducted to explore the source of heterogeneity subsequently based on the common layering characteristic of the publications. Moreover, sensitivity analysis was executed to further identify the source of heterogeneity and demonstrate the stability of the pooled results by omitting any single study. Results were visualized using Graphpad Prism software (version 7.0, San Diego, California). Finally, publication bias was shown as funnel plots and further validated by Egger’s test and Begg’s test[27,28] using Stata statistical software (version 12.0, College Station, TX).", "Two independent researchers conducted a thorough literature search on the electronic databases PubMed, Embase, and the Cochrane Library from inception up to June 2022. Appropriate search terms included MeSH terms, as well as the free text words “gastrointestinal stromal tumors” or “GISTs,” and “prognostic nutritional index” or “PNI,” and “survival” or “prognosis” or “recurrence” or “clinical outcome.” References cited by the identified documents were screened carefully to avoid missing possible eligible articles. This review proceeded in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses[23] and was registered in the PROSPERO database, an international register of systematic reviews (register number CRD42022345440).", "Prespecified acceptance criteria for studies included in the present meta-analysis were discussed by the authors and the final decision was made by the senior author when encountering disagreements, and was then approved by all authors. The inclusion criteria were: studies concerned with GISTs confirmed by pathology; studies provided the prognostic value of the PNI prior to treatment; studies with available hazard ratios (HRs) and 95% confidence intervals (CIs) for survival data of GISTs; availability of the full text published in English. The exclusion criteria were: studies irrelevant to PNI for prognosis of GISTs; duplicated publications; case reports, comments, conference abstracts, and reviews.", "Data extraction was carried out from publications that met the requirements after full-text reading by 2 investigators independently, including the first name of authors, year of publication, region, study type, age, gender, sample size, tumor type, recurrence risk according to the National Institutes of Health (NIH) criteria, treatment, observation period, follow-up, endpoint, cutoff value of PNI, and quality score. The HRs and 95% CIs for endpoints were extracted directly from multivariate or univariate analyses, as feasible. Because all studies met the inclusion criteria reported just for the recurrence-free survival (RFS), here, we took this parameter as the only endpoint.[8,11–18] The Newcastle–Ottawa quality assessment scale was utilized to assess the quality of the included studies.[24]", "The original data were synthesized using Review Manager software (version 5.4, Cochrane Collaboration, Copenhagen, Denmark). I-square (I2) statistical testing and Cochrane (Q) tests were used to investigate the heterogeneity among the eligible studies, with a P value of < .1 taken to indicate significant heterogeneity.[25] This was classified into 3 degrees according to the I2 results as follows: low (I2 < 25%), moderate (I2, 25–75%) and high (I2 > 75%).[26] A random effect model was used if heterogeneity was observed. A two-sided P value of < .05 was considered statistically significant. Subgroup analysis was conducted to explore the source of heterogeneity subsequently based on the common layering characteristic of the publications. Moreover, sensitivity analysis was executed to further identify the source of heterogeneity and demonstrate the stability of the pooled results by omitting any single study. Results were visualized using Graphpad Prism software (version 7.0, San Diego, California). Finally, publication bias was shown as funnel plots and further validated by Egger’s test and Begg’s test[27,28] using Stata statistical software (version 12.0, College Station, TX).", "3.1. Search results The specific literature search process is shown in the flowchart (Fig. 1). The initial literature search yielded 27 articles, 11 of which were retained for rigorous screening through recognition of titles and abstracts. After reading the full text of these 11 articles, 2 that did not provide enough data and another 2 found to be published by the same center[11,14] were excluded so that finally 8 retrospective studies including 2627 patients with GISTs that fully met the requirements were adopted for the current meta-analysis.[8,11–13,15–18]\nSearch flow diagram for studies included in the meta-analysis.\nAll of these eligible studies published between 2019 and 2022 evaluated the predictive capacity of PNI for the RFS of GISTs patients from China. The main treatment for GISTs in these studies was surgical resection, with 4 of them reporting primary GISTs treated solely by surgery,[8,13,16,18] while the other 4 reported the predictive effect of PNI on the prognosis of GISTs treated with surgery and followed by imatinib.[11,12,15,17] HRs and 95% CIs were extracted directly from the articles, accompanied by the cutoff values of PNI ranging from 40.7 to 51.3. The characteristics of the accepted studies and patients are summarized in Table 1.\nBaseline characteristics of included studies.\nHR = hazard ratio, MV = multivariate, NOS = Newcastle-Ottawa quality assessment, NR = not reported, PNI = prognostic nutritional index, RFS = recurrence-free survival, UV = univariate.\nThe specific literature search process is shown in the flowchart (Fig. 1). The initial literature search yielded 27 articles, 11 of which were retained for rigorous screening through recognition of titles and abstracts. After reading the full text of these 11 articles, 2 that did not provide enough data and another 2 found to be published by the same center[11,14] were excluded so that finally 8 retrospective studies including 2627 patients with GISTs that fully met the requirements were adopted for the current meta-analysis.[8,11–13,15–18]\nSearch flow diagram for studies included in the meta-analysis.\nAll of these eligible studies published between 2019 and 2022 evaluated the predictive capacity of PNI for the RFS of GISTs patients from China. The main treatment for GISTs in these studies was surgical resection, with 4 of them reporting primary GISTs treated solely by surgery,[8,13,16,18] while the other 4 reported the predictive effect of PNI on the prognosis of GISTs treated with surgery and followed by imatinib.[11,12,15,17] HRs and 95% CIs were extracted directly from the articles, accompanied by the cutoff values of PNI ranging from 40.7 to 51.3. The characteristics of the accepted studies and patients are summarized in Table 1.\nBaseline characteristics of included studies.\nHR = hazard ratio, MV = multivariate, NOS = Newcastle-Ottawa quality assessment, NR = not reported, PNI = prognostic nutritional index, RFS = recurrence-free survival, UV = univariate.\n3.2. Meta-analysis Eight studies reported the prognostic value of PNI for RFS of 2627 patients, with a pooled HR of 0.52 (95% CI: 0.40–0.68), indicating that patients with an elevated PNI had a better RFS relative to those with a lower PNI. Heterogeneity analysis revealed an I2 value of 38%, indicating moderate heterogeneity among the included studies, but this was not statistically significant (P = .13, Fig. 2). A random-effects model was used and subgroup analysis was performed to explore the differences between groups with respect to the characteristics of the different studies. Subgroup analysis according to sample size (cutoff point, 300) and treatment (surgery only vs. surgery and imatinib) showed lack of heterogeneity in the subgroup “sample size >300” (I2 value = 0; HR: 0.46, 95% CI: 0.36–0.58) and in the subgroup “surgery only” (I2 value = 0; HR: 0.45, 95% CI: 0.33–0.60). In contrast, significant heterogeneity was observed among the subgroup “sample size < 300” (I2 value = 74%; HR: 0.67, 95% CI: 0.47-0.95) and the subgroup “surgery and imatinib” (I2 value = 61%; HR: 0.63, 95% CI: 0.39–1.02). However, these results failed to identify the source of the heterogeneity, as subgroup analysis based on the sample size and treatment could not eliminate or even reduce it (Table 2).\nSubgroup analyses of PNI for RFS in GISTs patients.\nCI = confidence interval, GISTs = gastrointestinal stromal tumors, HR = hazard ratio, PNI = prognostic nutritional index, RFS = recurrence-free survival.\nForest plot of prognostic nutritional index (PNI) in predicting recurrence-free survival (RFS) of gastrointestinal stromal tumors (GISTs) patients.\nTo identify the source of heterogeneity, as well as document the stability of our results, a sensitivity analysis was carried out by omitting one study at a time and recalculating the summarized HRs for the remaining studies. The results changed only when the study by Li et al[12] was excluded (I2 value = 0; HR: 0.48, 95% CI: 0.39–0.59). In contrast, including that study resulted in an I2 value ranging from 38% to 47%, with a similar range of HRs (0.51–0.53) and 95% CIs (Fig. 3). The results of this sensitivity analysis thus indicated that the study published by Li et al in 2020[12] was the sole source of heterogeneity in the current meta-analysis.\nSensitivity analyses of HRs for recurrence-free survival (RFS) in gastrointestinal stromal tumors (GISTs) patients.\nEight studies reported the prognostic value of PNI for RFS of 2627 patients, with a pooled HR of 0.52 (95% CI: 0.40–0.68), indicating that patients with an elevated PNI had a better RFS relative to those with a lower PNI. Heterogeneity analysis revealed an I2 value of 38%, indicating moderate heterogeneity among the included studies, but this was not statistically significant (P = .13, Fig. 2). A random-effects model was used and subgroup analysis was performed to explore the differences between groups with respect to the characteristics of the different studies. Subgroup analysis according to sample size (cutoff point, 300) and treatment (surgery only vs. surgery and imatinib) showed lack of heterogeneity in the subgroup “sample size >300” (I2 value = 0; HR: 0.46, 95% CI: 0.36–0.58) and in the subgroup “surgery only” (I2 value = 0; HR: 0.45, 95% CI: 0.33–0.60). In contrast, significant heterogeneity was observed among the subgroup “sample size < 300” (I2 value = 74%; HR: 0.67, 95% CI: 0.47-0.95) and the subgroup “surgery and imatinib” (I2 value = 61%; HR: 0.63, 95% CI: 0.39–1.02). However, these results failed to identify the source of the heterogeneity, as subgroup analysis based on the sample size and treatment could not eliminate or even reduce it (Table 2).\nSubgroup analyses of PNI for RFS in GISTs patients.\nCI = confidence interval, GISTs = gastrointestinal stromal tumors, HR = hazard ratio, PNI = prognostic nutritional index, RFS = recurrence-free survival.\nForest plot of prognostic nutritional index (PNI) in predicting recurrence-free survival (RFS) of gastrointestinal stromal tumors (GISTs) patients.\nTo identify the source of heterogeneity, as well as document the stability of our results, a sensitivity analysis was carried out by omitting one study at a time and recalculating the summarized HRs for the remaining studies. The results changed only when the study by Li et al[12] was excluded (I2 value = 0; HR: 0.48, 95% CI: 0.39–0.59). In contrast, including that study resulted in an I2 value ranging from 38% to 47%, with a similar range of HRs (0.51–0.53) and 95% CIs (Fig. 3). The results of this sensitivity analysis thus indicated that the study published by Li et al in 2020[12] was the sole source of heterogeneity in the current meta-analysis.\nSensitivity analyses of HRs for recurrence-free survival (RFS) in gastrointestinal stromal tumors (GISTs) patients.", "The specific literature search process is shown in the flowchart (Fig. 1). The initial literature search yielded 27 articles, 11 of which were retained for rigorous screening through recognition of titles and abstracts. After reading the full text of these 11 articles, 2 that did not provide enough data and another 2 found to be published by the same center[11,14] were excluded so that finally 8 retrospective studies including 2627 patients with GISTs that fully met the requirements were adopted for the current meta-analysis.[8,11–13,15–18]\nSearch flow diagram for studies included in the meta-analysis.\nAll of these eligible studies published between 2019 and 2022 evaluated the predictive capacity of PNI for the RFS of GISTs patients from China. The main treatment for GISTs in these studies was surgical resection, with 4 of them reporting primary GISTs treated solely by surgery,[8,13,16,18] while the other 4 reported the predictive effect of PNI on the prognosis of GISTs treated with surgery and followed by imatinib.[11,12,15,17] HRs and 95% CIs were extracted directly from the articles, accompanied by the cutoff values of PNI ranging from 40.7 to 51.3. The characteristics of the accepted studies and patients are summarized in Table 1.\nBaseline characteristics of included studies.\nHR = hazard ratio, MV = multivariate, NOS = Newcastle-Ottawa quality assessment, NR = not reported, PNI = prognostic nutritional index, RFS = recurrence-free survival, UV = univariate.", "Eight studies reported the prognostic value of PNI for RFS of 2627 patients, with a pooled HR of 0.52 (95% CI: 0.40–0.68), indicating that patients with an elevated PNI had a better RFS relative to those with a lower PNI. Heterogeneity analysis revealed an I2 value of 38%, indicating moderate heterogeneity among the included studies, but this was not statistically significant (P = .13, Fig. 2). A random-effects model was used and subgroup analysis was performed to explore the differences between groups with respect to the characteristics of the different studies. Subgroup analysis according to sample size (cutoff point, 300) and treatment (surgery only vs. surgery and imatinib) showed lack of heterogeneity in the subgroup “sample size >300” (I2 value = 0; HR: 0.46, 95% CI: 0.36–0.58) and in the subgroup “surgery only” (I2 value = 0; HR: 0.45, 95% CI: 0.33–0.60). In contrast, significant heterogeneity was observed among the subgroup “sample size < 300” (I2 value = 74%; HR: 0.67, 95% CI: 0.47-0.95) and the subgroup “surgery and imatinib” (I2 value = 61%; HR: 0.63, 95% CI: 0.39–1.02). However, these results failed to identify the source of the heterogeneity, as subgroup analysis based on the sample size and treatment could not eliminate or even reduce it (Table 2).\nSubgroup analyses of PNI for RFS in GISTs patients.\nCI = confidence interval, GISTs = gastrointestinal stromal tumors, HR = hazard ratio, PNI = prognostic nutritional index, RFS = recurrence-free survival.\nForest plot of prognostic nutritional index (PNI) in predicting recurrence-free survival (RFS) of gastrointestinal stromal tumors (GISTs) patients.\nTo identify the source of heterogeneity, as well as document the stability of our results, a sensitivity analysis was carried out by omitting one study at a time and recalculating the summarized HRs for the remaining studies. The results changed only when the study by Li et al[12] was excluded (I2 value = 0; HR: 0.48, 95% CI: 0.39–0.59). In contrast, including that study resulted in an I2 value ranging from 38% to 47%, with a similar range of HRs (0.51–0.53) and 95% CIs (Fig. 3). The results of this sensitivity analysis thus indicated that the study published by Li et al in 2020[12] was the sole source of heterogeneity in the current meta-analysis.\nSensitivity analyses of HRs for recurrence-free survival (RFS) in gastrointestinal stromal tumors (GISTs) patients.", "The shape of the funnel plot showed symmetry of the whole dataset (Fig. 4) and indicated no apparent publication bias among the included studies. Further statistical analysis suggested that publication bias was not significant (Table 3, Begg’s test P = .711, Egger’s test P = .995). However, it should be noted that although there is no significant publication bias, there is clearly a geographical bias because all the studies were from China.\nAssessment for publication bias.\nPNI = prognostic nutritional index.\nFunnel plot.", "Decisions on treatment strategies for GISTs depend on the assessment of the risk of recurrence.[3] R0 resection does not usually imply cure for GISTs with a high preoperative and postoperative risk of recurrence. TKIs are recommended for increasing RFS.[2,3] Even though tumor location, size, mitotic index and tumor rupture have been identified as classic parameters for predicting recurrence of GISTs,[4–6] accuracy is often limited by the inability to obtain accurate pathological confirmation prior to treatment. Hematological indicators such as neutrophil to lymphocyte ratios had been shown to be useful in assessing the risk of GIST recurrence.[29] Similarly, recent studies showed that GIST patients with a high PNI tend to have a longer RFS, with HRs ranging from 0.17 to 0.59.[8,11–18] Moreover, a nomogram including tumor site, tumor size, mitotic index, tumor rupture, and PNI demonstrated better predictive ability than the commonly used risk stratification systems, such as modified NIH criteria and NIH–Miettinen criteria.[13]\nIn the current meta-analysis, a total of 8 studies including 2627 patients with GISTs was analyzed regarding the prognostic value of PNI.[8,11–13,15–18] The pooled results revealed that an elevated PNI prior to treatment was related to a longer RFS (HR, 0.52, 95% CI: 0.40–0.68). Subgroup analysis showed a positive prognostic impact of PNI on RFS regarding studies with a sample size >300 and those where patients had been treated only surgically. Sensitivity analysis enabled the identification of one study that was a source of heterogeneity (albeit this had not statistically significant (P > .1)). For that publication from 2020,[12] compared with the other studies analyzed here, we found no indication of the status of the GISTs included (primary or not, and localized or not). Possibly, GISTs at different stages included in that study may explain the heterogeneity due to the lack of its identification of the PNI as independent prognostic factor in by multiple regression analysis.\nA limitation of the current analysis is that, due to the lack of data, we were unable to explore relationships between PNI and other clinicopathologic features. Nutritional factors act on the development of cancers by regulating the balance between cell proliferation and death, appropriate cell differentiation, the expression of oncogenes and tumor-suppressor genes.[7] Serum albumin, as a common reference entity in assessments of nutritional status, is found to be closely connected with the prognosis of various malignancies.[30] Previous reviews had indicated that hypoalbuminemia is associated with poor cancer survival, including GI cancers.[30] However, it seems that serum albumin alone is not an independent predictor of prognosis of GISTs according to the limited studies included in the current meta-analysis.[15,18] Further verification is required for the prognostic value of serum albumin alone in GISTs. On the other hand, the interactions of various different immune organs, tissues, and cells mediates defense against tumors. Different tumor-infiltrating lymphocyte (TIL) phenotypes rather than the lymphocyte count alone can also be used to predict tumor prognosis.[31,32] Specifically, CD3+, CD8+, and CD56 + TILs were reported to be reliable independent predictors of disease-free survival of GIST patients.[33] Even after treatment with imatinib, enriched intratumoral CD3 + TILs could still be found and still correlated with a better progression-free survival in GISTs.[34] Based on the above data and the results of our meta-analysis, PNI, a parameter of immunonutritional status, can be expected to play an important role in prediction of GIST prognosis.\nThere are several limitations to the current meta-analysis. First, the study design of all the included studies was retrospective, but a randomized controlled trial is logically the pinnacle of the evidence pyramid.[35] Second, all of the studies included in this comprehensive analysis were from China, so a geographical bias is clearly present, and it remains unknown whether the current pooled results can be generalized to other Asian regions or western countries. Third, the risk stratification standards varied among the included studies, which we considered mixed unless the included publication clearly stated that the study population was of medium to high risk.[17] Fourth, most of the cutoff values for the PNI were determined by receiver operator characteristic curve analysis and were not consistent,[8,12,15,16,18] but half of them were concentrated around 47.5.[8,12,13,16] Therefore, optimal cutoff values still need to be determined by more well-designed studies. Fifth, the sample size of the included studies was <500, ranging from 200 to 445, which may make it difficult to draw firm conclusions. Finally, inclusion of only English literature may lead to a language bias.", "In conclusion, despite the above limitations, this meta-analysis is the first systematic review concerning the prognostic value of the PNI for GISTs. The pooled results demonstrate that an elevated PNI is associated with a favorable RFS in patients from China with GISTs. As a parameter of immunonutritional status, PNI may not only be helpful for pretreatment evaluation but also for guiding early nutritional and immunological intervention, although well-designed multi-regional studies not limited to China are required for confirmation of this hypothesis." ]

[ "intro", "methods", null, null, null, null, "results", "results", null, null, "discussion", null ]

[ "gastrointestinal stromal tumors (GISTs)", "prognostic nutritional index (PNI)", "Recurrence-free survival (RFS)" ]

[ 134, 123, 149, 229, 1604, 529, 109, 94 ]

[ "studies", "gists", "pni", "analysis", "value", "heterogeneity", "i2", "95", "rfs", "recurrence" ]

[ "gi malignancy convenience", "gists gastrointestinal stromal", "gi cancers 30", "gi mesenchymal tumors", "gastrointestinal stromal tumors" ]

[ "Aged", "Female", "Hepacivirus", "Hepatitis C Antibodies", "Hepatitis C, Chronic", "Humans", "Male", "Mass Screening", "Middle Aged", "Odds Ratio", "Prevalence", "Risk Factors", "United States", "Veterans" ]

[ "Background", "Abbreviations", "Competing interests", "Authors’ contributions" ]

[ "In 1990, serologic tests that detect antibodies to the hepatitis C virus (HCV) became commercially available in the United States. The Centers for Disease Control and Prevention (CDC) published guidelines on screening blood donors for HCV infection in 1991 and recommendations for HCV screening in persons with high risk behaviors in 1998\n[1,2] (Figure \n1). The US Veterans Health Administration (VHA) implemented HCV screening guidelines in 2001 that not only recommended screening veterans with the risk factors described in the CDC guidelines, but also screening any veteran with immoderate use of alcohol, a history of tattoos or repeated body piercing, intranasal cocaine use, multiple sexual partners, or Vietnam-era military service (i.e. dates of active military service between August 5, 1964 and May 7, 1975)\n[3]. In 2002, VHA introduced an electronic clinical reminder for HCV screening. In August 2012, CDC augmented risk-based HCV screening guidelines with a recommendation to perform one-time HCV screening in all persons born during 1945 – 1965, a birth cohort known to have a higher risk of having HCV infection\n[4,5]. In guidance published in June 2013, the United States Preventive Services Task Force also recommended one-time HCV screening of adults in this birth cohort\n[6]. Because this birth cohort overlaps with Vietnam-era veterans, we sought to estimate the proportion of veterans seen at the Atlanta VA Medical Center (AVAMC) who had ever been screened for HCV infection by birth year.\nTotal number of hepatitis C antibody tests performed and percent of tests that are positive, by year — Atlanta VA Medical Center, Atlanta, GA, USA. 1992 – 2011 (n = 91,240).", "VHA: Veterans health administration; HCV: Hepatitis C virus; RNA: Ribonucleic acid; HIPAA: Health insurance portability and accountability act; OR: Odds ratio; CI: Confidence interval.", "The authors declare that they have no competing interests.", "EC conceived and designed the study, performed data abstraction and analysis, and drafted the manuscript. CR provided input on study design, data analysis, data interpretation, statistical testing, and provided critical review of the manuscript. DR assisted with study design, obtained data, and provided input on data analysis and interpretation, and critically reviewed the manuscript. All authors read and approved the final manuscript." ]

[ null, null, null, null ]

[ "Background", "Methods", "Results", "Discussion", "Conclusions", "Abbreviations", "Competing interests", "Authors’ contributions" ]

[ "In 1990, serologic tests that detect antibodies to the hepatitis C virus (HCV) became commercially available in the United States. The Centers for Disease Control and Prevention (CDC) published guidelines on screening blood donors for HCV infection in 1991 and recommendations for HCV screening in persons with high risk behaviors in 1998\n[1,2] (Figure \n1). The US Veterans Health Administration (VHA) implemented HCV screening guidelines in 2001 that not only recommended screening veterans with the risk factors described in the CDC guidelines, but also screening any veteran with immoderate use of alcohol, a history of tattoos or repeated body piercing, intranasal cocaine use, multiple sexual partners, or Vietnam-era military service (i.e. dates of active military service between August 5, 1964 and May 7, 1975)\n[3]. In 2002, VHA introduced an electronic clinical reminder for HCV screening. In August 2012, CDC augmented risk-based HCV screening guidelines with a recommendation to perform one-time HCV screening in all persons born during 1945 – 1965, a birth cohort known to have a higher risk of having HCV infection\n[4,5]. In guidance published in June 2013, the United States Preventive Services Task Force also recommended one-time HCV screening of adults in this birth cohort\n[6]. Because this birth cohort overlaps with Vietnam-era veterans, we sought to estimate the proportion of veterans seen at the Atlanta VA Medical Center (AVAMC) who had ever been screened for HCV infection by birth year.\nTotal number of hepatitis C antibody tests performed and percent of tests that are positive, by year — Atlanta VA Medical Center, Atlanta, GA, USA. 1992 – 2011 (n = 91,240).", "We used an administrative database that contained the date of birth, gender, and race of veterans seen as inpatients or outpatients at the AVAMC between January 1, 2011 and December 31, 2011. All laboratory results were obtained from the AVAMC and included HCV antibody tests that were performed between 1992 and 2011 and all available HCV RNA viral loads. For veterans with more than one HCV antibody test, the HCV antibody status was considered positive if any HCV antibody test was positive. Any detectable value on either a quantitative or qualitative RNA viral load was considered positive. Continuous variables were compared using the Wilcoxon rank-sum test. Odds ratios and 95% confidence intervals were estimated using SAS version 9.2 (SAS institute, Cary, North Carolina). A P value of ≤ .05 was considered statistically significant. This study was approved by the Emory University Institutional Review Board and the VA Research and Development Committee. Informed consent was waived under a full HIPAA waiver.", "From 1992 through 2011, 91,240 HCV antibody tests were completed on 67,539 veterans at the AVAMC (Figure \n1). Before enhanced VHA screening efforts began in 2001, a median of 642 HCV antibody tests were done per year (interquartile range [IQR]: 329 – 1,451); since 2001, a median of 7,356 HCV antibody tests (IQR: 5,949 – 8,500) were done per year (p = 0.002).\nIn 2011, 87,144 veterans were seen at the AVAMC; data on age and sex was available in this database but information on race was largely missing (91% had a missing or unknown race) (Table \n1). Over 50% of veterans seen were between 50 and 69 years of age. While 89% of all veterans seen in 2011 are male, when examined by birth cohort, men made up 73% of those born after 1965. HCV antibody testing had ever been performed on 48% (41,556) of the veterans (Table \n2). Of those who had antibody testing, 49% (20,396) are African American, 37% (15,343) are Caucasian, 1% (390) are “other” race (includes Asian, Native Hawaiian, Pacific Islander, American Indian, or Alaska Native); race is unknown in 13% (5,427) (data not shown). HCV antibody was positive in 10% (4,107) of those tested (Table \n2). Of those with a positive HCV antibody, confirmatory RNA viral loads were performed in 96% (3,944). Chronic hepatitis C (i.e., a positive HCV antibody and a detectable RNA viral load) was identified in 73% (3,004).\nGender (by birth cohort), age, and race of veterans seen in 2011 — Atlanta VA Medical Center (n = 87,144)\nPerformance and positivity of hepatitis C antibody and RNA viral load tests for veterans seen in 2011, by birth cohort - Atlanta VA Medical Center (n=87,144)\nNote: HCV – Hepatitis C virus, AB – Antibody, no. - Number.\n*Percent is calculated using the HCV Ab positive number as the denominator.\nWhen the veterans seen in 2011 were classified by birth year, 27% were born before 1945, 54% were born during 1945 – 1965, and 19% were born after 1965. Among those born before 1945, 35% (8,378) were ever tested for HCV antibody; of those tested, 4% (335) were HCV antibody positive, and 56% (189) of those with a positive HCV antibody had confirmed, chronic HCV infection (Table \n2). Among those born during 1945 – 1965, 54% (25,097) had ever been tested; 15% (3,644) were HCV antibody positive, and 76% (2,775) of those with a positive antibody had confirmed, chronic infection. In those born after 1965, 48% (8,081) had been tested and 2% (128) were HCV antibody positive; 31% (40) of those with a positive HCV antibody had confirmed chronic HCV viremia. Over 97% with chronic HCV viremia are male; among those born after 1965, 90% of those with chronic HCV viremia are male (data not shown). Those born during 1945 – 1965 were more likely to have a HCV antibody performed when compared with those born in other years (54% vs. 41%, odds ratio [OR] 1.70, 95% Confidence Interval [CI] 1.65-1.74). Among veterans ever tested for HCV antibody, those born during 1945 – 1965 were 6 times more likely to have a positive HCV antibody (15% vs. 3%, OR 5.87, 95% CI 5.32-6.78) and 3 times more likely to have confirmed, chronic HCV infection (76% vs. 50%, OR 3.25, 95% CI 2.65-4.00).", "We found that nearly 50% of the veterans seen at the AVAMC in 2011 had received HCV antibody screening. Similarly, an analysis of National VHA HCV screening practices found that 53% of veterans with at least one outpatient visit at any VA clinic in 2011 had received HCV screening\n[7]. Civilian primary care settings report HCV screening rates of 1 – 8%\n[8-10] but interventions designed to enhance HCV screening have been shown to increase screening in high risk persons to 40%\n[5,10]. It is likely that the enhanced HCV screening recommendations for veterans and the electronic clinical reminder contributed to the higher HCV screening practices observed in our population and other VHA settings.\nAfter accounting for untested veterans, the overall HCV prevalence among veterans seen at the AVAMC in 2011 is between 5% and 10%. While this estimate is comparable to published estimates at other VHA settings and the national VA estimate\n[7,11-14], it is much higher than the estimated HCV prevalence of 1.6% obtained from a national civilian survey\n[15]. Even among those born during 1945 – 1965, the HCV tested prevalence is higher in the veteran population (15%) compared with the civilian population (3%)\n[15]. Although the higher HCV prevalence in veterans has been well described, reasons for the higher burden are not fully understood and are likely multifactorial.\nVeterans born during 1945 – 1965 were more likely to be screened for HCV, to have a positive HCV antibody, and to develop chronic HCV infection. The VHA recommendation to perform HCV screening in Vietnam-era veterans likely explains the higher HCV screening seen in those born during 1945 – 1965. Our finding confirms the higher HCV prevalence in those born during 1945 – 1965, supporting the birth cohort screening recommendations of CDC and USPSTF. While it is not fully understood why HCV prevalence is higher in this birth cohort, it has been hypothesized that it reflects incident HCV infections acquired through experimental intravenous illicit drug use during the 1970’s and 1980’s\n[16,17]. Additionally, higher HCV prevalence has been observed in similar birth cohorts outside of the United States, including in Scotland\n[18], England\n[19], and Cameroon\n[20]. Lastly, we found that those born during 1945 – 1965 were at increased risk of having chronic HCV infection (i.e. having a positive antibody and a positive viral load). Among those born after 1965, only 31% of those with a positive HCV antibody also had a positive viral load. There are two possible explanations for having a positive HCV antibody and a negative viral load; namely, a false positive HCV antibody or clearance of HCV viremia. While it is surprising that relatively few persons born after 1965 had evidence of chronic HCV infection, it is notable that there are more females in this cohort. Female sex has been associated with clearance of HCV infection in prospective studies of acute HCV\n[21-24]. While complex host and pathogen factors likely influence the development of chronic HCV infection, further exploration of the association between birth year and the development of chronic HCV infection is warranted.\nConfirmatory HCV RNA viral loads were performed in 96% of the HCV antibody positive persons. This high confirmatory testing rate was also seen in an analysis of national VA data\n[7]. In contrast, surveillance for HCV infections from eight civilian US sites found that confirmatory RNA viral loads were only performed in 50% of positive HCV antibody tests\n[25]. By reflexively performing HCV RNA viral load testing on positive HCV antibody tests, the AVAMC testing practices are consistent with the current CDC guidelines which recommend RNA viral loads on all reactive HCV antibody tests\n[26].\nOur analysis was limited to testing performed at the AVAMC and did not include testing done at other VA hospitals or testing done outside of VA system. However, test results from additional sources would likely only increase the screening estimates in our analysis. Because this analysis used administrative databases, information on HCV behavioral risk factors, medical comorbidities, and the rationale used by providers for HCV screening is not known. Information on race was missing for most veterans seen at the AVAMC in 2011. Other administrative databases from the AVAMC shows that 40% of veterans seen in 2011 are African American race, 40% Caucasian, and 2% “other” race; race was unknown in 18%. CDC and USPSTF recommend HCV screening for all persons born during 1945 – 1965, regardless of race. The veteran population in our analysis is older than the general population in the United States [http://www.census.gov/2010census/]. Lastly, because this analysis included a predominantly male population our findings may not be applicable in other settings.", "The enhanced screening efforts undertaken at the AVAMC in 2001 resulted in a significant increase in the annual number of HCV antibody tests performed. The diagnosis of chronic HCV infection allows for prevention interventions such as alcohol counseling, vaccination against hepatitis A and B, screening for advanced liver fibrosis, and referral for antiviral therapy. HCV antiviral management is rapidly evolving with the development of better tolerated and more efficacious therapies\n[27]. Achieving a sustained virologic response to antiviral therapy is known to significantly reduce the risk of liver failure\n[28], hepatocellular carcinoma\n[29], liver-related and all-cause mortality\n[30,31], and reduces the risk of further HCV transmission.\nWe found that many veterans born during 1945 – 1965 had been screened for HCV infection at the AVAMC even before the augmented CDC screening guidelines were published in 2012. However, given the high prevalence of disease in this birth cohort and the importance of HCV detection, continued screening practices that target this birth cohort are warranted.", "VHA: Veterans health administration; HCV: Hepatitis C virus; RNA: Ribonucleic acid; HIPAA: Health insurance portability and accountability act; OR: Odds ratio; CI: Confidence interval.", "The authors declare that they have no competing interests.", "EC conceived and designed the study, performed data abstraction and analysis, and drafted the manuscript. CR provided input on study design, data analysis, data interpretation, statistical testing, and provided critical review of the manuscript. DR assisted with study design, obtained data, and provided input on data analysis and interpretation, and critically reviewed the manuscript. All authors read and approved the final manuscript." ]

[ null, "methods", "results", "discussion", "conclusions", null, null, null ]

[ "Hepatitis C", "Screening", "Prevention & control" ]

[ 332, 36, 10, 75 ]

[ "hcv", "antibody", "hcv antibody", "screening", "veterans", "positive", "born", "1965", "hcv screening", "2011" ]

[ "providers hcv screening", "hcv screening adults", "report hcv screening", "hcv screening guidelines", "hcv screening civilian" ]

[ "Adult", "Male", "Female", "Humans", "Cardiorespiratory Fitness", "Cross-Sectional Studies", "Japan", "Muscle Strength", "Physical Fitness" ]

[ "Design, setting, and participants", "Measurements", "Medical checkup", "Bone stiffness and definition of low bone stiffness tendency", "Physical fitness test", "Statistical analysis", "Conclusion" ]

[ "This study was a cross-sectional analysis on the association of knee extensor muscle strength and CRF with bone stiffness. The eligibility criteria were: i) Sport Program Service (SPS, explained below) participants between April 1998 to July 2019, ii) aged ≥45 years, and iii) participants who had undergone bone stiffness measurements. SPS is a comprehensive medical checkup program primarily examining various domains of physical fitness held at the Yokohama Sports Medical Center. The SPS was initiated in April 1998 to improve the health status of people living or working in Yokohama City. Participants voluntarily apply to the service through the center’s website, and public information is published by the local government. This service receives an average of 10 people daily, totaling to 1,500 people annually. Participants eligible to use the service are those aged between 18 and 65 years, living or working in Yokohama City, and paid 15,000 JPY (approximately $142 in 2020); aged >65 years and paid 7,500 JPY ($71); and not living and working in Yokohama City, aged <65 years old, and paid 17,000 JPY ($161) and 8,500 JPY ($80). All data were selected at a single timepoint when participants first joined the service.\nPrior to joining the SPS, all participants provided their informed consent for data use. This study was conducted according to the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Yokohama Sports Medical Center (K-2019-07).", "Medical checkup Height and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nHeight and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nBone stiffness and definition of low bone stiffness tendency The generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nThe generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nPhysical fitness test Knee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.\nKnee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.", "Height and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.", "The generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.", "Knee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.", "Due to clear sex and age differences in the prevalence of osteoporosis,19 the participants were segregated by sex and age group (aged 45–54, 55–64, and ≥65 years), and categorized into tertiles based on knee extensor muscle strength. Thereafter, each age category was combined based on muscle strength, and new groups were created with age-adjusted tertiles. Continuous and categorical variables were expressed as median (interquartile rages) or mean (standard deviation), and percentage, respectively.\nTo reduce potential biases due to incomplete data,20 missing data were treated with multiple imputation methods using SPSS (IBM, Inc., Chicago, IL, USA) by creating a random number through the Markov chain Monte Carlo algorithm, wherein 20 para-complete datasets were produced.21 Blood glucose, total cholesterol, HDL cholesterol, LDL cholesterol, TG, ALP, and UA were used as auxiliary variables to account for the missing data. The auxiliary variables were not used in the main analysis. The 20 datasets were integrated with the standard Rubin’s technique. Every missing value is presented in Table 1.\nHDL, high-density lipoprotein; LDL, low-density lipoprotein cholesterol; OSI, osteo-sono assessment index; and YAM, young-adult mean.\nData are presented as median [interquartile ranges], mean (standard deviation), and number (percentage) unless specified.\nTo assess the association of each covariate, knee extensor muscle strength, and CRF with bone stiffness, univariable odds ratios and 95% CIs were calculated using a logistic regression model. The covariates included age,22 smoking status,23 drinking habits,24 BMI,25 systolic blood pressure,26 and menopause.23\nTo evaluate the association between knee extensor muscle strength and low bone stiffness tendency, multivariable odds ratios and 95% CIs were calculated using the lowest knee extensor muscle strength group as reference after adjusting for smoking status, drinking habits, BMI, systolic blood pressure, and menopause (for women only). In the mutually adjusted final model, CRF was entered into the final model. Moreover, a continuous variable of knee extensor muscle strength was used in another model to test linearity. The same analyses were repeated with CRF as the primary exposure and knee extensor muscle strength as the final covariate. To consider the moderating effect of menopausal status, a menopause-stratified multivariable analysis was also performed.\nTo evaluate the interactive association of knee extensor muscle strength and CRF with low bone stiffness tendency, a CRF-stratified (below or above the median) multivariable analysis was performed. Odds ratios and 95% CIs were calculated similarly as mentioned above, and the interaction term (knee extensor muscle strength*CRF) was entered into the models. A menopause-stratified multivariable analysis was also conducted.\nTo verify the validity of the applicable standards for low bone stiffness tendency, the following two sensitivity analyses were performed. First, we repeated the primary analysis by changing the definition of low bone stiffness tendency from 80% under the YAM to 70% under the YAM. Second, a complete-case analysis was conducted to distinguish findings with and without considering missing values. All sensitivity analysis data are shown in the eTable 1, eTable 2, eTable 3, eTable 4, eTable 5, eTable 6, and eTable 7.\nAll statistical analyses were performed using the SPSS version 25. A P-value <0.05 was considered significant.", "We found an additive association between knee extensor muscle strength and CRF for bone health. Moreover, an inverse association existed between knee extensor muscle strength and low bone stiffness tendency in both sexes. Accordingly, maintaining not only higher knee extensor muscle strength, but also higher CRF may be important to prevent or delay the onset of osteoporosis. From a practical viewpoint, these results suggest that middle-aged or older women and men should be encouraged to participate in high-intensity physical activities with mechanical loading, such as running, jumping rope, and resistance exercise. Further longitudinal studies are needed to investigate the relationship between physical fitness and bone status." ]

[ null, null, null, null, null, null, null ]

[ "INTRODUCTION", "METHODS", "Design, setting, and participants", "Measurements", "Medical checkup", "Bone stiffness and definition of low bone stiffness tendency", "Physical fitness test", "Statistical analysis", "RESULTS", "DISCUSSION", "Conclusion" ]

[ "Osteoporosis is characterized by low bone strength and an increased risk of bone fractures,1 which is highly associated with mortality.2,3 Moreover, as osteoporosis imposes a heavy economic burden on the gross domestic product,4 it is widely recognized as a serious public health concern not only in Japan but also in other aged societies. Currently, Japan experiences one of the most serious situations in Asia5 because the estimated number of patients with osteoporosis exceeds 12.8 million (men: 3 million; women: 9.8 million).6 To minimize the detrimental effects of osteoporotic fractures on a patient’s quality of life, early detection of low bone strength and preventive interventions preferably based on risk stratification are strongly needed.\nThe established risk factors for low bone strength are advanced age, female sex, genetic factors, low body weight, and physical inactivity, including low physical fitness represented as cardiorespiratory fitness (CRF) and/or grip strength.7,8 Compared to low CRF, high CRF represents an odds ratio of 0.29 (95% confidence interval [CI], 0.12–0.71) for the femoral neck T-score ≤ 2.5.7 The hazard ratio for osteoporotic fracture per 5 kg reduction in hand grip strength was 1.49 (95% CI, 1.18–1.95) in a 10-year prospective cohort study.8 Maintaining high levels of physical fitness, assessed via CRF and/or hand grip strength, is a key factor for preventing low bone strength, which consequently lowers the risk of osteoporosis development.\nGrip strength is often used as an indicator of muscle strength because it is valid and reliable for whole-body muscle strength.9 However, its use is controversial since it cannot be measured for antigravity muscles, such as the knee extensor muscle.10 As knee extensor muscle strength predicts the risk of falls and mobility, it can be used as a better indicator of bone strength.11,12 To our knowledge, it remains unclear whether knee extensor muscle strength is beneficially associated with bone strength. Moreover, CRF is also one of the predictors for low bone strength.7 However, information on the association between knee extensor muscle strength and bone strength according to varying levels of CRF is limited.\nThis study aimed to investigate the interactive associations of knee extensor muscle strength and CRF with bone stiffness as a marker of bone strength in Japanese adults. The findings from this cross-sectional study will allow us to design future longitudinal and clinical studies to investigate the preventive effect of various strategies for osteoporosis and possibly result in a decrease in osteoporotic fractures.", "Design, setting, and participants This study was a cross-sectional analysis on the association of knee extensor muscle strength and CRF with bone stiffness. The eligibility criteria were: i) Sport Program Service (SPS, explained below) participants between April 1998 to July 2019, ii) aged ≥45 years, and iii) participants who had undergone bone stiffness measurements. SPS is a comprehensive medical checkup program primarily examining various domains of physical fitness held at the Yokohama Sports Medical Center. The SPS was initiated in April 1998 to improve the health status of people living or working in Yokohama City. Participants voluntarily apply to the service through the center’s website, and public information is published by the local government. This service receives an average of 10 people daily, totaling to 1,500 people annually. Participants eligible to use the service are those aged between 18 and 65 years, living or working in Yokohama City, and paid 15,000 JPY (approximately $142 in 2020); aged >65 years and paid 7,500 JPY ($71); and not living and working in Yokohama City, aged <65 years old, and paid 17,000 JPY ($161) and 8,500 JPY ($80). All data were selected at a single timepoint when participants first joined the service.\nPrior to joining the SPS, all participants provided their informed consent for data use. This study was conducted according to the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Yokohama Sports Medical Center (K-2019-07).\nThis study was a cross-sectional analysis on the association of knee extensor muscle strength and CRF with bone stiffness. The eligibility criteria were: i) Sport Program Service (SPS, explained below) participants between April 1998 to July 2019, ii) aged ≥45 years, and iii) participants who had undergone bone stiffness measurements. SPS is a comprehensive medical checkup program primarily examining various domains of physical fitness held at the Yokohama Sports Medical Center. The SPS was initiated in April 1998 to improve the health status of people living or working in Yokohama City. Participants voluntarily apply to the service through the center’s website, and public information is published by the local government. This service receives an average of 10 people daily, totaling to 1,500 people annually. Participants eligible to use the service are those aged between 18 and 65 years, living or working in Yokohama City, and paid 15,000 JPY (approximately $142 in 2020); aged >65 years and paid 7,500 JPY ($71); and not living and working in Yokohama City, aged <65 years old, and paid 17,000 JPY ($161) and 8,500 JPY ($80). All data were selected at a single timepoint when participants first joined the service.\nPrior to joining the SPS, all participants provided their informed consent for data use. This study was conducted according to the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Yokohama Sports Medical Center (K-2019-07).\nMeasurements Medical checkup Height and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nHeight and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nBone stiffness and definition of low bone stiffness tendency The generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nThe generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nPhysical fitness test Knee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.\nKnee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.\nMedical checkup Height and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nHeight and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nBone stiffness and definition of low bone stiffness tendency The generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nThe generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nPhysical fitness test Knee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.\nKnee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.\nStatistical analysis Due to clear sex and age differences in the prevalence of osteoporosis,19 the participants were segregated by sex and age group (aged 45–54, 55–64, and ≥65 years), and categorized into tertiles based on knee extensor muscle strength. Thereafter, each age category was combined based on muscle strength, and new groups were created with age-adjusted tertiles. Continuous and categorical variables were expressed as median (interquartile rages) or mean (standard deviation), and percentage, respectively.\nTo reduce potential biases due to incomplete data,20 missing data were treated with multiple imputation methods using SPSS (IBM, Inc., Chicago, IL, USA) by creating a random number through the Markov chain Monte Carlo algorithm, wherein 20 para-complete datasets were produced.21 Blood glucose, total cholesterol, HDL cholesterol, LDL cholesterol, TG, ALP, and UA were used as auxiliary variables to account for the missing data. The auxiliary variables were not used in the main analysis. The 20 datasets were integrated with the standard Rubin’s technique. Every missing value is presented in Table 1.\nHDL, high-density lipoprotein; LDL, low-density lipoprotein cholesterol; OSI, osteo-sono assessment index; and YAM, young-adult mean.\nData are presented as median [interquartile ranges], mean (standard deviation), and number (percentage) unless specified.\nTo assess the association of each covariate, knee extensor muscle strength, and CRF with bone stiffness, univariable odds ratios and 95% CIs were calculated using a logistic regression model. The covariates included age,22 smoking status,23 drinking habits,24 BMI,25 systolic blood pressure,26 and menopause.23\nTo evaluate the association between knee extensor muscle strength and low bone stiffness tendency, multivariable odds ratios and 95% CIs were calculated using the lowest knee extensor muscle strength group as reference after adjusting for smoking status, drinking habits, BMI, systolic blood pressure, and menopause (for women only). In the mutually adjusted final model, CRF was entered into the final model. Moreover, a continuous variable of knee extensor muscle strength was used in another model to test linearity. The same analyses were repeated with CRF as the primary exposure and knee extensor muscle strength as the final covariate. To consider the moderating effect of menopausal status, a menopause-stratified multivariable analysis was also performed.\nTo evaluate the interactive association of knee extensor muscle strength and CRF with low bone stiffness tendency, a CRF-stratified (below or above the median) multivariable analysis was performed. Odds ratios and 95% CIs were calculated similarly as mentioned above, and the interaction term (knee extensor muscle strength*CRF) was entered into the models. A menopause-stratified multivariable analysis was also conducted.\nTo verify the validity of the applicable standards for low bone stiffness tendency, the following two sensitivity analyses were performed. First, we repeated the primary analysis by changing the definition of low bone stiffness tendency from 80% under the YAM to 70% under the YAM. Second, a complete-case analysis was conducted to distinguish findings with and without considering missing values. All sensitivity analysis data are shown in the eTable 1, eTable 2, eTable 3, eTable 4, eTable 5, eTable 6, and eTable 7.\nAll statistical analyses were performed using the SPSS version 25. A P-value <0.05 was considered significant.\nDue to clear sex and age differences in the prevalence of osteoporosis,19 the participants were segregated by sex and age group (aged 45–54, 55–64, and ≥65 years), and categorized into tertiles based on knee extensor muscle strength. Thereafter, each age category was combined based on muscle strength, and new groups were created with age-adjusted tertiles. Continuous and categorical variables were expressed as median (interquartile rages) or mean (standard deviation), and percentage, respectively.\nTo reduce potential biases due to incomplete data,20 missing data were treated with multiple imputation methods using SPSS (IBM, Inc., Chicago, IL, USA) by creating a random number through the Markov chain Monte Carlo algorithm, wherein 20 para-complete datasets were produced.21 Blood glucose, total cholesterol, HDL cholesterol, LDL cholesterol, TG, ALP, and UA were used as auxiliary variables to account for the missing data. The auxiliary variables were not used in the main analysis. The 20 datasets were integrated with the standard Rubin’s technique. Every missing value is presented in Table 1.\nHDL, high-density lipoprotein; LDL, low-density lipoprotein cholesterol; OSI, osteo-sono assessment index; and YAM, young-adult mean.\nData are presented as median [interquartile ranges], mean (standard deviation), and number (percentage) unless specified.\nTo assess the association of each covariate, knee extensor muscle strength, and CRF with bone stiffness, univariable odds ratios and 95% CIs were calculated using a logistic regression model. The covariates included age,22 smoking status,23 drinking habits,24 BMI,25 systolic blood pressure,26 and menopause.23\nTo evaluate the association between knee extensor muscle strength and low bone stiffness tendency, multivariable odds ratios and 95% CIs were calculated using the lowest knee extensor muscle strength group as reference after adjusting for smoking status, drinking habits, BMI, systolic blood pressure, and menopause (for women only). In the mutually adjusted final model, CRF was entered into the final model. Moreover, a continuous variable of knee extensor muscle strength was used in another model to test linearity. The same analyses were repeated with CRF as the primary exposure and knee extensor muscle strength as the final covariate. To consider the moderating effect of menopausal status, a menopause-stratified multivariable analysis was also performed.\nTo evaluate the interactive association of knee extensor muscle strength and CRF with low bone stiffness tendency, a CRF-stratified (below or above the median) multivariable analysis was performed. Odds ratios and 95% CIs were calculated similarly as mentioned above, and the interaction term (knee extensor muscle strength*CRF) was entered into the models. A menopause-stratified multivariable analysis was also conducted.\nTo verify the validity of the applicable standards for low bone stiffness tendency, the following two sensitivity analyses were performed. First, we repeated the primary analysis by changing the definition of low bone stiffness tendency from 80% under the YAM to 70% under the YAM. Second, a complete-case analysis was conducted to distinguish findings with and without considering missing values. All sensitivity analysis data are shown in the eTable 1, eTable 2, eTable 3, eTable 4, eTable 5, eTable 6, and eTable 7.\nAll statistical analyses were performed using the SPSS version 25. A P-value <0.05 was considered significant.", "This study was a cross-sectional analysis on the association of knee extensor muscle strength and CRF with bone stiffness. The eligibility criteria were: i) Sport Program Service (SPS, explained below) participants between April 1998 to July 2019, ii) aged ≥45 years, and iii) participants who had undergone bone stiffness measurements. SPS is a comprehensive medical checkup program primarily examining various domains of physical fitness held at the Yokohama Sports Medical Center. The SPS was initiated in April 1998 to improve the health status of people living or working in Yokohama City. Participants voluntarily apply to the service through the center’s website, and public information is published by the local government. This service receives an average of 10 people daily, totaling to 1,500 people annually. Participants eligible to use the service are those aged between 18 and 65 years, living or working in Yokohama City, and paid 15,000 JPY (approximately $142 in 2020); aged >65 years and paid 7,500 JPY ($71); and not living and working in Yokohama City, aged <65 years old, and paid 17,000 JPY ($161) and 8,500 JPY ($80). All data were selected at a single timepoint when participants first joined the service.\nPrior to joining the SPS, all participants provided their informed consent for data use. This study was conducted according to the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Yokohama Sports Medical Center (K-2019-07).", "Medical checkup Height and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nHeight and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.\nBone stiffness and definition of low bone stiffness tendency The generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nThe generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.\nPhysical fitness test Knee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.\nKnee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.", "Height and body weight of barefoot participants were measured using a calibrated height-weight scale (WB-510; Tanita Co., Tokyo, Japan). Body mass index (BMI) was calculated as body weight/height2 (kg/m2). After a 5-minute sitting position on a chair and medical examination by a physician, resting blood pressure was measured through the Riva-Rocci Korotkov method using a mercury sphygmomanometer. Blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, triglycerides (TG), alkaline phosphatase (ALP), and uric acid (UA) were sampled after 12-hour fasting and analyzed with Roche INTEGRA 400 plus (Roche International Ltd., Basle, Switzerland). Data on habitual alcohol drinking (yes or no), habitual smoking status (yes or no), and menopausal status (yes or no) were obtained through a self-reported questionnaire.", "The generally known method for assessing bone strength is dual energy X-ray absorptiometry (DXA), which is commonly used to diagnose osteoporosis. As DXA emits radiation to assess the bone, there are problems, such as radiation exposure, costliness, being time-consuming, access to the place where DXA is set up, and the need for radiologists. In contrast, quantitative ultrasound (QUS), which is used to assess bone stiffness as an objective marker of bone strength, is well established in Japan.6 Compared to DXA, QUS results in better outcomes for the patients as it avoids exposure to radiation and is inexpensive, highly portable, and efficient. Therefore, in this study, bone stiffness was assessed using a QUS device (AOS-100NW; Hitachi Aloka Medical, Ltd., Mitaka, Tokyo, Japan) that has a high correlation with other QUS devices or DXA (r = 0.804, P < 0.001).13,14 Maintenance and calibration were performed to preserve the quality of the results once per month. The method of measurement was as follows: the participants sat on a chair barefoot with their knees bent at 90°. A device was placed on their right calcaneal, and gel was applied on the membranes. The measurement was performed once for approximately 30 seconds. Bone stiffness was expressed using the osteo-sono assessment index, which was calculated using the speed of sound and transmission index15 and was highly reproducible.13 Young adult mean (YAM) was calculated based on average age of 20 to 44 years as 100%.16 Low bone stiffness tendency was defined as 80% under the YAM (YAM80%), according to the 2011 Japanese guideline for prevention and treatment of osteoporosis—executive summary.6 Similarly, a 70% under the YAM (YAM70%) was also calculated to analyze sensitivity.", "Knee extensor muscle strength, which assessed maximum voluntary knee extension torque, was measured using an isokinetic dynamometer (Cybex Humac Norm 770; Computer Sports Medicine Inc., Stoughton, MA, USA). The measurement process was as follows: the participants sat so that their knee and hip joint formed a right angle and performed warm-ups. Thereafter, the participants exerted isokinetic maximum voluntary knee extension at 60 degrees/s at least twice. Overall, the participants performed three repetitions with a 30-second break. The maximum value was considered as knee extensor strength (Nm) and adjusted by the participant’s own body weight (Nm/kg).\nCRF was assessed by applying physical working capacity at 75% of the maximum heart rate (PWC75%HRmax),17 which was highly correlated with maximal oxygen uptake (r = 0.942),18 using the submaximal graded exercise test method on an electronic bicycle ergometer (The Multi Exercise Test System, ML-1800, Fukuda-Denshi, Tokyo, Japan). On the graded exercise test, the rate of loading (10–60 W/min), which was an individualized ramp protocol, was decided by experts based on the participants’ age and habitual aerobic exercise. The target heart rate was set at 75% of the estimated maximum heart rate (220 minus age), and the test was ended upon reaching target value. Additionally, the participants could end the test when an abnormal electrocardiogram result (ST depression or frequent occurrence of the extrasystole) was confirmed by the cardiologists or when they could not pedal with the designated rhythm (50 rpm) while showing poor physical condition. Most participants ended the test at approximately 10 min.", "Due to clear sex and age differences in the prevalence of osteoporosis,19 the participants were segregated by sex and age group (aged 45–54, 55–64, and ≥65 years), and categorized into tertiles based on knee extensor muscle strength. Thereafter, each age category was combined based on muscle strength, and new groups were created with age-adjusted tertiles. Continuous and categorical variables were expressed as median (interquartile rages) or mean (standard deviation), and percentage, respectively.\nTo reduce potential biases due to incomplete data,20 missing data were treated with multiple imputation methods using SPSS (IBM, Inc., Chicago, IL, USA) by creating a random number through the Markov chain Monte Carlo algorithm, wherein 20 para-complete datasets were produced.21 Blood glucose, total cholesterol, HDL cholesterol, LDL cholesterol, TG, ALP, and UA were used as auxiliary variables to account for the missing data. The auxiliary variables were not used in the main analysis. The 20 datasets were integrated with the standard Rubin’s technique. Every missing value is presented in Table 1.\nHDL, high-density lipoprotein; LDL, low-density lipoprotein cholesterol; OSI, osteo-sono assessment index; and YAM, young-adult mean.\nData are presented as median [interquartile ranges], mean (standard deviation), and number (percentage) unless specified.\nTo assess the association of each covariate, knee extensor muscle strength, and CRF with bone stiffness, univariable odds ratios and 95% CIs were calculated using a logistic regression model. The covariates included age,22 smoking status,23 drinking habits,24 BMI,25 systolic blood pressure,26 and menopause.23\nTo evaluate the association between knee extensor muscle strength and low bone stiffness tendency, multivariable odds ratios and 95% CIs were calculated using the lowest knee extensor muscle strength group as reference after adjusting for smoking status, drinking habits, BMI, systolic blood pressure, and menopause (for women only). In the mutually adjusted final model, CRF was entered into the final model. Moreover, a continuous variable of knee extensor muscle strength was used in another model to test linearity. The same analyses were repeated with CRF as the primary exposure and knee extensor muscle strength as the final covariate. To consider the moderating effect of menopausal status, a menopause-stratified multivariable analysis was also performed.\nTo evaluate the interactive association of knee extensor muscle strength and CRF with low bone stiffness tendency, a CRF-stratified (below or above the median) multivariable analysis was performed. Odds ratios and 95% CIs were calculated similarly as mentioned above, and the interaction term (knee extensor muscle strength*CRF) was entered into the models. A menopause-stratified multivariable analysis was also conducted.\nTo verify the validity of the applicable standards for low bone stiffness tendency, the following two sensitivity analyses were performed. First, we repeated the primary analysis by changing the definition of low bone stiffness tendency from 80% under the YAM to 70% under the YAM. Second, a complete-case analysis was conducted to distinguish findings with and without considering missing values. All sensitivity analysis data are shown in the eTable 1, eTable 2, eTable 3, eTable 4, eTable 5, eTable 6, and eTable 7.\nAll statistical analyses were performed using the SPSS version 25. A P-value <0.05 was considered significant.", "Altogether, 18,161 adults (8,902 men and 9,259 women) joined the service from April 1998 to July 2017. Of them, 9,112 aged ≤45 years old and 220 without bone stiffness measurements were excluded (Figure 1). Of the final sample of 8,829 (3,731 men and 5,098 women), 542 men (14.5%) and 978 women (19.2%) had low bone stiffness tendency based on YAM80%. Similarly, 45 men (1.2%) and 49 women (1.0%) were classified as having low bone stiffness based on YAM70%. The lowest fitness category appears to have an inferior biomarker status, such as triglycerides than the other categories (Table 1).\nTable 2 shows the association between each potential covariate and bone stiffness. The association between smoking status and low bone stiffness tendency was found in men (P < 0.001), but not in women (P = 0.097). Low BMI, low knee extensor muscle strength, and low CRF were all associated with a higher prevalence of low bone stiffness tendency.\nCI, confidence interval.\naPrevalence per 1,000 persons.\nAn inverse association between knee extensor muscle strength and low bone stiffness tendency was observed after adjusting for potential confounders, such as age, smoking status, drinking habits, BMI, systolic blood pressure, menopause (for women only), and CRF in both sexes (P for linear trend <0.001 for both). Meanwhile, CRF and low bone stiffness tendency showed an inverse association in men (P for linear trend <0.001), but not in pre-menopausal, post-menopausal, and all women (P for linear trend = 0.634, 0.841, and 0.924, respectively), as shown in Table 3. Both knee extensor muscle strength and CRF had no interactive association with low bone stiffness tendency in both sexes (men; P = 0.836, all women; P = 0.700, post-menopausal women; P = 0.615), as shown in Table 4.\nValues are expressed as odds ratio (95% confidence interval).\naAdditionally adjusted smoking status, drinking habits, body mass index, systolic blood pressure, and menopause (for women only).\nbMutually adjusted cardiorespiratory fitness or knee extensor muscle strength plus variables in multivariable adjustmenta.\nCI, confidence interval.\naPrevalence per 1,000 persons.\nbAdjusted for age, smoking status, drinking habits, body mass index, systolic blood pressure, and menopause (for women only).\ncUsing the lowest cardiorespiratory fitness and the lowest knee extensor muscle strength as reference.", "Our primary findings were as follows. First, no interactive association between knee extensor muscle strength and CRF was found for low bone stiffness tendency in both sexes. Second, higher knee extensor muscle strength was associated with a lower prevalence of low bone stiffness tendency in both sexes, independent of CRF. Third, higher CRF was associated with a lower prevalence of low bone stiffness tendency in men but not in women, independent of knee extensor muscle strength. These findings suggest that a combination of muscle strength and CRF may carry an additive benefit, not synergistic, for bone health. However, the association between CRF and bone health may not be evident in adult Japanese women.\nContrary to our expectation, the knee extensor muscle strength and CRF had no interactive association with bone stiffness. Hence, each of the two physical fitness elements is independently or additively, not synergistically, associated with bone health. As described in the subsequent paragraphs, the possible mechanisms of muscle strength or CRF to bone strength may be different. However, it is unknown whether the two different pathways interfere with or work synergistically with each other, due to lack of findings from basic mechanistic studies. A previous randomized controlled trial has revealed that resistance exercise and a combination of resistance and aerobic exercise could prevent bone loss compared to aerobic exercise alone during a weight loss program.27 This finding supports the notion that a combined aerobic and resistance exercise may be recommended to prevent a loss of musculoskeletal function.28 This may be consistent with our results.\nKnee extensor muscle strength was positively associated with bone stiffness after adjusting for CRF in both sexes. According to Wolff’s law, theories have indicated the potential of the bone to adapt its remodeling response to external stressors.29 It is assumed that knee extensor muscle strength may play the role of an external stressor to improve bone stiffness. Meanwhile, another possible mechanism of association between muscle strength and bone stiffness is related to muscle functioning as an endocrine organ that secretes myokines. Previous physiological studies have reported that muscles can play a role in secreting myokines that activate bone metabolism, such as myostatin, insulin-like growth factor (IGF)-1, and IGF-2.30 A previous observational study has reported an association between muscle strength and nonadjacent bones.31 However, the details of this association remain unclear, so further investigations into the mechanisms are warranted. Because bone mineral density has been highly correlated with bone stiffness,13,14 these are reflected in our study’s results, as knee extensor muscle strength was associated with bone stiffness after adjusting for confounders.\nCRF was also deemed beneficial for bone health in men. A previous large-scale observational study has showed similar trends as this study.32 CRF partially reflects one’s physical activity level and has been utilized as an index of health outcome.33 Because physical activity produces mechanical loading to the musculoskeletal system, which enhances osteocyte activation and bone resorption and formation, the American College of Sport Medicine currently recommends weightbearing physical activities.34 This study identified a sex difference in the association between CRF and bone stiffness (ie, no association in women). From a biological viewpoint, one possible mechanism for bone loss could be greater age-dependent loss of estrogen in women, which reduces estrogen receptor and osteogenic response.35 Accordingly, no association between CRF and bone health was observed in women, as in a previous study.36\nThis study has several noteworthy strengths. First, we analyzed a large-scale population of participants across a wide age range (≥45 years old). A difference in the age of onset between men and women of osteoporosis caused by lowering bone strength was observed.19 The number of patients with osteoporosis drastically increases after menopause for women and after the age of 60 for men.19 Second, we reduced potential biases by using multiple imputation methods.21 Third, this study expanded the body of knowledge in this area by examining the joint associations between knee extensor muscle strength and CRF with bone stiffness. Fourth, we used an isokinetic knee extensor muscle strength dynamometer, which enabled precise measurements. Although it requires expert skills, isokinetic muscle strength that reflects active physical activity could be assessed with accuracy.37\nMeanwhile, there are also several limitations. First, causal associations among knee extensor muscle strength, CRF, and bone stiffness could not be assessed owing to the cross-sectional nature of the study. Second, there was no information on habitual dietary intake, such as protein, which could positively contribute to bone mineral density.38 Thus, the risk of residual confounding by dietary habits may exist in the results. Third, there was a lack of information on the use of osteoporosis medication and the history of surgery. Hence, bone stiffness could be underestimated. Finally, there was a lack of information on habitual physical activity. As half of the variances in CRF are accounted for by one’s genotype, CRF is not a direct marker of physical activity, although it may reflect an aspect of physical activity.39 Therefore, precise assessments of physical activity are preferable.\nConclusion We found an additive association between knee extensor muscle strength and CRF for bone health. Moreover, an inverse association existed between knee extensor muscle strength and low bone stiffness tendency in both sexes. Accordingly, maintaining not only higher knee extensor muscle strength, but also higher CRF may be important to prevent or delay the onset of osteoporosis. From a practical viewpoint, these results suggest that middle-aged or older women and men should be encouraged to participate in high-intensity physical activities with mechanical loading, such as running, jumping rope, and resistance exercise. Further longitudinal studies are needed to investigate the relationship between physical fitness and bone status.\nWe found an additive association between knee extensor muscle strength and CRF for bone health. Moreover, an inverse association existed between knee extensor muscle strength and low bone stiffness tendency in both sexes. Accordingly, maintaining not only higher knee extensor muscle strength, but also higher CRF may be important to prevent or delay the onset of osteoporosis. From a practical viewpoint, these results suggest that middle-aged or older women and men should be encouraged to participate in high-intensity physical activities with mechanical loading, such as running, jumping rope, and resistance exercise. Further longitudinal studies are needed to investigate the relationship between physical fitness and bone status.", "We found an additive association between knee extensor muscle strength and CRF for bone health. Moreover, an inverse association existed between knee extensor muscle strength and low bone stiffness tendency in both sexes. Accordingly, maintaining not only higher knee extensor muscle strength, but also higher CRF may be important to prevent or delay the onset of osteoporosis. From a practical viewpoint, these results suggest that middle-aged or older women and men should be encouraged to participate in high-intensity physical activities with mechanical loading, such as running, jumping rope, and resistance exercise. Further longitudinal studies are needed to investigate the relationship between physical fitness and bone status." ]

[ "intro", "methods", null, null, null, null, null, null, "results", "discussion", null ]

[ "knee extensor muscle strength", "cardiorespiratory fitness", "bone stiffness", "quantitative ultrasound" ]

[ 286, 1681, 182, 336, 313, 638, 122 ]

[ "bone", "strength", "knee", "bone stiffness", "stiffness", "participants", "muscle", "muscle strength", "extensor", "knee extensor" ]

[ "osteoporosis imposes heavy", "strategies osteoporosis", "associated mortality osteoporosis", "prevalence osteoporosis", "risk osteoporosis development" ]

[ "Male", "Humans", "Prostatic Neoplasms", "Prostate-Specific Antigen", "Early Detection of Cancer", "Case-Control Studies", "Bayes Theorem", "Vietnam", "Biopsy", "Lower Urinary Tract Symptoms", "Hospitals", "Asian People" ]

[ "Background", "Methods", "Study design and setting", "Participants", "Sample size", "Data collection and variables’ definition", "Statistical analysis", "Descriptive analysis", "Univariable logistic regression", "Bayesian model averaging (BMA) for model prediction", "CART model for PCa screening", "Association of epidemiological and behavioural characteristics with prostate cancer", "Association of I-PSS and PSA with prostate cancer", "BMA for PCa prediction", "CART for PCa screening", "Epidemiological and behavioral characteristics", "The role of PSA in PCa screening", "CART value for PCa screening" ]

[ "Prostate cancer (PCa) is common in men, especially in those aged 65 years and older. It has the second-highest incidence/prevalence (i.e., 30.7 per 100 000) and ranks fifth in cancer mortality rate among men (i.e., 7.7 per 100 000) worldwide [1]. In Vietnam, the incidence of PCa is 12.2 per 100 000, and the mortality rate is 5.1 per 100 000 as of 2020 [2]. Approximately 95–98% of PCa cases are adenocarcinomas that develop from adrenal duct cells [3]. PCa treatment depends primarily on the stage of development and the cell and patient characteristics. According to the American Cancer Society, PCa patients diagnosed at the localised or regional stage have a 5-year survival rate of over 90%. However, in the distant stage, the survival rate is only 30% [4]. Therefore, PCa should be detected at an early stage.\nProstate-specific antigen (PSA) is a serine protease in the kallikrein family and considered a tool for the screening and early detection of PCa [5]. It can help detect as early as nine years before having clinical symptoms [6]. There are two types of PCa screening studies using PSA, including population-based and hospital-based (or opportunistic) screenings [7]. The first type of screening deals with testing asymptomatic men with only PSA, and those with elevated PSA are immediately referred to biopsy. However, the latter type of screening involves testing men with some symptoms (e.g., lower urinary tract symptoms) using PSA and other clinical tools. Therefore, all men referred for biopsy in population-based screening are at lower risk of having PCa compared to that of hospital-based screening. PSA only based screening could accounted for 45–70% of the reduction in PCa mortality [8]; it could also induce the unnecessary biopsies [9]. In a 16 year follow-up of the European Randomized Study of Screening for Prostate Cancer (ERSPC), the unnecessary biopsy was 76% (i.e., 76% of elevated PSA cases have a negative biopsy) [10]. In addition, the optimal cut-off value of PSA for confirming PCa remains to be determined [11] [5]. In particular, even at a low level of PSA (that is, lower than 4 ng/mL), the false negative rate of PCa was high at 15%, whereas, at a high level of PSA (that is, higher than 10 ng/mL), the false positive rate was 50% [5].\nIn Vietnam, population-based PCa screening using PSA was conducted 12 years ago; it indicated a low prevalence of PCa (2.5%), but a high rate of medium grade lesions. The author also implied that the benefit of a mass screening program for PCa was not proven. Instead, a selective PCa screening in the usual care and at the hospital was superior in Vietnam. In hospital-based screening, combining clinical parameters, PSA, age, and other risk factors improved the prediction of prostate cancer [12–14]. International Prostate Symptom Score (I-PSS) is a screening scale for lower urinary tract symptoms and is used to screen non-specific prostate gland abnormalities. For PCa screening. For PCa screening, the I-PSS scale showed reasonable sensitivity (78%), but the specificity was not high (59.4%) [15]. A previous study showed that PSA screening performance varied with different I-PSS values. Therefore, combining PSA and I-PSS could improve the screening benefits [16]. There is, however, a paucity of such practical multivariable algorithm for hospital-based PCa screening in Vietnam.\nThe approach of PCa screening based on machine learning algorithms has only recently been applied. Algorithms including logistic regression, artificial neural networks, random forests, support vector machines, and extreme and light gradient boosting machines have been demonstrated to enhance PCa screening efficiency [13, 17–20]. However, these models do not help make clinical decisions in a step-to-step and intuitive manner. Classification and regression tree (CART) is an approach that allows physicians to apply results of the screening process directly and intuitively [17, 21].\nOur study aimed to investigate the association of PSA, I-PSS, epidemiological and behavioural characteristics with PCa and then used these factors to construct a classification and regression tree (CART) algorithm to select Vietnamese men with lower urinary tract symptoms (LUTS) for PCa biopsy. The algorithm is expected to aid in reducing the probability of a negative prostate biopsy (i.e., unnecessary biopsy) while maintaining the ability to reduce PCa mortality for Vietnamese patients.", " Study design and setting We conducted a case-control study at the MEDIC Medical Center, Ho Chi Minh City, Vietnam. MEDIC is the first and top modernity private medical centre in Vietnam. Every day, more than 4,000 patients visit the centre for examination and treatment. The study was approved by the local institutional ethics committee of the MEDIC Center, and the opinion was signed on 15th July, 2016.\nWe conducted a case-control study at the MEDIC Medical Center, Ho Chi Minh City, Vietnam. MEDIC is the first and top modernity private medical centre in Vietnam. Every day, more than 4,000 patients visit the centre for examination and treatment. The study was approved by the local institutional ethics committee of the MEDIC Center, and the opinion was signed on 15th July, 2016.\n Participants Our study participants were men aged ≥ 50 years who visited the MEDIC Centre in 2017–2018 with self-reported lower urinary tract symptoms. The inclusion criteria were abnormal lower urinary tract symptoms or enlarged prostate glands identified through DRE or ultrasound images. The exclusion criterions were acute prostatitis or refusal to participate in the study. All patients who meet the selection criteria were prescribed a biopsy. The case group was defined as having a positive biopsy result for PCa, and the control was defined as having a negative biopsy result. Biopsy based on 12-core Transrectal Ultrasound Guided Biopsy of the Prostate [22]. All patients provided written informed consent before participating in the study.\nOur study participants were men aged ≥ 50 years who visited the MEDIC Centre in 2017–2018 with self-reported lower urinary tract symptoms. The inclusion criteria were abnormal lower urinary tract symptoms or enlarged prostate glands identified through DRE or ultrasound images. The exclusion criterions were acute prostatitis or refusal to participate in the study. All patients who meet the selection criteria were prescribed a biopsy. The case group was defined as having a positive biopsy result for PCa, and the control was defined as having a negative biopsy result. Biopsy based on 12-core Transrectal Ultrasound Guided Biopsy of the Prostate [22]. All patients provided written informed consent before participating in the study.\n Sample size The minimum sample size estimated for each group of case-control studies was 116 patients to provide 90% power and 5% type I error to detect an odds ratio of 2.5. In Vietnam, PSA ≥ 10 ng/mL is considered as the high-risk group of PCa. Therefore, we chose the proportion of PSA ≥ 10 ng/mL equal to 23% in the control group as the proportion of controls with exposure in the sample size formula [23]. Our studies selected 130 patients for each group to ensure larger than the minimum sample size, hence the total patients was 260.\nThe minimum sample size estimated for each group of case-control studies was 116 patients to provide 90% power and 5% type I error to detect an odds ratio of 2.5. In Vietnam, PSA ≥ 10 ng/mL is considered as the high-risk group of PCa. Therefore, we chose the proportion of PSA ≥ 10 ng/mL equal to 23% in the control group as the proportion of controls with exposure in the sample size formula [23]. Our studies selected 130 patients for each group to ensure larger than the minimum sample size, hence the total patients was 260.\n Data collection and variables’ definition We collected epidemiological and behavioural characteristics through interviews using a questionnaire and collected clinical and subclinical information from medical records. Epidemiological characteristics included age, number of children, overweight/obesity (BMI ≥ 23 kg/m2 [24]), family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals. Lifestyle behaviours included physical activities (≥ 150 min/week for moderate or vigorous intensity [25]), current tobacco smoking, and heavy drinking (binge drinking (i.e., five drinks or more per occasion) on five or more days in the past month [26]). Food consumption behaviours were determined by the frequency of different food consumption types, including red meat, fruits, vegetables, nuts, vegetable oil, tea, and coffee.\nInternational Prostate Symptom Score (I-PSS) was used to assess seven lower urinary tract symptoms: incomplete emptying, frequency, intermittency, urgency, weak stream, straining, and nocturia. I-PSS Vietnamese version was published by the Vietnamese Ministry of Health and recommended for Benign Prostatic Hyperplasia assessment. Each item of I-PSS was classified on a zero to five scale, reflecting the severity of each symptom [27].\nEpidemiological and behavioural characteristics and I-PSS were assessed by only one oncologist for consistency. The oncologist was trained for conducting interviews before joining the study. The questionnaire consisted of ten interviews in a pilot sample for structure and content adaptation.\nSerum PSA was quantified by a 2-step immunoassay using light-emitting microparticle technology (CMIA) with Alinity CiCi (Abbott) testing machine system. The testing machine system was calibrated, and quality control was performed at least once every day or when changing the reagent batch [28].\nWe collected epidemiological and behavioural characteristics through interviews using a questionnaire and collected clinical and subclinical information from medical records. Epidemiological characteristics included age, number of children, overweight/obesity (BMI ≥ 23 kg/m2 [24]), family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals. Lifestyle behaviours included physical activities (≥ 150 min/week for moderate or vigorous intensity [25]), current tobacco smoking, and heavy drinking (binge drinking (i.e., five drinks or more per occasion) on five or more days in the past month [26]). Food consumption behaviours were determined by the frequency of different food consumption types, including red meat, fruits, vegetables, nuts, vegetable oil, tea, and coffee.\nInternational Prostate Symptom Score (I-PSS) was used to assess seven lower urinary tract symptoms: incomplete emptying, frequency, intermittency, urgency, weak stream, straining, and nocturia. I-PSS Vietnamese version was published by the Vietnamese Ministry of Health and recommended for Benign Prostatic Hyperplasia assessment. Each item of I-PSS was classified on a zero to five scale, reflecting the severity of each symptom [27].\nEpidemiological and behavioural characteristics and I-PSS were assessed by only one oncologist for consistency. The oncologist was trained for conducting interviews before joining the study. The questionnaire consisted of ten interviews in a pilot sample for structure and content adaptation.\nSerum PSA was quantified by a 2-step immunoassay using light-emitting microparticle technology (CMIA) with Alinity CiCi (Abbott) testing machine system. The testing machine system was calibrated, and quality control was performed at least once every day or when changing the reagent batch [28].\n Statistical analysis Descriptive analysis Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\nFrequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\n Univariable logistic regression A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\nA univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\n Bayesian model averaging (BMA) for model prediction A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\nA BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\n CART model for PCa screening CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\nCART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\n Descriptive analysis Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\nFrequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\n Univariable logistic regression A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\nA univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\n Bayesian model averaging (BMA) for model prediction A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\nA BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\n CART model for PCa screening CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\nCART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.", "We conducted a case-control study at the MEDIC Medical Center, Ho Chi Minh City, Vietnam. MEDIC is the first and top modernity private medical centre in Vietnam. Every day, more than 4,000 patients visit the centre for examination and treatment. The study was approved by the local institutional ethics committee of the MEDIC Center, and the opinion was signed on 15th July, 2016.", "Our study participants were men aged ≥ 50 years who visited the MEDIC Centre in 2017–2018 with self-reported lower urinary tract symptoms. The inclusion criteria were abnormal lower urinary tract symptoms or enlarged prostate glands identified through DRE or ultrasound images. The exclusion criterions were acute prostatitis or refusal to participate in the study. All patients who meet the selection criteria were prescribed a biopsy. The case group was defined as having a positive biopsy result for PCa, and the control was defined as having a negative biopsy result. Biopsy based on 12-core Transrectal Ultrasound Guided Biopsy of the Prostate [22]. All patients provided written informed consent before participating in the study.", "The minimum sample size estimated for each group of case-control studies was 116 patients to provide 90% power and 5% type I error to detect an odds ratio of 2.5. In Vietnam, PSA ≥ 10 ng/mL is considered as the high-risk group of PCa. Therefore, we chose the proportion of PSA ≥ 10 ng/mL equal to 23% in the control group as the proportion of controls with exposure in the sample size formula [23]. Our studies selected 130 patients for each group to ensure larger than the minimum sample size, hence the total patients was 260.", "We collected epidemiological and behavioural characteristics through interviews using a questionnaire and collected clinical and subclinical information from medical records. Epidemiological characteristics included age, number of children, overweight/obesity (BMI ≥ 23 kg/m2 [24]), family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals. Lifestyle behaviours included physical activities (≥ 150 min/week for moderate or vigorous intensity [25]), current tobacco smoking, and heavy drinking (binge drinking (i.e., five drinks or more per occasion) on five or more days in the past month [26]). Food consumption behaviours were determined by the frequency of different food consumption types, including red meat, fruits, vegetables, nuts, vegetable oil, tea, and coffee.\nInternational Prostate Symptom Score (I-PSS) was used to assess seven lower urinary tract symptoms: incomplete emptying, frequency, intermittency, urgency, weak stream, straining, and nocturia. I-PSS Vietnamese version was published by the Vietnamese Ministry of Health and recommended for Benign Prostatic Hyperplasia assessment. Each item of I-PSS was classified on a zero to five scale, reflecting the severity of each symptom [27].\nEpidemiological and behavioural characteristics and I-PSS were assessed by only one oncologist for consistency. The oncologist was trained for conducting interviews before joining the study. The questionnaire consisted of ten interviews in a pilot sample for structure and content adaptation.\nSerum PSA was quantified by a 2-step immunoassay using light-emitting microparticle technology (CMIA) with Alinity CiCi (Abbott) testing machine system. The testing machine system was calibrated, and quality control was performed at least once every day or when changing the reagent batch [28].", " Descriptive analysis Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\nFrequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\n Univariable logistic regression A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\nA univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\n Bayesian model averaging (BMA) for model prediction A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\nA BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\n CART model for PCa screening CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\nCART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.", "Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.", "A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.", "A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.", "CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.", "There were total 260 patients (130 in case group vs. 130 in control group) included in the study. The median age of cases was significantly higher than the controls (71 vs. 61). Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals. Physical exercise and fruit consumption were noted as protective factors of PCa (Table 1).\n\nTable 1Association of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regressionCases(n = 130)\nNo. (%)\nControls(n = 130)\nNo. (%)\nOR (95% CI)p-value\nEpidemiological characteristics\nAge (median [25–75 percentile])71 (64–78)66 (61–71)1.07 (1.04–1.11)< 0.001Overweight or Obesity (BMI ≥ 23)63 (48.5)49 (37.7)1.55 (0.92–2.63)0.103Family history of Prostate cancer (yes)2 (1.5)5 (3.9)0.39 (0.04–2.45)0.447Existing of urinary tract diseases (yes)17 (13.1)20 (15.4)0.82 (0.39–1.76)0.723History of urinary surgery (yes)10 (7.8)12 (9.2)0.83 (0.31–2.18)0.824Benign Prostate Hyperplasia (yes)14 (10.8)24 (18.5)0.53 (0.24–1.14)0.113Exposed to agrochemicals (yes)40 (30.8)20 (15.4)2.44 (1.29–4.73)0.005\nLifestyle behaviour\nPhysical activity (yes)68 (52.3)86 (66.2)0.65 (0.33–0.95)0.032Current tobacco smoking (yes)62 (47.7)48 (36.9)1.56 (0.92–2.64)0.103Heavy drinking (yes)14 (10.8)12 (9.2)1.19 (0.53–2.67)0.680\nFood consumption behaviour\nRed meat (≥ 3 times/week)107 (82.3)106 (81.5)1.05 (0.53–2.08)1.000Fruits (≥ 3 times/week)65 (50.0)83 (63.9)0.57 (0.33–0.96)0.033Vegetables (≥ 3 times/week)111 (85.4)120 (92.3)0.49 (0.19–1.16)0.114Nuts (≥ 3 times/week)9 (6.9)9 (6.9)1.00 (0.34–2.95)1.000Vegetable oil (≥ 3 times/week)104 (80.0)95 (73.1)1.47 (0.79–2.75)0.242Tea (≥ 3 times/week)56 (43.1)53 (40.8)1.10 (0.67–1.80)0.706Coffee (≥ 3 times/week)82 (63.1)80 (61.5)1.07 (0.63–1.82)0.898\n\nAssociation of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regression", "The PCa odds ratio for 1 ng/mL PSA increase was 1.06 (95% CI, 1.05–1.08). The PCa odds ratio for each I-PSS point increase was 1.14 (95% CI, 1.09–1.18). All the items of I-PSS significantly associated with PCa, except for the “Straining” item with lowest OR = 1.16 (95% CI, 0.98–1.38). “Nocturia” item had highest OR with 2.22 (95% CI, 1.79–2.74), and “Urgency” item came to the second with OR = 1.41 (95% CI, 1.23–1.62) (Table 2).\n\nTable 2Association of I-PSS and PSA with prostate cancer - univariable logistic regressionCases (n = 130)\nMedian\n\n(25–75 percentile)\nControls (n = 130)\nMedian\n\n(25–75 percentile)\nOR (95% CI)p\nPSA concentration (ng/mL)\n89.5 (39.1–100)12 (8–19)1.06 (1.05–1.08)< 0.001\nI-PSS (total score)\n\n14 (8–19)\n\n6.5 (3–12)\n\n1.14 (1.09–1.18)\n\n< 0.001\nIncomplete Emptying4 (1–5)1 (1–5)1.27 (1.12–1.44)Frequency (every 2 h)2 (0–2)0 (0–3)1.22 (1.08–1.37)Intermittency0 (0–1)0 (0–1)1.29 (1.02–1.64)Urgency2 (0–5)0 (0–1)1.41 (1.23–1.62)Weak Stream0 (0–2)0 (0–0)1.32 (1.12–1.55)Straining0 (0–1)0 (0–1)1.16 (0.98–1.38)Nocturia4 (3–5)2 (1–3)2.22 (1.79–2.74)\n\nAssociation of I-PSS and PSA with prostate cancer - univariable logistic regression", "To determine whether PCa could be predicted by PSA, I-PSS, epidemiological and behavioural variables. There were 27 models suggested from BMA process, among them the best 5 models are shown in Table 3. The most parsimonious model (i.e., minimum explanatory variables and maximum discrimination power) included two variables: I-PSS, and PSA concentration. The second parsimonious model contained I-PSS, PSA, and age. The Area Under the ROC Curve (AUC) of the most parsimonious model was not much different compared to the second parsimonious model (0.931 vs. 0.929). Because age is an important factor for PCa screening and diagnosis in many previous studies [29–31], it is also a critical factor for disease mechanisms from a clinical standpoint. Therefore, we chose the second model with three variables as the best model to use in the clinical setting (Table 3). This final model is also in light with the final model suggested by CART algorithm (details shown below).\n\nTable 3BMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCaModelVariableOR (95% CI)pR2 (%)AUCBICPosterior probability1IPSS1.12 (1.06–1.18)< 0.00149.20.931199.90.364PSA1.06 (1.04–1.08)< 0.001Intercept0.03 (0.01–0.08)< 0.0012IPSS1.11 (1.05–1.17)< 0.00150.30.929201.40.174PSA1.06 (1.04–1.07)< 0.001Age1.06 (1.01–1.10)0.047Intercept0.01 (0.00–0.04)< 0.0013IPSS1.13 (1.07–1.19)< 0.00150.10.929205.50.120PSA1.06 (1.04–1.08)< 0.001Fruits (≥ 3 times/week)0.50 (0.23–1.06)0.069Intercept0.05 (0.02–0.12)< 0.0014IPSS1.12 (1.06–1.19)< 0.00149.70.931203.60.057PSA1.06 (1.04–1.08)< 0.001Overweight or Obesity1.68 (0.80–3.53)0.174Intercept0.03 (0.01–0.07)< 0.0015IPSS1.12 (1.06–1.18)< 0.00149.70.931203.70.056PSA1.06 (1.04–1.08)< 0.001Vegetable oil (≥ 3 times/week)1.84 (0.75–4.52)0.186Intercept0.02 (0.01–0.07)< 0.001\n\nBMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCa", "CART was deployed with all independent variables input for PCa screening, and the final model is shown in (Fig. 1).\n\nFig. 1Trained CART in prostate cancer screening\n\nTrained CART in prostate cancer screening\nThe results indicated that PSA, I-PSS, and age played important roles in PCa screening. CART advised the following cut-off points in the marked screening sequence: 18 < PSA < 33.5 ng/mL, I-PSS ≥ 19, and age ≥ 71. Patients with PSA ≥ 33.5 ng/mL have a PCa risk was 91.2%; patients with PSA < 18 ng/mL and I-PSS < 19 have a PCa risk was 7.1%. Patient with 18 ≤ PSA < 33.5ng/mL and I-PSS < 19 have a PCa risk is 70% if age ≥ 71; and is 16% if age < 71.\nIn overall, CART reached high predictive value with AUC = 0.915. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CART at the 20% diagnosis probability threshold were 91.5%, 86.2%, 86.9%, 91.2% and 88.9% respectively; at 80% diagnosis probability threshold were 79.2%, 92.3%, 91.2%, 81.6%, and 85.8% respectively (Table 4).\n\nTable 4Diagnosis values of CART in PCa screeningProbability = 20%Probability = 50%Probability = 80%Sensitivity0.9150.9150.792Specificity0.8620.8620.923Positive predictive value0.8690.8690.912Negative predictive value0.9120.9120.816Accuracy0.8890.8890.858\n\nDiagnosis values of CART in PCa screening", "The study included 260 observations at the Medic Center HCMC with 130 in the case group and 130 in control group. Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals and protective factor included physical exercise and fruit consumption.\nPrevious studies found that farming and exposure to agricultural chemicals are risk factors for PCa but not for all agricultural chemicals [29, 30]. Exposure to a few specific pesticides including fonofos, malathion, terbufos, and azinphos-methyl, dimethoate associated with PCa [31–33]. Genomic analysis showed pesticides might interact with genetic variants in pathways related to neurotransmission release in PCa patients [34, 35]. Therefore, the relationship between exposure to agricultural chemicals and PCa is plausible. Further epidemiological and mechanism studies are needed to identify the relationships of PCa with specific agricultural chemicals, in particular in the Vietnamese context.\nAlthough our study initially found a relationship between physical exercise and PCa, there is a lack of evidence in the literature. Recent review and meta-analyses reveal that the association between regular physical activity and a low risk of prostate cancer remains elusive [36, 37]. Given also many general health benefits of physical activity, there is the need to clarify the role of physical activity in association with PCa in further studies.\nThe association of fruit consumption with PCa was shown in our study and recent studies [38, 39]. Total fruit intake significantly reduced PCa risk. However, our study did not analyze fruit subtypes. A previous study found that citrus fruit consumption is associated with PCa, but other fruit subtypes are not associated [38]. This relationship might be due to the anti-carcinogenic properties of vitamins and phytochemicals in citrus fruits [40, 41]. However, the causal relationship remains unclear because most findings are based on a cross-sectional study.\nCART model suggested that age is most important in epidemiological and behavioral characteristics. Therefore, age, PSA, and IPSS were combined in PCa screening CART model. Our study showed that 50% of patients were older than 71 years in cases and older than 66 in controls. PCa is a disease that commonly occurs in the elderly men. Previous studies found that 75–80% of new cases occur in men aged over 65 years [42, 43]. Another study in the United States had an average participant’s age was 66 years [44]. A study by European Association of Urology showed that PCa rarely occurred in men younger than 50 years; it also indicated that the median age of PCa patient was 70 years [45]. Giwercman et al. showed that age was the closest risk factor of PCa [46]. Similarly, our study detected age as an independent risk factor of PCa. According to the Bayesian Model Averaging process, the PCa risk increased by 6% each year of age increased.\nThe role of I-PSS in PCa screening.\nThe Prostate Symptom Score (I-PSS) with seven recommended questions became an international standard to assess the symptoms of urination dysfunction in patients during the previous month. This scale is able to monitor changes in symptoms over time or after an intervention. A symptom severity assessment with an I-PSS scale is an important part of the initial evaluation, diagnosis, prediction, and monitoring of response to treatment [47, 48]. Our study noted that I-PSS was used to detect the symptoms of PCa (p < 0.001) in both univariable and multivariable analyses. A cohort study by Martin et al. [49] detected an association between I-PSS and PCa. For overall PCa, men with I-PSS ≥ 20 had a 2.26-fold increased risk of PCa compared to those with no symptoms. For localised PCa, men with I-PSS ≥ 20 had a 4.6-fold increased risk of PCa compared to those with no symptoms [49]. A study by Hosseini et al. [15] showed an association between I-PSS and PCa. The mean I-PSS score of the PCa group was 16.05 and higher than that of the non-PCa group, with a mean I-PSS score of 6.84. The prevalence of patients with I-PSS ≥ 20 in the PCa group was 30.3%, which was higher than that in the non-PCa group. The sensitivity and specificity of I-PSS at cut-off I-PSS ≥ 20 were 78% and 59%, respectively [15]. Our study and data have provided evidence about the relationship of PCa screening value with I-PSS. In Vietnam, according to the Ministry of Health, I-PSS has not yet been recommended for PCa initial screening; however, I-PSS has been recommended for benign hypertrophy of prostate – a disease that has many symptoms similar to early-stage PCa symptoms. Our study recommended using I-PSS for initial screening for any patient who has self-reported lower urinary tract symptoms.\n The role of PSA in PCa screening Prostatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].\nProstatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].", "Prostatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].", "To remedy the inherent limitations of PSA in PCa screening, we used a combination of PSA with I-PSS and the main risk factors of PCa to build the CART model. Based on CART, patients with a PSA cut-off level > 33.5 ng/mL have a PCa risk of up to 91.2%. Patients with I-PSS < 19, and PSA < 18 ng/mL were at 7.1% risk. CART overcomes the limitations of using only I-PSS or PSA for screening. Other machine learning algorithms have been used in PCa screening in previous studies and have reached higher values than PSA only. In a study by Babaian et al. [59], a neural network algorithm for PCa screening showed an improved value compared to using only PSA, the specificity of the neural network was not good (lower than 65%) [59]. A study by Satoshi et al. [17] showed that artificial neural network, random forest, and support vector machine improved overall value when compared to only PSA; however, sensitivity and specificity were usually lower than 80% [17]. Our CART algorithm with three variables, PSA, I-PSS, and age, showed a relatively high predictive power (AUC = 0.915). In addition, CART algorithm could also support physicians to make clinical decisions in a step-to-step and intuitive manner; hence it has practical use in a daily clinical setting. At 20% diagnosis probability threshold, CART showed a high negative predictive value (91.2%), and at 80% diagnosis probability threshold, CART also had a high positive predictive value (91.2%). Therefore, we recommended 20% diagnosis probability threshold for negative prediction and 80% diagnosis probability threshold for referring prostate biopsy. Any other patients with a probability of PCa range from > 20% to < 80%, further tests including the digital rectal examination (DRE), PSA re-test after a month, and transrectal ultrasonography (TRUS) should be considered to reduce unnecessary biopsy while keeping the ability to diagnose PCa early.\nThe study has some limitations. First, we lack other tests such as DRE, TRUS, biomarkers that can contribute to making biopsy decisions [5, 7]. Second, the CART model has not yet been tested in different populations for the validity and reliability of the algorithm. Finally, we could not estimate the overdiagnosis rate of PCa in the study. It warrants further study in the near future to overcome these limitations." ]

[ null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null ]

[ "Background", "Methods", "Study design and setting", "Participants", "Sample size", "Data collection and variables’ definition", "Statistical analysis", "Descriptive analysis", "Univariable logistic regression", "Bayesian model averaging (BMA) for model prediction", "CART model for PCa screening", "Results", "Association of epidemiological and behavioural characteristics with prostate cancer", "Association of I-PSS and PSA with prostate cancer", "BMA for PCa prediction", "CART for PCa screening", "Discussion", "Epidemiological and behavioral characteristics", "The role of PSA in PCa screening", "CART value for PCa screening", "Conclusion" ]

[ "Prostate cancer (PCa) is common in men, especially in those aged 65 years and older. It has the second-highest incidence/prevalence (i.e., 30.7 per 100 000) and ranks fifth in cancer mortality rate among men (i.e., 7.7 per 100 000) worldwide [1]. In Vietnam, the incidence of PCa is 12.2 per 100 000, and the mortality rate is 5.1 per 100 000 as of 2020 [2]. Approximately 95–98% of PCa cases are adenocarcinomas that develop from adrenal duct cells [3]. PCa treatment depends primarily on the stage of development and the cell and patient characteristics. According to the American Cancer Society, PCa patients diagnosed at the localised or regional stage have a 5-year survival rate of over 90%. However, in the distant stage, the survival rate is only 30% [4]. Therefore, PCa should be detected at an early stage.\nProstate-specific antigen (PSA) is a serine protease in the kallikrein family and considered a tool for the screening and early detection of PCa [5]. It can help detect as early as nine years before having clinical symptoms [6]. There are two types of PCa screening studies using PSA, including population-based and hospital-based (or opportunistic) screenings [7]. The first type of screening deals with testing asymptomatic men with only PSA, and those with elevated PSA are immediately referred to biopsy. However, the latter type of screening involves testing men with some symptoms (e.g., lower urinary tract symptoms) using PSA and other clinical tools. Therefore, all men referred for biopsy in population-based screening are at lower risk of having PCa compared to that of hospital-based screening. PSA only based screening could accounted for 45–70% of the reduction in PCa mortality [8]; it could also induce the unnecessary biopsies [9]. In a 16 year follow-up of the European Randomized Study of Screening for Prostate Cancer (ERSPC), the unnecessary biopsy was 76% (i.e., 76% of elevated PSA cases have a negative biopsy) [10]. In addition, the optimal cut-off value of PSA for confirming PCa remains to be determined [11] [5]. In particular, even at a low level of PSA (that is, lower than 4 ng/mL), the false negative rate of PCa was high at 15%, whereas, at a high level of PSA (that is, higher than 10 ng/mL), the false positive rate was 50% [5].\nIn Vietnam, population-based PCa screening using PSA was conducted 12 years ago; it indicated a low prevalence of PCa (2.5%), but a high rate of medium grade lesions. The author also implied that the benefit of a mass screening program for PCa was not proven. Instead, a selective PCa screening in the usual care and at the hospital was superior in Vietnam. In hospital-based screening, combining clinical parameters, PSA, age, and other risk factors improved the prediction of prostate cancer [12–14]. International Prostate Symptom Score (I-PSS) is a screening scale for lower urinary tract symptoms and is used to screen non-specific prostate gland abnormalities. For PCa screening. For PCa screening, the I-PSS scale showed reasonable sensitivity (78%), but the specificity was not high (59.4%) [15]. A previous study showed that PSA screening performance varied with different I-PSS values. Therefore, combining PSA and I-PSS could improve the screening benefits [16]. There is, however, a paucity of such practical multivariable algorithm for hospital-based PCa screening in Vietnam.\nThe approach of PCa screening based on machine learning algorithms has only recently been applied. Algorithms including logistic regression, artificial neural networks, random forests, support vector machines, and extreme and light gradient boosting machines have been demonstrated to enhance PCa screening efficiency [13, 17–20]. However, these models do not help make clinical decisions in a step-to-step and intuitive manner. Classification and regression tree (CART) is an approach that allows physicians to apply results of the screening process directly and intuitively [17, 21].\nOur study aimed to investigate the association of PSA, I-PSS, epidemiological and behavioural characteristics with PCa and then used these factors to construct a classification and regression tree (CART) algorithm to select Vietnamese men with lower urinary tract symptoms (LUTS) for PCa biopsy. The algorithm is expected to aid in reducing the probability of a negative prostate biopsy (i.e., unnecessary biopsy) while maintaining the ability to reduce PCa mortality for Vietnamese patients.", " Study design and setting We conducted a case-control study at the MEDIC Medical Center, Ho Chi Minh City, Vietnam. MEDIC is the first and top modernity private medical centre in Vietnam. Every day, more than 4,000 patients visit the centre for examination and treatment. The study was approved by the local institutional ethics committee of the MEDIC Center, and the opinion was signed on 15th July, 2016.\nWe conducted a case-control study at the MEDIC Medical Center, Ho Chi Minh City, Vietnam. MEDIC is the first and top modernity private medical centre in Vietnam. Every day, more than 4,000 patients visit the centre for examination and treatment. The study was approved by the local institutional ethics committee of the MEDIC Center, and the opinion was signed on 15th July, 2016.\n Participants Our study participants were men aged ≥ 50 years who visited the MEDIC Centre in 2017–2018 with self-reported lower urinary tract symptoms. The inclusion criteria were abnormal lower urinary tract symptoms or enlarged prostate glands identified through DRE or ultrasound images. The exclusion criterions were acute prostatitis or refusal to participate in the study. All patients who meet the selection criteria were prescribed a biopsy. The case group was defined as having a positive biopsy result for PCa, and the control was defined as having a negative biopsy result. Biopsy based on 12-core Transrectal Ultrasound Guided Biopsy of the Prostate [22]. All patients provided written informed consent before participating in the study.\nOur study participants were men aged ≥ 50 years who visited the MEDIC Centre in 2017–2018 with self-reported lower urinary tract symptoms. The inclusion criteria were abnormal lower urinary tract symptoms or enlarged prostate glands identified through DRE or ultrasound images. The exclusion criterions were acute prostatitis or refusal to participate in the study. All patients who meet the selection criteria were prescribed a biopsy. The case group was defined as having a positive biopsy result for PCa, and the control was defined as having a negative biopsy result. Biopsy based on 12-core Transrectal Ultrasound Guided Biopsy of the Prostate [22]. All patients provided written informed consent before participating in the study.\n Sample size The minimum sample size estimated for each group of case-control studies was 116 patients to provide 90% power and 5% type I error to detect an odds ratio of 2.5. In Vietnam, PSA ≥ 10 ng/mL is considered as the high-risk group of PCa. Therefore, we chose the proportion of PSA ≥ 10 ng/mL equal to 23% in the control group as the proportion of controls with exposure in the sample size formula [23]. Our studies selected 130 patients for each group to ensure larger than the minimum sample size, hence the total patients was 260.\nThe minimum sample size estimated for each group of case-control studies was 116 patients to provide 90% power and 5% type I error to detect an odds ratio of 2.5. In Vietnam, PSA ≥ 10 ng/mL is considered as the high-risk group of PCa. Therefore, we chose the proportion of PSA ≥ 10 ng/mL equal to 23% in the control group as the proportion of controls with exposure in the sample size formula [23]. Our studies selected 130 patients for each group to ensure larger than the minimum sample size, hence the total patients was 260.\n Data collection and variables’ definition We collected epidemiological and behavioural characteristics through interviews using a questionnaire and collected clinical and subclinical information from medical records. Epidemiological characteristics included age, number of children, overweight/obesity (BMI ≥ 23 kg/m2 [24]), family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals. Lifestyle behaviours included physical activities (≥ 150 min/week for moderate or vigorous intensity [25]), current tobacco smoking, and heavy drinking (binge drinking (i.e., five drinks or more per occasion) on five or more days in the past month [26]). Food consumption behaviours were determined by the frequency of different food consumption types, including red meat, fruits, vegetables, nuts, vegetable oil, tea, and coffee.\nInternational Prostate Symptom Score (I-PSS) was used to assess seven lower urinary tract symptoms: incomplete emptying, frequency, intermittency, urgency, weak stream, straining, and nocturia. I-PSS Vietnamese version was published by the Vietnamese Ministry of Health and recommended for Benign Prostatic Hyperplasia assessment. Each item of I-PSS was classified on a zero to five scale, reflecting the severity of each symptom [27].\nEpidemiological and behavioural characteristics and I-PSS were assessed by only one oncologist for consistency. The oncologist was trained for conducting interviews before joining the study. The questionnaire consisted of ten interviews in a pilot sample for structure and content adaptation.\nSerum PSA was quantified by a 2-step immunoassay using light-emitting microparticle technology (CMIA) with Alinity CiCi (Abbott) testing machine system. The testing machine system was calibrated, and quality control was performed at least once every day or when changing the reagent batch [28].\nWe collected epidemiological and behavioural characteristics through interviews using a questionnaire and collected clinical and subclinical information from medical records. Epidemiological characteristics included age, number of children, overweight/obesity (BMI ≥ 23 kg/m2 [24]), family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals. Lifestyle behaviours included physical activities (≥ 150 min/week for moderate or vigorous intensity [25]), current tobacco smoking, and heavy drinking (binge drinking (i.e., five drinks or more per occasion) on five or more days in the past month [26]). Food consumption behaviours were determined by the frequency of different food consumption types, including red meat, fruits, vegetables, nuts, vegetable oil, tea, and coffee.\nInternational Prostate Symptom Score (I-PSS) was used to assess seven lower urinary tract symptoms: incomplete emptying, frequency, intermittency, urgency, weak stream, straining, and nocturia. I-PSS Vietnamese version was published by the Vietnamese Ministry of Health and recommended for Benign Prostatic Hyperplasia assessment. Each item of I-PSS was classified on a zero to five scale, reflecting the severity of each symptom [27].\nEpidemiological and behavioural characteristics and I-PSS were assessed by only one oncologist for consistency. The oncologist was trained for conducting interviews before joining the study. The questionnaire consisted of ten interviews in a pilot sample for structure and content adaptation.\nSerum PSA was quantified by a 2-step immunoassay using light-emitting microparticle technology (CMIA) with Alinity CiCi (Abbott) testing machine system. The testing machine system was calibrated, and quality control was performed at least once every day or when changing the reagent batch [28].\n Statistical analysis Descriptive analysis Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\nFrequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\n Univariable logistic regression A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\nA univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\n Bayesian model averaging (BMA) for model prediction A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\nA BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\n CART model for PCa screening CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\nCART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\n Descriptive analysis Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\nFrequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\n Univariable logistic regression A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\nA univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\n Bayesian model averaging (BMA) for model prediction A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\nA BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\n CART model for PCa screening CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\nCART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.", "We conducted a case-control study at the MEDIC Medical Center, Ho Chi Minh City, Vietnam. MEDIC is the first and top modernity private medical centre in Vietnam. Every day, more than 4,000 patients visit the centre for examination and treatment. The study was approved by the local institutional ethics committee of the MEDIC Center, and the opinion was signed on 15th July, 2016.", "Our study participants were men aged ≥ 50 years who visited the MEDIC Centre in 2017–2018 with self-reported lower urinary tract symptoms. The inclusion criteria were abnormal lower urinary tract symptoms or enlarged prostate glands identified through DRE or ultrasound images. The exclusion criterions were acute prostatitis or refusal to participate in the study. All patients who meet the selection criteria were prescribed a biopsy. The case group was defined as having a positive biopsy result for PCa, and the control was defined as having a negative biopsy result. Biopsy based on 12-core Transrectal Ultrasound Guided Biopsy of the Prostate [22]. All patients provided written informed consent before participating in the study.", "The minimum sample size estimated for each group of case-control studies was 116 patients to provide 90% power and 5% type I error to detect an odds ratio of 2.5. In Vietnam, PSA ≥ 10 ng/mL is considered as the high-risk group of PCa. Therefore, we chose the proportion of PSA ≥ 10 ng/mL equal to 23% in the control group as the proportion of controls with exposure in the sample size formula [23]. Our studies selected 130 patients for each group to ensure larger than the minimum sample size, hence the total patients was 260.", "We collected epidemiological and behavioural characteristics through interviews using a questionnaire and collected clinical and subclinical information from medical records. Epidemiological characteristics included age, number of children, overweight/obesity (BMI ≥ 23 kg/m2 [24]), family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals. Lifestyle behaviours included physical activities (≥ 150 min/week for moderate or vigorous intensity [25]), current tobacco smoking, and heavy drinking (binge drinking (i.e., five drinks or more per occasion) on five or more days in the past month [26]). Food consumption behaviours were determined by the frequency of different food consumption types, including red meat, fruits, vegetables, nuts, vegetable oil, tea, and coffee.\nInternational Prostate Symptom Score (I-PSS) was used to assess seven lower urinary tract symptoms: incomplete emptying, frequency, intermittency, urgency, weak stream, straining, and nocturia. I-PSS Vietnamese version was published by the Vietnamese Ministry of Health and recommended for Benign Prostatic Hyperplasia assessment. Each item of I-PSS was classified on a zero to five scale, reflecting the severity of each symptom [27].\nEpidemiological and behavioural characteristics and I-PSS were assessed by only one oncologist for consistency. The oncologist was trained for conducting interviews before joining the study. The questionnaire consisted of ten interviews in a pilot sample for structure and content adaptation.\nSerum PSA was quantified by a 2-step immunoassay using light-emitting microparticle technology (CMIA) with Alinity CiCi (Abbott) testing machine system. The testing machine system was calibrated, and quality control was performed at least once every day or when changing the reagent batch [28].", " Descriptive analysis Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\nFrequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.\n Univariable logistic regression A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\nA univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.\n Bayesian model averaging (BMA) for model prediction A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\nA BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.\n CART model for PCa screening CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.\nCART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.", "Frequency and percentage were used to describe qualitative variables, including overweight/obesity, family history of PCa, existence of urinary tract diseases, history of urinary surgery, benign prostate hyperplasia, and exposure to agrochemicals, lifestyle behaviour, and food consumption behaviour. The median and quartiles were used to describe quantitative variables, including I-PSS, PSA concentration, and age. All descriptive analyses were stratified by the case and control groups.", "A univariable logistic regression model was used to screen independent variables that were likely to be associated with PCa. The I-PSS score, PSA concentration, epidemiological characteristics, lifestyle behaviour, and food consumption behaviour were tested for association with PCa.", "A BMA approach was used to search for the most parsimonious model for PCa prediction (i.e., minimum explanatory variables and maximum discrimination power) using PSA, I-PSS, epidemiological and behavioural variables. In summary, if there is n variables, there will be 2n possible models constructing from n variables (not including interactive terms). BMA will construct all possible parsimonious prediction models based on the Bayesian Information Criteria (BIC), and posterior probabilities of these models. The final model with the high practical use in the clinical setting can be chosen based on BMA suggesting and clinical considerations.", "CART was performed using the rpart function in the rpart package, R language (version 4.0.3). Five-folds cross validation was used to training and testing CART model. All independent variables became CART input in this process. The CART pruning was controlled by the maximum depth of the tree set to 4 to construct a reasonable complexity, the minimum number of observations was allowed to be 10 at each node to ensure sufficient supporting data. Diagnosis values of CART including sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (1 – misclassification error) at the 20%, 50% and 80% probability cut-off were extracted.", " Association of epidemiological and behavioural characteristics with prostate cancer There were total 260 patients (130 in case group vs. 130 in control group) included in the study. The median age of cases was significantly higher than the controls (71 vs. 61). Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals. Physical exercise and fruit consumption were noted as protective factors of PCa (Table 1).\n\nTable 1Association of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regressionCases(n = 130)\nNo. (%)\nControls(n = 130)\nNo. (%)\nOR (95% CI)p-value\nEpidemiological characteristics\nAge (median [25–75 percentile])71 (64–78)66 (61–71)1.07 (1.04–1.11)< 0.001Overweight or Obesity (BMI ≥ 23)63 (48.5)49 (37.7)1.55 (0.92–2.63)0.103Family history of Prostate cancer (yes)2 (1.5)5 (3.9)0.39 (0.04–2.45)0.447Existing of urinary tract diseases (yes)17 (13.1)20 (15.4)0.82 (0.39–1.76)0.723History of urinary surgery (yes)10 (7.8)12 (9.2)0.83 (0.31–2.18)0.824Benign Prostate Hyperplasia (yes)14 (10.8)24 (18.5)0.53 (0.24–1.14)0.113Exposed to agrochemicals (yes)40 (30.8)20 (15.4)2.44 (1.29–4.73)0.005\nLifestyle behaviour\nPhysical activity (yes)68 (52.3)86 (66.2)0.65 (0.33–0.95)0.032Current tobacco smoking (yes)62 (47.7)48 (36.9)1.56 (0.92–2.64)0.103Heavy drinking (yes)14 (10.8)12 (9.2)1.19 (0.53–2.67)0.680\nFood consumption behaviour\nRed meat (≥ 3 times/week)107 (82.3)106 (81.5)1.05 (0.53–2.08)1.000Fruits (≥ 3 times/week)65 (50.0)83 (63.9)0.57 (0.33–0.96)0.033Vegetables (≥ 3 times/week)111 (85.4)120 (92.3)0.49 (0.19–1.16)0.114Nuts (≥ 3 times/week)9 (6.9)9 (6.9)1.00 (0.34–2.95)1.000Vegetable oil (≥ 3 times/week)104 (80.0)95 (73.1)1.47 (0.79–2.75)0.242Tea (≥ 3 times/week)56 (43.1)53 (40.8)1.10 (0.67–1.80)0.706Coffee (≥ 3 times/week)82 (63.1)80 (61.5)1.07 (0.63–1.82)0.898\n\nAssociation of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regression\nThere were total 260 patients (130 in case group vs. 130 in control group) included in the study. The median age of cases was significantly higher than the controls (71 vs. 61). Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals. Physical exercise and fruit consumption were noted as protective factors of PCa (Table 1).\n\nTable 1Association of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regressionCases(n = 130)\nNo. (%)\nControls(n = 130)\nNo. (%)\nOR (95% CI)p-value\nEpidemiological characteristics\nAge (median [25–75 percentile])71 (64–78)66 (61–71)1.07 (1.04–1.11)< 0.001Overweight or Obesity (BMI ≥ 23)63 (48.5)49 (37.7)1.55 (0.92–2.63)0.103Family history of Prostate cancer (yes)2 (1.5)5 (3.9)0.39 (0.04–2.45)0.447Existing of urinary tract diseases (yes)17 (13.1)20 (15.4)0.82 (0.39–1.76)0.723History of urinary surgery (yes)10 (7.8)12 (9.2)0.83 (0.31–2.18)0.824Benign Prostate Hyperplasia (yes)14 (10.8)24 (18.5)0.53 (0.24–1.14)0.113Exposed to agrochemicals (yes)40 (30.8)20 (15.4)2.44 (1.29–4.73)0.005\nLifestyle behaviour\nPhysical activity (yes)68 (52.3)86 (66.2)0.65 (0.33–0.95)0.032Current tobacco smoking (yes)62 (47.7)48 (36.9)1.56 (0.92–2.64)0.103Heavy drinking (yes)14 (10.8)12 (9.2)1.19 (0.53–2.67)0.680\nFood consumption behaviour\nRed meat (≥ 3 times/week)107 (82.3)106 (81.5)1.05 (0.53–2.08)1.000Fruits (≥ 3 times/week)65 (50.0)83 (63.9)0.57 (0.33–0.96)0.033Vegetables (≥ 3 times/week)111 (85.4)120 (92.3)0.49 (0.19–1.16)0.114Nuts (≥ 3 times/week)9 (6.9)9 (6.9)1.00 (0.34–2.95)1.000Vegetable oil (≥ 3 times/week)104 (80.0)95 (73.1)1.47 (0.79–2.75)0.242Tea (≥ 3 times/week)56 (43.1)53 (40.8)1.10 (0.67–1.80)0.706Coffee (≥ 3 times/week)82 (63.1)80 (61.5)1.07 (0.63–1.82)0.898\n\nAssociation of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regression\n Association of I-PSS and PSA with prostate cancer The PCa odds ratio for 1 ng/mL PSA increase was 1.06 (95% CI, 1.05–1.08). The PCa odds ratio for each I-PSS point increase was 1.14 (95% CI, 1.09–1.18). All the items of I-PSS significantly associated with PCa, except for the “Straining” item with lowest OR = 1.16 (95% CI, 0.98–1.38). “Nocturia” item had highest OR with 2.22 (95% CI, 1.79–2.74), and “Urgency” item came to the second with OR = 1.41 (95% CI, 1.23–1.62) (Table 2).\n\nTable 2Association of I-PSS and PSA with prostate cancer - univariable logistic regressionCases (n = 130)\nMedian\n\n(25–75 percentile)\nControls (n = 130)\nMedian\n\n(25–75 percentile)\nOR (95% CI)p\nPSA concentration (ng/mL)\n89.5 (39.1–100)12 (8–19)1.06 (1.05–1.08)< 0.001\nI-PSS (total score)\n\n14 (8–19)\n\n6.5 (3–12)\n\n1.14 (1.09–1.18)\n\n< 0.001\nIncomplete Emptying4 (1–5)1 (1–5)1.27 (1.12–1.44)Frequency (every 2 h)2 (0–2)0 (0–3)1.22 (1.08–1.37)Intermittency0 (0–1)0 (0–1)1.29 (1.02–1.64)Urgency2 (0–5)0 (0–1)1.41 (1.23–1.62)Weak Stream0 (0–2)0 (0–0)1.32 (1.12–1.55)Straining0 (0–1)0 (0–1)1.16 (0.98–1.38)Nocturia4 (3–5)2 (1–3)2.22 (1.79–2.74)\n\nAssociation of I-PSS and PSA with prostate cancer - univariable logistic regression\nThe PCa odds ratio for 1 ng/mL PSA increase was 1.06 (95% CI, 1.05–1.08). The PCa odds ratio for each I-PSS point increase was 1.14 (95% CI, 1.09–1.18). All the items of I-PSS significantly associated with PCa, except for the “Straining” item with lowest OR = 1.16 (95% CI, 0.98–1.38). “Nocturia” item had highest OR with 2.22 (95% CI, 1.79–2.74), and “Urgency” item came to the second with OR = 1.41 (95% CI, 1.23–1.62) (Table 2).\n\nTable 2Association of I-PSS and PSA with prostate cancer - univariable logistic regressionCases (n = 130)\nMedian\n\n(25–75 percentile)\nControls (n = 130)\nMedian\n\n(25–75 percentile)\nOR (95% CI)p\nPSA concentration (ng/mL)\n89.5 (39.1–100)12 (8–19)1.06 (1.05–1.08)< 0.001\nI-PSS (total score)\n\n14 (8–19)\n\n6.5 (3–12)\n\n1.14 (1.09–1.18)\n\n< 0.001\nIncomplete Emptying4 (1–5)1 (1–5)1.27 (1.12–1.44)Frequency (every 2 h)2 (0–2)0 (0–3)1.22 (1.08–1.37)Intermittency0 (0–1)0 (0–1)1.29 (1.02–1.64)Urgency2 (0–5)0 (0–1)1.41 (1.23–1.62)Weak Stream0 (0–2)0 (0–0)1.32 (1.12–1.55)Straining0 (0–1)0 (0–1)1.16 (0.98–1.38)Nocturia4 (3–5)2 (1–3)2.22 (1.79–2.74)\n\nAssociation of I-PSS and PSA with prostate cancer - univariable logistic regression\n BMA for PCa prediction To determine whether PCa could be predicted by PSA, I-PSS, epidemiological and behavioural variables. There were 27 models suggested from BMA process, among them the best 5 models are shown in Table 3. The most parsimonious model (i.e., minimum explanatory variables and maximum discrimination power) included two variables: I-PSS, and PSA concentration. The second parsimonious model contained I-PSS, PSA, and age. The Area Under the ROC Curve (AUC) of the most parsimonious model was not much different compared to the second parsimonious model (0.931 vs. 0.929). Because age is an important factor for PCa screening and diagnosis in many previous studies [29–31], it is also a critical factor for disease mechanisms from a clinical standpoint. Therefore, we chose the second model with three variables as the best model to use in the clinical setting (Table 3). This final model is also in light with the final model suggested by CART algorithm (details shown below).\n\nTable 3BMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCaModelVariableOR (95% CI)pR2 (%)AUCBICPosterior probability1IPSS1.12 (1.06–1.18)< 0.00149.20.931199.90.364PSA1.06 (1.04–1.08)< 0.001Intercept0.03 (0.01–0.08)< 0.0012IPSS1.11 (1.05–1.17)< 0.00150.30.929201.40.174PSA1.06 (1.04–1.07)< 0.001Age1.06 (1.01–1.10)0.047Intercept0.01 (0.00–0.04)< 0.0013IPSS1.13 (1.07–1.19)< 0.00150.10.929205.50.120PSA1.06 (1.04–1.08)< 0.001Fruits (≥ 3 times/week)0.50 (0.23–1.06)0.069Intercept0.05 (0.02–0.12)< 0.0014IPSS1.12 (1.06–1.19)< 0.00149.70.931203.60.057PSA1.06 (1.04–1.08)< 0.001Overweight or Obesity1.68 (0.80–3.53)0.174Intercept0.03 (0.01–0.07)< 0.0015IPSS1.12 (1.06–1.18)< 0.00149.70.931203.70.056PSA1.06 (1.04–1.08)< 0.001Vegetable oil (≥ 3 times/week)1.84 (0.75–4.52)0.186Intercept0.02 (0.01–0.07)< 0.001\n\nBMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCa\nTo determine whether PCa could be predicted by PSA, I-PSS, epidemiological and behavioural variables. There were 27 models suggested from BMA process, among them the best 5 models are shown in Table 3. The most parsimonious model (i.e., minimum explanatory variables and maximum discrimination power) included two variables: I-PSS, and PSA concentration. The second parsimonious model contained I-PSS, PSA, and age. The Area Under the ROC Curve (AUC) of the most parsimonious model was not much different compared to the second parsimonious model (0.931 vs. 0.929). Because age is an important factor for PCa screening and diagnosis in many previous studies [29–31], it is also a critical factor for disease mechanisms from a clinical standpoint. Therefore, we chose the second model with three variables as the best model to use in the clinical setting (Table 3). This final model is also in light with the final model suggested by CART algorithm (details shown below).\n\nTable 3BMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCaModelVariableOR (95% CI)pR2 (%)AUCBICPosterior probability1IPSS1.12 (1.06–1.18)< 0.00149.20.931199.90.364PSA1.06 (1.04–1.08)< 0.001Intercept0.03 (0.01–0.08)< 0.0012IPSS1.11 (1.05–1.17)< 0.00150.30.929201.40.174PSA1.06 (1.04–1.07)< 0.001Age1.06 (1.01–1.10)0.047Intercept0.01 (0.00–0.04)< 0.0013IPSS1.13 (1.07–1.19)< 0.00150.10.929205.50.120PSA1.06 (1.04–1.08)< 0.001Fruits (≥ 3 times/week)0.50 (0.23–1.06)0.069Intercept0.05 (0.02–0.12)< 0.0014IPSS1.12 (1.06–1.19)< 0.00149.70.931203.60.057PSA1.06 (1.04–1.08)< 0.001Overweight or Obesity1.68 (0.80–3.53)0.174Intercept0.03 (0.01–0.07)< 0.0015IPSS1.12 (1.06–1.18)< 0.00149.70.931203.70.056PSA1.06 (1.04–1.08)< 0.001Vegetable oil (≥ 3 times/week)1.84 (0.75–4.52)0.186Intercept0.02 (0.01–0.07)< 0.001\n\nBMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCa\n CART for PCa screening CART was deployed with all independent variables input for PCa screening, and the final model is shown in (Fig. 1).\n\nFig. 1Trained CART in prostate cancer screening\n\nTrained CART in prostate cancer screening\nThe results indicated that PSA, I-PSS, and age played important roles in PCa screening. CART advised the following cut-off points in the marked screening sequence: 18 < PSA < 33.5 ng/mL, I-PSS ≥ 19, and age ≥ 71. Patients with PSA ≥ 33.5 ng/mL have a PCa risk was 91.2%; patients with PSA < 18 ng/mL and I-PSS < 19 have a PCa risk was 7.1%. Patient with 18 ≤ PSA < 33.5ng/mL and I-PSS < 19 have a PCa risk is 70% if age ≥ 71; and is 16% if age < 71.\nIn overall, CART reached high predictive value with AUC = 0.915. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CART at the 20% diagnosis probability threshold were 91.5%, 86.2%, 86.9%, 91.2% and 88.9% respectively; at 80% diagnosis probability threshold were 79.2%, 92.3%, 91.2%, 81.6%, and 85.8% respectively (Table 4).\n\nTable 4Diagnosis values of CART in PCa screeningProbability = 20%Probability = 50%Probability = 80%Sensitivity0.9150.9150.792Specificity0.8620.8620.923Positive predictive value0.8690.8690.912Negative predictive value0.9120.9120.816Accuracy0.8890.8890.858\n\nDiagnosis values of CART in PCa screening\nCART was deployed with all independent variables input for PCa screening, and the final model is shown in (Fig. 1).\n\nFig. 1Trained CART in prostate cancer screening\n\nTrained CART in prostate cancer screening\nThe results indicated that PSA, I-PSS, and age played important roles in PCa screening. CART advised the following cut-off points in the marked screening sequence: 18 < PSA < 33.5 ng/mL, I-PSS ≥ 19, and age ≥ 71. Patients with PSA ≥ 33.5 ng/mL have a PCa risk was 91.2%; patients with PSA < 18 ng/mL and I-PSS < 19 have a PCa risk was 7.1%. Patient with 18 ≤ PSA < 33.5ng/mL and I-PSS < 19 have a PCa risk is 70% if age ≥ 71; and is 16% if age < 71.\nIn overall, CART reached high predictive value with AUC = 0.915. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CART at the 20% diagnosis probability threshold were 91.5%, 86.2%, 86.9%, 91.2% and 88.9% respectively; at 80% diagnosis probability threshold were 79.2%, 92.3%, 91.2%, 81.6%, and 85.8% respectively (Table 4).\n\nTable 4Diagnosis values of CART in PCa screeningProbability = 20%Probability = 50%Probability = 80%Sensitivity0.9150.9150.792Specificity0.8620.8620.923Positive predictive value0.8690.8690.912Negative predictive value0.9120.9120.816Accuracy0.8890.8890.858\n\nDiagnosis values of CART in PCa screening", "There were total 260 patients (130 in case group vs. 130 in control group) included in the study. The median age of cases was significantly higher than the controls (71 vs. 61). Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals. Physical exercise and fruit consumption were noted as protective factors of PCa (Table 1).\n\nTable 1Association of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regressionCases(n = 130)\nNo. (%)\nControls(n = 130)\nNo. (%)\nOR (95% CI)p-value\nEpidemiological characteristics\nAge (median [25–75 percentile])71 (64–78)66 (61–71)1.07 (1.04–1.11)< 0.001Overweight or Obesity (BMI ≥ 23)63 (48.5)49 (37.7)1.55 (0.92–2.63)0.103Family history of Prostate cancer (yes)2 (1.5)5 (3.9)0.39 (0.04–2.45)0.447Existing of urinary tract diseases (yes)17 (13.1)20 (15.4)0.82 (0.39–1.76)0.723History of urinary surgery (yes)10 (7.8)12 (9.2)0.83 (0.31–2.18)0.824Benign Prostate Hyperplasia (yes)14 (10.8)24 (18.5)0.53 (0.24–1.14)0.113Exposed to agrochemicals (yes)40 (30.8)20 (15.4)2.44 (1.29–4.73)0.005\nLifestyle behaviour\nPhysical activity (yes)68 (52.3)86 (66.2)0.65 (0.33–0.95)0.032Current tobacco smoking (yes)62 (47.7)48 (36.9)1.56 (0.92–2.64)0.103Heavy drinking (yes)14 (10.8)12 (9.2)1.19 (0.53–2.67)0.680\nFood consumption behaviour\nRed meat (≥ 3 times/week)107 (82.3)106 (81.5)1.05 (0.53–2.08)1.000Fruits (≥ 3 times/week)65 (50.0)83 (63.9)0.57 (0.33–0.96)0.033Vegetables (≥ 3 times/week)111 (85.4)120 (92.3)0.49 (0.19–1.16)0.114Nuts (≥ 3 times/week)9 (6.9)9 (6.9)1.00 (0.34–2.95)1.000Vegetable oil (≥ 3 times/week)104 (80.0)95 (73.1)1.47 (0.79–2.75)0.242Tea (≥ 3 times/week)56 (43.1)53 (40.8)1.10 (0.67–1.80)0.706Coffee (≥ 3 times/week)82 (63.1)80 (61.5)1.07 (0.63–1.82)0.898\n\nAssociation of epidemiological and behavioural characteristics with Prostate cancer – univariable logistic regression", "The PCa odds ratio for 1 ng/mL PSA increase was 1.06 (95% CI, 1.05–1.08). The PCa odds ratio for each I-PSS point increase was 1.14 (95% CI, 1.09–1.18). All the items of I-PSS significantly associated with PCa, except for the “Straining” item with lowest OR = 1.16 (95% CI, 0.98–1.38). “Nocturia” item had highest OR with 2.22 (95% CI, 1.79–2.74), and “Urgency” item came to the second with OR = 1.41 (95% CI, 1.23–1.62) (Table 2).\n\nTable 2Association of I-PSS and PSA with prostate cancer - univariable logistic regressionCases (n = 130)\nMedian\n\n(25–75 percentile)\nControls (n = 130)\nMedian\n\n(25–75 percentile)\nOR (95% CI)p\nPSA concentration (ng/mL)\n89.5 (39.1–100)12 (8–19)1.06 (1.05–1.08)< 0.001\nI-PSS (total score)\n\n14 (8–19)\n\n6.5 (3–12)\n\n1.14 (1.09–1.18)\n\n< 0.001\nIncomplete Emptying4 (1–5)1 (1–5)1.27 (1.12–1.44)Frequency (every 2 h)2 (0–2)0 (0–3)1.22 (1.08–1.37)Intermittency0 (0–1)0 (0–1)1.29 (1.02–1.64)Urgency2 (0–5)0 (0–1)1.41 (1.23–1.62)Weak Stream0 (0–2)0 (0–0)1.32 (1.12–1.55)Straining0 (0–1)0 (0–1)1.16 (0.98–1.38)Nocturia4 (3–5)2 (1–3)2.22 (1.79–2.74)\n\nAssociation of I-PSS and PSA with prostate cancer - univariable logistic regression", "To determine whether PCa could be predicted by PSA, I-PSS, epidemiological and behavioural variables. There were 27 models suggested from BMA process, among them the best 5 models are shown in Table 3. The most parsimonious model (i.e., minimum explanatory variables and maximum discrimination power) included two variables: I-PSS, and PSA concentration. The second parsimonious model contained I-PSS, PSA, and age. The Area Under the ROC Curve (AUC) of the most parsimonious model was not much different compared to the second parsimonious model (0.931 vs. 0.929). Because age is an important factor for PCa screening and diagnosis in many previous studies [29–31], it is also a critical factor for disease mechanisms from a clinical standpoint. Therefore, we chose the second model with three variables as the best model to use in the clinical setting (Table 3). This final model is also in light with the final model suggested by CART algorithm (details shown below).\n\nTable 3BMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCaModelVariableOR (95% CI)pR2 (%)AUCBICPosterior probability1IPSS1.12 (1.06–1.18)< 0.00149.20.931199.90.364PSA1.06 (1.04–1.08)< 0.001Intercept0.03 (0.01–0.08)< 0.0012IPSS1.11 (1.05–1.17)< 0.00150.30.929201.40.174PSA1.06 (1.04–1.07)< 0.001Age1.06 (1.01–1.10)0.047Intercept0.01 (0.00–0.04)< 0.0013IPSS1.13 (1.07–1.19)< 0.00150.10.929205.50.120PSA1.06 (1.04–1.08)< 0.001Fruits (≥ 3 times/week)0.50 (0.23–1.06)0.069Intercept0.05 (0.02–0.12)< 0.0014IPSS1.12 (1.06–1.19)< 0.00149.70.931203.60.057PSA1.06 (1.04–1.08)< 0.001Overweight or Obesity1.68 (0.80–3.53)0.174Intercept0.03 (0.01–0.07)< 0.0015IPSS1.12 (1.06–1.18)< 0.00149.70.931203.70.056PSA1.06 (1.04–1.08)< 0.001Vegetable oil (≥ 3 times/week)1.84 (0.75–4.52)0.186Intercept0.02 (0.01–0.07)< 0.001\n\nBMA prediction models using I-PSS, PSA, epidemiological, and behavioural characteristics for PCa", "CART was deployed with all independent variables input for PCa screening, and the final model is shown in (Fig. 1).\n\nFig. 1Trained CART in prostate cancer screening\n\nTrained CART in prostate cancer screening\nThe results indicated that PSA, I-PSS, and age played important roles in PCa screening. CART advised the following cut-off points in the marked screening sequence: 18 < PSA < 33.5 ng/mL, I-PSS ≥ 19, and age ≥ 71. Patients with PSA ≥ 33.5 ng/mL have a PCa risk was 91.2%; patients with PSA < 18 ng/mL and I-PSS < 19 have a PCa risk was 7.1%. Patient with 18 ≤ PSA < 33.5ng/mL and I-PSS < 19 have a PCa risk is 70% if age ≥ 71; and is 16% if age < 71.\nIn overall, CART reached high predictive value with AUC = 0.915. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CART at the 20% diagnosis probability threshold were 91.5%, 86.2%, 86.9%, 91.2% and 88.9% respectively; at 80% diagnosis probability threshold were 79.2%, 92.3%, 91.2%, 81.6%, and 85.8% respectively (Table 4).\n\nTable 4Diagnosis values of CART in PCa screeningProbability = 20%Probability = 50%Probability = 80%Sensitivity0.9150.9150.792Specificity0.8620.8620.923Positive predictive value0.8690.8690.912Negative predictive value0.9120.9120.816Accuracy0.8890.8890.858\n\nDiagnosis values of CART in PCa screening", " Epidemiological and behavioral characteristics The study included 260 observations at the Medic Center HCMC with 130 in the case group and 130 in control group. Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals and protective factor included physical exercise and fruit consumption.\nPrevious studies found that farming and exposure to agricultural chemicals are risk factors for PCa but not for all agricultural chemicals [29, 30]. Exposure to a few specific pesticides including fonofos, malathion, terbufos, and azinphos-methyl, dimethoate associated with PCa [31–33]. Genomic analysis showed pesticides might interact with genetic variants in pathways related to neurotransmission release in PCa patients [34, 35]. Therefore, the relationship between exposure to agricultural chemicals and PCa is plausible. Further epidemiological and mechanism studies are needed to identify the relationships of PCa with specific agricultural chemicals, in particular in the Vietnamese context.\nAlthough our study initially found a relationship between physical exercise and PCa, there is a lack of evidence in the literature. Recent review and meta-analyses reveal that the association between regular physical activity and a low risk of prostate cancer remains elusive [36, 37]. Given also many general health benefits of physical activity, there is the need to clarify the role of physical activity in association with PCa in further studies.\nThe association of fruit consumption with PCa was shown in our study and recent studies [38, 39]. Total fruit intake significantly reduced PCa risk. However, our study did not analyze fruit subtypes. A previous study found that citrus fruit consumption is associated with PCa, but other fruit subtypes are not associated [38]. This relationship might be due to the anti-carcinogenic properties of vitamins and phytochemicals in citrus fruits [40, 41]. However, the causal relationship remains unclear because most findings are based on a cross-sectional study.\nCART model suggested that age is most important in epidemiological and behavioral characteristics. Therefore, age, PSA, and IPSS were combined in PCa screening CART model. Our study showed that 50% of patients were older than 71 years in cases and older than 66 in controls. PCa is a disease that commonly occurs in the elderly men. Previous studies found that 75–80% of new cases occur in men aged over 65 years [42, 43]. Another study in the United States had an average participant’s age was 66 years [44]. A study by European Association of Urology showed that PCa rarely occurred in men younger than 50 years; it also indicated that the median age of PCa patient was 70 years [45]. Giwercman et al. showed that age was the closest risk factor of PCa [46]. Similarly, our study detected age as an independent risk factor of PCa. According to the Bayesian Model Averaging process, the PCa risk increased by 6% each year of age increased.\nThe role of I-PSS in PCa screening.\nThe Prostate Symptom Score (I-PSS) with seven recommended questions became an international standard to assess the symptoms of urination dysfunction in patients during the previous month. This scale is able to monitor changes in symptoms over time or after an intervention. A symptom severity assessment with an I-PSS scale is an important part of the initial evaluation, diagnosis, prediction, and monitoring of response to treatment [47, 48]. Our study noted that I-PSS was used to detect the symptoms of PCa (p < 0.001) in both univariable and multivariable analyses. A cohort study by Martin et al. [49] detected an association between I-PSS and PCa. For overall PCa, men with I-PSS ≥ 20 had a 2.26-fold increased risk of PCa compared to those with no symptoms. For localised PCa, men with I-PSS ≥ 20 had a 4.6-fold increased risk of PCa compared to those with no symptoms [49]. A study by Hosseini et al. [15] showed an association between I-PSS and PCa. The mean I-PSS score of the PCa group was 16.05 and higher than that of the non-PCa group, with a mean I-PSS score of 6.84. The prevalence of patients with I-PSS ≥ 20 in the PCa group was 30.3%, which was higher than that in the non-PCa group. The sensitivity and specificity of I-PSS at cut-off I-PSS ≥ 20 were 78% and 59%, respectively [15]. Our study and data have provided evidence about the relationship of PCa screening value with I-PSS. In Vietnam, according to the Ministry of Health, I-PSS has not yet been recommended for PCa initial screening; however, I-PSS has been recommended for benign hypertrophy of prostate – a disease that has many symptoms similar to early-stage PCa symptoms. Our study recommended using I-PSS for initial screening for any patient who has self-reported lower urinary tract symptoms.\n The role of PSA in PCa screening Prostatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].\nProstatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].\nThe study included 260 observations at the Medic Center HCMC with 130 in the case group and 130 in control group. Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals and protective factor included physical exercise and fruit consumption.\nPrevious studies found that farming and exposure to agricultural chemicals are risk factors for PCa but not for all agricultural chemicals [29, 30]. Exposure to a few specific pesticides including fonofos, malathion, terbufos, and azinphos-methyl, dimethoate associated with PCa [31–33]. Genomic analysis showed pesticides might interact with genetic variants in pathways related to neurotransmission release in PCa patients [34, 35]. Therefore, the relationship between exposure to agricultural chemicals and PCa is plausible. Further epidemiological and mechanism studies are needed to identify the relationships of PCa with specific agricultural chemicals, in particular in the Vietnamese context.\nAlthough our study initially found a relationship between physical exercise and PCa, there is a lack of evidence in the literature. Recent review and meta-analyses reveal that the association between regular physical activity and a low risk of prostate cancer remains elusive [36, 37]. Given also many general health benefits of physical activity, there is the need to clarify the role of physical activity in association with PCa in further studies.\nThe association of fruit consumption with PCa was shown in our study and recent studies [38, 39]. Total fruit intake significantly reduced PCa risk. However, our study did not analyze fruit subtypes. A previous study found that citrus fruit consumption is associated with PCa, but other fruit subtypes are not associated [38]. This relationship might be due to the anti-carcinogenic properties of vitamins and phytochemicals in citrus fruits [40, 41]. However, the causal relationship remains unclear because most findings are based on a cross-sectional study.\nCART model suggested that age is most important in epidemiological and behavioral characteristics. Therefore, age, PSA, and IPSS were combined in PCa screening CART model. Our study showed that 50% of patients were older than 71 years in cases and older than 66 in controls. PCa is a disease that commonly occurs in the elderly men. Previous studies found that 75–80% of new cases occur in men aged over 65 years [42, 43]. Another study in the United States had an average participant’s age was 66 years [44]. A study by European Association of Urology showed that PCa rarely occurred in men younger than 50 years; it also indicated that the median age of PCa patient was 70 years [45]. Giwercman et al. showed that age was the closest risk factor of PCa [46]. Similarly, our study detected age as an independent risk factor of PCa. According to the Bayesian Model Averaging process, the PCa risk increased by 6% each year of age increased.\nThe role of I-PSS in PCa screening.\nThe Prostate Symptom Score (I-PSS) with seven recommended questions became an international standard to assess the symptoms of urination dysfunction in patients during the previous month. This scale is able to monitor changes in symptoms over time or after an intervention. A symptom severity assessment with an I-PSS scale is an important part of the initial evaluation, diagnosis, prediction, and monitoring of response to treatment [47, 48]. Our study noted that I-PSS was used to detect the symptoms of PCa (p < 0.001) in both univariable and multivariable analyses. A cohort study by Martin et al. [49] detected an association between I-PSS and PCa. For overall PCa, men with I-PSS ≥ 20 had a 2.26-fold increased risk of PCa compared to those with no symptoms. For localised PCa, men with I-PSS ≥ 20 had a 4.6-fold increased risk of PCa compared to those with no symptoms [49]. A study by Hosseini et al. [15] showed an association between I-PSS and PCa. The mean I-PSS score of the PCa group was 16.05 and higher than that of the non-PCa group, with a mean I-PSS score of 6.84. The prevalence of patients with I-PSS ≥ 20 in the PCa group was 30.3%, which was higher than that in the non-PCa group. The sensitivity and specificity of I-PSS at cut-off I-PSS ≥ 20 were 78% and 59%, respectively [15]. Our study and data have provided evidence about the relationship of PCa screening value with I-PSS. In Vietnam, according to the Ministry of Health, I-PSS has not yet been recommended for PCa initial screening; however, I-PSS has been recommended for benign hypertrophy of prostate – a disease that has many symptoms similar to early-stage PCa symptoms. Our study recommended using I-PSS for initial screening for any patient who has self-reported lower urinary tract symptoms.\n The role of PSA in PCa screening Prostatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].\nProstatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].\n CART value for PCa screening To remedy the inherent limitations of PSA in PCa screening, we used a combination of PSA with I-PSS and the main risk factors of PCa to build the CART model. Based on CART, patients with a PSA cut-off level > 33.5 ng/mL have a PCa risk of up to 91.2%. Patients with I-PSS < 19, and PSA < 18 ng/mL were at 7.1% risk. CART overcomes the limitations of using only I-PSS or PSA for screening. Other machine learning algorithms have been used in PCa screening in previous studies and have reached higher values than PSA only. In a study by Babaian et al. [59], a neural network algorithm for PCa screening showed an improved value compared to using only PSA, the specificity of the neural network was not good (lower than 65%) [59]. A study by Satoshi et al. [17] showed that artificial neural network, random forest, and support vector machine improved overall value when compared to only PSA; however, sensitivity and specificity were usually lower than 80% [17]. Our CART algorithm with three variables, PSA, I-PSS, and age, showed a relatively high predictive power (AUC = 0.915). In addition, CART algorithm could also support physicians to make clinical decisions in a step-to-step and intuitive manner; hence it has practical use in a daily clinical setting. At 20% diagnosis probability threshold, CART showed a high negative predictive value (91.2%), and at 80% diagnosis probability threshold, CART also had a high positive predictive value (91.2%). Therefore, we recommended 20% diagnosis probability threshold for negative prediction and 80% diagnosis probability threshold for referring prostate biopsy. Any other patients with a probability of PCa range from > 20% to < 80%, further tests including the digital rectal examination (DRE), PSA re-test after a month, and transrectal ultrasonography (TRUS) should be considered to reduce unnecessary biopsy while keeping the ability to diagnose PCa early.\nThe study has some limitations. First, we lack other tests such as DRE, TRUS, biomarkers that can contribute to making biopsy decisions [5, 7]. Second, the CART model has not yet been tested in different populations for the validity and reliability of the algorithm. Finally, we could not estimate the overdiagnosis rate of PCa in the study. It warrants further study in the near future to overcome these limitations.\nTo remedy the inherent limitations of PSA in PCa screening, we used a combination of PSA with I-PSS and the main risk factors of PCa to build the CART model. Based on CART, patients with a PSA cut-off level > 33.5 ng/mL have a PCa risk of up to 91.2%. Patients with I-PSS < 19, and PSA < 18 ng/mL were at 7.1% risk. CART overcomes the limitations of using only I-PSS or PSA for screening. Other machine learning algorithms have been used in PCa screening in previous studies and have reached higher values than PSA only. In a study by Babaian et al. [59], a neural network algorithm for PCa screening showed an improved value compared to using only PSA, the specificity of the neural network was not good (lower than 65%) [59]. A study by Satoshi et al. [17] showed that artificial neural network, random forest, and support vector machine improved overall value when compared to only PSA; however, sensitivity and specificity were usually lower than 80% [17]. Our CART algorithm with three variables, PSA, I-PSS, and age, showed a relatively high predictive power (AUC = 0.915). In addition, CART algorithm could also support physicians to make clinical decisions in a step-to-step and intuitive manner; hence it has practical use in a daily clinical setting. At 20% diagnosis probability threshold, CART showed a high negative predictive value (91.2%), and at 80% diagnosis probability threshold, CART also had a high positive predictive value (91.2%). Therefore, we recommended 20% diagnosis probability threshold for negative prediction and 80% diagnosis probability threshold for referring prostate biopsy. Any other patients with a probability of PCa range from > 20% to < 80%, further tests including the digital rectal examination (DRE), PSA re-test after a month, and transrectal ultrasonography (TRUS) should be considered to reduce unnecessary biopsy while keeping the ability to diagnose PCa early.\nThe study has some limitations. First, we lack other tests such as DRE, TRUS, biomarkers that can contribute to making biopsy decisions [5, 7]. Second, the CART model has not yet been tested in different populations for the validity and reliability of the algorithm. Finally, we could not estimate the overdiagnosis rate of PCa in the study. It warrants further study in the near future to overcome these limitations.", "The study included 260 observations at the Medic Center HCMC with 130 in the case group and 130 in control group. Based on univariable logistic regression, the risk factors of PCa included age, exposure to agricultural chemicals and protective factor included physical exercise and fruit consumption.\nPrevious studies found that farming and exposure to agricultural chemicals are risk factors for PCa but not for all agricultural chemicals [29, 30]. Exposure to a few specific pesticides including fonofos, malathion, terbufos, and azinphos-methyl, dimethoate associated with PCa [31–33]. Genomic analysis showed pesticides might interact with genetic variants in pathways related to neurotransmission release in PCa patients [34, 35]. Therefore, the relationship between exposure to agricultural chemicals and PCa is plausible. Further epidemiological and mechanism studies are needed to identify the relationships of PCa with specific agricultural chemicals, in particular in the Vietnamese context.\nAlthough our study initially found a relationship between physical exercise and PCa, there is a lack of evidence in the literature. Recent review and meta-analyses reveal that the association between regular physical activity and a low risk of prostate cancer remains elusive [36, 37]. Given also many general health benefits of physical activity, there is the need to clarify the role of physical activity in association with PCa in further studies.\nThe association of fruit consumption with PCa was shown in our study and recent studies [38, 39]. Total fruit intake significantly reduced PCa risk. However, our study did not analyze fruit subtypes. A previous study found that citrus fruit consumption is associated with PCa, but other fruit subtypes are not associated [38]. This relationship might be due to the anti-carcinogenic properties of vitamins and phytochemicals in citrus fruits [40, 41]. However, the causal relationship remains unclear because most findings are based on a cross-sectional study.\nCART model suggested that age is most important in epidemiological and behavioral characteristics. Therefore, age, PSA, and IPSS were combined in PCa screening CART model. Our study showed that 50% of patients were older than 71 years in cases and older than 66 in controls. PCa is a disease that commonly occurs in the elderly men. Previous studies found that 75–80% of new cases occur in men aged over 65 years [42, 43]. Another study in the United States had an average participant’s age was 66 years [44]. A study by European Association of Urology showed that PCa rarely occurred in men younger than 50 years; it also indicated that the median age of PCa patient was 70 years [45]. Giwercman et al. showed that age was the closest risk factor of PCa [46]. Similarly, our study detected age as an independent risk factor of PCa. According to the Bayesian Model Averaging process, the PCa risk increased by 6% each year of age increased.\nThe role of I-PSS in PCa screening.\nThe Prostate Symptom Score (I-PSS) with seven recommended questions became an international standard to assess the symptoms of urination dysfunction in patients during the previous month. This scale is able to monitor changes in symptoms over time or after an intervention. A symptom severity assessment with an I-PSS scale is an important part of the initial evaluation, diagnosis, prediction, and monitoring of response to treatment [47, 48]. Our study noted that I-PSS was used to detect the symptoms of PCa (p < 0.001) in both univariable and multivariable analyses. A cohort study by Martin et al. [49] detected an association between I-PSS and PCa. For overall PCa, men with I-PSS ≥ 20 had a 2.26-fold increased risk of PCa compared to those with no symptoms. For localised PCa, men with I-PSS ≥ 20 had a 4.6-fold increased risk of PCa compared to those with no symptoms [49]. A study by Hosseini et al. [15] showed an association between I-PSS and PCa. The mean I-PSS score of the PCa group was 16.05 and higher than that of the non-PCa group, with a mean I-PSS score of 6.84. The prevalence of patients with I-PSS ≥ 20 in the PCa group was 30.3%, which was higher than that in the non-PCa group. The sensitivity and specificity of I-PSS at cut-off I-PSS ≥ 20 were 78% and 59%, respectively [15]. Our study and data have provided evidence about the relationship of PCa screening value with I-PSS. In Vietnam, according to the Ministry of Health, I-PSS has not yet been recommended for PCa initial screening; however, I-PSS has been recommended for benign hypertrophy of prostate – a disease that has many symptoms similar to early-stage PCa symptoms. Our study recommended using I-PSS for initial screening for any patient who has self-reported lower urinary tract symptoms.\n The role of PSA in PCa screening Prostatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].\nProstatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].", "Prostatic specific antigen (PSA) is an antigens-proteolytic protein that is secreted by prostate cells and excreted into the glandular microducts, which are largely poured into the sperm through the crystalline ducts, and smaller portions are poured into the serum and lymphatic secretions. PSA increases in PCa, prostate benign proliferation, and prostate inflammation after the procedure (cystoscopy, catheterisation of urethral, prostate massage, after a prostate biopsy within 4 weeks, after ejaculation within 48 h). PSA decreases by 50% when taking 5 alpha-reductase inhibitors with a continuous period of over 6 months [50].\nIn our study, PSA shown a significant associated with PCa and is an important predictor for PCa in both BMA and CART algorithm. Currently, all guidelines of the American and European Urogenital Societies use PSA cut-off levels ranging from 2 ng/mL to 4 ng/mL in order to make prostate biopsy decision [51]. Meanwhile, researchers chose PSA > 4 ng/mL as the cut-off level to ensure high sensitivity in screening [52–54]. According to Vietnam Ministry of Health guideline, PSA > 4 ng/mL has been recommended for selecting patients with lower urinary tract symptoms for a further clinical assessment for PCa diagnosis. The cut-off value of PSA for referring biopsy, however, is not determined. Previous studies showed that only using PSA for PCa screening before biopsy could tend to the high probability of a negative biopsy out of elevated PSA cases (high proportion of unnecessary biopsy). In PCa patients, only 65–75% of cases have PSA > 4 ng/mL; 35% of the remaining PSA cases remain at a normal level [55]. The study by Thompson et al. [56] in U.S. on cancer screening with 2950 men over 50 years old showed that 15.2% of patients had PSA < 4 ng/mL got prostate cancer, as well as 14.9% of the negative prediction group with a Gleason score of \\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\ge$$\\end{document}7 [56]. Wright et al. [57] found that a PSA threshold of > 4 ng/mL detected more cancer cases but increased unnecessary biopsy cases [57]. Morgan et al. [28] noted that the sensitivity of reached 98.2% at PSA cut-off level of > 4 ng/mL, and the sensitivity at PSA > 10 ng/mL was 91% with a specificity of 54% [28]. In some cases, the serum PSA values in the PCa and non-PCa groups overlapped, especially when PSA levels were 4–10 ng/mL. The PSA value in this range was called “diagnostic gray zone,“ according to Shariat and Karakiewicz [58].\nIn hospital-based PCa screening, combining PSA, clinical parameters, age, and other risk factors could reduce the rate of unnecessary biopsy while maintaining the ability to reduce PCa mortality [12–14].", "To remedy the inherent limitations of PSA in PCa screening, we used a combination of PSA with I-PSS and the main risk factors of PCa to build the CART model. Based on CART, patients with a PSA cut-off level > 33.5 ng/mL have a PCa risk of up to 91.2%. Patients with I-PSS < 19, and PSA < 18 ng/mL were at 7.1% risk. CART overcomes the limitations of using only I-PSS or PSA for screening. Other machine learning algorithms have been used in PCa screening in previous studies and have reached higher values than PSA only. In a study by Babaian et al. [59], a neural network algorithm for PCa screening showed an improved value compared to using only PSA, the specificity of the neural network was not good (lower than 65%) [59]. A study by Satoshi et al. [17] showed that artificial neural network, random forest, and support vector machine improved overall value when compared to only PSA; however, sensitivity and specificity were usually lower than 80% [17]. Our CART algorithm with three variables, PSA, I-PSS, and age, showed a relatively high predictive power (AUC = 0.915). In addition, CART algorithm could also support physicians to make clinical decisions in a step-to-step and intuitive manner; hence it has practical use in a daily clinical setting. At 20% diagnosis probability threshold, CART showed a high negative predictive value (91.2%), and at 80% diagnosis probability threshold, CART also had a high positive predictive value (91.2%). Therefore, we recommended 20% diagnosis probability threshold for negative prediction and 80% diagnosis probability threshold for referring prostate biopsy. Any other patients with a probability of PCa range from > 20% to < 80%, further tests including the digital rectal examination (DRE), PSA re-test after a month, and transrectal ultrasonography (TRUS) should be considered to reduce unnecessary biopsy while keeping the ability to diagnose PCa early.\nThe study has some limitations. First, we lack other tests such as DRE, TRUS, biomarkers that can contribute to making biopsy decisions [5, 7]. Second, the CART model has not yet been tested in different populations for the validity and reliability of the algorithm. Finally, we could not estimate the overdiagnosis rate of PCa in the study. It warrants further study in the near future to overcome these limitations.", "CART advised the following cut-off points in the marked screening sequence: 18 < PSA < 33.5 ng/mL, I-PSS ≥ 19, and age ≥ 71. Patients with PSA ≥ 33.5 ng/mL have a PCa risk was 91.2%; patients with PSA < 18 ng/mL and I-PSS < 19 have a PCa risk was 7.1%. Patient with 18 ≤ PSA < 33.5ng/mL and I-PSS < 19 have a PCa risk is 70% if age ≥ 71; and is 16% if age < 71. In overall, CART reached high predictive value with AUC = 0.915. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of CART at the 20% diagnosis probability threshold were 91.1%, 86.9%, 86.2%, 91.5% and 88.9%; at 80% diagnosis probability threshold were 81.6%, 91.2%, 92.3%, 79.2%, and 85.8%. I-PSS, PSA, and age had importance role in PCa screening. CART combining PSA, I-PSS, and age has practical use in hospital-based PCa screening in Vietnamese patients." ]

[ null, null, null, null, null, null, null, null, null, null, null, "results", null, null, null, null, "discussion", null, null, null, "conclusion" ]

[ "Prostate cancer", "PSA", "I-PSS", "Vietnamese patients", "Classification and regression tree", "Bayesian modeling averaging" ]

[ 913, 2890, 74, 128, 120, 348, 761, 82, 46, 112, 125, 357, 287, 353, 312, 2155, 590, 492 ]

[ "pca", "psa", "pss", "prostate", "cart", "screening", "ml", "ng ml", "ng", "study" ]

[ "increases pca prostate", "cancer pca", "screening prostate cancer", "psa prostate", "prostate specific antigen" ]

[ "Debridement", "Discitis", "Drainage", "Follow-Up Studies", "Humans", "Lumbar Vertebrae", "Minimally Invasive Surgical Procedures", "Retrospective Studies", "Spinal Fusion", "Tomography, X-Ray Computed", "Treatment Outcome" ]

[ "Background", "Methods", "Patients", "Operative procedure", "Outcome measures", "Statistical methods", "Demographic data", "Radiologic findings", "Clinical outcomes", "Follow‐up" ]

[ "Spondylodiscitis is an unusual infectious disease, which usually originates as a pathogenic infection of intervertebral discs and then spreads to neighboring vertebral bodies [1]. Mortality is around 2–3 % [2]. Its incidence varies between 0.2 % and 3.6 % after spine surgery [1, 3, 4]. There is no uniform standard for the treatment of spondylodiscitis. Conservative therapy including bracing and appropriate antibiotics is always adequate for the patients involving early detection of mild infection [5]. But delayed diagnosis and treatment of spondylitis are common because of their early variable clinical manifestations and indolent courses, which may lead to the failure of conservative treatment [5]. Surgical treatment is reserved for patients with failed conservative therapy, including intractable spinal pain, large epidural abscesses, and extensive vertebral body destruction [6]. The major purpose of surgical intervention in spondylodiscitis is to remove the infected tissues, relieve spinal pain, rebuild the spinal stability, and improve limb dysfunction. Open surgery has been advocated in the past [7–11]. However, whether the anterior or posterior approach, open surgery faced serious complications like nerve or vessel injuries due to extensive anatomical dissection and destruction of the stable spinal structure [12, 13]. Recent research favors a minimally invasive surgery(MIS) [7, 14, 15].\nExisting studies have focused on single-level or early-stage infectious spondylodiscitis, and good clinical results have been reported after percutaneous debridement and drainage [14, 16, 17]. However, there are few reports of the management of advanced multilevel infections. These are difficult to treat using open or endoscopic surgery in current clinical practice, because of mechanical instability of the affected multilevel segments caused by widespread destruction due to the disease process [14, 18–21]. To my knowledge, up to date, this is the first report to treat multilevel spondylodiscitis with MIS. MIS may provide minimized damage stable structure of the posterior spine and paraspinal soft tissues. However, it is difficult to identify anatomic landmarks in MIS that may lead to severe complications. Therefore, to increase the accuracy of debridement and drainage, CT-Guide was performed during the operation.\nThe purpose of this study was to evaluate the clinical effect of percutaneous debridement and drainage using intraoperative CT-Guide in the treatment of multilevel spondylodiscitis. Considering the particularity of tuberculous spondylodiscitis, it was not included in this study.", " Patients From January 2002 to May 2017, 23 patients with multilevel spondylodiscitis were treated with percutaneous debridement and drainage procedures in our department. There were 12 female and 11 male patients with an average age of 56.5 years (range from 40 to 65 years). All patients were treated conservatively (antibiotics and bed rest) or with open surgery in other hospitals before transfer to our department. There were 7 cases of infection after open or minimally surgery and 16 cases of unknown etiology.\nClinical diagnosis of spondylodiscitis was mainly based on routine blood tests including C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR); imaging examinations comprising X-ray, CT scan, and magnetic resonance imaging (MRI).\nFrom January 2002 to May 2017, 23 patients with multilevel spondylodiscitis were treated with percutaneous debridement and drainage procedures in our department. There were 12 female and 11 male patients with an average age of 56.5 years (range from 40 to 65 years). All patients were treated conservatively (antibiotics and bed rest) or with open surgery in other hospitals before transfer to our department. There were 7 cases of infection after open or minimally surgery and 16 cases of unknown etiology.\nClinical diagnosis of spondylodiscitis was mainly based on routine blood tests including C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR); imaging examinations comprising X-ray, CT scan, and magnetic resonance imaging (MRI).\n Operative procedure The patients were positioned in the prone position after induction of local anesthesia on a radiolucent surgical bed. Under CT-Guide (Brainlab® System), the target disc was located and the entry site was marked on the skin at a point 3–10 cm from the midline. All cases were treated by a transforaminal approach. The puncture direction was about 45 degrees abduction. The needle was punctured through the safety triangle to the infected vertebral space. On each side, a spinal needle was inserted directly into the infected disc and through the spinal needle, a guidewire was introduced. After a small incision (about 1 cm) was made, a dilator and a cannulated sleeve were guided through the wire into the targeted site. The infected tissue of the targeted disc was extracted with discectomy forceps through the cannulated sleeve if necessary. The same procedures were performed on the contralateral side. In the case of a paravertebral abscess, paravertebral tube drainage should be performed at the same time. This allowed for both sufficient biopsy material and extensive debridement of the necrosis and inflammatory tissue from a different direction. After biopsy and debridement, at least 1000 ml of the physiological saline was used for irrigation at each level. Finally, double cavity flushing drainage catheters were placed into the debrided segment and attached to the negative pressure suction for postoperative irrigation. Postoperatively, 1500 ml of broad-spectrum antibiotic saline was used irrigated locally every day via continuous irrigation and flushing. After the results of microbial culture became available, broad-spectrum antibiotics were replaced by those with more narrow-sensitivity. The antibiotic treatment in the perioperative period follows the relevant literature [5].\nThe criteria for the cessation of irrigation was: 1. complete disappearance of clinical symptoms; 2. clear fluid following flushing; 3. CRP declined to the normal range or the level before spondylodiscitis. If two of the above three indicators are met, the flushing will be stopped. After cessation of irrigation, the cannulae were removed after about 48 hours if CRP remained low.\nThe patients were positioned in the prone position after induction of local anesthesia on a radiolucent surgical bed. Under CT-Guide (Brainlab® System), the target disc was located and the entry site was marked on the skin at a point 3–10 cm from the midline. All cases were treated by a transforaminal approach. The puncture direction was about 45 degrees abduction. The needle was punctured through the safety triangle to the infected vertebral space. On each side, a spinal needle was inserted directly into the infected disc and through the spinal needle, a guidewire was introduced. After a small incision (about 1 cm) was made, a dilator and a cannulated sleeve were guided through the wire into the targeted site. The infected tissue of the targeted disc was extracted with discectomy forceps through the cannulated sleeve if necessary. The same procedures were performed on the contralateral side. In the case of a paravertebral abscess, paravertebral tube drainage should be performed at the same time. This allowed for both sufficient biopsy material and extensive debridement of the necrosis and inflammatory tissue from a different direction. After biopsy and debridement, at least 1000 ml of the physiological saline was used for irrigation at each level. Finally, double cavity flushing drainage catheters were placed into the debrided segment and attached to the negative pressure suction for postoperative irrigation. Postoperatively, 1500 ml of broad-spectrum antibiotic saline was used irrigated locally every day via continuous irrigation and flushing. After the results of microbial culture became available, broad-spectrum antibiotics were replaced by those with more narrow-sensitivity. The antibiotic treatment in the perioperative period follows the relevant literature [5].\nThe criteria for the cessation of irrigation was: 1. complete disappearance of clinical symptoms; 2. clear fluid following flushing; 3. CRP declined to the normal range or the level before spondylodiscitis. If two of the above three indicators are met, the flushing will be stopped. After cessation of irrigation, the cannulae were removed after about 48 hours if CRP remained low.\n Outcome measures All patients were followed up in the clinic at 1 month and then every 3 months to determine whether the infection remained under control after discharge [22]. All patients were followed up with X-ray and MRI at each visit [23]. The following factors were assessed: physical examination, laboratory tests, back pain score (visual analog scale, VAS), Oswestry disability index(ODI), and Macnab criteria(as proposed by Macnab I) [22]. After the operation, the patient was asked to identify which one of the four levels corresponded to their condition: excellent, good, fair, poor. No pain and no restriction of activity are excellent; occasional back or leg pain is good; intermittent pain affecting work and life is fair; no improvement or further operative intervention required is poor.\nAll patients were followed up in the clinic at 1 month and then every 3 months to determine whether the infection remained under control after discharge [22]. All patients were followed up with X-ray and MRI at each visit [23]. The following factors were assessed: physical examination, laboratory tests, back pain score (visual analog scale, VAS), Oswestry disability index(ODI), and Macnab criteria(as proposed by Macnab I) [22]. After the operation, the patient was asked to identify which one of the four levels corresponded to their condition: excellent, good, fair, poor. No pain and no restriction of activity are excellent; occasional back or leg pain is good; intermittent pain affecting work and life is fair; no improvement or further operative intervention required is poor.\n Statistical methods SPSS 23.0 was used for statistical analysis. Overall summary statistics were calculated in terms of means ± SD for continuous variables. In this study, t-test was used for measurement data. All statistical tests were bilateral, with P < 0.05 as the significance standard.\nSPSS 23.0 was used for statistical analysis. Overall summary statistics were calculated in terms of means ± SD for continuous variables. In this study, t-test was used for measurement data. All statistical tests were bilateral, with P < 0.05 as the significance standard.", "From January 2002 to May 2017, 23 patients with multilevel spondylodiscitis were treated with percutaneous debridement and drainage procedures in our department. There were 12 female and 11 male patients with an average age of 56.5 years (range from 40 to 65 years). All patients were treated conservatively (antibiotics and bed rest) or with open surgery in other hospitals before transfer to our department. There were 7 cases of infection after open or minimally surgery and 16 cases of unknown etiology.\nClinical diagnosis of spondylodiscitis was mainly based on routine blood tests including C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR); imaging examinations comprising X-ray, CT scan, and magnetic resonance imaging (MRI).", "The patients were positioned in the prone position after induction of local anesthesia on a radiolucent surgical bed. Under CT-Guide (Brainlab® System), the target disc was located and the entry site was marked on the skin at a point 3–10 cm from the midline. All cases were treated by a transforaminal approach. The puncture direction was about 45 degrees abduction. The needle was punctured through the safety triangle to the infected vertebral space. On each side, a spinal needle was inserted directly into the infected disc and through the spinal needle, a guidewire was introduced. After a small incision (about 1 cm) was made, a dilator and a cannulated sleeve were guided through the wire into the targeted site. The infected tissue of the targeted disc was extracted with discectomy forceps through the cannulated sleeve if necessary. The same procedures were performed on the contralateral side. In the case of a paravertebral abscess, paravertebral tube drainage should be performed at the same time. This allowed for both sufficient biopsy material and extensive debridement of the necrosis and inflammatory tissue from a different direction. After biopsy and debridement, at least 1000 ml of the physiological saline was used for irrigation at each level. Finally, double cavity flushing drainage catheters were placed into the debrided segment and attached to the negative pressure suction for postoperative irrigation. Postoperatively, 1500 ml of broad-spectrum antibiotic saline was used irrigated locally every day via continuous irrigation and flushing. After the results of microbial culture became available, broad-spectrum antibiotics were replaced by those with more narrow-sensitivity. The antibiotic treatment in the perioperative period follows the relevant literature [5].\nThe criteria for the cessation of irrigation was: 1. complete disappearance of clinical symptoms; 2. clear fluid following flushing; 3. CRP declined to the normal range or the level before spondylodiscitis. If two of the above three indicators are met, the flushing will be stopped. After cessation of irrigation, the cannulae were removed after about 48 hours if CRP remained low.", "All patients were followed up in the clinic at 1 month and then every 3 months to determine whether the infection remained under control after discharge [22]. All patients were followed up with X-ray and MRI at each visit [23]. The following factors were assessed: physical examination, laboratory tests, back pain score (visual analog scale, VAS), Oswestry disability index(ODI), and Macnab criteria(as proposed by Macnab I) [22]. After the operation, the patient was asked to identify which one of the four levels corresponded to their condition: excellent, good, fair, poor. No pain and no restriction of activity are excellent; occasional back or leg pain is good; intermittent pain affecting work and life is fair; no improvement or further operative intervention required is poor.", "SPSS 23.0 was used for statistical analysis. Overall summary statistics were calculated in terms of means ± SD for continuous variables. In this study, t-test was used for measurement data. All statistical tests were bilateral, with P < 0.05 as the significance standard.", "The 23 enrolled patients included 9 patients with Gram-positive (+), 6 patients with Gram-negative (-), 7 patients with Fungi, and 1 with mixed infection (Tables 1 and 2). Of the 23 patients, the maximum number of infected levels was 6 and the average number of infected levels was 3.2.\nTable 1Cultured pathogens in 23 patients received minimally invasive debridement and drainage treatmentCultured pathogensNumberGram-positive (+)9S. aureus4S. epidermidis3S. viridans1MRSA1Gram-negative (-)6E. coli3E. faecalis2Brucella1Fungi7Aspergillus fumigatus5Aspergillus flavus2Mixed infection1Aspergillus fumigatus and S. aureusTotal Number123Table 2Demographic data of seven patients with postoperative spinal infectionNOOperations before MISInternal fixation(Y/N)Fixation removed(Y/N)Cultured bacteriaCase 1open lumbar surgery + debridement and internal fixationYNAspergillus fumigatusCase 2Open internal fixation of lumbar spineYNAspergillus flavusCase 3Open Operation of malignant schwannomaN-E. coliCase 4Open internal fixation of lumbar spineN-S.epidermidisCase 5Open internal fixation of lumbar spineYNS. aureusCase 6Unidentified Lumbar infection + open debridement and internal fixationYNAspergillus fumigatusCase 7Open internal fixation of lumbar spineYNS. aureus\nCultured pathogens in 23 patients received minimally invasive debridement and drainage treatment\nAspergillus fumigatus and S. aureus\nTotal Number\n1\n23\nDemographic data of seven patients with postoperative spinal infection\nAt admission, all but one patient presented with a fever of more than 38.5 °C. All patients presented with ESR of more than 20 mm/hr (range, 50 to 115 mm/hr). The elevated CRP ranged from 14.9 mg/L to 30.6 mg/L with an average of 23 mg/L (Table 3). The white blood cell count was elevated above normal in only one case.\nTable 3The results of the outcome measures for Clinical and laboratory indicatorsLaboratory testsPre-Treatment(at admission)Post-Operation(1w)Post- Extubation(1 m)ESR (mm/hr)84.5 (50–115)23.4 (15–38)13.2 (6–19)CRP (mg/L)26.0 (14–30)16.5 (12–21)3.7 (2–10)Functional resultsVisual analog scale8.6 (6–10)4.5 (3–5)2.3 (1–3)Oswestry disability index69.3(56–89)34.2 (25–44)26.3 (13–42)\nThe results of the outcome measures for Clinical and laboratory indicators\nIn this study, 7 patients with spondylitis were infected after the operation, and 5 of them underwent internal fixation implantation. All patients received minimally invasive treatment without the removal of internal fixation (Table 2). Before coming to our hospital, only 2 patients had a culture report (fungal infection), and both of them underwent open debridement surgery in other hospitals, and then transferred to our hospital.", "In all patients, X-ray examination showed no signs of spinal instability; CT showed destruction of the vertebral endplate and varying degree of vertebral space collapse (Fig. 1). MRI revealed no compression of the spinal cord. There was no recurrence in the treated vertebral segment during follow-up, but two patients with fungal spinal infection (Case1 Fig. 2/3) (Case2 Fig. 4/5) progressed to involve adjacent segments. During postoperative follow-up, no deformities such as scoliosis or kyphosis were observed by plain radiography, and MRI showed varying degrees of spontaneous fusion [24].\nFig. 1A 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyedFig. 2Sequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)Fig. 3The same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discsFig. 4Sequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)Fig. 5The same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved\nA 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyed\nSequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)\nThe same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discs\nSequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)\nThe same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved", "Before surgery, all patients had significant back pain. There were 2 patients with radiating lower limb pain but no patients had muscle weakness, bowel or bladder dysfunction preoperatively. The operation time ranged from 30 minutes to 124 minutes for every spinal level with an average of 48 minutes. Intraoperative hemorrhage was minimal. In this patient population, the average delay of the diagnosis[25] was thirty-seven days. There were significant differences in VAS and ODI between the pre-treatment and post-operation (P > 0.05). And there were significant differences in VAS and ODI between the pre-treatment and post-extubation (P > 0.05) (Table 3).\nThe average drainage time was 14 days (5–26 days). No serious complications were found in the perioperative period. In one case (Case 6), a drainage tube became detached, and another puncture and catheterization were performed.", "Postoperative follow-up periods ranged from 1 year to 6.5 years, (mean 3.7 years). One patient (Case 2) was lost to follow-up at postoperative 12 months. One patient (Case 3) died from an abdominal neoplasm at postoperative 12 months. According to the classification system of Macnab [22], one patient (Case 2) had a good outcome at final follow-up, and the remainder were excellent." ]

[ null, null, null, null, null, null, null, null, null, null ]

[ "Background", "Methods", "Patients", "Operative procedure", "Outcome measures", "Statistical methods", "Results", "Demographic data", "Radiologic findings", "Clinical outcomes", "Follow‐up", "Discussion", "Conclusions" ]

[ "Spondylodiscitis is an unusual infectious disease, which usually originates as a pathogenic infection of intervertebral discs and then spreads to neighboring vertebral bodies [1]. Mortality is around 2–3 % [2]. Its incidence varies between 0.2 % and 3.6 % after spine surgery [1, 3, 4]. There is no uniform standard for the treatment of spondylodiscitis. Conservative therapy including bracing and appropriate antibiotics is always adequate for the patients involving early detection of mild infection [5]. But delayed diagnosis and treatment of spondylitis are common because of their early variable clinical manifestations and indolent courses, which may lead to the failure of conservative treatment [5]. Surgical treatment is reserved for patients with failed conservative therapy, including intractable spinal pain, large epidural abscesses, and extensive vertebral body destruction [6]. The major purpose of surgical intervention in spondylodiscitis is to remove the infected tissues, relieve spinal pain, rebuild the spinal stability, and improve limb dysfunction. Open surgery has been advocated in the past [7–11]. However, whether the anterior or posterior approach, open surgery faced serious complications like nerve or vessel injuries due to extensive anatomical dissection and destruction of the stable spinal structure [12, 13]. Recent research favors a minimally invasive surgery(MIS) [7, 14, 15].\nExisting studies have focused on single-level or early-stage infectious spondylodiscitis, and good clinical results have been reported after percutaneous debridement and drainage [14, 16, 17]. However, there are few reports of the management of advanced multilevel infections. These are difficult to treat using open or endoscopic surgery in current clinical practice, because of mechanical instability of the affected multilevel segments caused by widespread destruction due to the disease process [14, 18–21]. To my knowledge, up to date, this is the first report to treat multilevel spondylodiscitis with MIS. MIS may provide minimized damage stable structure of the posterior spine and paraspinal soft tissues. However, it is difficult to identify anatomic landmarks in MIS that may lead to severe complications. Therefore, to increase the accuracy of debridement and drainage, CT-Guide was performed during the operation.\nThe purpose of this study was to evaluate the clinical effect of percutaneous debridement and drainage using intraoperative CT-Guide in the treatment of multilevel spondylodiscitis. Considering the particularity of tuberculous spondylodiscitis, it was not included in this study.", " Patients From January 2002 to May 2017, 23 patients with multilevel spondylodiscitis were treated with percutaneous debridement and drainage procedures in our department. There were 12 female and 11 male patients with an average age of 56.5 years (range from 40 to 65 years). All patients were treated conservatively (antibiotics and bed rest) or with open surgery in other hospitals before transfer to our department. There were 7 cases of infection after open or minimally surgery and 16 cases of unknown etiology.\nClinical diagnosis of spondylodiscitis was mainly based on routine blood tests including C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR); imaging examinations comprising X-ray, CT scan, and magnetic resonance imaging (MRI).\nFrom January 2002 to May 2017, 23 patients with multilevel spondylodiscitis were treated with percutaneous debridement and drainage procedures in our department. There were 12 female and 11 male patients with an average age of 56.5 years (range from 40 to 65 years). All patients were treated conservatively (antibiotics and bed rest) or with open surgery in other hospitals before transfer to our department. There were 7 cases of infection after open or minimally surgery and 16 cases of unknown etiology.\nClinical diagnosis of spondylodiscitis was mainly based on routine blood tests including C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR); imaging examinations comprising X-ray, CT scan, and magnetic resonance imaging (MRI).\n Operative procedure The patients were positioned in the prone position after induction of local anesthesia on a radiolucent surgical bed. Under CT-Guide (Brainlab® System), the target disc was located and the entry site was marked on the skin at a point 3–10 cm from the midline. All cases were treated by a transforaminal approach. The puncture direction was about 45 degrees abduction. The needle was punctured through the safety triangle to the infected vertebral space. On each side, a spinal needle was inserted directly into the infected disc and through the spinal needle, a guidewire was introduced. After a small incision (about 1 cm) was made, a dilator and a cannulated sleeve were guided through the wire into the targeted site. The infected tissue of the targeted disc was extracted with discectomy forceps through the cannulated sleeve if necessary. The same procedures were performed on the contralateral side. In the case of a paravertebral abscess, paravertebral tube drainage should be performed at the same time. This allowed for both sufficient biopsy material and extensive debridement of the necrosis and inflammatory tissue from a different direction. After biopsy and debridement, at least 1000 ml of the physiological saline was used for irrigation at each level. Finally, double cavity flushing drainage catheters were placed into the debrided segment and attached to the negative pressure suction for postoperative irrigation. Postoperatively, 1500 ml of broad-spectrum antibiotic saline was used irrigated locally every day via continuous irrigation and flushing. After the results of microbial culture became available, broad-spectrum antibiotics were replaced by those with more narrow-sensitivity. The antibiotic treatment in the perioperative period follows the relevant literature [5].\nThe criteria for the cessation of irrigation was: 1. complete disappearance of clinical symptoms; 2. clear fluid following flushing; 3. CRP declined to the normal range or the level before spondylodiscitis. If two of the above three indicators are met, the flushing will be stopped. After cessation of irrigation, the cannulae were removed after about 48 hours if CRP remained low.\nThe patients were positioned in the prone position after induction of local anesthesia on a radiolucent surgical bed. Under CT-Guide (Brainlab® System), the target disc was located and the entry site was marked on the skin at a point 3–10 cm from the midline. All cases were treated by a transforaminal approach. The puncture direction was about 45 degrees abduction. The needle was punctured through the safety triangle to the infected vertebral space. On each side, a spinal needle was inserted directly into the infected disc and through the spinal needle, a guidewire was introduced. After a small incision (about 1 cm) was made, a dilator and a cannulated sleeve were guided through the wire into the targeted site. The infected tissue of the targeted disc was extracted with discectomy forceps through the cannulated sleeve if necessary. The same procedures were performed on the contralateral side. In the case of a paravertebral abscess, paravertebral tube drainage should be performed at the same time. This allowed for both sufficient biopsy material and extensive debridement of the necrosis and inflammatory tissue from a different direction. After biopsy and debridement, at least 1000 ml of the physiological saline was used for irrigation at each level. Finally, double cavity flushing drainage catheters were placed into the debrided segment and attached to the negative pressure suction for postoperative irrigation. Postoperatively, 1500 ml of broad-spectrum antibiotic saline was used irrigated locally every day via continuous irrigation and flushing. After the results of microbial culture became available, broad-spectrum antibiotics were replaced by those with more narrow-sensitivity. The antibiotic treatment in the perioperative period follows the relevant literature [5].\nThe criteria for the cessation of irrigation was: 1. complete disappearance of clinical symptoms; 2. clear fluid following flushing; 3. CRP declined to the normal range or the level before spondylodiscitis. If two of the above three indicators are met, the flushing will be stopped. After cessation of irrigation, the cannulae were removed after about 48 hours if CRP remained low.\n Outcome measures All patients were followed up in the clinic at 1 month and then every 3 months to determine whether the infection remained under control after discharge [22]. All patients were followed up with X-ray and MRI at each visit [23]. The following factors were assessed: physical examination, laboratory tests, back pain score (visual analog scale, VAS), Oswestry disability index(ODI), and Macnab criteria(as proposed by Macnab I) [22]. After the operation, the patient was asked to identify which one of the four levels corresponded to their condition: excellent, good, fair, poor. No pain and no restriction of activity are excellent; occasional back or leg pain is good; intermittent pain affecting work and life is fair; no improvement or further operative intervention required is poor.\nAll patients were followed up in the clinic at 1 month and then every 3 months to determine whether the infection remained under control after discharge [22]. All patients were followed up with X-ray and MRI at each visit [23]. The following factors were assessed: physical examination, laboratory tests, back pain score (visual analog scale, VAS), Oswestry disability index(ODI), and Macnab criteria(as proposed by Macnab I) [22]. After the operation, the patient was asked to identify which one of the four levels corresponded to their condition: excellent, good, fair, poor. No pain and no restriction of activity are excellent; occasional back or leg pain is good; intermittent pain affecting work and life is fair; no improvement or further operative intervention required is poor.\n Statistical methods SPSS 23.0 was used for statistical analysis. Overall summary statistics were calculated in terms of means ± SD for continuous variables. In this study, t-test was used for measurement data. All statistical tests were bilateral, with P < 0.05 as the significance standard.\nSPSS 23.0 was used for statistical analysis. Overall summary statistics were calculated in terms of means ± SD for continuous variables. In this study, t-test was used for measurement data. All statistical tests were bilateral, with P < 0.05 as the significance standard.", "From January 2002 to May 2017, 23 patients with multilevel spondylodiscitis were treated with percutaneous debridement and drainage procedures in our department. There were 12 female and 11 male patients with an average age of 56.5 years (range from 40 to 65 years). All patients were treated conservatively (antibiotics and bed rest) or with open surgery in other hospitals before transfer to our department. There were 7 cases of infection after open or minimally surgery and 16 cases of unknown etiology.\nClinical diagnosis of spondylodiscitis was mainly based on routine blood tests including C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR); imaging examinations comprising X-ray, CT scan, and magnetic resonance imaging (MRI).", "The patients were positioned in the prone position after induction of local anesthesia on a radiolucent surgical bed. Under CT-Guide (Brainlab® System), the target disc was located and the entry site was marked on the skin at a point 3–10 cm from the midline. All cases were treated by a transforaminal approach. The puncture direction was about 45 degrees abduction. The needle was punctured through the safety triangle to the infected vertebral space. On each side, a spinal needle was inserted directly into the infected disc and through the spinal needle, a guidewire was introduced. After a small incision (about 1 cm) was made, a dilator and a cannulated sleeve were guided through the wire into the targeted site. The infected tissue of the targeted disc was extracted with discectomy forceps through the cannulated sleeve if necessary. The same procedures were performed on the contralateral side. In the case of a paravertebral abscess, paravertebral tube drainage should be performed at the same time. This allowed for both sufficient biopsy material and extensive debridement of the necrosis and inflammatory tissue from a different direction. After biopsy and debridement, at least 1000 ml of the physiological saline was used for irrigation at each level. Finally, double cavity flushing drainage catheters were placed into the debrided segment and attached to the negative pressure suction for postoperative irrigation. Postoperatively, 1500 ml of broad-spectrum antibiotic saline was used irrigated locally every day via continuous irrigation and flushing. After the results of microbial culture became available, broad-spectrum antibiotics were replaced by those with more narrow-sensitivity. The antibiotic treatment in the perioperative period follows the relevant literature [5].\nThe criteria for the cessation of irrigation was: 1. complete disappearance of clinical symptoms; 2. clear fluid following flushing; 3. CRP declined to the normal range or the level before spondylodiscitis. If two of the above three indicators are met, the flushing will be stopped. After cessation of irrigation, the cannulae were removed after about 48 hours if CRP remained low.", "All patients were followed up in the clinic at 1 month and then every 3 months to determine whether the infection remained under control after discharge [22]. All patients were followed up with X-ray and MRI at each visit [23]. The following factors were assessed: physical examination, laboratory tests, back pain score (visual analog scale, VAS), Oswestry disability index(ODI), and Macnab criteria(as proposed by Macnab I) [22]. After the operation, the patient was asked to identify which one of the four levels corresponded to their condition: excellent, good, fair, poor. No pain and no restriction of activity are excellent; occasional back or leg pain is good; intermittent pain affecting work and life is fair; no improvement or further operative intervention required is poor.", "SPSS 23.0 was used for statistical analysis. Overall summary statistics were calculated in terms of means ± SD for continuous variables. In this study, t-test was used for measurement data. All statistical tests were bilateral, with P < 0.05 as the significance standard.", " Demographic data The 23 enrolled patients included 9 patients with Gram-positive (+), 6 patients with Gram-negative (-), 7 patients with Fungi, and 1 with mixed infection (Tables 1 and 2). Of the 23 patients, the maximum number of infected levels was 6 and the average number of infected levels was 3.2.\nTable 1Cultured pathogens in 23 patients received minimally invasive debridement and drainage treatmentCultured pathogensNumberGram-positive (+)9S. aureus4S. epidermidis3S. viridans1MRSA1Gram-negative (-)6E. coli3E. faecalis2Brucella1Fungi7Aspergillus fumigatus5Aspergillus flavus2Mixed infection1Aspergillus fumigatus and S. aureusTotal Number123Table 2Demographic data of seven patients with postoperative spinal infectionNOOperations before MISInternal fixation(Y/N)Fixation removed(Y/N)Cultured bacteriaCase 1open lumbar surgery + debridement and internal fixationYNAspergillus fumigatusCase 2Open internal fixation of lumbar spineYNAspergillus flavusCase 3Open Operation of malignant schwannomaN-E. coliCase 4Open internal fixation of lumbar spineN-S.epidermidisCase 5Open internal fixation of lumbar spineYNS. aureusCase 6Unidentified Lumbar infection + open debridement and internal fixationYNAspergillus fumigatusCase 7Open internal fixation of lumbar spineYNS. aureus\nCultured pathogens in 23 patients received minimally invasive debridement and drainage treatment\nAspergillus fumigatus and S. aureus\nTotal Number\n1\n23\nDemographic data of seven patients with postoperative spinal infection\nAt admission, all but one patient presented with a fever of more than 38.5 °C. All patients presented with ESR of more than 20 mm/hr (range, 50 to 115 mm/hr). The elevated CRP ranged from 14.9 mg/L to 30.6 mg/L with an average of 23 mg/L (Table 3). The white blood cell count was elevated above normal in only one case.\nTable 3The results of the outcome measures for Clinical and laboratory indicatorsLaboratory testsPre-Treatment(at admission)Post-Operation(1w)Post- Extubation(1 m)ESR (mm/hr)84.5 (50–115)23.4 (15–38)13.2 (6–19)CRP (mg/L)26.0 (14–30)16.5 (12–21)3.7 (2–10)Functional resultsVisual analog scale8.6 (6–10)4.5 (3–5)2.3 (1–3)Oswestry disability index69.3(56–89)34.2 (25–44)26.3 (13–42)\nThe results of the outcome measures for Clinical and laboratory indicators\nIn this study, 7 patients with spondylitis were infected after the operation, and 5 of them underwent internal fixation implantation. All patients received minimally invasive treatment without the removal of internal fixation (Table 2). Before coming to our hospital, only 2 patients had a culture report (fungal infection), and both of them underwent open debridement surgery in other hospitals, and then transferred to our hospital.\nThe 23 enrolled patients included 9 patients with Gram-positive (+), 6 patients with Gram-negative (-), 7 patients with Fungi, and 1 with mixed infection (Tables 1 and 2). Of the 23 patients, the maximum number of infected levels was 6 and the average number of infected levels was 3.2.\nTable 1Cultured pathogens in 23 patients received minimally invasive debridement and drainage treatmentCultured pathogensNumberGram-positive (+)9S. aureus4S. epidermidis3S. viridans1MRSA1Gram-negative (-)6E. coli3E. faecalis2Brucella1Fungi7Aspergillus fumigatus5Aspergillus flavus2Mixed infection1Aspergillus fumigatus and S. aureusTotal Number123Table 2Demographic data of seven patients with postoperative spinal infectionNOOperations before MISInternal fixation(Y/N)Fixation removed(Y/N)Cultured bacteriaCase 1open lumbar surgery + debridement and internal fixationYNAspergillus fumigatusCase 2Open internal fixation of lumbar spineYNAspergillus flavusCase 3Open Operation of malignant schwannomaN-E. coliCase 4Open internal fixation of lumbar spineN-S.epidermidisCase 5Open internal fixation of lumbar spineYNS. aureusCase 6Unidentified Lumbar infection + open debridement and internal fixationYNAspergillus fumigatusCase 7Open internal fixation of lumbar spineYNS. aureus\nCultured pathogens in 23 patients received minimally invasive debridement and drainage treatment\nAspergillus fumigatus and S. aureus\nTotal Number\n1\n23\nDemographic data of seven patients with postoperative spinal infection\nAt admission, all but one patient presented with a fever of more than 38.5 °C. All patients presented with ESR of more than 20 mm/hr (range, 50 to 115 mm/hr). The elevated CRP ranged from 14.9 mg/L to 30.6 mg/L with an average of 23 mg/L (Table 3). The white blood cell count was elevated above normal in only one case.\nTable 3The results of the outcome measures for Clinical and laboratory indicatorsLaboratory testsPre-Treatment(at admission)Post-Operation(1w)Post- Extubation(1 m)ESR (mm/hr)84.5 (50–115)23.4 (15–38)13.2 (6–19)CRP (mg/L)26.0 (14–30)16.5 (12–21)3.7 (2–10)Functional resultsVisual analog scale8.6 (6–10)4.5 (3–5)2.3 (1–3)Oswestry disability index69.3(56–89)34.2 (25–44)26.3 (13–42)\nThe results of the outcome measures for Clinical and laboratory indicators\nIn this study, 7 patients with spondylitis were infected after the operation, and 5 of them underwent internal fixation implantation. All patients received minimally invasive treatment without the removal of internal fixation (Table 2). Before coming to our hospital, only 2 patients had a culture report (fungal infection), and both of them underwent open debridement surgery in other hospitals, and then transferred to our hospital.\n Radiologic findings In all patients, X-ray examination showed no signs of spinal instability; CT showed destruction of the vertebral endplate and varying degree of vertebral space collapse (Fig. 1). MRI revealed no compression of the spinal cord. There was no recurrence in the treated vertebral segment during follow-up, but two patients with fungal spinal infection (Case1 Fig. 2/3) (Case2 Fig. 4/5) progressed to involve adjacent segments. During postoperative follow-up, no deformities such as scoliosis or kyphosis were observed by plain radiography, and MRI showed varying degrees of spontaneous fusion [24].\nFig. 1A 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyedFig. 2Sequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)Fig. 3The same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discsFig. 4Sequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)Fig. 5The same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved\nA 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyed\nSequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)\nThe same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discs\nSequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)\nThe same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved\nIn all patients, X-ray examination showed no signs of spinal instability; CT showed destruction of the vertebral endplate and varying degree of vertebral space collapse (Fig. 1). MRI revealed no compression of the spinal cord. There was no recurrence in the treated vertebral segment during follow-up, but two patients with fungal spinal infection (Case1 Fig. 2/3) (Case2 Fig. 4/5) progressed to involve adjacent segments. During postoperative follow-up, no deformities such as scoliosis or kyphosis were observed by plain radiography, and MRI showed varying degrees of spontaneous fusion [24].\nFig. 1A 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyedFig. 2Sequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)Fig. 3The same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discsFig. 4Sequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)Fig. 5The same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved\nA 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyed\nSequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)\nThe same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discs\nSequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)\nThe same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved\n Clinical outcomes Before surgery, all patients had significant back pain. There were 2 patients with radiating lower limb pain but no patients had muscle weakness, bowel or bladder dysfunction preoperatively. The operation time ranged from 30 minutes to 124 minutes for every spinal level with an average of 48 minutes. Intraoperative hemorrhage was minimal. In this patient population, the average delay of the diagnosis[25] was thirty-seven days. There were significant differences in VAS and ODI between the pre-treatment and post-operation (P > 0.05). And there were significant differences in VAS and ODI between the pre-treatment and post-extubation (P > 0.05) (Table 3).\nThe average drainage time was 14 days (5–26 days). No serious complications were found in the perioperative period. In one case (Case 6), a drainage tube became detached, and another puncture and catheterization were performed.\nBefore surgery, all patients had significant back pain. There were 2 patients with radiating lower limb pain but no patients had muscle weakness, bowel or bladder dysfunction preoperatively. The operation time ranged from 30 minutes to 124 minutes for every spinal level with an average of 48 minutes. Intraoperative hemorrhage was minimal. In this patient population, the average delay of the diagnosis[25] was thirty-seven days. There were significant differences in VAS and ODI between the pre-treatment and post-operation (P > 0.05). And there were significant differences in VAS and ODI between the pre-treatment and post-extubation (P > 0.05) (Table 3).\nThe average drainage time was 14 days (5–26 days). No serious complications were found in the perioperative period. In one case (Case 6), a drainage tube became detached, and another puncture and catheterization were performed.\n Follow‐up Postoperative follow-up periods ranged from 1 year to 6.5 years, (mean 3.7 years). One patient (Case 2) was lost to follow-up at postoperative 12 months. One patient (Case 3) died from an abdominal neoplasm at postoperative 12 months. According to the classification system of Macnab [22], one patient (Case 2) had a good outcome at final follow-up, and the remainder were excellent.\nPostoperative follow-up periods ranged from 1 year to 6.5 years, (mean 3.7 years). One patient (Case 2) was lost to follow-up at postoperative 12 months. One patient (Case 3) died from an abdominal neoplasm at postoperative 12 months. According to the classification system of Macnab [22], one patient (Case 2) had a good outcome at final follow-up, and the remainder were excellent.", "The 23 enrolled patients included 9 patients with Gram-positive (+), 6 patients with Gram-negative (-), 7 patients with Fungi, and 1 with mixed infection (Tables 1 and 2). Of the 23 patients, the maximum number of infected levels was 6 and the average number of infected levels was 3.2.\nTable 1Cultured pathogens in 23 patients received minimally invasive debridement and drainage treatmentCultured pathogensNumberGram-positive (+)9S. aureus4S. epidermidis3S. viridans1MRSA1Gram-negative (-)6E. coli3E. faecalis2Brucella1Fungi7Aspergillus fumigatus5Aspergillus flavus2Mixed infection1Aspergillus fumigatus and S. aureusTotal Number123Table 2Demographic data of seven patients with postoperative spinal infectionNOOperations before MISInternal fixation(Y/N)Fixation removed(Y/N)Cultured bacteriaCase 1open lumbar surgery + debridement and internal fixationYNAspergillus fumigatusCase 2Open internal fixation of lumbar spineYNAspergillus flavusCase 3Open Operation of malignant schwannomaN-E. coliCase 4Open internal fixation of lumbar spineN-S.epidermidisCase 5Open internal fixation of lumbar spineYNS. aureusCase 6Unidentified Lumbar infection + open debridement and internal fixationYNAspergillus fumigatusCase 7Open internal fixation of lumbar spineYNS. aureus\nCultured pathogens in 23 patients received minimally invasive debridement and drainage treatment\nAspergillus fumigatus and S. aureus\nTotal Number\n1\n23\nDemographic data of seven patients with postoperative spinal infection\nAt admission, all but one patient presented with a fever of more than 38.5 °C. All patients presented with ESR of more than 20 mm/hr (range, 50 to 115 mm/hr). The elevated CRP ranged from 14.9 mg/L to 30.6 mg/L with an average of 23 mg/L (Table 3). The white blood cell count was elevated above normal in only one case.\nTable 3The results of the outcome measures for Clinical and laboratory indicatorsLaboratory testsPre-Treatment(at admission)Post-Operation(1w)Post- Extubation(1 m)ESR (mm/hr)84.5 (50–115)23.4 (15–38)13.2 (6–19)CRP (mg/L)26.0 (14–30)16.5 (12–21)3.7 (2–10)Functional resultsVisual analog scale8.6 (6–10)4.5 (3–5)2.3 (1–3)Oswestry disability index69.3(56–89)34.2 (25–44)26.3 (13–42)\nThe results of the outcome measures for Clinical and laboratory indicators\nIn this study, 7 patients with spondylitis were infected after the operation, and 5 of them underwent internal fixation implantation. All patients received minimally invasive treatment without the removal of internal fixation (Table 2). Before coming to our hospital, only 2 patients had a culture report (fungal infection), and both of them underwent open debridement surgery in other hospitals, and then transferred to our hospital.", "In all patients, X-ray examination showed no signs of spinal instability; CT showed destruction of the vertebral endplate and varying degree of vertebral space collapse (Fig. 1). MRI revealed no compression of the spinal cord. There was no recurrence in the treated vertebral segment during follow-up, but two patients with fungal spinal infection (Case1 Fig. 2/3) (Case2 Fig. 4/5) progressed to involve adjacent segments. During postoperative follow-up, no deformities such as scoliosis or kyphosis were observed by plain radiography, and MRI showed varying degrees of spontaneous fusion [24].\nFig. 1A 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyedFig. 2Sequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)Fig. 3The same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discsFig. 4Sequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)Fig. 5The same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved\nA 56-year-old man was diagnosed as having T9-12/L2-3/L4-5 fungal spondylodiscitis. The CT scan showed the endplates were grossly destroyed\nSequential radiologic findings in a previously healthy 40-year-old man (case 1). Prior to primary lumbar discectomy, sagittal T2-weighted MRI showed a herniated disk at L3/4 (a). The original single-level infection was detected by T1-weighted MRI, approximately 2 weeks after primary discectomy (b). One month later, T1- and T2-weighted MRI showed the infection had progressed to L3/4 (c) and (d). Lesion debridement + bone graft fusion + pedicle screw fixation were performed in another hospital(e) and (f). CT showed that the infection had progressed to the adjacent L2/3 and L4/5 levels, with endplate destruction 2 months after fixation surgery(g) and (h)\nThe same 40-year-old female as shown in Fig. 2. Samples confirmed a fungal spondylodiscitis (case 1) using intraoperative CT-Guide(a). At five months after the minimally invasive debridement and irrigation, MRI showed that the L2-5 infections had resolved but the infection had progressed to the L1-2、T12-L1 and T11-12 levels (b and c). Postoperative minimally invasive debridement and irrigation picture(d). At the 48-month follow-up, MRI (e) and (f) showed all the infections had resolved and no further progression was observed; T1-weighted image showed increased signal with the extensive additional enhancement of the fat signals of vertebral bodies. The endplates were grossly deformed. T1 and T2-weighted show a narrow line at the site of the discs\nSequential radiologic findings in a previously healthy 49-year-old woman before admission to our institution (case 2). X-ray showed L4/5 internal fixation and fusion surgery (a and b). The original single-level infection(L4/5) was detected by MRI, approximately 40 days after the primary operation (c and d). Three months later, T1- and T2-weighted MRI showed the infection had progressed to L2/3 and L3/4 (e) and (f). Two months later, T1- and T2-weighted MRI showed the infection had progressed to L1/2 (g) and (h)\nThe same 49-year-old female as shown in Fig. 4 (case 2). Lesion debridement + fixation removal was performed. MRI in our institution showed that the infection had progressed to the L1/L2 level, with endplate destruction six weeks after fixation removal (a) and (b). Clinical photograph of postoperative percutaneous debridement and irrigation (c). At 12-month follow-up, MRI T1(d) and T2(e) showed all infection had resolved", "Before surgery, all patients had significant back pain. There were 2 patients with radiating lower limb pain but no patients had muscle weakness, bowel or bladder dysfunction preoperatively. The operation time ranged from 30 minutes to 124 minutes for every spinal level with an average of 48 minutes. Intraoperative hemorrhage was minimal. In this patient population, the average delay of the diagnosis[25] was thirty-seven days. There were significant differences in VAS and ODI between the pre-treatment and post-operation (P > 0.05). And there were significant differences in VAS and ODI between the pre-treatment and post-extubation (P > 0.05) (Table 3).\nThe average drainage time was 14 days (5–26 days). No serious complications were found in the perioperative period. In one case (Case 6), a drainage tube became detached, and another puncture and catheterization were performed.", "Postoperative follow-up periods ranged from 1 year to 6.5 years, (mean 3.7 years). One patient (Case 2) was lost to follow-up at postoperative 12 months. One patient (Case 3) died from an abdominal neoplasm at postoperative 12 months. According to the classification system of Macnab [22], one patient (Case 2) had a good outcome at final follow-up, and the remainder were excellent.", "Spondylodiscitis is a term encompassing vertebral osteomyelitis, spondylitis, and discitis, the incidence of which is increasing due to an increase in the susceptible population and improved diagnostics [26, 27]. The etiology of spondylodiscitis usually is monobacterial infection and in Europe,more than 50 % of cases are caused by Staphylococcus aureus [5, 28]. Fungal discitis is rare and to date, only case reports exist [26]. Fungal discitis is usually due to molds [29–32] and Candida species [33–38], which are consistent with our results.\nClinical presentation of spondylodiscitis especially early stage may be nonspecific. Refractory and unremitting back pain often requiring narcotic pain control is the most common clinical presentation, with fever and neurologic deficiency less frequently encountered [1, 39]. The clinical symptoms appear at an average of six weeks after the primary procedure [1]. Elevated ESR and CRP are extremely sensitive, but WBC count might be within the normal range [40]. In our series, CRP and ESR values were increased on admission in all patients, whereas white blood count was increased in only one patient. MRI is the gold standard in imaging studies to detect spondylodiscitis [5, 41]. MRI exhibits high specificity and sensitivity,which are extremely high at 96 % and 92 % respectively [23, 42, 43]. Therefore, we routinely performed an MRI during the follow-up.\nTreatment of spondylodiscitis, especially fungal infection, is often delayed because fungal organisms are slow-growing and difficult to identify by culture [25]. CT-guided needle biopsy has been recommended for the isolation of causative pathogens [44–46]. However, the aspirate is often inadequate. Percutaneous endoscopic aspirate has been reported to have a high accuracy rate in the detection of causative organisms [47]. In this study, the causative organisms were extracted through a cannula less than 1 cm diameter using discectomy forceps. Our study uses CT-Guide so the discectomy forceps were able to access the center of the lesion. Although the radiation dose of CT is higher than that of C-arm, CT-Guide is accurate and convenient for precise catheterization. We also consider that careful CT-Guide also provides potential benefit for theatre-users by reducing radiation exposure compared with fluoroscopically assisted spinal surgery [48, 49].\nMultilevel spondylodiscitis after surgery is an intractable and troublesome complication, which may not be resolved by simple surgical debridement. Major open surgery has important drawbacks in patients with multilevel infection because of concern that the extensive destruction and mechanical instability of the affected segments following this type of surgery may be associated with significant rates of perioperative complications and mortality [1, 50]. Minimally invasive endoscopic debridement with dilute betadine solution irrigation is an effective alternative to extensive open surgery for the treatment of single-level infectious spondylodiscitis,but the effectiveness of this procedure for extensive destruction of vertebral bodies and multilevel refractory infections may be limited because the thorough debridement of synchronous multiple lesions by endoscopic means is difficult and can exhaust both patient and surgeon [14, 15, 51, 52]. Minimally invasive drainage and continuous irrigation with local administration of antibiotic agents, including minimally invasive suction aspiration, have been found to be effective in patients with spondylodiscitis [53, 54].\nThere are several advantages of our method: (1) Continuous drainage can dilute the density of pathogens, which reduces pathogenic capacity (including the invasiveness of pathogens, external toxins, and endotoxins); (2) Minimally invasive implant of the drainage tube will not cause major surgical trauma and is conducive to the rehabilitation of patients; (3) Continuous perfusion does not destroy the body’s protective immune response; (4) Formation of hematoma as a culture medium is inhibited [12].\nIn our series, two patients with fungal infection showed progressive disease spreading to adjacent segments. A previous study has reported this unique pathological feature of spondylodiscitis, but the exact reason remains unclear[39]. It may depend on the premorbid general condition of the patient (malnutrition, immune suppression, malignancy), the type of fungal species, and delay in treatment[55]. Progressive disease may occur either above or below the lesion. Adjacent segment preventive catheterization was performed in one patient and achieved its purpose, but its reliable effect needs further study.\nThis study has several limitations. First, we were able to collect only 23 cases; firmer conclusions await large sample studies. Second, because our study was a retrospective non-control study of various disc infections, it is difficult to evaluate the efficacy of surgery independent of antimicrobial therapy. The feasibility and benefits of minimally invasive debridement and irrigation for the treatment of multilevel spondylitis need to be further evaluated in larger series as part of a prospectively controlled study. Third, in view of the particularity of tuberculosis treatment, the patients with tuberculosis spondylitis were not included in this study. In addition, this technology can’t be used for decompression and deformity correction. For patients with neurological compromise or spinal deformity, open surgery would be the requirement. However, in our cohort, enrolled patients had similar clinical features of multilevel spondylodiscitis and all operations were performed by the same experienced surgeon (XFZ).", "On the basis of these study findings, we believe that minimally invasive debridement and irrigation using intraoperative CT-Guide is an effective minimally invasive method for the treatment of multilevel advanced spondylodiscitis." ]

[ null, null, null, null, null, null, "results", null, null, null, null, "discussion", "conclusion" ]

[ "Spondylodiscitis", "Minimally invasive surgery", "Drainage" ]

[ 457, 1491, 139, 388, 154, 55, 459, 1215, 177, 89 ]

[ "patients", "showed", "infection", "mri", "debridement", "fixation", "case", "t1", "weighted", "progressed" ]

[ "spondylodiscitis effectiveness", "spondylodiscitis operations performed", "standard treatment spondylodiscitis", "treatment spondylodiscitis conservative", "surgical intervention spondylodiscitis" ]

[ "Acinetobacter baumannii", "Aminoglycosides", "Anti-Bacterial Agents", "Bacterial Proteins", "Drug Resistance, Bacterial", "Methyltransferases", "Microbial Sensitivity Tests", "RNA, Ribosomal, 16S" ]

[ "Sample collection and bacterial isolates", "Antimicrobial susceptibility testing", "DNA extraction, PCR, and multiplex-PCR", "Statistical analysis", "Patients, samples, and bacterial isolates", "Antimicrobial susceptibility pattern", "Gene profiles of the isolates" ]

[ "This study was performed on A. baumannii isolated from patients admitted to different educational hospitals in Sari, north of Iran, for 6 months (April 2019 to September 2019). The clinical specimens included blood, urine, respiratory secretions (bronchial lavage and tracheal secretions), CSF, and ulcer (surgical and burn wound). The clinical isolates were identified using conventional microbiological tests\n19\n and confirmed by polymerase chain reaction (PCR) amplification of the blaOXA-51 gene using specific primers\n20\n; the reaction conditions are shown in Table 1. \n\nTABLE 1:Primers used to amplify the blaOXA-51 and aminoglycoside resistance genes along with the conditions of PCR.Target genesPrimer sequences (5´-3´)Amplicon size (bp)94 °C94 °CAnnealing Temperature and time72 °C72 °CReference\nOXA-51TAATGCTTTGATCGGCCTTG3532 min25 sec51 °C for 30 sec30 sec5 min\n5\n\nTGGATTGCACTTCATCTTGG\n\n\n\n\n\n\n\nAPH(3′)-VIa\nCGGAAACAGCGTTTTAGA7172 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aphA6)\nTTCCTTTTGTCAGGTC\n\n\n\n\n\n\n\nAAC(3)-IIa\nATGCATACGCGGAAGGC8222 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacC2)\nTGCTGGCACGATCGGAG\n\n\n\n\n\n\n\nAAC(6′)-Ib\nTATGAGTGGCTAAATCGAT3952 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacA4)\nCCCGCTTTCTCGTAGCA\n\n\n\n\n\n\n\nANT(2\")-Ia\nATCTGCCGCTCTGGAT4052 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aadB)\nCGAGCCTGTAGGACT\n\n\n\n\n\n\n\nANT(3\")-Ia\nATGAGGGAAGCGGTGATCG7922 min25 sec62 °C for 30 sec30 sec5 min\n5\n\n(aadA1)\nTTATTTGCCGACTACCTTGGT\n\n\n\n\n\n\n\nArmA\nATTCTGCCTATCCTAATTGG3152 min25 sec49 °C for 30 sec30 sec5 min\n5\n\nACCTATACTTTATCGTCGTC\n\n\n\n\n\n\n\n", "The antibiotic susceptibility pattern of the isolates was determined by the disk agar diffusion method on Muller Hinton agar (Merck, Germany) according to the Clinical and Laboratory Standards Institute (CLSI) guidelines\n21\n. The antibiotics included piperacillin (100 µg), piperacillin-tazobactam (100/10 µg), imipenem (10 µg), meropenem (10 µg), doripenem (10 µg), ciprofloxacin (5 µg), levofloxacin (5 µg), trimethoprim-sulfamethoxazole (1.25-23.75 µg), ceftazidime (30 µg), cefotaxime (30 µg), and cefepime (30 µg) (MAST Co., England). The susceptibility pattern of the isolates against aminoglycosides including kanamycin, amikacin, spectinomycin, netilmicin, gentamicin, streptomycin, and tobramycin was determined using the micro-broth dilution method according to the CLSI guidelines\n21\n. For interpretation of the minimum inhibitory concentration (MIC) values, we referred to the CLSI guidelines and previous studies\n1\n\n,\n\n21\n\n,\n\n22\n. Escherichia coli ATCC 25922 and A. baumannii ATCC 19606 were used as control strains for antibiotic susceptibility testing. ", "DNA was extracted from all A. baumanniiisolates grown for 24 h using an alkaline lysis method with sodium dodecyl sulphate (SDS) and NaOH, as previously published\n23\n, with few modifications. In brief, first, we prepared a lysis buffer by dissolving 0.5 g of SDS and 0.4 g of NaOH in 200 µL of distilled water. Next, 4-6 colonies of the bacteria were suspended in 60 µL of lysis buffer and subsequently heated at 95 °C for 10 min. In the next step, the suspension was centrifuged at 13000 rpm for 5 min, and 180 µL of distilled water was added to the microtubes. The obtained supernatant was frozen at −20 °C until use as the extracted DNA in PCR. \nTwo sets of multiplex-PCR were used to detect AME-encoding genes in A. baumannii isolates using the specific primers shown in Table 1. APH(3′)-VIa (aphA6), ANT(2\")-Ia (aadB), and ArmA genes were detected in the same set; AAC(6′)-Ib (aacA4) and AAC(3)-IIa (aacC2) were identified in the second set; and the ANT(3\")-Ia (aadA1) gene was detected by PCR alone. The PCR and multiplex-PCR were performed in 25 µL of final volume containing 12.5 µL of the master mix (Ampliqon, Denmark), 10 pmol of each primer (Bioneer, South Korea), and 500 ng of template DNA; the reaction solutions were brought to the desired volume through the addition of distilled water. The genes were amplified under standard conditions using a thermocycler machine (Bio-Rad, USA). All reactions were performed in 34 cycles, and the conditions are shown in Table 1.", "The data were analyzed using SPSS (version 21). Categorical data were analyzed using the Fisher’s exact test, and a P-value less than 0.05 was considered statistically significant. In addition, an independent t-test was used to examine the mean age of the subjects. ", "In this study, 100 non-duplicated A. baumannii clinical isolates were collected from 100 patients admitted to the teaching and educational hospitals of Sari, north of Iran. All isolates identified using the phenotypic method contained the blaOXA-51 gene according to the PCR results. The mean age of the patients was 42.08±25.08 years (minimum age: 6 months; highest age: 88 years), and 50% of the patients were male. There was no significant difference between men and women in terms of mean age (p=0.64). Most of the bacterial isolates (34%) were obtained from patients admitted to the burn wards, while 29%, 21%, and 16% of the isolates were collected from the ICU, surgery, and pediatric wards, respectively. The most common type of specimen (73%) for isolation of the bacteria was the wound samples; however, 15% and 12% of other clinical isolates were obtained from urine and blood cultures, respectively.", "According to the results of the disk agar diffusion method, the most and least effective antibiotics in the present study were imipenem and ciprofloxacin, with resistance rates of 75% and 100%, respectively (Table 2). Moreover, 94% of the isolates were detected as multi-drug resistant (MDR), and the most MDR isolates were collected from wound samples. Table 2 presents the antibiotic resistance patterns of all A. baumannii clinical isolates in this study based on hospital wards, as well as sample types. Resistance to the tested antibiotics was not significantly correlated with the sample types and hospital wards where the samples were collected.\n\nTABLE 2:Antimicrobial susceptibility pattern of the Acinetobacter baumannii clinical isolates in disk agar diffusion method.AntibioticsNo. (%) of resistant, intermediate resistant or susceptible isolates in terms of \nSusceptibilityTotalHospital wards Sample types \n\n(n=100)BurnICUSurgeryPediatric\nP-value\nWoundUrineBlood\nP-value\n\n\n\n(n=34)(n=29)(n=21)s (n=16)\n(n=73)(n=15)(n=12)\nPIPR8628 (82.3)28 (96.5)19 (90.4)11 (68.7)0.41262 (84.9)14 (93.3)10 (83.3)0.917\nI105 (14.7)1 (3.4)2 (9.5)2 (12.5)\n8 (10.9)1 (6.6)1 (8.3)\n\nS41 (2.9)003 (18.7)\n3 (4.1)01 (8.3)\nPIP-TAZR7826 (76.4)24 (82.7)16 (76.1)12 (75)0.10454 (73.9)14 (93.3)10 (83.3)0.372\nI104 (11.7)2 (6.8)3 (14.2)1 (6.2)\n8 (10.9)02 (16.6)\n\nS124 (11.7)3 (10.3)2 (9.5)3 (18.7)\n11 (15)1 (6.6)0\nCAZR7625 (73.5)22 (75.8)18 (85.7)11 (68.7)0.74352 (71.2)15 (100)9 (75)0.559\nI00000\n000\n\nS249 (26.4)1 (3.4)3 (14.2)5 (31.2)\n21 (28.7)03 (25)\nCTXR9332 (94.1)28 (96.5)19 (90.4)14 (87.5)0.76267 (91.7)15 (100)11 (91.6)0.618\nI02 (5.8)1 (3.4)2 (9.5)2 (12.5)\n6 (8.2)01 (8.3)\n\nS70000\n000\nCEFR9232 (94.1)28 (96.5)18 (85.7)14 (87.5)0.44867 (91.7)14 (93.3)11 (91.6)0.728\nI41 (2.9)1 (3.4)2 (9.5)0\n2 (2.7)1 (6.6)1 (8.3)\n\nS41 (2.9)01 (4.7)2 (12.5)\n4 (5.4)00\nIMIR7527 (79.4)25 (86.2)17 (80.9)10 (62.5)0.61755 (75.3)12 (80)8 (66.6)0.873\nI114 (11.7)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)1 (6.6)1 (8.3)\n\nS147 (20.5)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)2 (13.3)3 (25)\nMERR9733 (97)28 (96.5)20 (95.2)16 (100)0.96470 (95.8)15 (100)12 100)0.667\nI00000\n000\n\nS31 (2.9)1 (3.4)1 (4.7)0\n3 (4.1)00\nDORR9632 (94.1)28 (96.5)20 (95.2)16 (100)0.79769 (94.5)15 (100)12 (100)0.913\nI21 (2.9)1 (3.4)00\n2 (2.7)00\n\nS21 (2.9)01 (5)0\n2 (2.7)00\nCIPR10034 (100)29 (100)21 (100)16 (100)0.10073 (100)15 (100)12 (100)0.100\nI00000\n000\n\nS00000\n000\nLEVR9331 (91.1)27 (93.1)20 (95.2)15 (93.7)0.72567 (91.7)14 (93.3)12 (100)0.842\nI32 (5.8)001 (6.2)\n3 (4.1)00\n\nS41 (2.9)2 (6.8)1 (4.7)0\n3 (4.1)1 (6.6)0\nSXTR9231 (91.1)27 (93.1)18 (85.7)16 (100)0.93568 (93.1)12 (80)12 (100)0.216\nI31 (2.9)1 (3.4)1 (4.7)0\n1 (1.3)2 (13.3)0\n\nS52 (5.8)1 (3.4)2 (9.5)0\n4 (5.4)1 (6.6)0\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\n\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\nMoreover, according to the MIC results, the resistance rate against gentamicin, kanamycin, tobramycin, and streptomycin was 94%, while the highest susceptibility (20%) of A. baumannii isolates was observed against netilmicin. In contrast, 74%, 68%, and 78% of our clinical isolates were resistant to amikacin, netilmicin, and spectinomycin, respectively. The MIC ranges of aminoglycosides and their relationship with the presence of AMEs-encoding genes are shown in Table 3.\n\nTABLE 3:Aminoglycoside resistance pattern of the Acinetobacter baumannii clinical isolates in this study. AntibioticsMIC rangesNo. of the isolates with \n(µg/mL)Susceptibility\nAPH(3′)-VIa\n\nANT(2\")-Ia\n\nANT(3\")-Ia\n\nAAC(6′)-Ib\n\nAAC(3)-IIa\n\narmA\nTotal\n\npattern\n(aphA6)\n\n(aadB)\n\n(aadA1)\n\n(aacA4)\n\n(aacC2)\n(n=22)\n\n\n\n(n=77)(n=73)(n=33)(n=33)(n=19)\n\nGentamicin ≤4S------6\n8I5311-1-\n16-32R---\n--94\n64-128R5-----\n\n≥256R677032321921\n\nMIC50\n5122565125122561024256\nMIC90\n102425610242562561024512Tobramycin≤4S33---14\n8I1----22\n16-32R2-1---94\n64-128R-21423\n\n≥256R716831291716\n\nMIC50\n256512512256512512512\nMIC90\n10241024102451210245121024Amikacin≤16S1310662316\n32I710665110\n64R-1----74\n128R-4----\n\n≥256R574821211218\n\nMIC50\n2561024512512512256512\nMIC90\n51251251210245122561024Netilmicin≤8S883-1220\n16I6--4-612\n32R810464268\n64-128R571324\n\n≥256R50482520128\n\nMIC50\n25612864256256128128\nMIC90\n25612864512512128256Kanamycin≤16S42----4\n32I22----2\n64R-3-1--94\n128R322-21\n\n≥256R686431321721\n\nMIC50\n643212864256128128\nMIC90\n6425625664256256256Streptomycin≤4S1-11--6\n8S11-1--\n\n16S----\n-\n\n32I-------\n64-128R33-32-94\n≥256R726932281718\n\nMIC50\n256128256256256512256\nMIC90\n2561285121024128512512Spectinomycin≤4S11---312\n8S7522--\n\n16S\n-\n---\n\n32I83-1-510\n64-128R21-22178\n≥256R596331281713\n\nMIC50\n2562565126464256256\nMIC90\n256512256128128256512\nR: resistant; I: intermediate resistant; S: susceptible. Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.\n\n\nR: resistant; I: intermediate resistant; S: susceptible.\n Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.", "The frequency of each aminoglycoside resistance gene and its relation with the MIC ranges are shown in Table 3. In total, the proportions of aminoglycoside resistance genes among our clinical isolates of A. baumannii were as follows: APH(3′)-VIa (aphA6) (77%), ANT(2\")-Ia (aadB) (73%), ANT(3\")-Ia (aadA1) (33%), AAC(6′)-Ib (aacA4) (33%), AAC(3)-IIa (aacC2) (19%), and ArmA (22%). The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the isolates is shown in Table 4. There was a significant association between the presence of all resistance genes and the non-susceptibility (resistance or intermediate resistance) to all aminoglycosides, except armA and resistance to netilmicin. Important data from this table indicates that in some groups, such as gentamicin- and tobramycin-resistant groups, all resistant isolates contained some AMEs-encoding genes such as aacC2, aacA4, and aadA1. \n\nTABLE 4:The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the A. baumannii clinical isolates.AntibioticsSusceptibility PatternNo. (%) of the isolates contained \n\n\nAPH(3′)-VIa (aphA6)\n\nP-value\n\nANT(2\")-Ia (aadB)\n\nP-value\n\nANT(3\")-Ia (aadA1)\n\nP-value\n\nAAC(6′)-Ib (aacA4)\n\nP-value\n\nAAC(3)-IIa (aacC2)\n\nP-value\n\narmA\n\nP-value\n\n\n(n=77)\n(n=73)\n(n=33)\n(n=33)\n(n=19)\n(n=22)\nGentamicinNon-susceptible77 (100)0.01273 (100)0.02333 (100)0.03333 (100)0.03319 (100)0.00722 (100)0.021\nSusceptible0\n0\n0\n0\n0\n0\nTobramycinNon-susceptible74 (96.1)0.01970 (95.8)0.02933 (100)0.00333 (100)0.00319 (100)0.00721 (95.4)0.024\nSusceptible3 (3.8)\n3 (4.1)\n0\n0\n0\n1 (4.5)\nAmikacinNon-susceptible64 (83.1)0.03663 (86.3)0.03727 (81.8)0.03827 (81.8)0.03817 (89.4)0.02419 (86.3)0.033\nSusceptible13 (16.8)\n10 (13.6)\n6 (18.1)\n6 (18.1)\n2 (10.5)\n3 (13.6)\nNetilmicinNon-susceptible63 (81.8)0.04265 (89.04)0.03930 (90.9)0.01429 (87.8)0.03218 (94.7)0.01814 (63.6)0.072\nSusceptible15 (19.4)\n8 (10.9)\n3 (9.09)\n4 (12.1)\n1 (5.2)\n8 (36.3)\nKanamycinNon-susceptible73 (94.8)0.02971 (97.2)0.02333 (100)0.00333 (100)0.00319 (100)0.00722 (100)0.021\nSusceptible4 (5.1)\n2 (2.7)\n0\n0\n0\n0\nStreptomycinNon-susceptible75 (97.4)0.01572 (98.6)0.01932 (96.9)0.01931 (93.9)0.02119 (100)0.00718 (81.8)0.044\nSusceptible2 (2.5)\n1 (1.3)\n1 (3.03)\n2 (6.06)\n0\n4 (18.1)\nSpectinomycinNon-susceptible69 (89.6(0.02867 (91.7)0.03731 (93.9)0.02131 (93.9)0.02119 (100)0.00719 (86.3)0.033\nSusceptible8 (10.3)\n6 (8.2)\n2 (6.06)\n2 (6.06)\n0\n3 (13.6)\n\n\nIn addition, we detected 22 gene profiles among all clinical isolates of A. baumannii (Table 5). The most prevalent combination gene profiles in the present study included: 1) APH(3')-VIa + ANT(2\")-Ia with 39 isolates containing these genes, among which 100% isolates were resistant towards kanamycin, while almost 95% were resistant against netilmicin and 97.4% were resistant to tobramycin and gentamicin, and 2) AAC(3)-IIa + AAC(6')-Ib + ANT(3\")-Ia + APH(3')-VIa + ANT(2\")-Ia with 14 isolates, among which 100% were resistant to gentamicin, kanamycin, and streptomycin, while almost 93% were resistant against tobramycin and spectinomycin. However, 15 isolates showed an AME gene profile with one AME gene, most of which were resistant to tested aminoglycosides. Other AME-encoding gene profiles were detected at a low rate (Table 5). However, 15, 52, 12, 5, 14, and 2 isolates in the present study contained 1, 2, 3, 4, 5, and 6 AME genes, respectively. The most prevalent gene profiles exhibited the simultaneous presence of 2 genes followed by 5 genes and 3 AME genes.\n\nTABLE 5:Gene profiles of the Acinetobacter baumannii clinical isolates and their association with aminoglycoside resistance.Gene profile (No.)Gentamicin Tobramycin Amikacin Netilmicin Kanamycin Streptomycin Spectinomycin \nNSSNSSNSSNSSNSSNSSNSS\n(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)\nArmA (9)9 (100)-9 (100)-7 (77.7)2 (22.2)3 (33.3)6 (66.6)9 (100)\n-\n7 (77.7)2 (22.2)9 (100)-\naphA6 (2)2 (100)-2 (100)--2 (100)-2 (100)1 (50)1 (50)2 (100)--2 (100)\naadB (4)4 (100)-3 (75)1 (25)3 (75)1 (25)2 (50)2 (50)3 (75)1 (25)4 (100)-3 (75)1 (25)\nArmA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aadA1 (2)2 (100)-2 (100)-2 (100)-1 (50)1 (50)2 (100)\n-\n2 (100)-2 (100)-\naadA1 + armA (2)2 (100)-2 (100)-1 (50)1 (50)2 (100)-2 (100)\n-\n1 (50)1 (50)2 (100)-\naadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naadB + aadA1 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naphA6 + aadB (39)38 (97.4)1 (2.56)38 (97.4)1 (2.56)34 (87.1)5 (12.8)37 (94.8)2 (5.1)39 (100)-33 (84.6)6 (15.3)32 (82)7 (17.9)\naacA4 + aphA6 (3)3 (100)-3 (100)-3 (100)-3 (100)-3 (100)\n-\n3 (100)-3 (100)-\naadB + aadA1 +\naphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacA4 + aadB +\naadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadB + aphA6 (1)1 (100)-1 (100)-1 (100)--1 (100)1 (100)\n-\n-1 (100)1 (100)-\naadB + armA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aacC2 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + armA\n+ aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacC2 + aacA4 + aadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacC2 + aacA4\n+ aadB + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacA4 + aadB +\naadA1 + aphA6 (4)4 (100)-4 (100)-4 (100)-4 (100)-4 (100)\n-\n4 (100)-4 (100)-\naacC2 + aacA4 + aadA1 + aphA6 + aadB (14)14 (100)-13 (92.8)1 (7.1)12 (85.7)2 (14.2)12 (85.7)2 (14.2)14 (100)-14 (100)-13 (92.8)1 (7.1)\naacC2 + aacA4 + aadA1 + aadB + armA + aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible.\n\n\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible." ]

[ null, null, null, null, null, null, null ]

[ "INTRODUCTION", "METHODS", "Sample collection and bacterial isolates", "Antimicrobial susceptibility testing", "DNA extraction, PCR, and multiplex-PCR", "Statistical analysis", "RESULTS", "Patients, samples, and bacterial isolates", "Antimicrobial susceptibility pattern", "Gene profiles of the isolates", "DISCUSSION", "CONCLUSIONS" ]

[ "\nAcinetobacter baumannii, living in the soil, the water, and different hospital environments, is an important opportunistic pathogen that causes nosocomial infections such as pneumonia, urinary tract infections, intravenous catheter-associated infections, and ventilation-associated infections, particularly in intensive care units\n1\n\n-\n\n4\n. The ability of this microorganism to remain in the hospital environment and to spread among the patients, along with their resistance to several antibiotics, are the main driving forces behind large-scale recurrent events in different countries\n5\n.\nThe major antibiotics used for the treatment of infections caused by this organism are beta-lactams, aminoglycosides, fluoroquinolones, and carbapenems; however, A. baumannii has shown different rates of resistance against these antimicrobial agents\n6\n\n-\n\n8\n. These infections are difficult, costly, and sometimes impossible to treat owing to the high ability of A. baumannii to acquire antibiotic resistance genes and the development of multidrug-resistant (MDR) strains\n9\n\n,\n\n10\n. Aminoglycosides are one of the main drugs used for the treatment of Acinetobacter infections\n11\n; however, recently, the resistance of A. baumannii to these antibiotics has also increased. Two main mechanisms of resistance to aminoglycosides are the alteration of the ribosome structure caused by mutations in the ribosomal 16S rRNA and the enzymatic resistance mechanism\n12\n. The enzymatic alteration of the aminoglycoside molecule at -OH or -NH2 groups by aminoglycoside-modifying enzymes (AMEs) is the most important resistance mechanism\n12\n\n-\n\n14\n. AMEs are classified into three major groups: aminoglycoside phosphotransferase (APH), aminoglycoside acetyltransferase (AAC), aminoglycoside nucleotidyltransferase (ANT), and aminoglycoside adenylyltransferase (AAD)\n5\n\n,\n\n13\n. Aminoglycoside acetyltransferases cause acetylation of the -NH2 groups of aminoglycosides at the 1, 3, 2', and 6' positions using acetyl coenzyme A as a donor substrate\n15\n. Aminoglycoside phosphotransferases phosphorylate the hydroxyl groups present in the structure of aminoglycosides at the 4, 6, 9, 3', 2'', 3'', and 7'' positions (seven different groups) with the help of ATP; the largest enzymatic group in this family is the APH(3′)-I group\n16\n. The proportion of strains harboring the aphA6 gene in A. baumannii is widespread, and this enzyme is the cause of resistance to neomycin, amikacin, kanamycin, paromomycin, ribostamycin, butirosin, and isepamicin\n17\n. Aminoglycoside nucleotidyltransferases are classified into 5 groups, and the genes encoding these enzymes can be found in chromosomes or transferred by plasmids and transposons\n12\n. These enzymes transfer an AMP group from ATP to a hydroxyl group at the 2'', 3'', 4', 6, and 9 positions of the aminoglycoside molecule\n13\n. In addition to AMEs, 16S rRNA methylation by the ArmA enzyme is a novel mechanism that contributes to the high level of aminoglycoside resistance in A. baumannii, as reported in the Far East, Europe, and North America\n5\n. This enzyme can be transferred by class 1 integrons and is often detected in carbapenem-resistant A. baumannii isolates\n18\n. This study aimed to investigate the role of some important aminoglycoside-modifying enzymes and 16S rRNA methylase (ArmA) in the resistance of A. baumannii clinical isolates to aminoglycosides in Sari, located north of Iran.", " Sample collection and bacterial isolates This study was performed on A. baumannii isolated from patients admitted to different educational hospitals in Sari, north of Iran, for 6 months (April 2019 to September 2019). The clinical specimens included blood, urine, respiratory secretions (bronchial lavage and tracheal secretions), CSF, and ulcer (surgical and burn wound). The clinical isolates were identified using conventional microbiological tests\n19\n and confirmed by polymerase chain reaction (PCR) amplification of the blaOXA-51 gene using specific primers\n20\n; the reaction conditions are shown in Table 1. \n\nTABLE 1:Primers used to amplify the blaOXA-51 and aminoglycoside resistance genes along with the conditions of PCR.Target genesPrimer sequences (5´-3´)Amplicon size (bp)94 °C94 °CAnnealing Temperature and time72 °C72 °CReference\nOXA-51TAATGCTTTGATCGGCCTTG3532 min25 sec51 °C for 30 sec30 sec5 min\n5\n\nTGGATTGCACTTCATCTTGG\n\n\n\n\n\n\n\nAPH(3′)-VIa\nCGGAAACAGCGTTTTAGA7172 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aphA6)\nTTCCTTTTGTCAGGTC\n\n\n\n\n\n\n\nAAC(3)-IIa\nATGCATACGCGGAAGGC8222 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacC2)\nTGCTGGCACGATCGGAG\n\n\n\n\n\n\n\nAAC(6′)-Ib\nTATGAGTGGCTAAATCGAT3952 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacA4)\nCCCGCTTTCTCGTAGCA\n\n\n\n\n\n\n\nANT(2\")-Ia\nATCTGCCGCTCTGGAT4052 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aadB)\nCGAGCCTGTAGGACT\n\n\n\n\n\n\n\nANT(3\")-Ia\nATGAGGGAAGCGGTGATCG7922 min25 sec62 °C for 30 sec30 sec5 min\n5\n\n(aadA1)\nTTATTTGCCGACTACCTTGGT\n\n\n\n\n\n\n\nArmA\nATTCTGCCTATCCTAATTGG3152 min25 sec49 °C for 30 sec30 sec5 min\n5\n\nACCTATACTTTATCGTCGTC\n\n\n\n\n\n\n\n\nThis study was performed on A. baumannii isolated from patients admitted to different educational hospitals in Sari, north of Iran, for 6 months (April 2019 to September 2019). The clinical specimens included blood, urine, respiratory secretions (bronchial lavage and tracheal secretions), CSF, and ulcer (surgical and burn wound). The clinical isolates were identified using conventional microbiological tests\n19\n and confirmed by polymerase chain reaction (PCR) amplification of the blaOXA-51 gene using specific primers\n20\n; the reaction conditions are shown in Table 1. \n\nTABLE 1:Primers used to amplify the blaOXA-51 and aminoglycoside resistance genes along with the conditions of PCR.Target genesPrimer sequences (5´-3´)Amplicon size (bp)94 °C94 °CAnnealing Temperature and time72 °C72 °CReference\nOXA-51TAATGCTTTGATCGGCCTTG3532 min25 sec51 °C for 30 sec30 sec5 min\n5\n\nTGGATTGCACTTCATCTTGG\n\n\n\n\n\n\n\nAPH(3′)-VIa\nCGGAAACAGCGTTTTAGA7172 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aphA6)\nTTCCTTTTGTCAGGTC\n\n\n\n\n\n\n\nAAC(3)-IIa\nATGCATACGCGGAAGGC8222 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacC2)\nTGCTGGCACGATCGGAG\n\n\n\n\n\n\n\nAAC(6′)-Ib\nTATGAGTGGCTAAATCGAT3952 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacA4)\nCCCGCTTTCTCGTAGCA\n\n\n\n\n\n\n\nANT(2\")-Ia\nATCTGCCGCTCTGGAT4052 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aadB)\nCGAGCCTGTAGGACT\n\n\n\n\n\n\n\nANT(3\")-Ia\nATGAGGGAAGCGGTGATCG7922 min25 sec62 °C for 30 sec30 sec5 min\n5\n\n(aadA1)\nTTATTTGCCGACTACCTTGGT\n\n\n\n\n\n\n\nArmA\nATTCTGCCTATCCTAATTGG3152 min25 sec49 °C for 30 sec30 sec5 min\n5\n\nACCTATACTTTATCGTCGTC\n\n\n\n\n\n\n\n\n Antimicrobial susceptibility testing The antibiotic susceptibility pattern of the isolates was determined by the disk agar diffusion method on Muller Hinton agar (Merck, Germany) according to the Clinical and Laboratory Standards Institute (CLSI) guidelines\n21\n. The antibiotics included piperacillin (100 µg), piperacillin-tazobactam (100/10 µg), imipenem (10 µg), meropenem (10 µg), doripenem (10 µg), ciprofloxacin (5 µg), levofloxacin (5 µg), trimethoprim-sulfamethoxazole (1.25-23.75 µg), ceftazidime (30 µg), cefotaxime (30 µg), and cefepime (30 µg) (MAST Co., England). The susceptibility pattern of the isolates against aminoglycosides including kanamycin, amikacin, spectinomycin, netilmicin, gentamicin, streptomycin, and tobramycin was determined using the micro-broth dilution method according to the CLSI guidelines\n21\n. For interpretation of the minimum inhibitory concentration (MIC) values, we referred to the CLSI guidelines and previous studies\n1\n\n,\n\n21\n\n,\n\n22\n. Escherichia coli ATCC 25922 and A. baumannii ATCC 19606 were used as control strains for antibiotic susceptibility testing. \nThe antibiotic susceptibility pattern of the isolates was determined by the disk agar diffusion method on Muller Hinton agar (Merck, Germany) according to the Clinical and Laboratory Standards Institute (CLSI) guidelines\n21\n. The antibiotics included piperacillin (100 µg), piperacillin-tazobactam (100/10 µg), imipenem (10 µg), meropenem (10 µg), doripenem (10 µg), ciprofloxacin (5 µg), levofloxacin (5 µg), trimethoprim-sulfamethoxazole (1.25-23.75 µg), ceftazidime (30 µg), cefotaxime (30 µg), and cefepime (30 µg) (MAST Co., England). The susceptibility pattern of the isolates against aminoglycosides including kanamycin, amikacin, spectinomycin, netilmicin, gentamicin, streptomycin, and tobramycin was determined using the micro-broth dilution method according to the CLSI guidelines\n21\n. For interpretation of the minimum inhibitory concentration (MIC) values, we referred to the CLSI guidelines and previous studies\n1\n\n,\n\n21\n\n,\n\n22\n. Escherichia coli ATCC 25922 and A. baumannii ATCC 19606 were used as control strains for antibiotic susceptibility testing. \n DNA extraction, PCR, and multiplex-PCR DNA was extracted from all A. baumanniiisolates grown for 24 h using an alkaline lysis method with sodium dodecyl sulphate (SDS) and NaOH, as previously published\n23\n, with few modifications. In brief, first, we prepared a lysis buffer by dissolving 0.5 g of SDS and 0.4 g of NaOH in 200 µL of distilled water. Next, 4-6 colonies of the bacteria were suspended in 60 µL of lysis buffer and subsequently heated at 95 °C for 10 min. In the next step, the suspension was centrifuged at 13000 rpm for 5 min, and 180 µL of distilled water was added to the microtubes. The obtained supernatant was frozen at −20 °C until use as the extracted DNA in PCR. \nTwo sets of multiplex-PCR were used to detect AME-encoding genes in A. baumannii isolates using the specific primers shown in Table 1. APH(3′)-VIa (aphA6), ANT(2\")-Ia (aadB), and ArmA genes were detected in the same set; AAC(6′)-Ib (aacA4) and AAC(3)-IIa (aacC2) were identified in the second set; and the ANT(3\")-Ia (aadA1) gene was detected by PCR alone. The PCR and multiplex-PCR were performed in 25 µL of final volume containing 12.5 µL of the master mix (Ampliqon, Denmark), 10 pmol of each primer (Bioneer, South Korea), and 500 ng of template DNA; the reaction solutions were brought to the desired volume through the addition of distilled water. The genes were amplified under standard conditions using a thermocycler machine (Bio-Rad, USA). All reactions were performed in 34 cycles, and the conditions are shown in Table 1.\nDNA was extracted from all A. baumanniiisolates grown for 24 h using an alkaline lysis method with sodium dodecyl sulphate (SDS) and NaOH, as previously published\n23\n, with few modifications. In brief, first, we prepared a lysis buffer by dissolving 0.5 g of SDS and 0.4 g of NaOH in 200 µL of distilled water. Next, 4-6 colonies of the bacteria were suspended in 60 µL of lysis buffer and subsequently heated at 95 °C for 10 min. In the next step, the suspension was centrifuged at 13000 rpm for 5 min, and 180 µL of distilled water was added to the microtubes. The obtained supernatant was frozen at −20 °C until use as the extracted DNA in PCR. \nTwo sets of multiplex-PCR were used to detect AME-encoding genes in A. baumannii isolates using the specific primers shown in Table 1. APH(3′)-VIa (aphA6), ANT(2\")-Ia (aadB), and ArmA genes were detected in the same set; AAC(6′)-Ib (aacA4) and AAC(3)-IIa (aacC2) were identified in the second set; and the ANT(3\")-Ia (aadA1) gene was detected by PCR alone. The PCR and multiplex-PCR were performed in 25 µL of final volume containing 12.5 µL of the master mix (Ampliqon, Denmark), 10 pmol of each primer (Bioneer, South Korea), and 500 ng of template DNA; the reaction solutions were brought to the desired volume through the addition of distilled water. The genes were amplified under standard conditions using a thermocycler machine (Bio-Rad, USA). All reactions were performed in 34 cycles, and the conditions are shown in Table 1.\n Statistical analysis The data were analyzed using SPSS (version 21). Categorical data were analyzed using the Fisher’s exact test, and a P-value less than 0.05 was considered statistically significant. In addition, an independent t-test was used to examine the mean age of the subjects. \nThe data were analyzed using SPSS (version 21). Categorical data were analyzed using the Fisher’s exact test, and a P-value less than 0.05 was considered statistically significant. In addition, an independent t-test was used to examine the mean age of the subjects. ", "This study was performed on A. baumannii isolated from patients admitted to different educational hospitals in Sari, north of Iran, for 6 months (April 2019 to September 2019). The clinical specimens included blood, urine, respiratory secretions (bronchial lavage and tracheal secretions), CSF, and ulcer (surgical and burn wound). The clinical isolates were identified using conventional microbiological tests\n19\n and confirmed by polymerase chain reaction (PCR) amplification of the blaOXA-51 gene using specific primers\n20\n; the reaction conditions are shown in Table 1. \n\nTABLE 1:Primers used to amplify the blaOXA-51 and aminoglycoside resistance genes along with the conditions of PCR.Target genesPrimer sequences (5´-3´)Amplicon size (bp)94 °C94 °CAnnealing Temperature and time72 °C72 °CReference\nOXA-51TAATGCTTTGATCGGCCTTG3532 min25 sec51 °C for 30 sec30 sec5 min\n5\n\nTGGATTGCACTTCATCTTGG\n\n\n\n\n\n\n\nAPH(3′)-VIa\nCGGAAACAGCGTTTTAGA7172 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aphA6)\nTTCCTTTTGTCAGGTC\n\n\n\n\n\n\n\nAAC(3)-IIa\nATGCATACGCGGAAGGC8222 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacC2)\nTGCTGGCACGATCGGAG\n\n\n\n\n\n\n\nAAC(6′)-Ib\nTATGAGTGGCTAAATCGAT3952 min25 sec54 °C for 30 sec30 sec5 min\n5\n\n(aacA4)\nCCCGCTTTCTCGTAGCA\n\n\n\n\n\n\n\nANT(2\")-Ia\nATCTGCCGCTCTGGAT4052 min25 sec49 °C for 30 sec30 sec5 min\n5\n\n(aadB)\nCGAGCCTGTAGGACT\n\n\n\n\n\n\n\nANT(3\")-Ia\nATGAGGGAAGCGGTGATCG7922 min25 sec62 °C for 30 sec30 sec5 min\n5\n\n(aadA1)\nTTATTTGCCGACTACCTTGGT\n\n\n\n\n\n\n\nArmA\nATTCTGCCTATCCTAATTGG3152 min25 sec49 °C for 30 sec30 sec5 min\n5\n\nACCTATACTTTATCGTCGTC\n\n\n\n\n\n\n\n", "The antibiotic susceptibility pattern of the isolates was determined by the disk agar diffusion method on Muller Hinton agar (Merck, Germany) according to the Clinical and Laboratory Standards Institute (CLSI) guidelines\n21\n. The antibiotics included piperacillin (100 µg), piperacillin-tazobactam (100/10 µg), imipenem (10 µg), meropenem (10 µg), doripenem (10 µg), ciprofloxacin (5 µg), levofloxacin (5 µg), trimethoprim-sulfamethoxazole (1.25-23.75 µg), ceftazidime (30 µg), cefotaxime (30 µg), and cefepime (30 µg) (MAST Co., England). The susceptibility pattern of the isolates against aminoglycosides including kanamycin, amikacin, spectinomycin, netilmicin, gentamicin, streptomycin, and tobramycin was determined using the micro-broth dilution method according to the CLSI guidelines\n21\n. For interpretation of the minimum inhibitory concentration (MIC) values, we referred to the CLSI guidelines and previous studies\n1\n\n,\n\n21\n\n,\n\n22\n. Escherichia coli ATCC 25922 and A. baumannii ATCC 19606 were used as control strains for antibiotic susceptibility testing. ", "DNA was extracted from all A. baumanniiisolates grown for 24 h using an alkaline lysis method with sodium dodecyl sulphate (SDS) and NaOH, as previously published\n23\n, with few modifications. In brief, first, we prepared a lysis buffer by dissolving 0.5 g of SDS and 0.4 g of NaOH in 200 µL of distilled water. Next, 4-6 colonies of the bacteria were suspended in 60 µL of lysis buffer and subsequently heated at 95 °C for 10 min. In the next step, the suspension was centrifuged at 13000 rpm for 5 min, and 180 µL of distilled water was added to the microtubes. The obtained supernatant was frozen at −20 °C until use as the extracted DNA in PCR. \nTwo sets of multiplex-PCR were used to detect AME-encoding genes in A. baumannii isolates using the specific primers shown in Table 1. APH(3′)-VIa (aphA6), ANT(2\")-Ia (aadB), and ArmA genes were detected in the same set; AAC(6′)-Ib (aacA4) and AAC(3)-IIa (aacC2) were identified in the second set; and the ANT(3\")-Ia (aadA1) gene was detected by PCR alone. The PCR and multiplex-PCR were performed in 25 µL of final volume containing 12.5 µL of the master mix (Ampliqon, Denmark), 10 pmol of each primer (Bioneer, South Korea), and 500 ng of template DNA; the reaction solutions were brought to the desired volume through the addition of distilled water. The genes were amplified under standard conditions using a thermocycler machine (Bio-Rad, USA). All reactions were performed in 34 cycles, and the conditions are shown in Table 1.", "The data were analyzed using SPSS (version 21). Categorical data were analyzed using the Fisher’s exact test, and a P-value less than 0.05 was considered statistically significant. In addition, an independent t-test was used to examine the mean age of the subjects. ", " Patients, samples, and bacterial isolates In this study, 100 non-duplicated A. baumannii clinical isolates were collected from 100 patients admitted to the teaching and educational hospitals of Sari, north of Iran. All isolates identified using the phenotypic method contained the blaOXA-51 gene according to the PCR results. The mean age of the patients was 42.08±25.08 years (minimum age: 6 months; highest age: 88 years), and 50% of the patients were male. There was no significant difference between men and women in terms of mean age (p=0.64). Most of the bacterial isolates (34%) were obtained from patients admitted to the burn wards, while 29%, 21%, and 16% of the isolates were collected from the ICU, surgery, and pediatric wards, respectively. The most common type of specimen (73%) for isolation of the bacteria was the wound samples; however, 15% and 12% of other clinical isolates were obtained from urine and blood cultures, respectively.\nIn this study, 100 non-duplicated A. baumannii clinical isolates were collected from 100 patients admitted to the teaching and educational hospitals of Sari, north of Iran. All isolates identified using the phenotypic method contained the blaOXA-51 gene according to the PCR results. The mean age of the patients was 42.08±25.08 years (minimum age: 6 months; highest age: 88 years), and 50% of the patients were male. There was no significant difference between men and women in terms of mean age (p=0.64). Most of the bacterial isolates (34%) were obtained from patients admitted to the burn wards, while 29%, 21%, and 16% of the isolates were collected from the ICU, surgery, and pediatric wards, respectively. The most common type of specimen (73%) for isolation of the bacteria was the wound samples; however, 15% and 12% of other clinical isolates were obtained from urine and blood cultures, respectively.\n Antimicrobial susceptibility pattern According to the results of the disk agar diffusion method, the most and least effective antibiotics in the present study were imipenem and ciprofloxacin, with resistance rates of 75% and 100%, respectively (Table 2). Moreover, 94% of the isolates were detected as multi-drug resistant (MDR), and the most MDR isolates were collected from wound samples. Table 2 presents the antibiotic resistance patterns of all A. baumannii clinical isolates in this study based on hospital wards, as well as sample types. Resistance to the tested antibiotics was not significantly correlated with the sample types and hospital wards where the samples were collected.\n\nTABLE 2:Antimicrobial susceptibility pattern of the Acinetobacter baumannii clinical isolates in disk agar diffusion method.AntibioticsNo. (%) of resistant, intermediate resistant or susceptible isolates in terms of \nSusceptibilityTotalHospital wards Sample types \n\n(n=100)BurnICUSurgeryPediatric\nP-value\nWoundUrineBlood\nP-value\n\n\n\n(n=34)(n=29)(n=21)s (n=16)\n(n=73)(n=15)(n=12)\nPIPR8628 (82.3)28 (96.5)19 (90.4)11 (68.7)0.41262 (84.9)14 (93.3)10 (83.3)0.917\nI105 (14.7)1 (3.4)2 (9.5)2 (12.5)\n8 (10.9)1 (6.6)1 (8.3)\n\nS41 (2.9)003 (18.7)\n3 (4.1)01 (8.3)\nPIP-TAZR7826 (76.4)24 (82.7)16 (76.1)12 (75)0.10454 (73.9)14 (93.3)10 (83.3)0.372\nI104 (11.7)2 (6.8)3 (14.2)1 (6.2)\n8 (10.9)02 (16.6)\n\nS124 (11.7)3 (10.3)2 (9.5)3 (18.7)\n11 (15)1 (6.6)0\nCAZR7625 (73.5)22 (75.8)18 (85.7)11 (68.7)0.74352 (71.2)15 (100)9 (75)0.559\nI00000\n000\n\nS249 (26.4)1 (3.4)3 (14.2)5 (31.2)\n21 (28.7)03 (25)\nCTXR9332 (94.1)28 (96.5)19 (90.4)14 (87.5)0.76267 (91.7)15 (100)11 (91.6)0.618\nI02 (5.8)1 (3.4)2 (9.5)2 (12.5)\n6 (8.2)01 (8.3)\n\nS70000\n000\nCEFR9232 (94.1)28 (96.5)18 (85.7)14 (87.5)0.44867 (91.7)14 (93.3)11 (91.6)0.728\nI41 (2.9)1 (3.4)2 (9.5)0\n2 (2.7)1 (6.6)1 (8.3)\n\nS41 (2.9)01 (4.7)2 (12.5)\n4 (5.4)00\nIMIR7527 (79.4)25 (86.2)17 (80.9)10 (62.5)0.61755 (75.3)12 (80)8 (66.6)0.873\nI114 (11.7)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)1 (6.6)1 (8.3)\n\nS147 (20.5)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)2 (13.3)3 (25)\nMERR9733 (97)28 (96.5)20 (95.2)16 (100)0.96470 (95.8)15 (100)12 100)0.667\nI00000\n000\n\nS31 (2.9)1 (3.4)1 (4.7)0\n3 (4.1)00\nDORR9632 (94.1)28 (96.5)20 (95.2)16 (100)0.79769 (94.5)15 (100)12 (100)0.913\nI21 (2.9)1 (3.4)00\n2 (2.7)00\n\nS21 (2.9)01 (5)0\n2 (2.7)00\nCIPR10034 (100)29 (100)21 (100)16 (100)0.10073 (100)15 (100)12 (100)0.100\nI00000\n000\n\nS00000\n000\nLEVR9331 (91.1)27 (93.1)20 (95.2)15 (93.7)0.72567 (91.7)14 (93.3)12 (100)0.842\nI32 (5.8)001 (6.2)\n3 (4.1)00\n\nS41 (2.9)2 (6.8)1 (4.7)0\n3 (4.1)1 (6.6)0\nSXTR9231 (91.1)27 (93.1)18 (85.7)16 (100)0.93568 (93.1)12 (80)12 (100)0.216\nI31 (2.9)1 (3.4)1 (4.7)0\n1 (1.3)2 (13.3)0\n\nS52 (5.8)1 (3.4)2 (9.5)0\n4 (5.4)1 (6.6)0\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\n\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\nMoreover, according to the MIC results, the resistance rate against gentamicin, kanamycin, tobramycin, and streptomycin was 94%, while the highest susceptibility (20%) of A. baumannii isolates was observed against netilmicin. In contrast, 74%, 68%, and 78% of our clinical isolates were resistant to amikacin, netilmicin, and spectinomycin, respectively. The MIC ranges of aminoglycosides and their relationship with the presence of AMEs-encoding genes are shown in Table 3.\n\nTABLE 3:Aminoglycoside resistance pattern of the Acinetobacter baumannii clinical isolates in this study. AntibioticsMIC rangesNo. of the isolates with \n(µg/mL)Susceptibility\nAPH(3′)-VIa\n\nANT(2\")-Ia\n\nANT(3\")-Ia\n\nAAC(6′)-Ib\n\nAAC(3)-IIa\n\narmA\nTotal\n\npattern\n(aphA6)\n\n(aadB)\n\n(aadA1)\n\n(aacA4)\n\n(aacC2)\n(n=22)\n\n\n\n(n=77)(n=73)(n=33)(n=33)(n=19)\n\nGentamicin ≤4S------6\n8I5311-1-\n16-32R---\n--94\n64-128R5-----\n\n≥256R677032321921\n\nMIC50\n5122565125122561024256\nMIC90\n102425610242562561024512Tobramycin≤4S33---14\n8I1----22\n16-32R2-1---94\n64-128R-21423\n\n≥256R716831291716\n\nMIC50\n256512512256512512512\nMIC90\n10241024102451210245121024Amikacin≤16S1310662316\n32I710665110\n64R-1----74\n128R-4----\n\n≥256R574821211218\n\nMIC50\n2561024512512512256512\nMIC90\n51251251210245122561024Netilmicin≤8S883-1220\n16I6--4-612\n32R810464268\n64-128R571324\n\n≥256R50482520128\n\nMIC50\n25612864256256128128\nMIC90\n25612864512512128256Kanamycin≤16S42----4\n32I22----2\n64R-3-1--94\n128R322-21\n\n≥256R686431321721\n\nMIC50\n643212864256128128\nMIC90\n6425625664256256256Streptomycin≤4S1-11--6\n8S11-1--\n\n16S----\n-\n\n32I-------\n64-128R33-32-94\n≥256R726932281718\n\nMIC50\n256128256256256512256\nMIC90\n2561285121024128512512Spectinomycin≤4S11---312\n8S7522--\n\n16S\n-\n---\n\n32I83-1-510\n64-128R21-22178\n≥256R596331281713\n\nMIC50\n2562565126464256256\nMIC90\n256512256128128256512\nR: resistant; I: intermediate resistant; S: susceptible. Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.\n\n\nR: resistant; I: intermediate resistant; S: susceptible.\n Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.\nAccording to the results of the disk agar diffusion method, the most and least effective antibiotics in the present study were imipenem and ciprofloxacin, with resistance rates of 75% and 100%, respectively (Table 2). Moreover, 94% of the isolates were detected as multi-drug resistant (MDR), and the most MDR isolates were collected from wound samples. Table 2 presents the antibiotic resistance patterns of all A. baumannii clinical isolates in this study based on hospital wards, as well as sample types. Resistance to the tested antibiotics was not significantly correlated with the sample types and hospital wards where the samples were collected.\n\nTABLE 2:Antimicrobial susceptibility pattern of the Acinetobacter baumannii clinical isolates in disk agar diffusion method.AntibioticsNo. (%) of resistant, intermediate resistant or susceptible isolates in terms of \nSusceptibilityTotalHospital wards Sample types \n\n(n=100)BurnICUSurgeryPediatric\nP-value\nWoundUrineBlood\nP-value\n\n\n\n(n=34)(n=29)(n=21)s (n=16)\n(n=73)(n=15)(n=12)\nPIPR8628 (82.3)28 (96.5)19 (90.4)11 (68.7)0.41262 (84.9)14 (93.3)10 (83.3)0.917\nI105 (14.7)1 (3.4)2 (9.5)2 (12.5)\n8 (10.9)1 (6.6)1 (8.3)\n\nS41 (2.9)003 (18.7)\n3 (4.1)01 (8.3)\nPIP-TAZR7826 (76.4)24 (82.7)16 (76.1)12 (75)0.10454 (73.9)14 (93.3)10 (83.3)0.372\nI104 (11.7)2 (6.8)3 (14.2)1 (6.2)\n8 (10.9)02 (16.6)\n\nS124 (11.7)3 (10.3)2 (9.5)3 (18.7)\n11 (15)1 (6.6)0\nCAZR7625 (73.5)22 (75.8)18 (85.7)11 (68.7)0.74352 (71.2)15 (100)9 (75)0.559\nI00000\n000\n\nS249 (26.4)1 (3.4)3 (14.2)5 (31.2)\n21 (28.7)03 (25)\nCTXR9332 (94.1)28 (96.5)19 (90.4)14 (87.5)0.76267 (91.7)15 (100)11 (91.6)0.618\nI02 (5.8)1 (3.4)2 (9.5)2 (12.5)\n6 (8.2)01 (8.3)\n\nS70000\n000\nCEFR9232 (94.1)28 (96.5)18 (85.7)14 (87.5)0.44867 (91.7)14 (93.3)11 (91.6)0.728\nI41 (2.9)1 (3.4)2 (9.5)0\n2 (2.7)1 (6.6)1 (8.3)\n\nS41 (2.9)01 (4.7)2 (12.5)\n4 (5.4)00\nIMIR7527 (79.4)25 (86.2)17 (80.9)10 (62.5)0.61755 (75.3)12 (80)8 (66.6)0.873\nI114 (11.7)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)1 (6.6)1 (8.3)\n\nS147 (20.5)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)2 (13.3)3 (25)\nMERR9733 (97)28 (96.5)20 (95.2)16 (100)0.96470 (95.8)15 (100)12 100)0.667\nI00000\n000\n\nS31 (2.9)1 (3.4)1 (4.7)0\n3 (4.1)00\nDORR9632 (94.1)28 (96.5)20 (95.2)16 (100)0.79769 (94.5)15 (100)12 (100)0.913\nI21 (2.9)1 (3.4)00\n2 (2.7)00\n\nS21 (2.9)01 (5)0\n2 (2.7)00\nCIPR10034 (100)29 (100)21 (100)16 (100)0.10073 (100)15 (100)12 (100)0.100\nI00000\n000\n\nS00000\n000\nLEVR9331 (91.1)27 (93.1)20 (95.2)15 (93.7)0.72567 (91.7)14 (93.3)12 (100)0.842\nI32 (5.8)001 (6.2)\n3 (4.1)00\n\nS41 (2.9)2 (6.8)1 (4.7)0\n3 (4.1)1 (6.6)0\nSXTR9231 (91.1)27 (93.1)18 (85.7)16 (100)0.93568 (93.1)12 (80)12 (100)0.216\nI31 (2.9)1 (3.4)1 (4.7)0\n1 (1.3)2 (13.3)0\n\nS52 (5.8)1 (3.4)2 (9.5)0\n4 (5.4)1 (6.6)0\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\n\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\nMoreover, according to the MIC results, the resistance rate against gentamicin, kanamycin, tobramycin, and streptomycin was 94%, while the highest susceptibility (20%) of A. baumannii isolates was observed against netilmicin. In contrast, 74%, 68%, and 78% of our clinical isolates were resistant to amikacin, netilmicin, and spectinomycin, respectively. The MIC ranges of aminoglycosides and their relationship with the presence of AMEs-encoding genes are shown in Table 3.\n\nTABLE 3:Aminoglycoside resistance pattern of the Acinetobacter baumannii clinical isolates in this study. AntibioticsMIC rangesNo. of the isolates with \n(µg/mL)Susceptibility\nAPH(3′)-VIa\n\nANT(2\")-Ia\n\nANT(3\")-Ia\n\nAAC(6′)-Ib\n\nAAC(3)-IIa\n\narmA\nTotal\n\npattern\n(aphA6)\n\n(aadB)\n\n(aadA1)\n\n(aacA4)\n\n(aacC2)\n(n=22)\n\n\n\n(n=77)(n=73)(n=33)(n=33)(n=19)\n\nGentamicin ≤4S------6\n8I5311-1-\n16-32R---\n--94\n64-128R5-----\n\n≥256R677032321921\n\nMIC50\n5122565125122561024256\nMIC90\n102425610242562561024512Tobramycin≤4S33---14\n8I1----22\n16-32R2-1---94\n64-128R-21423\n\n≥256R716831291716\n\nMIC50\n256512512256512512512\nMIC90\n10241024102451210245121024Amikacin≤16S1310662316\n32I710665110\n64R-1----74\n128R-4----\n\n≥256R574821211218\n\nMIC50\n2561024512512512256512\nMIC90\n51251251210245122561024Netilmicin≤8S883-1220\n16I6--4-612\n32R810464268\n64-128R571324\n\n≥256R50482520128\n\nMIC50\n25612864256256128128\nMIC90\n25612864512512128256Kanamycin≤16S42----4\n32I22----2\n64R-3-1--94\n128R322-21\n\n≥256R686431321721\n\nMIC50\n643212864256128128\nMIC90\n6425625664256256256Streptomycin≤4S1-11--6\n8S11-1--\n\n16S----\n-\n\n32I-------\n64-128R33-32-94\n≥256R726932281718\n\nMIC50\n256128256256256512256\nMIC90\n2561285121024128512512Spectinomycin≤4S11---312\n8S7522--\n\n16S\n-\n---\n\n32I83-1-510\n64-128R21-22178\n≥256R596331281713\n\nMIC50\n2562565126464256256\nMIC90\n256512256128128256512\nR: resistant; I: intermediate resistant; S: susceptible. Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.\n\n\nR: resistant; I: intermediate resistant; S: susceptible.\n Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.\n Gene profiles of the isolates The frequency of each aminoglycoside resistance gene and its relation with the MIC ranges are shown in Table 3. In total, the proportions of aminoglycoside resistance genes among our clinical isolates of A. baumannii were as follows: APH(3′)-VIa (aphA6) (77%), ANT(2\")-Ia (aadB) (73%), ANT(3\")-Ia (aadA1) (33%), AAC(6′)-Ib (aacA4) (33%), AAC(3)-IIa (aacC2) (19%), and ArmA (22%). The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the isolates is shown in Table 4. There was a significant association between the presence of all resistance genes and the non-susceptibility (resistance or intermediate resistance) to all aminoglycosides, except armA and resistance to netilmicin. Important data from this table indicates that in some groups, such as gentamicin- and tobramycin-resistant groups, all resistant isolates contained some AMEs-encoding genes such as aacC2, aacA4, and aadA1. \n\nTABLE 4:The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the A. baumannii clinical isolates.AntibioticsSusceptibility PatternNo. (%) of the isolates contained \n\n\nAPH(3′)-VIa (aphA6)\n\nP-value\n\nANT(2\")-Ia (aadB)\n\nP-value\n\nANT(3\")-Ia (aadA1)\n\nP-value\n\nAAC(6′)-Ib (aacA4)\n\nP-value\n\nAAC(3)-IIa (aacC2)\n\nP-value\n\narmA\n\nP-value\n\n\n(n=77)\n(n=73)\n(n=33)\n(n=33)\n(n=19)\n(n=22)\nGentamicinNon-susceptible77 (100)0.01273 (100)0.02333 (100)0.03333 (100)0.03319 (100)0.00722 (100)0.021\nSusceptible0\n0\n0\n0\n0\n0\nTobramycinNon-susceptible74 (96.1)0.01970 (95.8)0.02933 (100)0.00333 (100)0.00319 (100)0.00721 (95.4)0.024\nSusceptible3 (3.8)\n3 (4.1)\n0\n0\n0\n1 (4.5)\nAmikacinNon-susceptible64 (83.1)0.03663 (86.3)0.03727 (81.8)0.03827 (81.8)0.03817 (89.4)0.02419 (86.3)0.033\nSusceptible13 (16.8)\n10 (13.6)\n6 (18.1)\n6 (18.1)\n2 (10.5)\n3 (13.6)\nNetilmicinNon-susceptible63 (81.8)0.04265 (89.04)0.03930 (90.9)0.01429 (87.8)0.03218 (94.7)0.01814 (63.6)0.072\nSusceptible15 (19.4)\n8 (10.9)\n3 (9.09)\n4 (12.1)\n1 (5.2)\n8 (36.3)\nKanamycinNon-susceptible73 (94.8)0.02971 (97.2)0.02333 (100)0.00333 (100)0.00319 (100)0.00722 (100)0.021\nSusceptible4 (5.1)\n2 (2.7)\n0\n0\n0\n0\nStreptomycinNon-susceptible75 (97.4)0.01572 (98.6)0.01932 (96.9)0.01931 (93.9)0.02119 (100)0.00718 (81.8)0.044\nSusceptible2 (2.5)\n1 (1.3)\n1 (3.03)\n2 (6.06)\n0\n4 (18.1)\nSpectinomycinNon-susceptible69 (89.6(0.02867 (91.7)0.03731 (93.9)0.02131 (93.9)0.02119 (100)0.00719 (86.3)0.033\nSusceptible8 (10.3)\n6 (8.2)\n2 (6.06)\n2 (6.06)\n0\n3 (13.6)\n\n\nIn addition, we detected 22 gene profiles among all clinical isolates of A. baumannii (Table 5). The most prevalent combination gene profiles in the present study included: 1) APH(3')-VIa + ANT(2\")-Ia with 39 isolates containing these genes, among which 100% isolates were resistant towards kanamycin, while almost 95% were resistant against netilmicin and 97.4% were resistant to tobramycin and gentamicin, and 2) AAC(3)-IIa + AAC(6')-Ib + ANT(3\")-Ia + APH(3')-VIa + ANT(2\")-Ia with 14 isolates, among which 100% were resistant to gentamicin, kanamycin, and streptomycin, while almost 93% were resistant against tobramycin and spectinomycin. However, 15 isolates showed an AME gene profile with one AME gene, most of which were resistant to tested aminoglycosides. Other AME-encoding gene profiles were detected at a low rate (Table 5). However, 15, 52, 12, 5, 14, and 2 isolates in the present study contained 1, 2, 3, 4, 5, and 6 AME genes, respectively. The most prevalent gene profiles exhibited the simultaneous presence of 2 genes followed by 5 genes and 3 AME genes.\n\nTABLE 5:Gene profiles of the Acinetobacter baumannii clinical isolates and their association with aminoglycoside resistance.Gene profile (No.)Gentamicin Tobramycin Amikacin Netilmicin Kanamycin Streptomycin Spectinomycin \nNSSNSSNSSNSSNSSNSSNSS\n(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)\nArmA (9)9 (100)-9 (100)-7 (77.7)2 (22.2)3 (33.3)6 (66.6)9 (100)\n-\n7 (77.7)2 (22.2)9 (100)-\naphA6 (2)2 (100)-2 (100)--2 (100)-2 (100)1 (50)1 (50)2 (100)--2 (100)\naadB (4)4 (100)-3 (75)1 (25)3 (75)1 (25)2 (50)2 (50)3 (75)1 (25)4 (100)-3 (75)1 (25)\nArmA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aadA1 (2)2 (100)-2 (100)-2 (100)-1 (50)1 (50)2 (100)\n-\n2 (100)-2 (100)-\naadA1 + armA (2)2 (100)-2 (100)-1 (50)1 (50)2 (100)-2 (100)\n-\n1 (50)1 (50)2 (100)-\naadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naadB + aadA1 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naphA6 + aadB (39)38 (97.4)1 (2.56)38 (97.4)1 (2.56)34 (87.1)5 (12.8)37 (94.8)2 (5.1)39 (100)-33 (84.6)6 (15.3)32 (82)7 (17.9)\naacA4 + aphA6 (3)3 (100)-3 (100)-3 (100)-3 (100)-3 (100)\n-\n3 (100)-3 (100)-\naadB + aadA1 +\naphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacA4 + aadB +\naadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadB + aphA6 (1)1 (100)-1 (100)-1 (100)--1 (100)1 (100)\n-\n-1 (100)1 (100)-\naadB + armA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aacC2 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + armA\n+ aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacC2 + aacA4 + aadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacC2 + aacA4\n+ aadB + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacA4 + aadB +\naadA1 + aphA6 (4)4 (100)-4 (100)-4 (100)-4 (100)-4 (100)\n-\n4 (100)-4 (100)-\naacC2 + aacA4 + aadA1 + aphA6 + aadB (14)14 (100)-13 (92.8)1 (7.1)12 (85.7)2 (14.2)12 (85.7)2 (14.2)14 (100)-14 (100)-13 (92.8)1 (7.1)\naacC2 + aacA4 + aadA1 + aadB + armA + aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible.\n\n\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible.\nThe frequency of each aminoglycoside resistance gene and its relation with the MIC ranges are shown in Table 3. In total, the proportions of aminoglycoside resistance genes among our clinical isolates of A. baumannii were as follows: APH(3′)-VIa (aphA6) (77%), ANT(2\")-Ia (aadB) (73%), ANT(3\")-Ia (aadA1) (33%), AAC(6′)-Ib (aacA4) (33%), AAC(3)-IIa (aacC2) (19%), and ArmA (22%). The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the isolates is shown in Table 4. There was a significant association between the presence of all resistance genes and the non-susceptibility (resistance or intermediate resistance) to all aminoglycosides, except armA and resistance to netilmicin. Important data from this table indicates that in some groups, such as gentamicin- and tobramycin-resistant groups, all resistant isolates contained some AMEs-encoding genes such as aacC2, aacA4, and aadA1. \n\nTABLE 4:The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the A. baumannii clinical isolates.AntibioticsSusceptibility PatternNo. (%) of the isolates contained \n\n\nAPH(3′)-VIa (aphA6)\n\nP-value\n\nANT(2\")-Ia (aadB)\n\nP-value\n\nANT(3\")-Ia (aadA1)\n\nP-value\n\nAAC(6′)-Ib (aacA4)\n\nP-value\n\nAAC(3)-IIa (aacC2)\n\nP-value\n\narmA\n\nP-value\n\n\n(n=77)\n(n=73)\n(n=33)\n(n=33)\n(n=19)\n(n=22)\nGentamicinNon-susceptible77 (100)0.01273 (100)0.02333 (100)0.03333 (100)0.03319 (100)0.00722 (100)0.021\nSusceptible0\n0\n0\n0\n0\n0\nTobramycinNon-susceptible74 (96.1)0.01970 (95.8)0.02933 (100)0.00333 (100)0.00319 (100)0.00721 (95.4)0.024\nSusceptible3 (3.8)\n3 (4.1)\n0\n0\n0\n1 (4.5)\nAmikacinNon-susceptible64 (83.1)0.03663 (86.3)0.03727 (81.8)0.03827 (81.8)0.03817 (89.4)0.02419 (86.3)0.033\nSusceptible13 (16.8)\n10 (13.6)\n6 (18.1)\n6 (18.1)\n2 (10.5)\n3 (13.6)\nNetilmicinNon-susceptible63 (81.8)0.04265 (89.04)0.03930 (90.9)0.01429 (87.8)0.03218 (94.7)0.01814 (63.6)0.072\nSusceptible15 (19.4)\n8 (10.9)\n3 (9.09)\n4 (12.1)\n1 (5.2)\n8 (36.3)\nKanamycinNon-susceptible73 (94.8)0.02971 (97.2)0.02333 (100)0.00333 (100)0.00319 (100)0.00722 (100)0.021\nSusceptible4 (5.1)\n2 (2.7)\n0\n0\n0\n0\nStreptomycinNon-susceptible75 (97.4)0.01572 (98.6)0.01932 (96.9)0.01931 (93.9)0.02119 (100)0.00718 (81.8)0.044\nSusceptible2 (2.5)\n1 (1.3)\n1 (3.03)\n2 (6.06)\n0\n4 (18.1)\nSpectinomycinNon-susceptible69 (89.6(0.02867 (91.7)0.03731 (93.9)0.02131 (93.9)0.02119 (100)0.00719 (86.3)0.033\nSusceptible8 (10.3)\n6 (8.2)\n2 (6.06)\n2 (6.06)\n0\n3 (13.6)\n\n\nIn addition, we detected 22 gene profiles among all clinical isolates of A. baumannii (Table 5). The most prevalent combination gene profiles in the present study included: 1) APH(3')-VIa + ANT(2\")-Ia with 39 isolates containing these genes, among which 100% isolates were resistant towards kanamycin, while almost 95% were resistant against netilmicin and 97.4% were resistant to tobramycin and gentamicin, and 2) AAC(3)-IIa + AAC(6')-Ib + ANT(3\")-Ia + APH(3')-VIa + ANT(2\")-Ia with 14 isolates, among which 100% were resistant to gentamicin, kanamycin, and streptomycin, while almost 93% were resistant against tobramycin and spectinomycin. However, 15 isolates showed an AME gene profile with one AME gene, most of which were resistant to tested aminoglycosides. Other AME-encoding gene profiles were detected at a low rate (Table 5). However, 15, 52, 12, 5, 14, and 2 isolates in the present study contained 1, 2, 3, 4, 5, and 6 AME genes, respectively. The most prevalent gene profiles exhibited the simultaneous presence of 2 genes followed by 5 genes and 3 AME genes.\n\nTABLE 5:Gene profiles of the Acinetobacter baumannii clinical isolates and their association with aminoglycoside resistance.Gene profile (No.)Gentamicin Tobramycin Amikacin Netilmicin Kanamycin Streptomycin Spectinomycin \nNSSNSSNSSNSSNSSNSSNSS\n(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)\nArmA (9)9 (100)-9 (100)-7 (77.7)2 (22.2)3 (33.3)6 (66.6)9 (100)\n-\n7 (77.7)2 (22.2)9 (100)-\naphA6 (2)2 (100)-2 (100)--2 (100)-2 (100)1 (50)1 (50)2 (100)--2 (100)\naadB (4)4 (100)-3 (75)1 (25)3 (75)1 (25)2 (50)2 (50)3 (75)1 (25)4 (100)-3 (75)1 (25)\nArmA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aadA1 (2)2 (100)-2 (100)-2 (100)-1 (50)1 (50)2 (100)\n-\n2 (100)-2 (100)-\naadA1 + armA (2)2 (100)-2 (100)-1 (50)1 (50)2 (100)-2 (100)\n-\n1 (50)1 (50)2 (100)-\naadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naadB + aadA1 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naphA6 + aadB (39)38 (97.4)1 (2.56)38 (97.4)1 (2.56)34 (87.1)5 (12.8)37 (94.8)2 (5.1)39 (100)-33 (84.6)6 (15.3)32 (82)7 (17.9)\naacA4 + aphA6 (3)3 (100)-3 (100)-3 (100)-3 (100)-3 (100)\n-\n3 (100)-3 (100)-\naadB + aadA1 +\naphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacA4 + aadB +\naadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadB + aphA6 (1)1 (100)-1 (100)-1 (100)--1 (100)1 (100)\n-\n-1 (100)1 (100)-\naadB + armA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aacC2 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + armA\n+ aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacC2 + aacA4 + aadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacC2 + aacA4\n+ aadB + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacA4 + aadB +\naadA1 + aphA6 (4)4 (100)-4 (100)-4 (100)-4 (100)-4 (100)\n-\n4 (100)-4 (100)-\naacC2 + aacA4 + aadA1 + aphA6 + aadB (14)14 (100)-13 (92.8)1 (7.1)12 (85.7)2 (14.2)12 (85.7)2 (14.2)14 (100)-14 (100)-13 (92.8)1 (7.1)\naacC2 + aacA4 + aadA1 + aadB + armA + aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible.\n\n\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible.", "In this study, 100 non-duplicated A. baumannii clinical isolates were collected from 100 patients admitted to the teaching and educational hospitals of Sari, north of Iran. All isolates identified using the phenotypic method contained the blaOXA-51 gene according to the PCR results. The mean age of the patients was 42.08±25.08 years (minimum age: 6 months; highest age: 88 years), and 50% of the patients were male. There was no significant difference between men and women in terms of mean age (p=0.64). Most of the bacterial isolates (34%) were obtained from patients admitted to the burn wards, while 29%, 21%, and 16% of the isolates were collected from the ICU, surgery, and pediatric wards, respectively. The most common type of specimen (73%) for isolation of the bacteria was the wound samples; however, 15% and 12% of other clinical isolates were obtained from urine and blood cultures, respectively.", "According to the results of the disk agar diffusion method, the most and least effective antibiotics in the present study were imipenem and ciprofloxacin, with resistance rates of 75% and 100%, respectively (Table 2). Moreover, 94% of the isolates were detected as multi-drug resistant (MDR), and the most MDR isolates were collected from wound samples. Table 2 presents the antibiotic resistance patterns of all A. baumannii clinical isolates in this study based on hospital wards, as well as sample types. Resistance to the tested antibiotics was not significantly correlated with the sample types and hospital wards where the samples were collected.\n\nTABLE 2:Antimicrobial susceptibility pattern of the Acinetobacter baumannii clinical isolates in disk agar diffusion method.AntibioticsNo. (%) of resistant, intermediate resistant or susceptible isolates in terms of \nSusceptibilityTotalHospital wards Sample types \n\n(n=100)BurnICUSurgeryPediatric\nP-value\nWoundUrineBlood\nP-value\n\n\n\n(n=34)(n=29)(n=21)s (n=16)\n(n=73)(n=15)(n=12)\nPIPR8628 (82.3)28 (96.5)19 (90.4)11 (68.7)0.41262 (84.9)14 (93.3)10 (83.3)0.917\nI105 (14.7)1 (3.4)2 (9.5)2 (12.5)\n8 (10.9)1 (6.6)1 (8.3)\n\nS41 (2.9)003 (18.7)\n3 (4.1)01 (8.3)\nPIP-TAZR7826 (76.4)24 (82.7)16 (76.1)12 (75)0.10454 (73.9)14 (93.3)10 (83.3)0.372\nI104 (11.7)2 (6.8)3 (14.2)1 (6.2)\n8 (10.9)02 (16.6)\n\nS124 (11.7)3 (10.3)2 (9.5)3 (18.7)\n11 (15)1 (6.6)0\nCAZR7625 (73.5)22 (75.8)18 (85.7)11 (68.7)0.74352 (71.2)15 (100)9 (75)0.559\nI00000\n000\n\nS249 (26.4)1 (3.4)3 (14.2)5 (31.2)\n21 (28.7)03 (25)\nCTXR9332 (94.1)28 (96.5)19 (90.4)14 (87.5)0.76267 (91.7)15 (100)11 (91.6)0.618\nI02 (5.8)1 (3.4)2 (9.5)2 (12.5)\n6 (8.2)01 (8.3)\n\nS70000\n000\nCEFR9232 (94.1)28 (96.5)18 (85.7)14 (87.5)0.44867 (91.7)14 (93.3)11 (91.6)0.728\nI41 (2.9)1 (3.4)2 (9.5)0\n2 (2.7)1 (6.6)1 (8.3)\n\nS41 (2.9)01 (4.7)2 (12.5)\n4 (5.4)00\nIMIR7527 (79.4)25 (86.2)17 (80.9)10 (62.5)0.61755 (75.3)12 (80)8 (66.6)0.873\nI114 (11.7)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)1 (6.6)1 (8.3)\n\nS147 (20.5)2 (6.8)2 (9.5)3 (18.7)\n9 (12.3)2 (13.3)3 (25)\nMERR9733 (97)28 (96.5)20 (95.2)16 (100)0.96470 (95.8)15 (100)12 100)0.667\nI00000\n000\n\nS31 (2.9)1 (3.4)1 (4.7)0\n3 (4.1)00\nDORR9632 (94.1)28 (96.5)20 (95.2)16 (100)0.79769 (94.5)15 (100)12 (100)0.913\nI21 (2.9)1 (3.4)00\n2 (2.7)00\n\nS21 (2.9)01 (5)0\n2 (2.7)00\nCIPR10034 (100)29 (100)21 (100)16 (100)0.10073 (100)15 (100)12 (100)0.100\nI00000\n000\n\nS00000\n000\nLEVR9331 (91.1)27 (93.1)20 (95.2)15 (93.7)0.72567 (91.7)14 (93.3)12 (100)0.842\nI32 (5.8)001 (6.2)\n3 (4.1)00\n\nS41 (2.9)2 (6.8)1 (4.7)0\n3 (4.1)1 (6.6)0\nSXTR9231 (91.1)27 (93.1)18 (85.7)16 (100)0.93568 (93.1)12 (80)12 (100)0.216\nI31 (2.9)1 (3.4)1 (4.7)0\n1 (1.3)2 (13.3)0\n\nS52 (5.8)1 (3.4)2 (9.5)0\n4 (5.4)1 (6.6)0\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\n\n\nPIP: piperacillin; PIP-TAZ: piperacillin-tazobactam; CAZ: ceftazidime; CTX: cefotaxime; CEF: cefepime; IMI: imipenem; MER: meropenem; DOR: doripenem; CIP: ciprofloxacin; LEV: levofloxacin; SXT: trimethoprim-sulfamethoxazole; R: resistant; I: intermediate resistant; and S: susceptible.\nMoreover, according to the MIC results, the resistance rate against gentamicin, kanamycin, tobramycin, and streptomycin was 94%, while the highest susceptibility (20%) of A. baumannii isolates was observed against netilmicin. In contrast, 74%, 68%, and 78% of our clinical isolates were resistant to amikacin, netilmicin, and spectinomycin, respectively. The MIC ranges of aminoglycosides and their relationship with the presence of AMEs-encoding genes are shown in Table 3.\n\nTABLE 3:Aminoglycoside resistance pattern of the Acinetobacter baumannii clinical isolates in this study. AntibioticsMIC rangesNo. of the isolates with \n(µg/mL)Susceptibility\nAPH(3′)-VIa\n\nANT(2\")-Ia\n\nANT(3\")-Ia\n\nAAC(6′)-Ib\n\nAAC(3)-IIa\n\narmA\nTotal\n\npattern\n(aphA6)\n\n(aadB)\n\n(aadA1)\n\n(aacA4)\n\n(aacC2)\n(n=22)\n\n\n\n(n=77)(n=73)(n=33)(n=33)(n=19)\n\nGentamicin ≤4S------6\n8I5311-1-\n16-32R---\n--94\n64-128R5-----\n\n≥256R677032321921\n\nMIC50\n5122565125122561024256\nMIC90\n102425610242562561024512Tobramycin≤4S33---14\n8I1----22\n16-32R2-1---94\n64-128R-21423\n\n≥256R716831291716\n\nMIC50\n256512512256512512512\nMIC90\n10241024102451210245121024Amikacin≤16S1310662316\n32I710665110\n64R-1----74\n128R-4----\n\n≥256R574821211218\n\nMIC50\n2561024512512512256512\nMIC90\n51251251210245122561024Netilmicin≤8S883-1220\n16I6--4-612\n32R810464268\n64-128R571324\n\n≥256R50482520128\n\nMIC50\n25612864256256128128\nMIC90\n25612864512512128256Kanamycin≤16S42----4\n32I22----2\n64R-3-1--94\n128R322-21\n\n≥256R686431321721\n\nMIC50\n643212864256128128\nMIC90\n6425625664256256256Streptomycin≤4S1-11--6\n8S11-1--\n\n16S----\n-\n\n32I-------\n64-128R33-32-94\n≥256R726932281718\n\nMIC50\n256128256256256512256\nMIC90\n2561285121024128512512Spectinomycin≤4S11---312\n8S7522--\n\n16S\n-\n---\n\n32I83-1-510\n64-128R21-22178\n≥256R596331281713\n\nMIC50\n2562565126464256256\nMIC90\n256512256128128256512\nR: resistant; I: intermediate resistant; S: susceptible. Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.\n\n\nR: resistant; I: intermediate resistant; S: susceptible.\n Notes: MIC\n50\n: Minimum inhibitory concentration required to inhibit the growth of 50% of organisms; MIC\n90: Minimum inhibitory concentration required to inhibit the growth of 90% of organisms.", "The frequency of each aminoglycoside resistance gene and its relation with the MIC ranges are shown in Table 3. In total, the proportions of aminoglycoside resistance genes among our clinical isolates of A. baumannii were as follows: APH(3′)-VIa (aphA6) (77%), ANT(2\")-Ia (aadB) (73%), ANT(3\")-Ia (aadA1) (33%), AAC(6′)-Ib (aacA4) (33%), AAC(3)-IIa (aacC2) (19%), and ArmA (22%). The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the isolates is shown in Table 4. There was a significant association between the presence of all resistance genes and the non-susceptibility (resistance or intermediate resistance) to all aminoglycosides, except armA and resistance to netilmicin. Important data from this table indicates that in some groups, such as gentamicin- and tobramycin-resistant groups, all resistant isolates contained some AMEs-encoding genes such as aacC2, aacA4, and aadA1. \n\nTABLE 4:The relationship between the presence of aminoglycoside resistance genes and the aminoglycoside susceptibility pattern of the A. baumannii clinical isolates.AntibioticsSusceptibility PatternNo. (%) of the isolates contained \n\n\nAPH(3′)-VIa (aphA6)\n\nP-value\n\nANT(2\")-Ia (aadB)\n\nP-value\n\nANT(3\")-Ia (aadA1)\n\nP-value\n\nAAC(6′)-Ib (aacA4)\n\nP-value\n\nAAC(3)-IIa (aacC2)\n\nP-value\n\narmA\n\nP-value\n\n\n(n=77)\n(n=73)\n(n=33)\n(n=33)\n(n=19)\n(n=22)\nGentamicinNon-susceptible77 (100)0.01273 (100)0.02333 (100)0.03333 (100)0.03319 (100)0.00722 (100)0.021\nSusceptible0\n0\n0\n0\n0\n0\nTobramycinNon-susceptible74 (96.1)0.01970 (95.8)0.02933 (100)0.00333 (100)0.00319 (100)0.00721 (95.4)0.024\nSusceptible3 (3.8)\n3 (4.1)\n0\n0\n0\n1 (4.5)\nAmikacinNon-susceptible64 (83.1)0.03663 (86.3)0.03727 (81.8)0.03827 (81.8)0.03817 (89.4)0.02419 (86.3)0.033\nSusceptible13 (16.8)\n10 (13.6)\n6 (18.1)\n6 (18.1)\n2 (10.5)\n3 (13.6)\nNetilmicinNon-susceptible63 (81.8)0.04265 (89.04)0.03930 (90.9)0.01429 (87.8)0.03218 (94.7)0.01814 (63.6)0.072\nSusceptible15 (19.4)\n8 (10.9)\n3 (9.09)\n4 (12.1)\n1 (5.2)\n8 (36.3)\nKanamycinNon-susceptible73 (94.8)0.02971 (97.2)0.02333 (100)0.00333 (100)0.00319 (100)0.00722 (100)0.021\nSusceptible4 (5.1)\n2 (2.7)\n0\n0\n0\n0\nStreptomycinNon-susceptible75 (97.4)0.01572 (98.6)0.01932 (96.9)0.01931 (93.9)0.02119 (100)0.00718 (81.8)0.044\nSusceptible2 (2.5)\n1 (1.3)\n1 (3.03)\n2 (6.06)\n0\n4 (18.1)\nSpectinomycinNon-susceptible69 (89.6(0.02867 (91.7)0.03731 (93.9)0.02131 (93.9)0.02119 (100)0.00719 (86.3)0.033\nSusceptible8 (10.3)\n6 (8.2)\n2 (6.06)\n2 (6.06)\n0\n3 (13.6)\n\n\nIn addition, we detected 22 gene profiles among all clinical isolates of A. baumannii (Table 5). The most prevalent combination gene profiles in the present study included: 1) APH(3')-VIa + ANT(2\")-Ia with 39 isolates containing these genes, among which 100% isolates were resistant towards kanamycin, while almost 95% were resistant against netilmicin and 97.4% were resistant to tobramycin and gentamicin, and 2) AAC(3)-IIa + AAC(6')-Ib + ANT(3\")-Ia + APH(3')-VIa + ANT(2\")-Ia with 14 isolates, among which 100% were resistant to gentamicin, kanamycin, and streptomycin, while almost 93% were resistant against tobramycin and spectinomycin. However, 15 isolates showed an AME gene profile with one AME gene, most of which were resistant to tested aminoglycosides. Other AME-encoding gene profiles were detected at a low rate (Table 5). However, 15, 52, 12, 5, 14, and 2 isolates in the present study contained 1, 2, 3, 4, 5, and 6 AME genes, respectively. The most prevalent gene profiles exhibited the simultaneous presence of 2 genes followed by 5 genes and 3 AME genes.\n\nTABLE 5:Gene profiles of the Acinetobacter baumannii clinical isolates and their association with aminoglycoside resistance.Gene profile (No.)Gentamicin Tobramycin Amikacin Netilmicin Kanamycin Streptomycin Spectinomycin \nNSSNSSNSSNSSNSSNSSNSS\n(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)(%)\nArmA (9)9 (100)-9 (100)-7 (77.7)2 (22.2)3 (33.3)6 (66.6)9 (100)\n-\n7 (77.7)2 (22.2)9 (100)-\naphA6 (2)2 (100)-2 (100)--2 (100)-2 (100)1 (50)1 (50)2 (100)--2 (100)\naadB (4)4 (100)-3 (75)1 (25)3 (75)1 (25)2 (50)2 (50)3 (75)1 (25)4 (100)-3 (75)1 (25)\nArmA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aadA1 (2)2 (100)-2 (100)-2 (100)-1 (50)1 (50)2 (100)\n-\n2 (100)-2 (100)-\naadA1 + armA (2)2 (100)-2 (100)-1 (50)1 (50)2 (100)-2 (100)\n-\n1 (50)1 (50)2 (100)-\naadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naadB + aadA1 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naphA6 + aadB (39)38 (97.4)1 (2.56)38 (97.4)1 (2.56)34 (87.1)5 (12.8)37 (94.8)2 (5.1)39 (100)-33 (84.6)6 (15.3)32 (82)7 (17.9)\naacA4 + aphA6 (3)3 (100)-3 (100)-3 (100)-3 (100)-3 (100)\n-\n3 (100)-3 (100)-\naadB + aadA1 +\naphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacA4 + aadB +\naadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadB + aphA6 (1)1 (100)-1 (100)-1 (100)--1 (100)1 (100)\n-\n-1 (100)1 (100)-\naadB + armA + aphA6 (3)3 (100)-3 (100)-2 (66.6)1 (33.3)2 (66.6)1 (33.3)3 (100)\n-\n3 (100)-2 (66.6)1 (33.3)\naacA4 + aacC2 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + armA\n+ aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)\n-\n2 (100)-2 (100)-\naacC2 + aacA4 + aadA1 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)\n-\n1 (100)-1 (100)-\naacA4 + aadA1 + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacC2 + aacA4\n+ aadB + aphA6 (1)1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-1 (100)-\naacA4 + aadB +\naadA1 + aphA6 (4)4 (100)-4 (100)-4 (100)-4 (100)-4 (100)\n-\n4 (100)-4 (100)-\naacC2 + aacA4 + aadA1 + aphA6 + aadB (14)14 (100)-13 (92.8)1 (7.1)12 (85.7)2 (14.2)12 (85.7)2 (14.2)14 (100)-14 (100)-13 (92.8)1 (7.1)\naacC2 + aacA4 + aadA1 + aadB + armA + aphA6 (2)2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-2 (100)-\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible.\n\n\nNS: non-susceptible (resistant and intermediate resistant); S: susceptible.", "Overuse and misuse of antibiotics in the treatment of infections caused by A. baumannii has led to the emergence of MDR isolates in hospitals and health centers\n24\n. The spread of AME-encoding genes among the clinical isolates of A. baumannii is an important concern in the prescription of these traditional and effective antibiotics, as 94% of our isolates were resistant to kanamycin, gentamicin, streptomycin, and tobramycin. However, we found that netilmicin was the most effective aminoglycoside, as this antibiotic is not commonly used in the treatment of bacterial infections. This finding was similar to that of another Iranian study\n1\n. However, their isolates revealed an MIC50 ≤8 µg/mL, while in the present study it ranged from 128 µg/mL, indicating an increased resistance rate in our region. However, the MIC ranges of other clinically important aminoglycosides such as amikacin, gentamicin, and tobramycin in the present study were significantly higher than those reported in previous studies in Iran and other countries\n1\n\n,\n\n5\n\n,\n\n25\n, while these ranges were almost similar to those reported by Yoo Jin Cho et al. in 2009\n22\n. \nThe molecular analysis of AME-encoding genes in the present study showed a high frequency of aphA6, aadB, aadA1, aacA4, aacC2, and armA genes, consistent with the previous studies from Iran\n1\n\n,\n\n5\n. Given that AME-encoding genes can spread by transferable genetic elements\n18\n, this high proportion would be justified. Possibly, these resistance genes can spread between different gram-negative bacteria such as Pseudomonas aeruginosa and Enterobacteriaceae. Further confirmation of this hypothesis can be found in another study conducted in Iran on the clinical isolates of P. aeruginosa, according to which aadB and aacA4 were the most prevalent AME genes\n26\n. In a study performed by Lee et al. in Korea, the highest frequency was reported for aphA6 (71%), aacC1 (56%), and aadB (48%)\n27\n.\nIn addition, a high proportion of aphA6 and aadB was reported by Akers et al. in USA in agreement with the present study; almost 42% of their isolates were collected from the burn ward and ICU. However, the resistance rates toward gentamicin and amikacin in their isolates were 96.6% and 57.1%, respectively\n25\n. However, aphA6 confers resistance to amikacin and kanamycin\n17\n. Interestingly, 74% and 88.3% of our isolates containing aphA6 exhibited MIC values of ≥256 µg/mL for amikacin and kanamycin, respectively. Moreover, a study carried out in Poland revealed that aphA6 was the second most prevalent AME gene (78.7%) among 61 A. baumannii isolates\n28\n. However, aadB, the second most prevalent AME gene in the current study, confers resistance to gentamicin, tobramycin, and kanamycin in gram-negative bacteria\n26\n, while 95.8%, 93.1%, and 87.6% of our isolates containing aadB showed an MIC range of ≥256 µg/mL for these antibiotics, respectively. \nIn addition, we found that 33% of our isolates contained the aacA4 gene. Other research performed in the USA detected only one isolate carrying this gene from blood and wound infections that were resistant to gentamicin, tobramycin, and amikacin\n25\n. However, 96.9% of our aadB-positive isolates showed a ≥256 µg/mL MIC range for gentamicin and kanamycin; 87.8% of the isolates exhibited this MIC range for tobramycin, while in a previous study in Iran, this percentage was 26.4%\n5\n. It is noteworthy that this gene was reported as the second most prevalent AME gene carryied by class 1 integrons among clinical isolates of P. aeruginosa in Iran\n26\n. However, 83.6% of the aminoglycoside-resistant A. baumannii isolates in South Korea contained the aacA4 gene, while their MIC ranges were 64 to greater than 1024 µg/mL\n22\n. Sheikhalizadeh et al. reported that 27.6% isolated exhibited the aadA1 gene proportion, which was almost concordant with the results of the present study\n5\n, while another Iranian study detected 26.4%, 31%, and 54.5% exhibiting this gene among the sequence group (SG) of A. baumannii, 1, 2, and 3, respectively. Any isolates belonging to SG4-9 contained this resistance gene\n1\n. In addition, we detected that 19% of our isolates carried aacC2, while Akers et al. reported a 3.7% proportion of this gene\n25\n, and another Iranian study detected a proportion of 8.04% for this gene owing to which all isolates were non-susceptible to kanamycin\n5\n. However, 89.4% of our isolates containing this gene showed an MIC range of ≥256 µg/mL for spectinomycin, streptomycin, kanamycin, and tobramycin, while 100% of them exhibited this MIC range for gentamicin. Moreover, according to research by Hasani et al., this gene was detected in SG1-4\n1\n, while Nowak et al. did not detect this gene among their isolates\n28\n.\nAdditionally, the armA gene, which is an effective factor in the development of resistance to aminoglycosides in A. baumannii, can be placed on plasmids and frequently recognized in carbapenem-resistant isolates\n18\n. This gene encodes a 16S rRNA methylase, resulting in limited access of aminoglycosides to the ribosome of the bacteria and causing high-level aminoglycoside resistance (HLAR) against gentamicin, tobramycin, amikacin, and kanamycin\n1\n. Surprisingly, among 22 armA-positive A. baumannii isolates in this study, 21 (95.4%), 16 (72.7%), 18 (81.8%), and 21 (95.4%) isolates showed high-level resistance (MIC≥256 µg/mL) to gentamicin, tobramycin, amikacin, and kanamycin, respectively, with an MIC50≥128 μg/mL. Considering that most isolates in the present study were MDR, and a high proportion of strains harboring the AME genes was detected, the simultaneous presence of carbapenem-resistance genes and AME genes in A. baumannii has been proven\n18\n\n,\n\n28\n; this assumption may also be true for our isolates. However, 75-97% of our isolates were resistant to carbapenems and other β-lactams. Other studies from South Korea, Iran, and North America have reported armA production by A. baumannii\n\n1\n\n,\n\n27\n\n,\n\n29\n. Additionally, other researchers have revealed the role of the armA gene in high-level resistance to amikacin and gentamicin\n22\n\n,\n\n30\n.\nIn addition to the material presented, the most important problem observed in our study was the simultaneous presence of aminoglycoside resistance genes. We detected 22 gene profiles, while Nowak et al. detected only 3 combinations of AME genes from 61 carbapenem-resistant and aminoglycoside non-susceptible A. baumannii isolates\n28\n. Our most prevalent combinations were APH(3')-VIa+ANT(2\")-Ia (39 isolates) with 95-100% resistance rates against aminoglycosides and AAC(3)-IIa+AAC(6')-Ib+ANT(3\")-Ia+APH(3')-VIa+ANT(2\")-Ia (14 isolates) of which 93-100% were resistant to aminoglycosides. The common point between our study and the study by Nowak et al. was the presence of aphA6 among most of the isolates. However, Akers et al. detected 16 AME gene profiles, of which 12 (75%) isolates had a combination of these genes\n25\n. The most prevalent (38/107 isolates) combination of their study included APH(3')-Ia +ANT(2'')-Ia, and 35 (92.1%) were concurrently resistant to gentamicin, tobramycin, and amikacin. Nevertheless, 85% of our A. baumannii isolates carried more than one AME gene, of which 52 (61.1%) contained 2 AME genes concurrently and most of them were resistant to all tested aminoglycosides. Moreover, we found that as the number of AME genes increased, the likelihood of resistance to aminoglycosides, especially gentamicin, tobramycin, streptomycin, and kanamycin, increased. Due to the higher proportion of strains harboring the AME genes, especially aph, it may be better to use phosphotransferases and acetyltransferase inhibitors such as the bovine antimicrobial peptide indolicidin, as previously reported\n31\n, in combination with aminoglycosides in our region, Iran. ", "High-level aminoglycoside MIC ranges in isolates with the simultaneous presence of AME and ArmA-encoding genes indicated the importance of these genes in resistance to aminoglycosides in A. baumannii. However, it seems that the selection of the appropriate antibiotic based on antimicrobial susceptibility testing and the use of combination therapy would be effective in overcoming this problem in such countries. Therefore, it is necessary to collect data from monitoring studies for the prevention, treatment, and control of the infections caused by this microorganism." ]

[ "intro", "methods", null, null, null, null, "results", null, null, null, "discussion", "conclusions" ]

[ "Acinetobacter baumannii", "Aminoglycoside-modifying enzymes", "ArmA", "Aminoglycoside resistance" ]

[ 280, 215, 314, 54, 185, 1237, 1503 ]

[ "100", "100 100", "100 100 100", "isolates", "resistant", "resistance", "apha6", "12", "genes", "gene" ]

[ "introduction acinetobacter baumannii", "acinetobacter baumannii clinical", "baumannii antibiotics increased", "resistant baumannii isolates", "resistance aminoglycosides baumannii" ]

[ "Adolescent", "Adult", "Alcohol Drinking", "Case-Control Studies", "Comorbidity", "Delivery, Obstetric", "Eclampsia", "Educational Status", "Female", "Humans", "Kenya", "Maternal Mortality", "Medical Audit", "Middle Aged", "Parity", "Pregnancy", "Prenatal Care", "Referral and Consultation", "Retrospective Studies", "Risk Factors", "Tachycardia", "Tertiary Care Centers", "Young Adult" ]

[ "Background", "Statistical analyses", "Abbreviations", "Competing interests", "Authors’ contributions", "Pre-publication history" ]

[ "The maternal mortality ratio (MMR) is defined as “the ratio of the number of maternal deaths during a given period per 100,000 live births during the same time-period”. The global MMR is 210 per 100,000 live births [1]. Despite worldwide declines since 1990, the MMR is 15 times higher in developing than developed regions [1]. Sub-Saharan Africa has the highest MMR at 500 per 100,000 live births. In developed regions the MMR is 16 per 100,000 live births [1]. The target for Millennium Development Goal (MDG) Five is to reduce the global MMR by three quarters and to achieve universal access to reproductive health by 2015 [2]. In Kenya, the MMR has remained at 400-600 per 100,000 live births over the past decade - resulting in little or no progress being made towards achieving MDG Five [1,3].\nThe main direct causes of maternal death in developing countries include haemorrhage, sepsis, obstructed labour and hypertensive disorders [4]. The risk of death from haemorrhage is one in 1,000 deliveries in developing countries, compared with one in 100,000 in developed countries, and accounts for one third of the maternal deaths in Africa [5]. A study in Canada found increased risk of eclampsia among women with existing heart disease and anaemia [6]. A retrospective study undertaken at a tertiary hospital in Nigeria in 2007 found that the most common risk factors for maternal mortality were primaparity, haemorrhage, anaemia, eclampsia and malaria [7]. Risk factors for complications arising from infections include birthing under unhygienic conditions, poor nutrition, anaemia, caesarean section, membrane rupture, prolonged labour, retained products and haemorrhage [8].\nIn developing countries, indirect causes of maternal death include both previously existing diseases and diseases that develop during pregnancy. These include HIV, malaria, tuberculosis, diabetes, and cardiovascular disease, all of which and have an enormous impact on maternal and fetal outcomes during pregnancy [4].\nMany individual and socioeconomic factors have been associated with high maternal mortality. These include lack of education, parity, previous obstetric history, employment, socioeconomic status, and types of care seeking behaviours during pregnancy. There is also evidence of increased risk of death among women who are less than 24 and older than 35 years [9]. A study in Tanzania found that low level of spouse education was a risk factor for maternal mortality [10]. Lack of knowledge regarding the need for skilled attendants is a barrier to women seeking care, especially during birth emergencies. A survey conducted in Kenya in 2006 showed that 15% of pregnant women were not informed of the importance of hospital deliveries [11]. In Nigeria, a cross-sectional survey revealed that the most common risk factors for maternal death were primigravidity (19%), and unbooked status (19%) [12]. Poverty has also been associated with adverse maternal outcomes, not directly, but as a contributor to maternal ability to access and utilise care where complications occur [13,14]. There is also evidence that contraceptive use is efficient for the primary prevention of maternal mortality in developing countries by about 44% [15].\nAntenatal care (ANC) is very important during pregnancy. International organizations recommend a minimum of four visits, the administration of two doses of tetanus toxoid and folic acid supplementation during ANC attendance [16]. When women receive good care during the pre-partum period, they have been shown to be at less risk of maternal morbidity and mortality, since they had a higher likelihood of using a professional health facility during birth [10,17].\nIn the Kenya Demographic and Health Survey (2008-2009), it was reported that 92% of women received ANC from a skilled provider (doctor, nurse, or midwife), especially those who were more educated and resided in urban areas [3]. The report further showed that 83% of women who visited public hospitals were required to pay for antenatal services, which may explain why only 47% of antenatal women attended the recommended four visits [3]. Women had also been required to pay for delivery services until June 2013, when the Kenyan government rolled out a program where pregnant women can receive free maternity services in public hospitals.\nHealth systems functioning with adequate equipment, resources and trained personnel to handle maternal complications can reduce the risks of mortality. In Africa maternal deaths are associated with delayed referrals for women from lower level facilities, and where referral systems are not well equipped to handle emergency obstetric care [18]. The presence of skilled attendants during birth is also important in managing life threatening complications. In Kenya, the use of skilled attendants at delivery is currently 50% [19].\nThe Delay Model by McCarthy and Maine is a conceptual framework that has been used to assess factors contributing to maternal mortality in developing countries [20]. This framework attributes mortality to certain determinants that contribute to the delay in deciding to seek care, the delay in reaching a health facility, and the delay in receiving quality care upon reaching a health facility. In Kenya there has been insufficient progress made towards achieving MDG Five. The aim of this study is to identify risk factors associated with maternal mortality in a tertiary level hospital in Kenya. Using a framework adapted from the Delay Model, this study analyses four sets of determinants: individual and socio-demographic, maternal history, reproductive or obstetric, and hospital admission/health system.", "Analyses were performed using Stata version 10.0 (Stata-Corp, College Station, TX, USA). Following initial data checking and exploratory analysis, univariable logistic regression analysis was conducted for each potential risk factor. The multivariable models initially included all variables with p < 0.2 in the univariable models. Backward stepwise multiple logistic regression was undertaken separately for the four groups of risk factors in the framework adapted from the Delay Model, (individual and socio-demographic; maternal history; reproductive or obstetric; and admission). Variables were removed from the models where p-values > = 0.1 on the Likelihood Ratio Test. The variables in each of the final models were then included in a combined model and removed where p-values > = 0.1 in order to derive a final parsimonious model. Odds ratios (ORs), 95% confidence intervals and p-values are reported for all models. The reference group was the category with the lowest expected risk of death, or if there were few cases in this category, the group with the majority of respondents.\nAssuming the probability of exposure in controls was 40% and the ratio of cases to controls was 1:2, with 80% power and a 5% level of significance, a sample of approximately 450 women (150 cases and 300 controls) was needed to detect an odds ratio of approximately 0.5 or 1.8.\nEthical approval was sought from the Human Research Ethics Committee (HREC) at the University of Newcastle and The Institute for Research and Ethics Committee (IREC) in Kenya.", "MDG: Millennium development goal; MMR: Maternal mortality ratio; ANC: Antenatal care; MTRH: Moi teaching and referral hospital; NBU: Newborn unit; WHO: World Health Organization; HIV: Human immunodeficiency virus.", "The authors declare that they have no competing interests.", "FY conceived the study. FY, CD, JB, JSW, and PN all contributed to the protocol design, questionnaire design and ethics application process. FY contributed in data collection and extraction. PN contributed in providing consultation and advice during data extraction. FY, CD, PN and JB contributed to data analysis and writing of the paper. CD, JB, FY and JSW contributed to the drafting and editing the paper. All authors contributed to reviewing the paper and approved the final version for publication.", "The pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1471-2393/14/38/prepub\n" ]

[ null, null, null, null, null, null ]

[ "Background", "Methods", "Statistical analyses", "Results", "Discussion", "Conclusions", "Abbreviations", "Competing interests", "Authors’ contributions", "Pre-publication history" ]

[ "The maternal mortality ratio (MMR) is defined as “the ratio of the number of maternal deaths during a given period per 100,000 live births during the same time-period”. The global MMR is 210 per 100,000 live births [1]. Despite worldwide declines since 1990, the MMR is 15 times higher in developing than developed regions [1]. Sub-Saharan Africa has the highest MMR at 500 per 100,000 live births. In developed regions the MMR is 16 per 100,000 live births [1]. The target for Millennium Development Goal (MDG) Five is to reduce the global MMR by three quarters and to achieve universal access to reproductive health by 2015 [2]. In Kenya, the MMR has remained at 400-600 per 100,000 live births over the past decade - resulting in little or no progress being made towards achieving MDG Five [1,3].\nThe main direct causes of maternal death in developing countries include haemorrhage, sepsis, obstructed labour and hypertensive disorders [4]. The risk of death from haemorrhage is one in 1,000 deliveries in developing countries, compared with one in 100,000 in developed countries, and accounts for one third of the maternal deaths in Africa [5]. A study in Canada found increased risk of eclampsia among women with existing heart disease and anaemia [6]. A retrospective study undertaken at a tertiary hospital in Nigeria in 2007 found that the most common risk factors for maternal mortality were primaparity, haemorrhage, anaemia, eclampsia and malaria [7]. Risk factors for complications arising from infections include birthing under unhygienic conditions, poor nutrition, anaemia, caesarean section, membrane rupture, prolonged labour, retained products and haemorrhage [8].\nIn developing countries, indirect causes of maternal death include both previously existing diseases and diseases that develop during pregnancy. These include HIV, malaria, tuberculosis, diabetes, and cardiovascular disease, all of which and have an enormous impact on maternal and fetal outcomes during pregnancy [4].\nMany individual and socioeconomic factors have been associated with high maternal mortality. These include lack of education, parity, previous obstetric history, employment, socioeconomic status, and types of care seeking behaviours during pregnancy. There is also evidence of increased risk of death among women who are less than 24 and older than 35 years [9]. A study in Tanzania found that low level of spouse education was a risk factor for maternal mortality [10]. Lack of knowledge regarding the need for skilled attendants is a barrier to women seeking care, especially during birth emergencies. A survey conducted in Kenya in 2006 showed that 15% of pregnant women were not informed of the importance of hospital deliveries [11]. In Nigeria, a cross-sectional survey revealed that the most common risk factors for maternal death were primigravidity (19%), and unbooked status (19%) [12]. Poverty has also been associated with adverse maternal outcomes, not directly, but as a contributor to maternal ability to access and utilise care where complications occur [13,14]. There is also evidence that contraceptive use is efficient for the primary prevention of maternal mortality in developing countries by about 44% [15].\nAntenatal care (ANC) is very important during pregnancy. International organizations recommend a minimum of four visits, the administration of two doses of tetanus toxoid and folic acid supplementation during ANC attendance [16]. When women receive good care during the pre-partum period, they have been shown to be at less risk of maternal morbidity and mortality, since they had a higher likelihood of using a professional health facility during birth [10,17].\nIn the Kenya Demographic and Health Survey (2008-2009), it was reported that 92% of women received ANC from a skilled provider (doctor, nurse, or midwife), especially those who were more educated and resided in urban areas [3]. The report further showed that 83% of women who visited public hospitals were required to pay for antenatal services, which may explain why only 47% of antenatal women attended the recommended four visits [3]. Women had also been required to pay for delivery services until June 2013, when the Kenyan government rolled out a program where pregnant women can receive free maternity services in public hospitals.\nHealth systems functioning with adequate equipment, resources and trained personnel to handle maternal complications can reduce the risks of mortality. In Africa maternal deaths are associated with delayed referrals for women from lower level facilities, and where referral systems are not well equipped to handle emergency obstetric care [18]. The presence of skilled attendants during birth is also important in managing life threatening complications. In Kenya, the use of skilled attendants at delivery is currently 50% [19].\nThe Delay Model by McCarthy and Maine is a conceptual framework that has been used to assess factors contributing to maternal mortality in developing countries [20]. This framework attributes mortality to certain determinants that contribute to the delay in deciding to seek care, the delay in reaching a health facility, and the delay in receiving quality care upon reaching a health facility. In Kenya there has been insufficient progress made towards achieving MDG Five. The aim of this study is to identify risk factors associated with maternal mortality in a tertiary level hospital in Kenya. Using a framework adapted from the Delay Model, this study analyses four sets of determinants: individual and socio-demographic, maternal history, reproductive or obstetric, and hospital admission/health system.", "An unmatched case control study of women who delivered between January 2004 and March 2011 was conducted at Moi Teaching and Referral Hospital (MTRH) located in the Western region of the Rift Valley Province, Kenya [21]. As the second largest national hospital in Kenya with over 800 beds, MTRH provides a range of curative, preventive and rehabilitative health services to a population of about 400,000 inhabitants, and an indigent referral population of 16 million from Northern and Western Kenya [21]. The Mother and Baby Unit at MTRH at has an antenatal ward, post natal ward, labour ward, Newborn Unit (NBU) and two theatres dedicated for obstetrics. The bed capacity is approximately 20 for the antenatal and labour wards, and 50 for the post natal wards [21].\nCases (n = 150) were maternal deaths identified from a manual review of hospital records. Two controls (n = 300) were selected per case. Controls were surviving women who were admitted immediately preceding and following cases. Cases were selected retrospectively and sequentially from the most recent delivery until the required sample size was achieved. Trained staff collected information using a standard audit form. Abortion related deaths were excluded from the study.\nMaternal hospital death was the outcome. This was a clearly defined adverse event certified by medical personnel. The data collection form included: mother’s age, mother’s marital status, mother’s education, spouse’s education, mother’s occupation, spouse’s occupation, and the source of funding for the delivery. Information relating to the mother’s medical history included: smoking, alcohol use, contraceptive use, previous abortion, previous twins, gravida, and pre-existing medical conditions. Obstetric or reproductive factors were pregnancy stage, labour stage, number of ANC visits, and place of ANC care. Health system factors included mode of delivery, qualification of birth attendant, and referral from another facility (yes/no). Information on the mother’s admission factors comprised: clinical cause of death or diagnosis on admission (e.g. eclampsia, dystocia haemorrhage, or comorbid causes), diastolic blood pressure (millimetres of mercury/mm Hg), systolic blood pressure (mm HG), haemoglobin level (grams per decilitre g/dL), pulse rate (beats per minute/bpm), and temperature (degrees Celsius/°C). The primary obstetric cause of death was that documented in the patient hospital and post mortem records.\n Statistical analyses Analyses were performed using Stata version 10.0 (Stata-Corp, College Station, TX, USA). Following initial data checking and exploratory analysis, univariable logistic regression analysis was conducted for each potential risk factor. The multivariable models initially included all variables with p < 0.2 in the univariable models. Backward stepwise multiple logistic regression was undertaken separately for the four groups of risk factors in the framework adapted from the Delay Model, (individual and socio-demographic; maternal history; reproductive or obstetric; and admission). Variables were removed from the models where p-values > = 0.1 on the Likelihood Ratio Test. The variables in each of the final models were then included in a combined model and removed where p-values > = 0.1 in order to derive a final parsimonious model. Odds ratios (ORs), 95% confidence intervals and p-values are reported for all models. The reference group was the category with the lowest expected risk of death, or if there were few cases in this category, the group with the majority of respondents.\nAssuming the probability of exposure in controls was 40% and the ratio of cases to controls was 1:2, with 80% power and a 5% level of significance, a sample of approximately 450 women (150 cases and 300 controls) was needed to detect an odds ratio of approximately 0.5 or 1.8.\nEthical approval was sought from the Human Research Ethics Committee (HREC) at the University of Newcastle and The Institute for Research and Ethics Committee (IREC) in Kenya.\nAnalyses were performed using Stata version 10.0 (Stata-Corp, College Station, TX, USA). Following initial data checking and exploratory analysis, univariable logistic regression analysis was conducted for each potential risk factor. The multivariable models initially included all variables with p < 0.2 in the univariable models. Backward stepwise multiple logistic regression was undertaken separately for the four groups of risk factors in the framework adapted from the Delay Model, (individual and socio-demographic; maternal history; reproductive or obstetric; and admission). Variables were removed from the models where p-values > = 0.1 on the Likelihood Ratio Test. The variables in each of the final models were then included in a combined model and removed where p-values > = 0.1 in order to derive a final parsimonious model. Odds ratios (ORs), 95% confidence intervals and p-values are reported for all models. The reference group was the category with the lowest expected risk of death, or if there were few cases in this category, the group with the majority of respondents.\nAssuming the probability of exposure in controls was 40% and the ratio of cases to controls was 1:2, with 80% power and a 5% level of significance, a sample of approximately 450 women (150 cases and 300 controls) was needed to detect an odds ratio of approximately 0.5 or 1.8.\nEthical approval was sought from the Human Research Ethics Committee (HREC) at the University of Newcastle and The Institute for Research and Ethics Committee (IREC) in Kenya.", "Analyses were performed using Stata version 10.0 (Stata-Corp, College Station, TX, USA). Following initial data checking and exploratory analysis, univariable logistic regression analysis was conducted for each potential risk factor. The multivariable models initially included all variables with p < 0.2 in the univariable models. Backward stepwise multiple logistic regression was undertaken separately for the four groups of risk factors in the framework adapted from the Delay Model, (individual and socio-demographic; maternal history; reproductive or obstetric; and admission). Variables were removed from the models where p-values > = 0.1 on the Likelihood Ratio Test. The variables in each of the final models were then included in a combined model and removed where p-values > = 0.1 in order to derive a final parsimonious model. Odds ratios (ORs), 95% confidence intervals and p-values are reported for all models. The reference group was the category with the lowest expected risk of death, or if there were few cases in this category, the group with the majority of respondents.\nAssuming the probability of exposure in controls was 40% and the ratio of cases to controls was 1:2, with 80% power and a 5% level of significance, a sample of approximately 450 women (150 cases and 300 controls) was needed to detect an odds ratio of approximately 0.5 or 1.8.\nEthical approval was sought from the Human Research Ethics Committee (HREC) at the University of Newcastle and The Institute for Research and Ethics Committee (IREC) in Kenya.", "Table 1 shows the demographic factors associated with maternal mortality. In this model, mother’s age and mother’s education were significantly associated with mortality. Relative to controls, cases had three times the odds of being aged 35-45 years rather than 15-24 years (OR 3.1, 95% CI 1.5- 6.2; p < 0.0001). Cases had eight times the odds of having no education versus secondary education compared with controls (OR 8.0, 95% CI 4.0-16.3; p < 0.0001).\nIndividual and Socio-demographic risk factors for maternal mortality in a tertiary hospital in Kenya from January 2004 to March 2011\n*Spouse’s education was not included in the multiple regression model due to high cases of missing data and correlation with mothers education.\naAdjusted for variables included in the final demographic model.\n†P- value for Likelihood Ratio Test in the adjusted model.\nReference category for logistic regression represented by 1.\nNumbers may not add to total sample due to missing values.\nTable 2 shows the association between maternal history of prevailing conditions and obstetric and reproductive factors with maternal mortality. After adjusting for all other factors in the model, cases had higher odds than controls of having a history of maternal alcohol use (OR 2.5, 95% CI 1.2-5.3; p = 0.018), more than five previous pregnancies (OR 2.6, 95% CI 1.4-4.8; p = 0.0049), and a history of pre-existing illnesses (OR 3.0, 95% CI 1.7-5.3; p < 0.0001). Contraceptive use was protective (OR 0.3 95% CI 0.1-0.6; p = 0.0007). Table 2 also shows obstetric and reproductive characteristics associated maternal mortality. Compared to controls, cases had higher odds of assisted or caesarean deliveries (OR 3.0 95% CI 1.5-5.6; p < 0.0006). Cases had higher odds than controls of having a doctor, rather than a nurse or midwife attend the birth (OR 4.1 95% CI 2.2-7.6; p < 0.0001). Relative to controls, cases had almost nine times the odds of arriving at hospital at the puerperium stage (OR 8.9, 95% CI 3.5, 22.7; p < 0.0001), and almost six times the odds of lack of antenatal care (OR 5.7, 95% CI 2.6-12.4; p < 0.0001).\nMother’s history of prevailing conditions and obstetric characteristics associated with maternal mortality in a tertiary hospital in Kenya from January 2004 to March 2011\n*These include: HIV, malaria, rheumatic heart disease, cardiovascular disease, and diabetes.\n†P- value for Likelihood Ratio Test in the adjusted model.\naAdjusted for variables included in the final demographic model.\nReference category for logistic regression represented by 1.\n††Did not include ANC place because of high correlation with number of ANC visits.\nNumbers may not add to total sample due to missing values.\nTable 3 shows maternal admission factors associated with mortality. Admission from comorbid complications (OR 6.7, 95% CI 3.8-11.8; p < 0.0001), eclampsia (OR 4.7, 95% CI 1.6, 13.7; p = 0.0038), non-normal blood pressure (OR 7.5, 95% CI 1.5-37.7; p = 0.0039), tachycardia (OR 16.5, 95% CI 4.8-57.3; p < 0.0001), and being referred to MTRH (OR 3.3, 95% CI 1.9-5.7; p < 0.0001) were all statistically significant risk factors for maternal mortality.\nMaternal admission factors associated with maternal mortality in a tertiary hospital in Kenya from January 2004 to March 2011\n**Diastolic blood pressure was used as a proxy for systolic because of high correlation.\n†Temp and haemoglobin were omitted in the adjusted model because of too many missing values.\n††P- value for Likelihood Ratio Test in the adjusted model.\naAdjusted for variables included in the final demographic model.\nReference category for logistic regression represented by 1.\nNumbers may not add to total sample due to missing values.\nTable 4 shows the multivariable analysis combining all factors from the previous models. Statistically significant risk factors for maternal mortality included: no education, relative to secondary education (OR 3.3, 95% CI 1.1-10.4, p = 0.0284), history of pre-existing medical conditions (OR 3.9, 95% CI 1.7-9.2, p = 0.0016), doctor attendance at birth (OR 4.6, 95% CI 2.1-10.1, p = 0.0001), having no antenatal visits (OR 4.1, 95% CI 1.6-10.4, p = 0.0007), being admitted with eclampsia (OR 10.9 95% CI 3.7-31.9, p < 0.0001), having comorbid complications on admission (OR 9.0, 95% CI 4.2-19.3, p < 0.0001), having elevated pulse (OR 10.7, 95% CI 2.7-43.4, p = 0.0002), and being referred to MTRH (OR 2.1, 95% CI 1.0-4.3, p = 0.0459).\nMultivariable model showing risk factors for maternal mortality in a tertiary hospital in Kenya from January 2004 to March 2011\nFinal model included all variables with P-value of 0.1 or less on the likelihood ratio test.\nReference category for logistic regression represented by 1.\n†These include: HIV, malaria, rheumatic heart disease, cardiovascular disease, and diabetes.\nNumbers may not add to total sample due to missing values; the final model included 367 observations.", "In the multivariable analysis of each of the four groups of risk factors (socio-demographic, maternal history, reproductive/ obstetric and admission factors) variables significantly associated with maternal mortality included: age, education, alcohol use, contraceptive use, gravida, pre-existing medical conditions, mode of delivery, type of birth attendant, pregnancy stage, number of ANC visits, having comorbid complications on admission, eclampsia, diastolic blood pressure, elevated pulse, and referral status. However, in the final model combining only significant factors from the four separate sets of analyses into a parsimonious model, only education, underlying medical conditions, birth attendant, number of ANC visits, having comorbid complications, eclampsia, having an elevated pulse on admission, and referral status were significant risk factors for maternal mortality.\nCases had three times the odds of having no education versus secondary education compared with controls. This is in agreement with another study that also reported a higher risk of mortality among illiterate women [22]. This finding is important since it emphasizes the role of education for both the mother and her spouse in obtaining and understanding the benefits of good health and being able to make appropriate decisions during pregnancy. It is important to note that despite the woman’s weaker role in decision-making in African settings, education has a strong influence on mortality. In this study, we used mother’s education as a proxy for the husband’s education. Although there was considerable missing data for spouse’s education, there was correlation between these two education variables.\nHaving no antenatal care during pregnancy was associated with mortality in this study, a finding which corresponds with those of other studies [22,23]. Antenatal care is important in screening for pre-existing illnesses and complications in the early stages of pregnancy that could impact adversely during pregnancy and childbirth [24]. Since ANC coverage is high in Kenya, there is a need to scale up interventions that empower women to make at least four visits during pregnancy as recommended by international organizations [16,19].\nThe findings here were that comorbid conditions including HIV, malaria, rheumatic heart disease, cardiovascular disease, and diabetes contributed to maternal deaths. This is contrary to other research showing that direct pregnancy complications are the leading causes of maternal deaths [4]. However, other research shows that the significant increase in MMRs in Sub-Saharan Africa are predominantly due to increasing HIV prevalence in that region [25]. The finding that the odds of comorbid conditions were higher in cases than controls also demonstrates the importance of ANC for screening, detection and management of underlying illnesses that could potentially pose a threat to the mother during pregnancy and childbirth.\nContraceptive use was protective for maternal mortality, which coincides with findings from another study that found that maternal mortality would be 77% higher globally in the absence of family planning programs and contraceptive use [15]. The role of contraceptives in tackling maternal mortality has been through reducing exposure to incidence of pregnancy, lowering hazards of fragility from high parity pregnancies, reducing vulnerability to abortion risks, and postponing pregnancies, especially in countries with high fertility rates [15].\nIn this study, hypertensive disorders during pregnancy were higher among cases than controls. Our study demonstrated increased odds of eclampsia in cases, which is in agreement with another study that found that the delay in diagnosis, triage, transport and treatment of eclampsia increases the risk of maternal death [26]. There is evidence that screening for hypertensive conditions during the antenatal period plays a significant role in reducing the risk of death to the mother [13]. This study also found higher odds of elevated pulse amongst cases, which could explain the increased risk of death due to eclampsia. After adjusting for other factors, haemorrhage was not significantly associated with mortality possibly as a result of hospital protocols for management of haemorrhage.\nThis study found health care system related factors that identified cases as being at risk including doctor attendance at birth and referrals. Cases had higher odds than controls of a doctor attending their delivery, potentially because they were diagnosed with the most difficult complications. This has been previously reported, especially in low resource settings where uptake of professional birth attendants is low hence women only seek help when the condition is critical or too late for the doctor to save their lives [4]. Cases had twice the odds of referral relative to controls, potentially because the number of referrals represented over half of the cases who were referred following complications of birth.\nThis study provides information that is important for the identification of risk factors that contribute to maternal mortality in the second largest referral hospital in Kenya. It also provides information that will aid in identifying areas of improving health facilities locally and nationally in terms of referrals, antenatal care, and the availability of skilled birth attendants who are able to manage pregnancy related complications. This study is timely given the free maternity program roll out in Kenya since June 2013. Importantly, these findings will inform policy makers about ways of strengthening the health system and promoting more hospital births.\nThis study has some limitations. Firstly, it only includes deaths that occurred during the hospital admission and therefore the risk factors identified here were specifically associated with in-hospital mortality. Pregnancy related mortality that occurs outside hospital may have other risk factors that were not identified here. Secondly, bias may have resulted from the misclassification of causes of death data and missing information in some fields.", "This study highlights risk factors for mortality at a tertiary hospital in Kenya showing the importance of antenatal care and maternal education in preventing maternal mortality. The findings are timely given Kenya’s limited progress towards achieving MDG Five by 2015. Antenatal visits provide opportunities for the detection of risk factors for eclampsia and other underlying illnesses that may put a mother at risk during birth. There is need to focus on integrated care throughout the pregnancy by improving women’s knowledge and empowering them to take an active role in their own health as well as gaining access to skilled care at birth and during pregnancy.", "MDG: Millennium development goal; MMR: Maternal mortality ratio; ANC: Antenatal care; MTRH: Moi teaching and referral hospital; NBU: Newborn unit; WHO: World Health Organization; HIV: Human immunodeficiency virus.", "The authors declare that they have no competing interests.", "FY conceived the study. FY, CD, JB, JSW, and PN all contributed to the protocol design, questionnaire design and ethics application process. FY contributed in data collection and extraction. PN contributed in providing consultation and advice during data extraction. FY, CD, PN and JB contributed to data analysis and writing of the paper. CD, JB, FY and JSW contributed to the drafting and editing the paper. All authors contributed to reviewing the paper and approved the final version for publication.", "The pre-publication history for this paper can be accessed here:\n\nhttp://www.biomedcentral.com/1471-2393/14/38/prepub\n" ]

[ null, "methods", null, "results", "discussion", "conclusions", null, null, null, null ]

[ "Maternal mortality", "Tertiary hospital", "Risk factors", "Kenya" ]

[ 1052, 302, 42, 10, 96, 16 ]

[ "maternal", "mortality", "risk", "factors", "cases", "95", "model", "maternal mortality", "95 ci", "ci" ]

[ "high maternal mortality", "risks mortality africa", "mortality developing countries", "maternal deaths africa", "mmr maternal mortality" ]

[ "Adult", "Aged", "Back Pain", "Child", "Humans", "Middle Aged", "Musculoskeletal Diseases", "Retrospective Studies", "Tuberculosis", "Turkey" ]

[ "Materials and methods", "Results", "Bursit case" ]

[ "This retrospective study was conducted as a descriptive single-center experience. Patients who were followed up in Osmaniye Tuberculosis Dispensary between 2004 and 2020 were screened from the dispensary records. This dispensary is a governmental institution located in the south of the country with a population of approximately 350,000 (~ 90 % urban and ~ 10 % rural) that provides free service (including medicines) to all patients. In addition, all TB patients living in this area have to apply to this dispensary to provide directly observed therapy since it is the only TB center in the city. Among them, cases with musculoskeletal system involvement were noted. All patients were diagnosed with tuberculosis according to the histopathologic examinations by a pathologist. The biopsy samples were evaluated grossly and processed using the routine histopathologic technique (fixation and paraffin embedding) and then stained with Haematoxylin-Eosin.\nDefinite diagnosis of musculoskeletal TB was made when either (I) joint aspiration or tissue biopsy/fine-needle aspiration cytology (FNAC) revealed acid-fast bacilli (AFB) detection by smear examination, (II) culture of the synovial fluid (or tissue aspirated) grew Mycobacterium tuberculosis, or (III) histopathology/FNAC revealed granulomatous lesions with or without caseation with AFB positivity. Probable TB was considered if there was no explicit evidence of AFB or granuloma, but clinical, radiological, and serological evidence suggested TB and the patient responded to empirical antitubercular therapy. Other tests include tuberculin skin sensitivity (PPD) test and TB interferon γ-assays. Radiology included X-ray, magnetic resonance imaging (MRI), or computed tomography (CT) of the organ involved. CT-guided biopsy or fine-needle aspiration and arthroscopic biopsies were also performed in some cases. The clinical and demographic characteristics, including age, gender, involvement location and duration of illness, presenting complaint, local examination findings, treatment outcome were retrieved and analyzed from the case files. TB is a notifiable disease, and data are recorded prospectively by our specialist TB nurses. Patients with missing data in terms of diagnosis, clinical follow-up, or treatment, and patients transferred to another tuberculosis center were excluded.\nTypical treatment of all cases included the use of a four-drug regimen: isoniazid, rifampicin, ethambutol, and pyrazinamide for the initiative phase of two months, followed by 4–10 months of continuation phase with isoniazid and rifampicin. If the case was a recurrence TB, streptomycin applied to the patients intravenously (IV) is added to the treatment regimen for first two months. The drug doses were adjusted according to the weight of the patients. Drug susceptibility testing for the first-line drugs was conducted for patients who did not respond to treatment, and regimens were changed based on the susceptibility results or strong clinical suspicion of unresponsiveness.\nStatistical analyses were performed using SPSS Statistics version 17.0 (IBM). The normality of data analyzed by using Kolmogorov-Smirnov. The descriptive statistics were reported as mean ± standard deviation, medians, and ranges (min-max).\nThe current study protocol was approved by the Local Ethics Committee of Adana City Training and Research Hospital (decision number: 321/2018). This study was exempted from informed consent form by this ethics committee, since it was a retrospective study. All authors confirmed that all methods were performed in accordance with the relevant guidelines and regulations.", "A total of 949 patients were followed up due to tuberculosis in our center between 2004 and 2020. Overall, 31 (3.2 % of all TB cases) patients (14 males, 17 females) with a mean age of 44.2 ± 16.7 years (ranges: 3 to 75 years) had musculoskeletal tuberculosis. Demographic features are summarized in Table 1.\nTable 1Demographic FeaturesVariablesResultsAge (years)a44.2± 16.749 (3-75)Gender n, (%) - Male14 (45.2) - Female17 (54.8)Age group (years) n, (%) - Pediatric (<18)1 (3.2) - Adult (18-64)28 (90.3) - Geriatric (≥ 65)2 (6.5)Race n, (%) - Turkish29 (93.5) - Afghanistan1 (3.2) - Syria1 (3.2)Occupation n, (%) - Housewife12 (38.7) - Butcher4 (12.9) - Student4 (12.9) - Retired4 (12.9) - Worker2 (6.5) - Farmer2 (6.5) - Cooker1 (3.2) - Craftsman1 (3.2) - Cooker1 (3.2)Family history of TB n, (%)6 (19.4)Concomitant Pulmonary TB n, (%)6 (19.4)TB Tuberculosisamean ± S.D and median (minimum-maximum)\nDemographic Features\nTB Tuberculosis\namean ± S.D and median (minimum-maximum)\nClinical presentations of the cases are given in Table 2. Back pain (n = 22, 70.9 %) and shoulder pain (n = 2, 6.5 %) were the most seen symptoms, and thoracic or lumbar vertebra was the most involved location (74.2 %). The example images of a shoulder and thoracic vertebra TB are also shown in Figs. 1 and 2, respectively. Most of the cases (58.1 %) were diagnosed in tertiary hospitals. The mean duration of treatment was 12.9 ± 5.5 months (range, 1.3 week to 27.7 months). Two of the patients had a recurrent illness and were treated with the HRZE + S (isoniazid, rifampicin, ethambutol, and pyrazinamide + IV streptomycin) regimen (Table 2).\nTable 2Clinical PresentationVariablen%Symptoms - Back pain2270.9 - Mass on back and neck13.2 - Knee pain13.2 - Wrist swelling13.2 - Chest pain13.2 - Hand pain13.2 - Shoulder pain26.5 - Abdominal/hip pain13.2Location - Vertebra2374.2 - Hand26.5 - Knee joint13.2 - Costa13.2 - Shoulder13.2 - Patella13.2 - Psoas muscle26.5Vertebra Location - Thoracic1032.3 - Lumbar1238.7 - Sacrococcygeal13.2Center of Diagnosis - Secondary1341.9 - Tertiary1858.1Treatment duration (months)a12.9± 5.5 & 12.0 (1.3-27.7)Outcome - Treatment completed2683.8 - Treatment is going on39.6 - Exitus13.2 - Leaved treatment13.2Primer TB2993.5Recurrence TB26.5Treatment Regimen - HRZE2993.5 - HRZE + S26.5TB Tuberculosis, HRZE isoniazid + rifampicin + ethambutol + pyrazinamide, HRZE + S isoniazid + rifampicin + ethambutol + pyrazinamide + streptomycinamean ± S.D & median (percentile 25-percentile 75)Fig. 1Granulomatosis inflammatory reaction against M. tuberculosis with caseous necrosis (white arrow) seen in muscle tissue (M) (×200, H&E)Fig. 2Granulomatous inflammatory reaction involving caseified necrosis that destroys bone tissue (x200, H&E)\nClinical Presentation\nTB Tuberculosis, HRZE isoniazid + rifampicin + ethambutol + pyrazinamide, HRZE + S isoniazid + rifampicin + ethambutol + pyrazinamide + streptomycin\namean ± S.D & median (percentile 25-percentile 75)\nGranulomatosis inflammatory reaction against M. tuberculosis with caseous necrosis (white arrow) seen in muscle tissue (M) (×200, H&E)\nGranulomatous inflammatory reaction involving caseified necrosis that destroys bone tissue (x200, H&E)\nBursit case The example images are from a 49-year-old man who presented with fatigue, left shoulder pain, and restriction in shoulder movements (Fig. 3). Laboratory investigations yielded increased levels of C-reactive protein and erythrocyte sedimentation rate. MRI showed joint effusion, synovial hypertrophy, bursitis, rice bodies, and cortical erosions. The pathological examination was consistent with lymphohistiocytic inflammatory response, including giant cells and caseified necrosis in adipose and connective tissue (i.e., granulomatous inflammatory response). The patient was received a 12 month-treatment (2 months HRZE + 10 months HR). After 12 months of treatment, the patient’s treatment file was closed as ‘treatment completion’ after his complaints completely disappeared.\nFig. 3Preoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nPreoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nThe example images are from a 49-year-old man who presented with fatigue, left shoulder pain, and restriction in shoulder movements (Fig. 3). Laboratory investigations yielded increased levels of C-reactive protein and erythrocyte sedimentation rate. MRI showed joint effusion, synovial hypertrophy, bursitis, rice bodies, and cortical erosions. The pathological examination was consistent with lymphohistiocytic inflammatory response, including giant cells and caseified necrosis in adipose and connective tissue (i.e., granulomatous inflammatory response). The patient was received a 12 month-treatment (2 months HRZE + 10 months HR). After 12 months of treatment, the patient’s treatment file was closed as ‘treatment completion’ after his complaints completely disappeared.\nFig. 3Preoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nPreoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case", "The example images are from a 49-year-old man who presented with fatigue, left shoulder pain, and restriction in shoulder movements (Fig. 3). Laboratory investigations yielded increased levels of C-reactive protein and erythrocyte sedimentation rate. MRI showed joint effusion, synovial hypertrophy, bursitis, rice bodies, and cortical erosions. The pathological examination was consistent with lymphohistiocytic inflammatory response, including giant cells and caseified necrosis in adipose and connective tissue (i.e., granulomatous inflammatory response). The patient was received a 12 month-treatment (2 months HRZE + 10 months HR). After 12 months of treatment, the patient’s treatment file was closed as ‘treatment completion’ after his complaints completely disappeared.\nFig. 3Preoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nPreoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case" ]

[ null, null, null ]

[ "Introduction", "Materials and methods", "Results", "Bursit case", "Discussion", "Conclusions" ]

[ "Tuberculosis (TB) is a chronic infectious disease mainly caused by Mycobacterium tuberculosis. TB is a public health problem, especially in underdeveloped or developing countries, and remains a major cause of morbidity and mortality [1]. As reported by World Health Organization (WHO), the estimated global incidence of TB cases was 10.0 million in 2018 [2]. In Turkey, as a developing country, the TB incidence (the number of new TB cases per 100,000 populations per year) decreased annually, from 29.8 to 2005 to 14.4 in 2018 [3, 4].\nMore than 80 % of TB cases are pulmonary TB. However, several manifestations of extra-pulmonary TB have been reported, including musculoskeletal system TB [5]. Although pulmonary TB has been extensively covered in the literature [6–8], publications on musculoskeletal TB are relatively limited, and have been reported mostly in case reports [9–11]. In addition, the diagnosis of musculoskeletal system TB can be challenging, particularly for the physicians who are not familiar, because TB can present in atypical locations and mimic tumoral lesions [12]. Therefore, a biopsy should be taken to confirm the diagnosis.\nHistopathologically, a granulomatous inflammatory reaction that occurs against mycobacterial species is composed of epithelioid histiocytes, giant cells, lymphocytes, and some of them have centrally located caseous necrosis. The granuloma can not only occur against TB disease, but also against fungi as an infectious agent, sarcoidosis as an immunological reaction, or against a foreign body. Histochemically, Mycobacterium tuberculosis bacilli can be demonstrated inside the granuloma, mainly in necrotic areas with Erlich-Ziehl-Neelsen (EZN) stain to confirm the diagnosis. Also, the diagnosis can be made by culture and amplification of Mycobacterium tuberculosis DNA using the polymerase chain reaction (PCR) method. The diagnosis of musculoskeletal TB is usually tricky and could be delayed. A positive skin tuberculin test or abnormal chest radiograph will support the diagnosis though it is not excluded by negative results [13].\nRespecting immunosuppression, the incidence of musculoskeletal TB and all other extra-pulmonary TB infections depend on the grade of weakening cellular immunity. People with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) and latent TB are also far more likely to progress active disease, with a nearly 10 % risk of developing active disease each year, in comparison with a lifelong reactivation risk of about 5 % in an HIV/AIDS-negative population. As impairment of the immune system progresses, patients living with HIV/AIDS are more likely to develop extra-pulmonary TB, just like musculoskeletal TB [14].\nKeeping in mind the increased prevalence of TB in recent years [15], we deem it important to review musculoskeletal involvement of TB. Accordingly, the objective of this study was to elaborate musculoskeletal system TB as a single-center experience.", "This retrospective study was conducted as a descriptive single-center experience. Patients who were followed up in Osmaniye Tuberculosis Dispensary between 2004 and 2020 were screened from the dispensary records. This dispensary is a governmental institution located in the south of the country with a population of approximately 350,000 (~ 90 % urban and ~ 10 % rural) that provides free service (including medicines) to all patients. In addition, all TB patients living in this area have to apply to this dispensary to provide directly observed therapy since it is the only TB center in the city. Among them, cases with musculoskeletal system involvement were noted. All patients were diagnosed with tuberculosis according to the histopathologic examinations by a pathologist. The biopsy samples were evaluated grossly and processed using the routine histopathologic technique (fixation and paraffin embedding) and then stained with Haematoxylin-Eosin.\nDefinite diagnosis of musculoskeletal TB was made when either (I) joint aspiration or tissue biopsy/fine-needle aspiration cytology (FNAC) revealed acid-fast bacilli (AFB) detection by smear examination, (II) culture of the synovial fluid (or tissue aspirated) grew Mycobacterium tuberculosis, or (III) histopathology/FNAC revealed granulomatous lesions with or without caseation with AFB positivity. Probable TB was considered if there was no explicit evidence of AFB or granuloma, but clinical, radiological, and serological evidence suggested TB and the patient responded to empirical antitubercular therapy. Other tests include tuberculin skin sensitivity (PPD) test and TB interferon γ-assays. Radiology included X-ray, magnetic resonance imaging (MRI), or computed tomography (CT) of the organ involved. CT-guided biopsy or fine-needle aspiration and arthroscopic biopsies were also performed in some cases. The clinical and demographic characteristics, including age, gender, involvement location and duration of illness, presenting complaint, local examination findings, treatment outcome were retrieved and analyzed from the case files. TB is a notifiable disease, and data are recorded prospectively by our specialist TB nurses. Patients with missing data in terms of diagnosis, clinical follow-up, or treatment, and patients transferred to another tuberculosis center were excluded.\nTypical treatment of all cases included the use of a four-drug regimen: isoniazid, rifampicin, ethambutol, and pyrazinamide for the initiative phase of two months, followed by 4–10 months of continuation phase with isoniazid and rifampicin. If the case was a recurrence TB, streptomycin applied to the patients intravenously (IV) is added to the treatment regimen for first two months. The drug doses were adjusted according to the weight of the patients. Drug susceptibility testing for the first-line drugs was conducted for patients who did not respond to treatment, and regimens were changed based on the susceptibility results or strong clinical suspicion of unresponsiveness.\nStatistical analyses were performed using SPSS Statistics version 17.0 (IBM). The normality of data analyzed by using Kolmogorov-Smirnov. The descriptive statistics were reported as mean ± standard deviation, medians, and ranges (min-max).\nThe current study protocol was approved by the Local Ethics Committee of Adana City Training and Research Hospital (decision number: 321/2018). This study was exempted from informed consent form by this ethics committee, since it was a retrospective study. All authors confirmed that all methods were performed in accordance with the relevant guidelines and regulations.", "A total of 949 patients were followed up due to tuberculosis in our center between 2004 and 2020. Overall, 31 (3.2 % of all TB cases) patients (14 males, 17 females) with a mean age of 44.2 ± 16.7 years (ranges: 3 to 75 years) had musculoskeletal tuberculosis. Demographic features are summarized in Table 1.\nTable 1Demographic FeaturesVariablesResultsAge (years)a44.2± 16.749 (3-75)Gender n, (%) - Male14 (45.2) - Female17 (54.8)Age group (years) n, (%) - Pediatric (<18)1 (3.2) - Adult (18-64)28 (90.3) - Geriatric (≥ 65)2 (6.5)Race n, (%) - Turkish29 (93.5) - Afghanistan1 (3.2) - Syria1 (3.2)Occupation n, (%) - Housewife12 (38.7) - Butcher4 (12.9) - Student4 (12.9) - Retired4 (12.9) - Worker2 (6.5) - Farmer2 (6.5) - Cooker1 (3.2) - Craftsman1 (3.2) - Cooker1 (3.2)Family history of TB n, (%)6 (19.4)Concomitant Pulmonary TB n, (%)6 (19.4)TB Tuberculosisamean ± S.D and median (minimum-maximum)\nDemographic Features\nTB Tuberculosis\namean ± S.D and median (minimum-maximum)\nClinical presentations of the cases are given in Table 2. Back pain (n = 22, 70.9 %) and shoulder pain (n = 2, 6.5 %) were the most seen symptoms, and thoracic or lumbar vertebra was the most involved location (74.2 %). The example images of a shoulder and thoracic vertebra TB are also shown in Figs. 1 and 2, respectively. Most of the cases (58.1 %) were diagnosed in tertiary hospitals. The mean duration of treatment was 12.9 ± 5.5 months (range, 1.3 week to 27.7 months). Two of the patients had a recurrent illness and were treated with the HRZE + S (isoniazid, rifampicin, ethambutol, and pyrazinamide + IV streptomycin) regimen (Table 2).\nTable 2Clinical PresentationVariablen%Symptoms - Back pain2270.9 - Mass on back and neck13.2 - Knee pain13.2 - Wrist swelling13.2 - Chest pain13.2 - Hand pain13.2 - Shoulder pain26.5 - Abdominal/hip pain13.2Location - Vertebra2374.2 - Hand26.5 - Knee joint13.2 - Costa13.2 - Shoulder13.2 - Patella13.2 - Psoas muscle26.5Vertebra Location - Thoracic1032.3 - Lumbar1238.7 - Sacrococcygeal13.2Center of Diagnosis - Secondary1341.9 - Tertiary1858.1Treatment duration (months)a12.9± 5.5 & 12.0 (1.3-27.7)Outcome - Treatment completed2683.8 - Treatment is going on39.6 - Exitus13.2 - Leaved treatment13.2Primer TB2993.5Recurrence TB26.5Treatment Regimen - HRZE2993.5 - HRZE + S26.5TB Tuberculosis, HRZE isoniazid + rifampicin + ethambutol + pyrazinamide, HRZE + S isoniazid + rifampicin + ethambutol + pyrazinamide + streptomycinamean ± S.D & median (percentile 25-percentile 75)Fig. 1Granulomatosis inflammatory reaction against M. tuberculosis with caseous necrosis (white arrow) seen in muscle tissue (M) (×200, H&E)Fig. 2Granulomatous inflammatory reaction involving caseified necrosis that destroys bone tissue (x200, H&E)\nClinical Presentation\nTB Tuberculosis, HRZE isoniazid + rifampicin + ethambutol + pyrazinamide, HRZE + S isoniazid + rifampicin + ethambutol + pyrazinamide + streptomycin\namean ± S.D & median (percentile 25-percentile 75)\nGranulomatosis inflammatory reaction against M. tuberculosis with caseous necrosis (white arrow) seen in muscle tissue (M) (×200, H&E)\nGranulomatous inflammatory reaction involving caseified necrosis that destroys bone tissue (x200, H&E)\nBursit case The example images are from a 49-year-old man who presented with fatigue, left shoulder pain, and restriction in shoulder movements (Fig. 3). Laboratory investigations yielded increased levels of C-reactive protein and erythrocyte sedimentation rate. MRI showed joint effusion, synovial hypertrophy, bursitis, rice bodies, and cortical erosions. The pathological examination was consistent with lymphohistiocytic inflammatory response, including giant cells and caseified necrosis in adipose and connective tissue (i.e., granulomatous inflammatory response). The patient was received a 12 month-treatment (2 months HRZE + 10 months HR). After 12 months of treatment, the patient’s treatment file was closed as ‘treatment completion’ after his complaints completely disappeared.\nFig. 3Preoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nPreoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nThe example images are from a 49-year-old man who presented with fatigue, left shoulder pain, and restriction in shoulder movements (Fig. 3). Laboratory investigations yielded increased levels of C-reactive protein and erythrocyte sedimentation rate. MRI showed joint effusion, synovial hypertrophy, bursitis, rice bodies, and cortical erosions. The pathological examination was consistent with lymphohistiocytic inflammatory response, including giant cells and caseified necrosis in adipose and connective tissue (i.e., granulomatous inflammatory response). The patient was received a 12 month-treatment (2 months HRZE + 10 months HR). After 12 months of treatment, the patient’s treatment file was closed as ‘treatment completion’ after his complaints completely disappeared.\nFig. 3Preoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nPreoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case", "The example images are from a 49-year-old man who presented with fatigue, left shoulder pain, and restriction in shoulder movements (Fig. 3). Laboratory investigations yielded increased levels of C-reactive protein and erythrocyte sedimentation rate. MRI showed joint effusion, synovial hypertrophy, bursitis, rice bodies, and cortical erosions. The pathological examination was consistent with lymphohistiocytic inflammatory response, including giant cells and caseified necrosis in adipose and connective tissue (i.e., granulomatous inflammatory response). The patient was received a 12 month-treatment (2 months HRZE + 10 months HR). After 12 months of treatment, the patient’s treatment file was closed as ‘treatment completion’ after his complaints completely disappeared.\nFig. 3Preoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case\nPreoperative (left) and postoperative (right) Magnetic Rezonans Imaging (sagittal plane T1) of bursitis case", "The majority of extra-pulmonary TB cases involve the pleura, musculoskeletal and lymphatic systems. Most cases verified to have a previous pulmonary TB origin [16]. TB bacilli spread over the pleural cavity, starting from a pulmonary infection focus on that occasion, immigrates through the blood vessels or lymphatics to other organs developing extrapulmonary TB.\nThe distribution of age and site of musculoskeletal TB has been previously studied. Overall, of all TB cases, 1-4 % showed musculoskeletal involvement. In a retrospective and observational study conducted in a tertiary area with the highest prevalence of TB worldwide, three peaks in the first, third, and sixth decade of life were found [17]. However, some reports highlighted a bimodal age distribution [18]. In a nine-year single-center experience, the rate of musculoskeletal TB was found as 4 % [19]. In our study, the rate of musculoskeletal system TB was 3.2 %, which was compatible with the previous reports. On the other hand, the mean age was 44 years, and 90 % of all cases were adult patients. We did not find an age peak in childhood or in the advanced age group. Musculoskeletal TB is distributed almost equally among males and females in our sample group.\nIn the literature, most of the TB cases present with spine involvement. More than 10 % of patients with extra-pulmonary TB have skeletal involvement [20]. The most common form of skeletal TB is the Pott’s disease, comprising approximately half of musculoskeletal TB cases, and followed by TB arthritis and extra-spinal TB osteomyelitis [21]. In the retrospective study done by Held et al. [17], 78 % of the cases had spine TB while the remaining 21.6 % had extra-spinal diseases, comprising hip, knee, foot/ankle, shoulder, elbow, wrist, and others. The thoracic region of the vertebral column is commonly involved [20]. Likewise, in our study, the prevalent anatomically affected location was the spine (23 of 31 patients; 75 %) as well as hands, knee joint, costa, shoulder, patella, and psoas muscle. Only two cases in this study were involved with psoas muscle. These two cases had both concomitant pulmonary TB. The psoas muscle is a retroperitoneal muscle that originates from the lateral borders of T12 to L5 vertebrae and ends as a tendon that inserts into the lesser trochanter. The primary TB of iliopsoas compartment abscess with occult cause rarely encountered in the clinical practice and is generally idiopathic.\nThe joints’ TB infection comes after hematogenous spread or direct invasion from neighboring tissue of TB osteomyelitis. Mostly monoarticular joints are involved such as the knee and hip. Oligoarticular/polyarticular patterns are very rare, ranging from 5 to 15 % of cases, sometimes with small joint involvement, and ordinary in immunosuppressed patients [22]. A patient in our series (male, 38-year-old, butcher), TB was detected in the biopsy performed a long time after the local infectious swelling developed with a knife sticking in the left hand at the workplace.\nThe delays in the diagnosis of musculoskeletal TB have been sufficiently presented [13]. This may be due to the patients’ uncertain histories, perhaps complicated by inaccurate stories of irrelevant trauma, and lack of presence of a concomitant pulmonary involvement. In the current study, complaints of 70.9 % of cases have been started as back pain. Therefore, these patients have been investigated in neurosurgery, orthopedic and traumatology, or algology clinics for a long time. The patients usually presented with nonspecific symptoms such as back pain, mass on back/neck, knee pain, wrist swelling, chest pain, shoulder pain, hand pain, or abdominal pain.\nThere were some limitations to our work. Primarily, because of the retrospective nature of our study, it was not possible to obtain detailed information of every patient. Furthermore, because the patient registry systems of dispensaries and hospitals are not integrated, monitoring and clinical information, such as misdiagnosis or delayed diagnosis for other reasons, that are not recorded in the ledger could not be accessed. In Turkey, all patients suspected with TB, whether they are pediatrics or adults, are directed to dispensaries to confirm their diagnosis and to give their treatment. However, dispensaries do not have a chance to diagnose possible overlooked cases unless clinicians from other branches express suspicion of TB. Even if all musculoskeletal TB patients living in this area have to apply to this dispensary as it is the only TB center in the city, the present results should not be generalized to the entire Turkey.", "To sum up, although it is a very rare disease, surgeons and clinicians should be aware of the diagnosis of musculoskeletal TB infection especially the presence of pain not relieved despite the physical therapy and B symptoms such as fever and weight loss. Further, in these cases, confused with cancer clinically and radiologically, it would be appropriate to take a biopsy to make the diagnosis at an earlier stage and exclude the cancer. In addition, cases in which granulomatous inflammatory reaction is reported should be referred to the dispensary immediately. On the assumption that the diagnosis and treatment is delayed, spinal damage in addition to the bones and joints of the patients may be an inevitable consequence. Consequently, musculoskeletal TB needs a complex approach and cooperation." ]

[ "introduction", null, null, null, "discussion", "conclusion" ]

[ "Bursitis", "Extrapulmonary", "Pott's spine", "Tuberculosis dispensary" ]

[ 645, 1070, 183 ]

[ "tb", "cases", "patients", "musculoskeletal", "treatment", "tuberculosis", "diagnosis", "months", "pain", "musculoskeletal tb" ]

[ "musculoskeletal tb distributed", "musculoskeletal tb cases", "musculoskeletal tuberculosis", "diagnosis musculoskeletal tb", "incidence musculoskeletal tb" ]

[ "Hospital Mortality", "Hospitals", "Humans", "Noninvasive Ventilation", "Pulmonary Disease, Chronic Obstructive", "Respiration, Artificial", "Respiratory Insufficiency", "Retrospective Studies" ]

[ "Methods", "Setting", "Definitions", "Statistical Analysis", "Results", "Discussion", "Conclusions" ]

[ "This retrospective cohort study included AECOPD-related hospitalizations to acute-care VA hospitals between October 1, 2011 and September 30, 2017. This work was approved by the Institutional Review Boards and Research and Development Committee at the Iowa City VA Health Care System [IRB 201712713] as part of a larger study with a previous publication.10 The study was conducted in accordance with the Declaration of Helsinki. A waiver of informed consent was granted for this retrospective study because the study examined only existing patient-level data. Patients’ data were kept confidential.\nSetting We obtained data from the Veterans Informatics and Computing Infrastructure (VINCI), an integrated system that includes VA’s electronic health records and administrative data. Admissions to VA acute-care hospitals were identified via the Corporate Data Warehouse (CDW) using the inpatient domain. These datasets contain patient demographics, including residential address and ZIP code, diagnosis and procedure codes during admission, admission source, and admission and discharge dates.\nWe obtained data from the Veterans Informatics and Computing Infrastructure (VINCI), an integrated system that includes VA’s electronic health records and administrative data. Admissions to VA acute-care hospitals were identified via the Corporate Data Warehouse (CDW) using the inpatient domain. These datasets contain patient demographics, including residential address and ZIP code, diagnosis and procedure codes during admission, admission source, and admission and discharge dates.\nDefinitions We identified patients hospitalized with AECOPD based on the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM) and using the following criteria: 1) a principal diagnosis of COPD (ICD-9-CM codes: 490.x, 491.xx, 492.xx, and 496.xx; or ICD-10-CM codes: J41, J43, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J41.8, J42, or J44.9) or 2) a principal diagnosis of acute respiratory failure (518.81, 518.82, 518.84, or 799.1; ICD-10-CM: J96.00, J96.01, J96.02, J96.11, J96.12, J96.20, J96.21, J96.22, J96.90, J96.92, or R06.03) with a secondary AECOPD diagnosis (ICD-9-CM codes: 491.21, 491.22; ICD-10-CM codes: J44.1, J44.0).\nNIV use was defined using ICD-9-CM procedure code 93.90 or, for ICD-10, codes 5A09357, 5A09457, and 5A09557. IMV use was defined using ICD-9-CM procedure codes 96.04 and 96.70–96.72 or, for ICD-10, codes 0BH17EZ, 0BH18EZ, 5A1935Z, 5A1945Z, and 5A1955Z. Primary ventilation support was defined based on whether the patient received NIV and/or IMV within one day from the admission date.\nPatient rurality was defined using census tracts based on Rural Urban Commuting Area (RUCA) codes. RUCA codes reflect measures of urbanization, commuting, and population density.11–15 RUCA codes were further condensed to designate an area as urban (RUCA codes: 1 and 1.1), rural (RUCA codes: 2, 2.1, 3, 4, 4.1, 5, 5.1, 6, 7, 7.1, 7.2, 8, 8.1, 8.2, and 9), or isolated rural (RUCA codes: 10, 10.2, 10.2, and 10.3), using the categories defined by the VA Office of Rural Health.16 Travel time to the nearest VA hospital was determined from VA Planning Systems and Support Group (PSSG) geo-coded enrollment files. PSSG calculates distances to tertiary care VA sites for all enrolled patients using actual longitude and latitude coordinates of patient residences and the nearest VA hospitals. Travel time to the nearest VA hospital was estimated using geospatial technologies, which reflect roads and average driving conditions.11 Comorbidities were defined using ICD-9-CM and ICD-10-CM diagnosis codes within 1 year prior to admission in the inpatient, outpatient, or fee-basis data files, except for pneumonia, which was defined based on the presence of the corresponding diagnosis code during the hospitalization, as previously described.17 Severity of illness was quantified using a modified APACHE score (mAPACHE), which includes vital signs and commonly obtained laboratory values,12,18,19 and has also been externally validated.20 In brief, mAPACHE uses the same scoring assignments as APACHE III and includes all the predictor variables from the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas, and mechanical ventilator components. mAPACHE variables include age, comorbidities, mean arterial blood pressure, heart rate, respiratory rate, temperature, hematocrit, white blood cells, sodium, blood urea nitrogen, creatinine, glucose, albumin, and bilirubin. We retrieved all the data from electronic medical records, as described previously.12,18,19 Obstructive sleep apnea was defined using ICD-9 diagnosis code 327.2 or 780.57, or ICD-10 diagnosis codes G47.30–G47.39.\nHospital rurality was defined using the same methods as patient rurality, but the facilities in rural and isolated rural areas were collapsed to one category (rural). Hospital complexity was defined by VA as 1 (high resource), 2 (medium resource), and 3 (low resource).21 Hospital COPD-case volume was classified as high (above the median) or low (below the median) based on the total COPD volume during the study period. Hospital length of stay was calculated from the admission and discharge dates, in days. Hospital mortality was defined using the date of death listed in the VA Vital Status File and the occurrence of this date between the admission and discharge dates inclusive.\nWe included admissions with AECOPD at 121 VA acute-care facilities from 2011 to 2017. We excluded records admitted to facilities with complexity 3 (low resource) and newly opened facilities because these facilities may not provide ventilator support. We also excluded patients in hospices (V667/Z51.5 code within 1 year prior to the hospitalization date) and admissions of patients who received no oral or intravenous glucocorticoids during the hospitalizations.\nWe identified patients hospitalized with AECOPD based on the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM) and using the following criteria: 1) a principal diagnosis of COPD (ICD-9-CM codes: 490.x, 491.xx, 492.xx, and 496.xx; or ICD-10-CM codes: J41, J43, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J41.8, J42, or J44.9) or 2) a principal diagnosis of acute respiratory failure (518.81, 518.82, 518.84, or 799.1; ICD-10-CM: J96.00, J96.01, J96.02, J96.11, J96.12, J96.20, J96.21, J96.22, J96.90, J96.92, or R06.03) with a secondary AECOPD diagnosis (ICD-9-CM codes: 491.21, 491.22; ICD-10-CM codes: J44.1, J44.0).\nNIV use was defined using ICD-9-CM procedure code 93.90 or, for ICD-10, codes 5A09357, 5A09457, and 5A09557. IMV use was defined using ICD-9-CM procedure codes 96.04 and 96.70–96.72 or, for ICD-10, codes 0BH17EZ, 0BH18EZ, 5A1935Z, 5A1945Z, and 5A1955Z. Primary ventilation support was defined based on whether the patient received NIV and/or IMV within one day from the admission date.\nPatient rurality was defined using census tracts based on Rural Urban Commuting Area (RUCA) codes. RUCA codes reflect measures of urbanization, commuting, and population density.11–15 RUCA codes were further condensed to designate an area as urban (RUCA codes: 1 and 1.1), rural (RUCA codes: 2, 2.1, 3, 4, 4.1, 5, 5.1, 6, 7, 7.1, 7.2, 8, 8.1, 8.2, and 9), or isolated rural (RUCA codes: 10, 10.2, 10.2, and 10.3), using the categories defined by the VA Office of Rural Health.16 Travel time to the nearest VA hospital was determined from VA Planning Systems and Support Group (PSSG) geo-coded enrollment files. PSSG calculates distances to tertiary care VA sites for all enrolled patients using actual longitude and latitude coordinates of patient residences and the nearest VA hospitals. Travel time to the nearest VA hospital was estimated using geospatial technologies, which reflect roads and average driving conditions.11 Comorbidities were defined using ICD-9-CM and ICD-10-CM diagnosis codes within 1 year prior to admission in the inpatient, outpatient, or fee-basis data files, except for pneumonia, which was defined based on the presence of the corresponding diagnosis code during the hospitalization, as previously described.17 Severity of illness was quantified using a modified APACHE score (mAPACHE), which includes vital signs and commonly obtained laboratory values,12,18,19 and has also been externally validated.20 In brief, mAPACHE uses the same scoring assignments as APACHE III and includes all the predictor variables from the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas, and mechanical ventilator components. mAPACHE variables include age, comorbidities, mean arterial blood pressure, heart rate, respiratory rate, temperature, hematocrit, white blood cells, sodium, blood urea nitrogen, creatinine, glucose, albumin, and bilirubin. We retrieved all the data from electronic medical records, as described previously.12,18,19 Obstructive sleep apnea was defined using ICD-9 diagnosis code 327.2 or 780.57, or ICD-10 diagnosis codes G47.30–G47.39.\nHospital rurality was defined using the same methods as patient rurality, but the facilities in rural and isolated rural areas were collapsed to one category (rural). Hospital complexity was defined by VA as 1 (high resource), 2 (medium resource), and 3 (low resource).21 Hospital COPD-case volume was classified as high (above the median) or low (below the median) based on the total COPD volume during the study period. Hospital length of stay was calculated from the admission and discharge dates, in days. Hospital mortality was defined using the date of death listed in the VA Vital Status File and the occurrence of this date between the admission and discharge dates inclusive.\nWe included admissions with AECOPD at 121 VA acute-care facilities from 2011 to 2017. We excluded records admitted to facilities with complexity 3 (low resource) and newly opened facilities because these facilities may not provide ventilator support. We also excluded patients in hospices (V667/Z51.5 code within 1 year prior to the hospitalization date) and admissions of patients who received no oral or intravenous glucocorticoids during the hospitalizations.\nStatistical Analysis As NIV use may vary depending on the patient’s severity of illness (acuity), we calculated the risk-standardized NIV % to assess the NIV variation across the hospitals. We followed a similar approach to that used in a previous study.8 In brief, we created a hierarchical multivariable logistic regression model with NIV use within the first 24 hours of admission as the dependent variable (outcome) among records started on ventilation (records without NIV or invasive ventilation within first 24 hours were not used). Hospital was included as a random effect. Models were adjusted for severity of illness (mAPACHE) and several comorbidities (obstructive sleep apnea, presence of comorbid pneumonia, diabetes mellitus, congestive heart failure, hypertension, peripheral vascular disease, coronary artery disease, cancer, chronic kidney disease, stroke, and liver disease).22 Similarly to the approach used for publicly reported hospital performance metrics,23,24 we calculated the expected and predicted proportion of NIV use for each hospital using these models. Specifically, the expected rate is calculated based on hospital patient characteristics and does not include the hierarchical model hospital-specific random intercepts, whereas the predicted rate does incorporate hospital-specific random intercepts. The risk-standardized proportion of NIV use for each hospital was determined by multiplying the overall unadjusted proportion of NIV use by the ratio of the predicted to expected proportion for each hospital. Then, we stratified hospitals into quartiles based on hospital risk-standardized NIV % and compared patient characteristics among quartiles for trend and differences between Q1 and Q4. We used linear regression to examine trends between risk-standardized NIV % quartiles (Q1 to Q4) for continuous variables (p for trend), and a Cochran–Mantel–Haenszel test to examine trends between risk-standardized NIV % quartiles for categorical variables. The t-test (for continuous variables) and chi-squared test (for categorical variables) were also used to compare differences in characteristics between the lowest (Q1) and highest (Q4) quartiles of risk-standardized NIV %. Subsequently, we compared the risk-standardized NIV % using the Wilcoxon–Mann–Whitney test, and unadjusted hospital mortality using the chi-squared test between: 1) rural and urban, 2) low-volume and high-volume, and 3) complexity 2 (medium-resource) and complexity 1 (high-resource) hospitals. We also created a hierarchical logistic regression model to examine the association of hospital characteristics with hospital mortality adjusted for severity of illness and accounting for repeated admissions. All statistical analyses were conducted using SAS Enterprise Guide, 2014 (SAS Institute Inc).\nAs NIV use may vary depending on the patient’s severity of illness (acuity), we calculated the risk-standardized NIV % to assess the NIV variation across the hospitals. We followed a similar approach to that used in a previous study.8 In brief, we created a hierarchical multivariable logistic regression model with NIV use within the first 24 hours of admission as the dependent variable (outcome) among records started on ventilation (records without NIV or invasive ventilation within first 24 hours were not used). Hospital was included as a random effect. Models were adjusted for severity of illness (mAPACHE) and several comorbidities (obstructive sleep apnea, presence of comorbid pneumonia, diabetes mellitus, congestive heart failure, hypertension, peripheral vascular disease, coronary artery disease, cancer, chronic kidney disease, stroke, and liver disease).22 Similarly to the approach used for publicly reported hospital performance metrics,23,24 we calculated the expected and predicted proportion of NIV use for each hospital using these models. Specifically, the expected rate is calculated based on hospital patient characteristics and does not include the hierarchical model hospital-specific random intercepts, whereas the predicted rate does incorporate hospital-specific random intercepts. The risk-standardized proportion of NIV use for each hospital was determined by multiplying the overall unadjusted proportion of NIV use by the ratio of the predicted to expected proportion for each hospital. Then, we stratified hospitals into quartiles based on hospital risk-standardized NIV % and compared patient characteristics among quartiles for trend and differences between Q1 and Q4. We used linear regression to examine trends between risk-standardized NIV % quartiles (Q1 to Q4) for continuous variables (p for trend), and a Cochran–Mantel–Haenszel test to examine trends between risk-standardized NIV % quartiles for categorical variables. The t-test (for continuous variables) and chi-squared test (for categorical variables) were also used to compare differences in characteristics between the lowest (Q1) and highest (Q4) quartiles of risk-standardized NIV %. Subsequently, we compared the risk-standardized NIV % using the Wilcoxon–Mann–Whitney test, and unadjusted hospital mortality using the chi-squared test between: 1) rural and urban, 2) low-volume and high-volume, and 3) complexity 2 (medium-resource) and complexity 1 (high-resource) hospitals. We also created a hierarchical logistic regression model to examine the association of hospital characteristics with hospital mortality adjusted for severity of illness and accounting for repeated admissions. All statistical analyses were conducted using SAS Enterprise Guide, 2014 (SAS Institute Inc).", "We obtained data from the Veterans Informatics and Computing Infrastructure (VINCI), an integrated system that includes VA’s electronic health records and administrative data. Admissions to VA acute-care hospitals were identified via the Corporate Data Warehouse (CDW) using the inpatient domain. These datasets contain patient demographics, including residential address and ZIP code, diagnosis and procedure codes during admission, admission source, and admission and discharge dates.", "We identified patients hospitalized with AECOPD based on the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM) and using the following criteria: 1) a principal diagnosis of COPD (ICD-9-CM codes: 490.x, 491.xx, 492.xx, and 496.xx; or ICD-10-CM codes: J41, J43, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J41.8, J42, or J44.9) or 2) a principal diagnosis of acute respiratory failure (518.81, 518.82, 518.84, or 799.1; ICD-10-CM: J96.00, J96.01, J96.02, J96.11, J96.12, J96.20, J96.21, J96.22, J96.90, J96.92, or R06.03) with a secondary AECOPD diagnosis (ICD-9-CM codes: 491.21, 491.22; ICD-10-CM codes: J44.1, J44.0).\nNIV use was defined using ICD-9-CM procedure code 93.90 or, for ICD-10, codes 5A09357, 5A09457, and 5A09557. IMV use was defined using ICD-9-CM procedure codes 96.04 and 96.70–96.72 or, for ICD-10, codes 0BH17EZ, 0BH18EZ, 5A1935Z, 5A1945Z, and 5A1955Z. Primary ventilation support was defined based on whether the patient received NIV and/or IMV within one day from the admission date.\nPatient rurality was defined using census tracts based on Rural Urban Commuting Area (RUCA) codes. RUCA codes reflect measures of urbanization, commuting, and population density.11–15 RUCA codes were further condensed to designate an area as urban (RUCA codes: 1 and 1.1), rural (RUCA codes: 2, 2.1, 3, 4, 4.1, 5, 5.1, 6, 7, 7.1, 7.2, 8, 8.1, 8.2, and 9), or isolated rural (RUCA codes: 10, 10.2, 10.2, and 10.3), using the categories defined by the VA Office of Rural Health.16 Travel time to the nearest VA hospital was determined from VA Planning Systems and Support Group (PSSG) geo-coded enrollment files. PSSG calculates distances to tertiary care VA sites for all enrolled patients using actual longitude and latitude coordinates of patient residences and the nearest VA hospitals. Travel time to the nearest VA hospital was estimated using geospatial technologies, which reflect roads and average driving conditions.11 Comorbidities were defined using ICD-9-CM and ICD-10-CM diagnosis codes within 1 year prior to admission in the inpatient, outpatient, or fee-basis data files, except for pneumonia, which was defined based on the presence of the corresponding diagnosis code during the hospitalization, as previously described.17 Severity of illness was quantified using a modified APACHE score (mAPACHE), which includes vital signs and commonly obtained laboratory values,12,18,19 and has also been externally validated.20 In brief, mAPACHE uses the same scoring assignments as APACHE III and includes all the predictor variables from the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas, and mechanical ventilator components. mAPACHE variables include age, comorbidities, mean arterial blood pressure, heart rate, respiratory rate, temperature, hematocrit, white blood cells, sodium, blood urea nitrogen, creatinine, glucose, albumin, and bilirubin. We retrieved all the data from electronic medical records, as described previously.12,18,19 Obstructive sleep apnea was defined using ICD-9 diagnosis code 327.2 or 780.57, or ICD-10 diagnosis codes G47.30–G47.39.\nHospital rurality was defined using the same methods as patient rurality, but the facilities in rural and isolated rural areas were collapsed to one category (rural). Hospital complexity was defined by VA as 1 (high resource), 2 (medium resource), and 3 (low resource).21 Hospital COPD-case volume was classified as high (above the median) or low (below the median) based on the total COPD volume during the study period. Hospital length of stay was calculated from the admission and discharge dates, in days. Hospital mortality was defined using the date of death listed in the VA Vital Status File and the occurrence of this date between the admission and discharge dates inclusive.\nWe included admissions with AECOPD at 121 VA acute-care facilities from 2011 to 2017. We excluded records admitted to facilities with complexity 3 (low resource) and newly opened facilities because these facilities may not provide ventilator support. We also excluded patients in hospices (V667/Z51.5 code within 1 year prior to the hospitalization date) and admissions of patients who received no oral or intravenous glucocorticoids during the hospitalizations.", "As NIV use may vary depending on the patient’s severity of illness (acuity), we calculated the risk-standardized NIV % to assess the NIV variation across the hospitals. We followed a similar approach to that used in a previous study.8 In brief, we created a hierarchical multivariable logistic regression model with NIV use within the first 24 hours of admission as the dependent variable (outcome) among records started on ventilation (records without NIV or invasive ventilation within first 24 hours were not used). Hospital was included as a random effect. Models were adjusted for severity of illness (mAPACHE) and several comorbidities (obstructive sleep apnea, presence of comorbid pneumonia, diabetes mellitus, congestive heart failure, hypertension, peripheral vascular disease, coronary artery disease, cancer, chronic kidney disease, stroke, and liver disease).22 Similarly to the approach used for publicly reported hospital performance metrics,23,24 we calculated the expected and predicted proportion of NIV use for each hospital using these models. Specifically, the expected rate is calculated based on hospital patient characteristics and does not include the hierarchical model hospital-specific random intercepts, whereas the predicted rate does incorporate hospital-specific random intercepts. The risk-standardized proportion of NIV use for each hospital was determined by multiplying the overall unadjusted proportion of NIV use by the ratio of the predicted to expected proportion for each hospital. Then, we stratified hospitals into quartiles based on hospital risk-standardized NIV % and compared patient characteristics among quartiles for trend and differences between Q1 and Q4. We used linear regression to examine trends between risk-standardized NIV % quartiles (Q1 to Q4) for continuous variables (p for trend), and a Cochran–Mantel–Haenszel test to examine trends between risk-standardized NIV % quartiles for categorical variables. The t-test (for continuous variables) and chi-squared test (for categorical variables) were also used to compare differences in characteristics between the lowest (Q1) and highest (Q4) quartiles of risk-standardized NIV %. Subsequently, we compared the risk-standardized NIV % using the Wilcoxon–Mann–Whitney test, and unadjusted hospital mortality using the chi-squared test between: 1) rural and urban, 2) low-volume and high-volume, and 3) complexity 2 (medium-resource) and complexity 1 (high-resource) hospitals. We also created a hierarchical logistic regression model to examine the association of hospital characteristics with hospital mortality adjusted for severity of illness and accounting for repeated admissions. All statistical analyses were conducted using SAS Enterprise Guide, 2014 (SAS Institute Inc).", "Of 64,895 admissions with AECOPD exacerbations at 121 VA acute-care facilities from 2011 to 2017, 22,847 records met the exclusion criteria (Figure 1). The final cohort included 42,048 admissions in 106 VA hospitals and all of them were ICU admissions. We stratified these records by ventilator support within the first 24 hours of admission (primary ventilation support). Figure 1 also shows the outcomes of admissions stratified by ventilation support.Figure 1Patient flowchart and outcomes by ventilation support within one day from admission.Abbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation.\nPatient flowchart and outcomes by ventilation support within one day from admission.\nThe percentage of total admissions who received any ventilation support (NIV, IMV, or both) within the first 24 hours across all 106 hospitals ranged from 0.8% to 13.7%, with a median of 5.4% (interquartile interval [IQI]=3.7–7.8%) (Figure 2). The percentage of total admissions who received primary NIV ranged from 0% to 9%, with a median of 2.8% (IQI=1.2–4.5%). Among only those admissions who received ventilation support within the first 24 hours (primary NIV, IMV, or both), the unadjusted NIV % use ranged from 0 to 100, with a median of 54.7% (IQI=34.8–68.2%).Figure 2Percentage of ventilatory support within one day from admission across the 106 facilities.Abbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation.\nPercentage of ventilatory support within one day from admission across the 106 facilities.\nThe risk-standardized NIV % (the adjusted initial NIV of those admissions receiving ventilation support in the initial 24 hours) ranged from 10.8% to 86.6%, with a median of 58.0% (IQI=41.9–64.4%). Table 1 shows the characteristics of the hospitals stratified by risk-standardized NIV % into quartiles. Risk-standardized NIV % use accounted for severity of illness and comorbidities. Quartile 1 (Q1) included hospitals with the lowest risk-standardized NIV while quartile 4 (Q4) included hospitals with the highest risk-standardized NIV; other patient- and hospital-level characteristics are reported by risk-standardized NIV % quartiles. There were more black and rural patients in the highest risk-standardized NIV % relative to lower quartiles. Patients in the highest risk-standardized NIV % quartile had longer travel time to the nearest VA hospitals than those in the lowest quartiles. The IMV rates were lower and hospital length of stay was longer in hospitals in the highest NIV quartiles relative to the lowest quartiles.Table 1Patient and Hospital Characteristics of 106 Hospitals Stratified by Risk-Standardized Non-Invasive Ventilation Percentage Within One Day from AdmissionNIV Q1 (N=11,810)NIV Q2 (N=10,031)NIV Q3 (N=11,719)NIV Q4 (N=8488)p Value for Trendp Value Q1 vs Q4*Risk-standardized NIV %10.8–41.9%42.2–56.7%58.0–64.3%64.4–86.6%N hospital26272726Age, mean (SD)70.29 (8.69)69.92 (9.01)70.49 (9.74)70.33 (9.21)0.100.79Sex (female), N (%)507 (4.29)471 (4.70)476 (4.06)350 (4.12)0.110.55Race, N (%)<0.001<0.001 White9126 (77.30)7505 (74.82)9047 (77.20)6249 (73.62) Black1896 (16.05)1780 (17.74)1989 (16.97)1637 (19.29) Other364 (3.08)365 (3.64)262 (2.24)290 (3.42) Missing421 (3.56)381 (3.80)421 (3.59)312 (3.68)Patient residential location, N (%)<0.001<0.001 Urban7454 (63.12)6999 (69.77)7422 (63.33)5155 (60.73) Rural3549 (30.05)2416 (24.09)3525 (30.08)2717 (32.01) Isolated478 (4.05)347 (3.46)468 (3.99)354 (4.17) Missing329 (2.79)269 (2.68)304 (2.59)262 (3.09)Obstructive sleep apnea4372 (37.02)3895 (38.83)4568 (38.98)3059 (36.04)<0.0010.15Diabetes mellitus1964 (16.63)1643 (16.38)2349 (20.04)1614 (19.02)<0.001<0.001Congestive heart failure1532 (12.97)1386 (13.82)1759 (15.01)1179 (13.89)<0.0010.058Pneumonia6377 (54.00)5532 (55.15)6472 (55.23)4783 (56.35)0.010<0.001Hypertension665 (5.63)690 (6.88)1038 (8.86)795 (9.37)<0.001<0.001Peripheral vascular disease903 (7.65)746 (7.44)1028 (8.77)676 (7.96)0.0010.40Cancer931 (7.88)814 (8.11)1126 (9.61)788 (9.28)<0.001<0.001Coronary artery disease565 (4.78)569 (5.67)780 (6.66)530 (6.24)<0.001<0.001Chronic kidney disease770 (6.52)709 (7.07)925 (7.89)673 (7.93)<0.001<0.001Stroke627 (5.31)550 (5.48)727 (6.20)521 (6.14)0.0060.012Liver disease398 (3.37)338 (3.37)424 (3.62)320 (3.77)0.340.13Admission source, N (%)<0.001<0.001 Hospital515 (4.36)337 (3.36)371 (3.17)238 (2.82) Nursing home140 (1.19)135 (1.35)170 (1.45)131 (1.55) Other facility20 (0.17)11 (0.11)12 (0.10)26 (0.31) Outpatient11,100 (93.99)9494 (94.65)11,041 (94.21)7963 (94.44) Unknown35 (0.30)54 (0.54)125 (1.07)74 (0.88)Travel time to VAMC (min), mean (SD)67.26 (68.46)68.11 (73.11)72.51 (55.21)82.71 (76.47)<0.001<0.001Any IMV during hospital stay, N (%)604 (5.11)486 (4.84)437 (3.73)277 (3.26)<0.001<0.001mAPACHE, mean (SD)35.73 (11.95)35.00 (12.02)35.30 (11.91)35.63 (11.98)0.600.55Hospital mortality, N (%)303 (2.57)303 (3.02)315 (2.69)238 (2.80)0.210.30Hospital length of stay (days), mean (SD)5.54 (15.21)6.32 (27.04)7.13 (40.05)7.68 (43.30)<0.001<0.001Note: *Q1 vs Q4 using chi-squared or t-test.Abbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation; Q, quartile; SE, standard error; VAMC, Veterans Health Administration Medical Center.\n\nPatient and Hospital Characteristics of 106 Hospitals Stratified by Risk-Standardized Non-Invasive Ventilation Percentage Within One Day from Admission\nNote: *Q1 vs Q4 using chi-squared or t-test.\nAbbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation; Q, quartile; SE, standard error; VAMC, Veterans Health Administration Medical Center.\nTable 2 shows a risk-standardized NIV % of 55.1% (min=10.8%; max=86.6%) in urban hospitals, while the NIV % was 65.3.5% (min=34.2%; max=84.2%) in rural hospitals (p=0.047). There was no difference in risk-standardized NIV % between high- and low-COPD-volume facilities (p=0.33), or between complexity 1 (high-resource) and complexity 2 (medium-resource) hospitals (p=0.45).Table 2Risk-Standardized Non-Invasive Ventilation Percentage Stratified by Hospital CharacteristicsNumber of HospitalsNumber of PatientsRisk-Standardized NIV %, Median (Min–Max)p Value*Overall10642,04857.3 (10.8–86.6)Rural/urban0.047 Rural10254865.3 (34.2–84.2) Urban9639,50055.1 (10.8–86.6)Hospital volume0.33 Low5312,81359.90 (10.8–86.6) High5329,23553.43 (13.7–84.9)Hospital complexity0.45 2 (Medium resource)15294462.14 (20.4–86.6) 1 (High resource)9139,10455.71 (10.8–84.9)Note: *Wilcoxon two-sample test.\n\nRisk-Standardized Non-Invasive Ventilation Percentage Stratified by Hospital Characteristics\nNote: *Wilcoxon two-sample test.\nTable 3 shows unadjusted mortality stratified by hospital characteristics. After adjusting for patient acuity and taking into account repeated admissions, admission at an urban hospital was not associated with mortality (odds ratio [OR]=0.90; 95% CI=0.68 to 1.19, p=0.46) relative to admission at a rural hospital. Similarly, admission to a low-volume hospital was not associated with mortality (OR=1.00; 95% CI=0.86 to 1.18, p=0.96) relative to admission at a high-volume hospital, but admission to a medium-resource facility (complexity 2) was associated with increased mortality (OR=1.55; 95% CI=1.20 to 2.00, p<0.001) relative to admission at a high-resource facility.Table 3Unadjusted Mortality Stratified by Hospital CharacteristicsNumber of HospitalsNumber of PatientsUnadjusted Hospital Mortality, Deaths (%)p Value*Overall10642,0481159 (2.8%)Rural/urban0.22 Rural10254880 (3.14%) Urban9639,5001079 (2.73%)Hospital volume0.004 Low5312,813398 (3.11%) High5329,235761 (2.60%)Hospital complexity<0.001 2 (Medium resource)152944128 (4.35%) 1 (High resource)9139,1041031 (2.64%)Note: *Chi-squared test.\n\nUnadjusted Mortality Stratified by Hospital Characteristics\nNote: *Chi-squared test.", "In a cohort of 42,048 patients admitted with AECOPD to VA hospitals over 6 years, we observed considerable variation in NIV use across hospitals. After taking into account patient acuity using a validated critical illness severity score18,20,25 and comorbidities, we observed that hospitals in the highest NIV use quartile cared for more rural patients, used IMV less, and had longer length of hospital stay, but had no difference in mortality relative to hospitals in the lowest quartiles. The NIV use rate was higher in rural than in urban hospitals. Hospital mortality did not differ between rural and urban facilities.\nIt is well established that NIV reduces the need for IMV and hospital length of stay, and improves mortality in acute respiratory failure due to AECOPD26 and heart failure,27 but its role in other causes of respiratory failure, such as pneumonia, is controversial.28 In a study using 2011 claims data from California, the most common reason for NIV (26.1%) was pneumonia. Among hospitalizations with NIV as the primary ventilator support, hospitals that used NIV for strong-evidence conditions (COPD and heart failure) had lower NIV failure rates.9 We found that concomitant pneumonia was present in more than 50% of the AECOPD patients requiring NIV and/or IMV within 24 hours of admission. Although this may merely represent pneumonia overdiagnosis or using an ICD code to order a chest X-ray, pneumonia and AECOPD frequently coexist.29,30 In our analysis, we included only admissions with a primary diagnosis of AECOPD. We also adjusted for concomitant pneumonia when we calculated the risk-standardized NIV use because pneumonia is associated with worse outcomes.30\nSeveral observational studies conducted in various countries have reported approximately a five-fold increase in NIV use rates in the early 2010s compared to those in the early 2000s.31–33 Using the Premier inpatient database, Stefan et al showed that initial NIV use increased by 15.1% annually in the USA.33 In 2001, NIV use was 5.9% but by 2011 it was 14.8%. This increase concurred with a decline in IMV use. The widespread use of NIV is due to its easy application. This may be the reason why we observed an inverse association between NIV and IMV in the hospitals, and not necessarily that NIV reduced the need for IMV.\nA plethora of studies have shown significant variation in NIV use across hospitals.8,9,34 Mehta et al showed that the risk-standardized NIV % rate among 37,516 hospitalizations for AECOPD across 250 hospitals in California was 10% (IQI=1.6–35.7%).9 In another cross-sectional analysis of 77,576 hospitalizations across 386 US hospitals, the risk-standardized NIV % among those admissions requiring ventilation support ranged from 47.4% at the 10th percentile to 84.7% at the 90th percentile.8 We also observed significant variation even though we conducted the study in a single health system that has published guidelines to provide care for COPD patients.35\nNIV variation across hospitals may be related to the case mix of patients admitted to those hospitals. Large tertiary centers may care for very sick patients, and for that reason, IMV (and not NIV) is more frequently used there than in small rural hospitals. Adjusting for patient illness severity is crucial for studying NIV variation across hospitals. Prior reports studying risk-standardized NIV use were limited because patient acuity was not included in the analysis. Instead, comorbidities identified using ICD-9 codes were used as surrogates for severity of illness. In our analysis, we not only adjusted for comorbidities, but also accounted for severity of illness. Adjusting for patient acuity is critical as NIV may be underused or overused. NIV may be unnecessary in low-acuity patients, but it may be harmful in high-acuity patients who may require IMV, and delays in initiating IMV in these patients may increase the level of mortality.25,36 Our findings showed that rural hospitals used NIV more, which is in agreement with previous literature.8\nNumerous hospital factors may also be responsible for variations in NIV use. In the early years of NIV, lack of equipment was the main reason for underuse.37 Subsequent surveys showed that insufficient training of physicians was the main reason for NIV underuse, followed by lack of equipment.38 Inadequate training of respiratory therapists was another contributor. A survey of Canadian physicians conducted in 2003 did not show variation in practice among various facilities, but rather variation among specialties.39 A national survey of VA physicians and respiratory therapists in 2004 revealed that lack of training and experience was a significant factor in NIV underutilization.40 A study using a qualitative approach with in-depth interviews of 32 participants in seven US hospitals, including nurses, physicians, respiratory therapists, and leaders, showed that respiratory therapist autonomy, interdisciplinary teamwork, staff education, and the presence of policies and protocols were features of high-performing hospitals.41\nLindenauer et al demonstrated that among several hospital characteristics, including teaching status, staffing levels, and hospital location and volume, only the presence of intensivists and hospital rurality was associated with high risk-adjusted NIV use.8 Although we found that rural hospitals more frequently use NIV, we did not find any difference in risk-standardized NIV % between medium- and high-resource hospitals, or between low- and high-volume hospitals. We did find fewer black and rural patients in hospitals with higher risk-standardized NIV %. This finding may be related to how and where patients seek medical care (eg, rural patients may seek care in nearby low-resource hospitals). Racial disparities in treatment are also possible. A previous study in VA demonstrated that rates of IMV were higher in black than in white patients.42\nAnother finding of this study is that hospitals in the highest risk-standardized NIV % use quartiles had longer hospital length of stay and received more rural patients, indicating that these hospitals delay discharging rural patients (perhaps because of a lack of skilled nursing and rehabilitation facilities in the area).\nOur study has several limitations. It was conducted in a single health-care system, with most of the patients being male. Therefore, we should generalize our findings with caution. Because we did not have smoking exposure data or pulmonary function data, we cannot confirm the diagnosis of COPD. We cannot exclude the misdiagnosis of hypercapnic respiratory failure due to obesity hypoventilation as COPD-related respiratory failure. Nevertheless, we included only patients who received oral steroids and we took obstructive sleep apnea into account to calculate our risk-standardized NIV %. In addition, our previous work, which used less stringent ICD code criteria, showed an accuracy in identifying AECOPD of between 80% and 90%.10 We did not exclude patients with obstructive sleep apnea, as obstructive sleep apnea–COPD overlap is common, in particular among patients with advanced disease.43 Excluding those patients would have resulted in a sample that was not a good representative of the true population. We have no data regarding NIV modes used (eg, BIPAP). Physiological parameters such as partial pressure of CO2 in the arterial blood, which are indicators of patient acuity, were not available. Moreover, we did not have data from civilian hospitals. Veterans may have been hospitalized at civilian hospitals owing to ease of access or convenience. There were no available data regarding the staffing levels of hospitals, including whether a board-certified critical care physician or ICU telemedicine was available, which are associated with improved outcomes. The above limitations do not undermine the strengths of our study, which are the large sample size, the strict exclusion criteria, and the fact that we used an illness severity scoring system to calculate the risk-standardized NIV %.", "Among patients admitted with AECOPD in VA hospitals, NIV use varied significantly across hospitals, with rural hospitals having higher risk-standardized NIV % than urban ones. Hospitals with the highest NIV rates cared for more rural patients and had longer hospital length of stay than hospitals with the lowest NIV rates. Further research should investigate the exact mechanism of variation in NIV use between rural and urban hospitals." ]

[ null, null, null, null, null, null, null ]

[ "Introduction", "Methods", "Setting", "Definitions", "Statistical Analysis", "Results", "Discussion", "Conclusions" ]

[ "Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality in the USA1 and is associated with high resource utilization.2,3 COPD patients experience exacerbations of the disease (AECOPD), defined as acute worsening of respiratory symptoms according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines.2 Standard therapy for moderate AECOPDs includes bronchodilators, glucocorticoids, and antibiotics.\nSevere AECOPDs, defined as those requiring an emergency room visit or hospitalization, have a 1-year mortality of 26.2% and are associated with high rates of hospital readmission.4 AECOPD-related hospitalizations are also associated with reduced quality of life and are responsible for 70% of the total direct health-care costs for COPD.5,6 Severe AECOPD may lead to acute respiratory failure requiring supplemental oxygen and/or ventilation support in addition to standard therapy. The most effective treatment for acute respiratory failure due to severe AECOPD is non-invasive ventilation (NIV). NIV reduces the need for invasive mechanical ventilation (IMV) by 64% and mortality by 46%, according to a 2017 meta-analysis.7\nDespite the benefit of NIV in severe AECOPDs, NIV use in AECOPD-related hospitalizations varies significantly across the USA.8,9 This variation in NIV use may be due to hospital- and patient-level characteristics, such as the patient’s severity of illness (acuity). Facilities with high-acuity patients may use NIV more frequently. Nevertheless, prior studies examining NIV variation across US hospitals did not account for patient acuity.8,9 Variation in NIV use may also be related to the heterogeneity in its adoption across hospitals. High-resource hospitals may use NIV more frequently, as opposed to low-resource facilities, which may often transfer patients who require NIV because they do not feel comfortable treating these patients in case of NIV failure. We hypothesized that NIV varies significantly across US hospitals, even after adjusting for patient acuity. Our primary objective was to investigate NIV use in AECOPD-related hospitalizations across the Veterans Health Administration (VA) and assess whether patient characteristics (eg, demographics, residential location, and comorbidities) or hospital characteristics (eg, location, volume, and resources are) were associated with NIV use.", "This retrospective cohort study included AECOPD-related hospitalizations to acute-care VA hospitals between October 1, 2011 and September 30, 2017. This work was approved by the Institutional Review Boards and Research and Development Committee at the Iowa City VA Health Care System [IRB 201712713] as part of a larger study with a previous publication.10 The study was conducted in accordance with the Declaration of Helsinki. A waiver of informed consent was granted for this retrospective study because the study examined only existing patient-level data. Patients’ data were kept confidential.\nSetting We obtained data from the Veterans Informatics and Computing Infrastructure (VINCI), an integrated system that includes VA’s electronic health records and administrative data. Admissions to VA acute-care hospitals were identified via the Corporate Data Warehouse (CDW) using the inpatient domain. These datasets contain patient demographics, including residential address and ZIP code, diagnosis and procedure codes during admission, admission source, and admission and discharge dates.\nWe obtained data from the Veterans Informatics and Computing Infrastructure (VINCI), an integrated system that includes VA’s electronic health records and administrative data. Admissions to VA acute-care hospitals were identified via the Corporate Data Warehouse (CDW) using the inpatient domain. These datasets contain patient demographics, including residential address and ZIP code, diagnosis and procedure codes during admission, admission source, and admission and discharge dates.\nDefinitions We identified patients hospitalized with AECOPD based on the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM) and using the following criteria: 1) a principal diagnosis of COPD (ICD-9-CM codes: 490.x, 491.xx, 492.xx, and 496.xx; or ICD-10-CM codes: J41, J43, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J41.8, J42, or J44.9) or 2) a principal diagnosis of acute respiratory failure (518.81, 518.82, 518.84, or 799.1; ICD-10-CM: J96.00, J96.01, J96.02, J96.11, J96.12, J96.20, J96.21, J96.22, J96.90, J96.92, or R06.03) with a secondary AECOPD diagnosis (ICD-9-CM codes: 491.21, 491.22; ICD-10-CM codes: J44.1, J44.0).\nNIV use was defined using ICD-9-CM procedure code 93.90 or, for ICD-10, codes 5A09357, 5A09457, and 5A09557. IMV use was defined using ICD-9-CM procedure codes 96.04 and 96.70–96.72 or, for ICD-10, codes 0BH17EZ, 0BH18EZ, 5A1935Z, 5A1945Z, and 5A1955Z. Primary ventilation support was defined based on whether the patient received NIV and/or IMV within one day from the admission date.\nPatient rurality was defined using census tracts based on Rural Urban Commuting Area (RUCA) codes. RUCA codes reflect measures of urbanization, commuting, and population density.11–15 RUCA codes were further condensed to designate an area as urban (RUCA codes: 1 and 1.1), rural (RUCA codes: 2, 2.1, 3, 4, 4.1, 5, 5.1, 6, 7, 7.1, 7.2, 8, 8.1, 8.2, and 9), or isolated rural (RUCA codes: 10, 10.2, 10.2, and 10.3), using the categories defined by the VA Office of Rural Health.16 Travel time to the nearest VA hospital was determined from VA Planning Systems and Support Group (PSSG) geo-coded enrollment files. PSSG calculates distances to tertiary care VA sites for all enrolled patients using actual longitude and latitude coordinates of patient residences and the nearest VA hospitals. Travel time to the nearest VA hospital was estimated using geospatial technologies, which reflect roads and average driving conditions.11 Comorbidities were defined using ICD-9-CM and ICD-10-CM diagnosis codes within 1 year prior to admission in the inpatient, outpatient, or fee-basis data files, except for pneumonia, which was defined based on the presence of the corresponding diagnosis code during the hospitalization, as previously described.17 Severity of illness was quantified using a modified APACHE score (mAPACHE), which includes vital signs and commonly obtained laboratory values,12,18,19 and has also been externally validated.20 In brief, mAPACHE uses the same scoring assignments as APACHE III and includes all the predictor variables from the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas, and mechanical ventilator components. mAPACHE variables include age, comorbidities, mean arterial blood pressure, heart rate, respiratory rate, temperature, hematocrit, white blood cells, sodium, blood urea nitrogen, creatinine, glucose, albumin, and bilirubin. We retrieved all the data from electronic medical records, as described previously.12,18,19 Obstructive sleep apnea was defined using ICD-9 diagnosis code 327.2 or 780.57, or ICD-10 diagnosis codes G47.30–G47.39.\nHospital rurality was defined using the same methods as patient rurality, but the facilities in rural and isolated rural areas were collapsed to one category (rural). Hospital complexity was defined by VA as 1 (high resource), 2 (medium resource), and 3 (low resource).21 Hospital COPD-case volume was classified as high (above the median) or low (below the median) based on the total COPD volume during the study period. Hospital length of stay was calculated from the admission and discharge dates, in days. Hospital mortality was defined using the date of death listed in the VA Vital Status File and the occurrence of this date between the admission and discharge dates inclusive.\nWe included admissions with AECOPD at 121 VA acute-care facilities from 2011 to 2017. We excluded records admitted to facilities with complexity 3 (low resource) and newly opened facilities because these facilities may not provide ventilator support. We also excluded patients in hospices (V667/Z51.5 code within 1 year prior to the hospitalization date) and admissions of patients who received no oral or intravenous glucocorticoids during the hospitalizations.\nWe identified patients hospitalized with AECOPD based on the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM) and using the following criteria: 1) a principal diagnosis of COPD (ICD-9-CM codes: 490.x, 491.xx, 492.xx, and 496.xx; or ICD-10-CM codes: J41, J43, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J41.8, J42, or J44.9) or 2) a principal diagnosis of acute respiratory failure (518.81, 518.82, 518.84, or 799.1; ICD-10-CM: J96.00, J96.01, J96.02, J96.11, J96.12, J96.20, J96.21, J96.22, J96.90, J96.92, or R06.03) with a secondary AECOPD diagnosis (ICD-9-CM codes: 491.21, 491.22; ICD-10-CM codes: J44.1, J44.0).\nNIV use was defined using ICD-9-CM procedure code 93.90 or, for ICD-10, codes 5A09357, 5A09457, and 5A09557. IMV use was defined using ICD-9-CM procedure codes 96.04 and 96.70–96.72 or, for ICD-10, codes 0BH17EZ, 0BH18EZ, 5A1935Z, 5A1945Z, and 5A1955Z. Primary ventilation support was defined based on whether the patient received NIV and/or IMV within one day from the admission date.\nPatient rurality was defined using census tracts based on Rural Urban Commuting Area (RUCA) codes. RUCA codes reflect measures of urbanization, commuting, and population density.11–15 RUCA codes were further condensed to designate an area as urban (RUCA codes: 1 and 1.1), rural (RUCA codes: 2, 2.1, 3, 4, 4.1, 5, 5.1, 6, 7, 7.1, 7.2, 8, 8.1, 8.2, and 9), or isolated rural (RUCA codes: 10, 10.2, 10.2, and 10.3), using the categories defined by the VA Office of Rural Health.16 Travel time to the nearest VA hospital was determined from VA Planning Systems and Support Group (PSSG) geo-coded enrollment files. PSSG calculates distances to tertiary care VA sites for all enrolled patients using actual longitude and latitude coordinates of patient residences and the nearest VA hospitals. Travel time to the nearest VA hospital was estimated using geospatial technologies, which reflect roads and average driving conditions.11 Comorbidities were defined using ICD-9-CM and ICD-10-CM diagnosis codes within 1 year prior to admission in the inpatient, outpatient, or fee-basis data files, except for pneumonia, which was defined based on the presence of the corresponding diagnosis code during the hospitalization, as previously described.17 Severity of illness was quantified using a modified APACHE score (mAPACHE), which includes vital signs and commonly obtained laboratory values,12,18,19 and has also been externally validated.20 In brief, mAPACHE uses the same scoring assignments as APACHE III and includes all the predictor variables from the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas, and mechanical ventilator components. mAPACHE variables include age, comorbidities, mean arterial blood pressure, heart rate, respiratory rate, temperature, hematocrit, white blood cells, sodium, blood urea nitrogen, creatinine, glucose, albumin, and bilirubin. We retrieved all the data from electronic medical records, as described previously.12,18,19 Obstructive sleep apnea was defined using ICD-9 diagnosis code 327.2 or 780.57, or ICD-10 diagnosis codes G47.30–G47.39.\nHospital rurality was defined using the same methods as patient rurality, but the facilities in rural and isolated rural areas were collapsed to one category (rural). Hospital complexity was defined by VA as 1 (high resource), 2 (medium resource), and 3 (low resource).21 Hospital COPD-case volume was classified as high (above the median) or low (below the median) based on the total COPD volume during the study period. Hospital length of stay was calculated from the admission and discharge dates, in days. Hospital mortality was defined using the date of death listed in the VA Vital Status File and the occurrence of this date between the admission and discharge dates inclusive.\nWe included admissions with AECOPD at 121 VA acute-care facilities from 2011 to 2017. We excluded records admitted to facilities with complexity 3 (low resource) and newly opened facilities because these facilities may not provide ventilator support. We also excluded patients in hospices (V667/Z51.5 code within 1 year prior to the hospitalization date) and admissions of patients who received no oral or intravenous glucocorticoids during the hospitalizations.\nStatistical Analysis As NIV use may vary depending on the patient’s severity of illness (acuity), we calculated the risk-standardized NIV % to assess the NIV variation across the hospitals. We followed a similar approach to that used in a previous study.8 In brief, we created a hierarchical multivariable logistic regression model with NIV use within the first 24 hours of admission as the dependent variable (outcome) among records started on ventilation (records without NIV or invasive ventilation within first 24 hours were not used). Hospital was included as a random effect. Models were adjusted for severity of illness (mAPACHE) and several comorbidities (obstructive sleep apnea, presence of comorbid pneumonia, diabetes mellitus, congestive heart failure, hypertension, peripheral vascular disease, coronary artery disease, cancer, chronic kidney disease, stroke, and liver disease).22 Similarly to the approach used for publicly reported hospital performance metrics,23,24 we calculated the expected and predicted proportion of NIV use for each hospital using these models. Specifically, the expected rate is calculated based on hospital patient characteristics and does not include the hierarchical model hospital-specific random intercepts, whereas the predicted rate does incorporate hospital-specific random intercepts. The risk-standardized proportion of NIV use for each hospital was determined by multiplying the overall unadjusted proportion of NIV use by the ratio of the predicted to expected proportion for each hospital. Then, we stratified hospitals into quartiles based on hospital risk-standardized NIV % and compared patient characteristics among quartiles for trend and differences between Q1 and Q4. We used linear regression to examine trends between risk-standardized NIV % quartiles (Q1 to Q4) for continuous variables (p for trend), and a Cochran–Mantel–Haenszel test to examine trends between risk-standardized NIV % quartiles for categorical variables. The t-test (for continuous variables) and chi-squared test (for categorical variables) were also used to compare differences in characteristics between the lowest (Q1) and highest (Q4) quartiles of risk-standardized NIV %. Subsequently, we compared the risk-standardized NIV % using the Wilcoxon–Mann–Whitney test, and unadjusted hospital mortality using the chi-squared test between: 1) rural and urban, 2) low-volume and high-volume, and 3) complexity 2 (medium-resource) and complexity 1 (high-resource) hospitals. We also created a hierarchical logistic regression model to examine the association of hospital characteristics with hospital mortality adjusted for severity of illness and accounting for repeated admissions. All statistical analyses were conducted using SAS Enterprise Guide, 2014 (SAS Institute Inc).\nAs NIV use may vary depending on the patient’s severity of illness (acuity), we calculated the risk-standardized NIV % to assess the NIV variation across the hospitals. We followed a similar approach to that used in a previous study.8 In brief, we created a hierarchical multivariable logistic regression model with NIV use within the first 24 hours of admission as the dependent variable (outcome) among records started on ventilation (records without NIV or invasive ventilation within first 24 hours were not used). Hospital was included as a random effect. Models were adjusted for severity of illness (mAPACHE) and several comorbidities (obstructive sleep apnea, presence of comorbid pneumonia, diabetes mellitus, congestive heart failure, hypertension, peripheral vascular disease, coronary artery disease, cancer, chronic kidney disease, stroke, and liver disease).22 Similarly to the approach used for publicly reported hospital performance metrics,23,24 we calculated the expected and predicted proportion of NIV use for each hospital using these models. Specifically, the expected rate is calculated based on hospital patient characteristics and does not include the hierarchical model hospital-specific random intercepts, whereas the predicted rate does incorporate hospital-specific random intercepts. The risk-standardized proportion of NIV use for each hospital was determined by multiplying the overall unadjusted proportion of NIV use by the ratio of the predicted to expected proportion for each hospital. Then, we stratified hospitals into quartiles based on hospital risk-standardized NIV % and compared patient characteristics among quartiles for trend and differences between Q1 and Q4. We used linear regression to examine trends between risk-standardized NIV % quartiles (Q1 to Q4) for continuous variables (p for trend), and a Cochran–Mantel–Haenszel test to examine trends between risk-standardized NIV % quartiles for categorical variables. The t-test (for continuous variables) and chi-squared test (for categorical variables) were also used to compare differences in characteristics between the lowest (Q1) and highest (Q4) quartiles of risk-standardized NIV %. Subsequently, we compared the risk-standardized NIV % using the Wilcoxon–Mann–Whitney test, and unadjusted hospital mortality using the chi-squared test between: 1) rural and urban, 2) low-volume and high-volume, and 3) complexity 2 (medium-resource) and complexity 1 (high-resource) hospitals. We also created a hierarchical logistic regression model to examine the association of hospital characteristics with hospital mortality adjusted for severity of illness and accounting for repeated admissions. All statistical analyses were conducted using SAS Enterprise Guide, 2014 (SAS Institute Inc).", "We obtained data from the Veterans Informatics and Computing Infrastructure (VINCI), an integrated system that includes VA’s electronic health records and administrative data. Admissions to VA acute-care hospitals were identified via the Corporate Data Warehouse (CDW) using the inpatient domain. These datasets contain patient demographics, including residential address and ZIP code, diagnosis and procedure codes during admission, admission source, and admission and discharge dates.", "We identified patients hospitalized with AECOPD based on the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM) and using the following criteria: 1) a principal diagnosis of COPD (ICD-9-CM codes: 490.x, 491.xx, 492.xx, and 496.xx; or ICD-10-CM codes: J41, J43, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J41.8, J42, or J44.9) or 2) a principal diagnosis of acute respiratory failure (518.81, 518.82, 518.84, or 799.1; ICD-10-CM: J96.00, J96.01, J96.02, J96.11, J96.12, J96.20, J96.21, J96.22, J96.90, J96.92, or R06.03) with a secondary AECOPD diagnosis (ICD-9-CM codes: 491.21, 491.22; ICD-10-CM codes: J44.1, J44.0).\nNIV use was defined using ICD-9-CM procedure code 93.90 or, for ICD-10, codes 5A09357, 5A09457, and 5A09557. IMV use was defined using ICD-9-CM procedure codes 96.04 and 96.70–96.72 or, for ICD-10, codes 0BH17EZ, 0BH18EZ, 5A1935Z, 5A1945Z, and 5A1955Z. Primary ventilation support was defined based on whether the patient received NIV and/or IMV within one day from the admission date.\nPatient rurality was defined using census tracts based on Rural Urban Commuting Area (RUCA) codes. RUCA codes reflect measures of urbanization, commuting, and population density.11–15 RUCA codes were further condensed to designate an area as urban (RUCA codes: 1 and 1.1), rural (RUCA codes: 2, 2.1, 3, 4, 4.1, 5, 5.1, 6, 7, 7.1, 7.2, 8, 8.1, 8.2, and 9), or isolated rural (RUCA codes: 10, 10.2, 10.2, and 10.3), using the categories defined by the VA Office of Rural Health.16 Travel time to the nearest VA hospital was determined from VA Planning Systems and Support Group (PSSG) geo-coded enrollment files. PSSG calculates distances to tertiary care VA sites for all enrolled patients using actual longitude and latitude coordinates of patient residences and the nearest VA hospitals. Travel time to the nearest VA hospital was estimated using geospatial technologies, which reflect roads and average driving conditions.11 Comorbidities were defined using ICD-9-CM and ICD-10-CM diagnosis codes within 1 year prior to admission in the inpatient, outpatient, or fee-basis data files, except for pneumonia, which was defined based on the presence of the corresponding diagnosis code during the hospitalization, as previously described.17 Severity of illness was quantified using a modified APACHE score (mAPACHE), which includes vital signs and commonly obtained laboratory values,12,18,19 and has also been externally validated.20 In brief, mAPACHE uses the same scoring assignments as APACHE III and includes all the predictor variables from the APACHE III excluding the Glasgow Coma Scale, urine output, arterial blood gas, and mechanical ventilator components. mAPACHE variables include age, comorbidities, mean arterial blood pressure, heart rate, respiratory rate, temperature, hematocrit, white blood cells, sodium, blood urea nitrogen, creatinine, glucose, albumin, and bilirubin. We retrieved all the data from electronic medical records, as described previously.12,18,19 Obstructive sleep apnea was defined using ICD-9 diagnosis code 327.2 or 780.57, or ICD-10 diagnosis codes G47.30–G47.39.\nHospital rurality was defined using the same methods as patient rurality, but the facilities in rural and isolated rural areas were collapsed to one category (rural). Hospital complexity was defined by VA as 1 (high resource), 2 (medium resource), and 3 (low resource).21 Hospital COPD-case volume was classified as high (above the median) or low (below the median) based on the total COPD volume during the study period. Hospital length of stay was calculated from the admission and discharge dates, in days. Hospital mortality was defined using the date of death listed in the VA Vital Status File and the occurrence of this date between the admission and discharge dates inclusive.\nWe included admissions with AECOPD at 121 VA acute-care facilities from 2011 to 2017. We excluded records admitted to facilities with complexity 3 (low resource) and newly opened facilities because these facilities may not provide ventilator support. We also excluded patients in hospices (V667/Z51.5 code within 1 year prior to the hospitalization date) and admissions of patients who received no oral or intravenous glucocorticoids during the hospitalizations.", "As NIV use may vary depending on the patient’s severity of illness (acuity), we calculated the risk-standardized NIV % to assess the NIV variation across the hospitals. We followed a similar approach to that used in a previous study.8 In brief, we created a hierarchical multivariable logistic regression model with NIV use within the first 24 hours of admission as the dependent variable (outcome) among records started on ventilation (records without NIV or invasive ventilation within first 24 hours were not used). Hospital was included as a random effect. Models were adjusted for severity of illness (mAPACHE) and several comorbidities (obstructive sleep apnea, presence of comorbid pneumonia, diabetes mellitus, congestive heart failure, hypertension, peripheral vascular disease, coronary artery disease, cancer, chronic kidney disease, stroke, and liver disease).22 Similarly to the approach used for publicly reported hospital performance metrics,23,24 we calculated the expected and predicted proportion of NIV use for each hospital using these models. Specifically, the expected rate is calculated based on hospital patient characteristics and does not include the hierarchical model hospital-specific random intercepts, whereas the predicted rate does incorporate hospital-specific random intercepts. The risk-standardized proportion of NIV use for each hospital was determined by multiplying the overall unadjusted proportion of NIV use by the ratio of the predicted to expected proportion for each hospital. Then, we stratified hospitals into quartiles based on hospital risk-standardized NIV % and compared patient characteristics among quartiles for trend and differences between Q1 and Q4. We used linear regression to examine trends between risk-standardized NIV % quartiles (Q1 to Q4) for continuous variables (p for trend), and a Cochran–Mantel–Haenszel test to examine trends between risk-standardized NIV % quartiles for categorical variables. The t-test (for continuous variables) and chi-squared test (for categorical variables) were also used to compare differences in characteristics between the lowest (Q1) and highest (Q4) quartiles of risk-standardized NIV %. Subsequently, we compared the risk-standardized NIV % using the Wilcoxon–Mann–Whitney test, and unadjusted hospital mortality using the chi-squared test between: 1) rural and urban, 2) low-volume and high-volume, and 3) complexity 2 (medium-resource) and complexity 1 (high-resource) hospitals. We also created a hierarchical logistic regression model to examine the association of hospital characteristics with hospital mortality adjusted for severity of illness and accounting for repeated admissions. All statistical analyses were conducted using SAS Enterprise Guide, 2014 (SAS Institute Inc).", "Of 64,895 admissions with AECOPD exacerbations at 121 VA acute-care facilities from 2011 to 2017, 22,847 records met the exclusion criteria (Figure 1). The final cohort included 42,048 admissions in 106 VA hospitals and all of them were ICU admissions. We stratified these records by ventilator support within the first 24 hours of admission (primary ventilation support). Figure 1 also shows the outcomes of admissions stratified by ventilation support.Figure 1Patient flowchart and outcomes by ventilation support within one day from admission.Abbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation.\nPatient flowchart and outcomes by ventilation support within one day from admission.\nThe percentage of total admissions who received any ventilation support (NIV, IMV, or both) within the first 24 hours across all 106 hospitals ranged from 0.8% to 13.7%, with a median of 5.4% (interquartile interval [IQI]=3.7–7.8%) (Figure 2). The percentage of total admissions who received primary NIV ranged from 0% to 9%, with a median of 2.8% (IQI=1.2–4.5%). Among only those admissions who received ventilation support within the first 24 hours (primary NIV, IMV, or both), the unadjusted NIV % use ranged from 0 to 100, with a median of 54.7% (IQI=34.8–68.2%).Figure 2Percentage of ventilatory support within one day from admission across the 106 facilities.Abbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation.\nPercentage of ventilatory support within one day from admission across the 106 facilities.\nThe risk-standardized NIV % (the adjusted initial NIV of those admissions receiving ventilation support in the initial 24 hours) ranged from 10.8% to 86.6%, with a median of 58.0% (IQI=41.9–64.4%). Table 1 shows the characteristics of the hospitals stratified by risk-standardized NIV % into quartiles. Risk-standardized NIV % use accounted for severity of illness and comorbidities. Quartile 1 (Q1) included hospitals with the lowest risk-standardized NIV while quartile 4 (Q4) included hospitals with the highest risk-standardized NIV; other patient- and hospital-level characteristics are reported by risk-standardized NIV % quartiles. There were more black and rural patients in the highest risk-standardized NIV % relative to lower quartiles. Patients in the highest risk-standardized NIV % quartile had longer travel time to the nearest VA hospitals than those in the lowest quartiles. The IMV rates were lower and hospital length of stay was longer in hospitals in the highest NIV quartiles relative to the lowest quartiles.Table 1Patient and Hospital Characteristics of 106 Hospitals Stratified by Risk-Standardized Non-Invasive Ventilation Percentage Within One Day from AdmissionNIV Q1 (N=11,810)NIV Q2 (N=10,031)NIV Q3 (N=11,719)NIV Q4 (N=8488)p Value for Trendp Value Q1 vs Q4*Risk-standardized NIV %10.8–41.9%42.2–56.7%58.0–64.3%64.4–86.6%N hospital26272726Age, mean (SD)70.29 (8.69)69.92 (9.01)70.49 (9.74)70.33 (9.21)0.100.79Sex (female), N (%)507 (4.29)471 (4.70)476 (4.06)350 (4.12)0.110.55Race, N (%)<0.001<0.001 White9126 (77.30)7505 (74.82)9047 (77.20)6249 (73.62) Black1896 (16.05)1780 (17.74)1989 (16.97)1637 (19.29) Other364 (3.08)365 (3.64)262 (2.24)290 (3.42) Missing421 (3.56)381 (3.80)421 (3.59)312 (3.68)Patient residential location, N (%)<0.001<0.001 Urban7454 (63.12)6999 (69.77)7422 (63.33)5155 (60.73) Rural3549 (30.05)2416 (24.09)3525 (30.08)2717 (32.01) Isolated478 (4.05)347 (3.46)468 (3.99)354 (4.17) Missing329 (2.79)269 (2.68)304 (2.59)262 (3.09)Obstructive sleep apnea4372 (37.02)3895 (38.83)4568 (38.98)3059 (36.04)<0.0010.15Diabetes mellitus1964 (16.63)1643 (16.38)2349 (20.04)1614 (19.02)<0.001<0.001Congestive heart failure1532 (12.97)1386 (13.82)1759 (15.01)1179 (13.89)<0.0010.058Pneumonia6377 (54.00)5532 (55.15)6472 (55.23)4783 (56.35)0.010<0.001Hypertension665 (5.63)690 (6.88)1038 (8.86)795 (9.37)<0.001<0.001Peripheral vascular disease903 (7.65)746 (7.44)1028 (8.77)676 (7.96)0.0010.40Cancer931 (7.88)814 (8.11)1126 (9.61)788 (9.28)<0.001<0.001Coronary artery disease565 (4.78)569 (5.67)780 (6.66)530 (6.24)<0.001<0.001Chronic kidney disease770 (6.52)709 (7.07)925 (7.89)673 (7.93)<0.001<0.001Stroke627 (5.31)550 (5.48)727 (6.20)521 (6.14)0.0060.012Liver disease398 (3.37)338 (3.37)424 (3.62)320 (3.77)0.340.13Admission source, N (%)<0.001<0.001 Hospital515 (4.36)337 (3.36)371 (3.17)238 (2.82) Nursing home140 (1.19)135 (1.35)170 (1.45)131 (1.55) Other facility20 (0.17)11 (0.11)12 (0.10)26 (0.31) Outpatient11,100 (93.99)9494 (94.65)11,041 (94.21)7963 (94.44) Unknown35 (0.30)54 (0.54)125 (1.07)74 (0.88)Travel time to VAMC (min), mean (SD)67.26 (68.46)68.11 (73.11)72.51 (55.21)82.71 (76.47)<0.001<0.001Any IMV during hospital stay, N (%)604 (5.11)486 (4.84)437 (3.73)277 (3.26)<0.001<0.001mAPACHE, mean (SD)35.73 (11.95)35.00 (12.02)35.30 (11.91)35.63 (11.98)0.600.55Hospital mortality, N (%)303 (2.57)303 (3.02)315 (2.69)238 (2.80)0.210.30Hospital length of stay (days), mean (SD)5.54 (15.21)6.32 (27.04)7.13 (40.05)7.68 (43.30)<0.001<0.001Note: *Q1 vs Q4 using chi-squared or t-test.Abbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation; Q, quartile; SE, standard error; VAMC, Veterans Health Administration Medical Center.\n\nPatient and Hospital Characteristics of 106 Hospitals Stratified by Risk-Standardized Non-Invasive Ventilation Percentage Within One Day from Admission\nNote: *Q1 vs Q4 using chi-squared or t-test.\nAbbreviations: IMV, invasive mechanical ventilation; NIV, non-invasive ventilation; Q, quartile; SE, standard error; VAMC, Veterans Health Administration Medical Center.\nTable 2 shows a risk-standardized NIV % of 55.1% (min=10.8%; max=86.6%) in urban hospitals, while the NIV % was 65.3.5% (min=34.2%; max=84.2%) in rural hospitals (p=0.047). There was no difference in risk-standardized NIV % between high- and low-COPD-volume facilities (p=0.33), or between complexity 1 (high-resource) and complexity 2 (medium-resource) hospitals (p=0.45).Table 2Risk-Standardized Non-Invasive Ventilation Percentage Stratified by Hospital CharacteristicsNumber of HospitalsNumber of PatientsRisk-Standardized NIV %, Median (Min–Max)p Value*Overall10642,04857.3 (10.8–86.6)Rural/urban0.047 Rural10254865.3 (34.2–84.2) Urban9639,50055.1 (10.8–86.6)Hospital volume0.33 Low5312,81359.90 (10.8–86.6) High5329,23553.43 (13.7–84.9)Hospital complexity0.45 2 (Medium resource)15294462.14 (20.4–86.6) 1 (High resource)9139,10455.71 (10.8–84.9)Note: *Wilcoxon two-sample test.\n\nRisk-Standardized Non-Invasive Ventilation Percentage Stratified by Hospital Characteristics\nNote: *Wilcoxon two-sample test.\nTable 3 shows unadjusted mortality stratified by hospital characteristics. After adjusting for patient acuity and taking into account repeated admissions, admission at an urban hospital was not associated with mortality (odds ratio [OR]=0.90; 95% CI=0.68 to 1.19, p=0.46) relative to admission at a rural hospital. Similarly, admission to a low-volume hospital was not associated with mortality (OR=1.00; 95% CI=0.86 to 1.18, p=0.96) relative to admission at a high-volume hospital, but admission to a medium-resource facility (complexity 2) was associated with increased mortality (OR=1.55; 95% CI=1.20 to 2.00, p<0.001) relative to admission at a high-resource facility.Table 3Unadjusted Mortality Stratified by Hospital CharacteristicsNumber of HospitalsNumber of PatientsUnadjusted Hospital Mortality, Deaths (%)p Value*Overall10642,0481159 (2.8%)Rural/urban0.22 Rural10254880 (3.14%) Urban9639,5001079 (2.73%)Hospital volume0.004 Low5312,813398 (3.11%) High5329,235761 (2.60%)Hospital complexity<0.001 2 (Medium resource)152944128 (4.35%) 1 (High resource)9139,1041031 (2.64%)Note: *Chi-squared test.\n\nUnadjusted Mortality Stratified by Hospital Characteristics\nNote: *Chi-squared test.", "In a cohort of 42,048 patients admitted with AECOPD to VA hospitals over 6 years, we observed considerable variation in NIV use across hospitals. After taking into account patient acuity using a validated critical illness severity score18,20,25 and comorbidities, we observed that hospitals in the highest NIV use quartile cared for more rural patients, used IMV less, and had longer length of hospital stay, but had no difference in mortality relative to hospitals in the lowest quartiles. The NIV use rate was higher in rural than in urban hospitals. Hospital mortality did not differ between rural and urban facilities.\nIt is well established that NIV reduces the need for IMV and hospital length of stay, and improves mortality in acute respiratory failure due to AECOPD26 and heart failure,27 but its role in other causes of respiratory failure, such as pneumonia, is controversial.28 In a study using 2011 claims data from California, the most common reason for NIV (26.1%) was pneumonia. Among hospitalizations with NIV as the primary ventilator support, hospitals that used NIV for strong-evidence conditions (COPD and heart failure) had lower NIV failure rates.9 We found that concomitant pneumonia was present in more than 50% of the AECOPD patients requiring NIV and/or IMV within 24 hours of admission. Although this may merely represent pneumonia overdiagnosis or using an ICD code to order a chest X-ray, pneumonia and AECOPD frequently coexist.29,30 In our analysis, we included only admissions with a primary diagnosis of AECOPD. We also adjusted for concomitant pneumonia when we calculated the risk-standardized NIV use because pneumonia is associated with worse outcomes.30\nSeveral observational studies conducted in various countries have reported approximately a five-fold increase in NIV use rates in the early 2010s compared to those in the early 2000s.31–33 Using the Premier inpatient database, Stefan et al showed that initial NIV use increased by 15.1% annually in the USA.33 In 2001, NIV use was 5.9% but by 2011 it was 14.8%. This increase concurred with a decline in IMV use. The widespread use of NIV is due to its easy application. This may be the reason why we observed an inverse association between NIV and IMV in the hospitals, and not necessarily that NIV reduced the need for IMV.\nA plethora of studies have shown significant variation in NIV use across hospitals.8,9,34 Mehta et al showed that the risk-standardized NIV % rate among 37,516 hospitalizations for AECOPD across 250 hospitals in California was 10% (IQI=1.6–35.7%).9 In another cross-sectional analysis of 77,576 hospitalizations across 386 US hospitals, the risk-standardized NIV % among those admissions requiring ventilation support ranged from 47.4% at the 10th percentile to 84.7% at the 90th percentile.8 We also observed significant variation even though we conducted the study in a single health system that has published guidelines to provide care for COPD patients.35\nNIV variation across hospitals may be related to the case mix of patients admitted to those hospitals. Large tertiary centers may care for very sick patients, and for that reason, IMV (and not NIV) is more frequently used there than in small rural hospitals. Adjusting for patient illness severity is crucial for studying NIV variation across hospitals. Prior reports studying risk-standardized NIV use were limited because patient acuity was not included in the analysis. Instead, comorbidities identified using ICD-9 codes were used as surrogates for severity of illness. In our analysis, we not only adjusted for comorbidities, but also accounted for severity of illness. Adjusting for patient acuity is critical as NIV may be underused or overused. NIV may be unnecessary in low-acuity patients, but it may be harmful in high-acuity patients who may require IMV, and delays in initiating IMV in these patients may increase the level of mortality.25,36 Our findings showed that rural hospitals used NIV more, which is in agreement with previous literature.8\nNumerous hospital factors may also be responsible for variations in NIV use. In the early years of NIV, lack of equipment was the main reason for underuse.37 Subsequent surveys showed that insufficient training of physicians was the main reason for NIV underuse, followed by lack of equipment.38 Inadequate training of respiratory therapists was another contributor. A survey of Canadian physicians conducted in 2003 did not show variation in practice among various facilities, but rather variation among specialties.39 A national survey of VA physicians and respiratory therapists in 2004 revealed that lack of training and experience was a significant factor in NIV underutilization.40 A study using a qualitative approach with in-depth interviews of 32 participants in seven US hospitals, including nurses, physicians, respiratory therapists, and leaders, showed that respiratory therapist autonomy, interdisciplinary teamwork, staff education, and the presence of policies and protocols were features of high-performing hospitals.41\nLindenauer et al demonstrated that among several hospital characteristics, including teaching status, staffing levels, and hospital location and volume, only the presence of intensivists and hospital rurality was associated with high risk-adjusted NIV use.8 Although we found that rural hospitals more frequently use NIV, we did not find any difference in risk-standardized NIV % between medium- and high-resource hospitals, or between low- and high-volume hospitals. We did find fewer black and rural patients in hospitals with higher risk-standardized NIV %. This finding may be related to how and where patients seek medical care (eg, rural patients may seek care in nearby low-resource hospitals). Racial disparities in treatment are also possible. A previous study in VA demonstrated that rates of IMV were higher in black than in white patients.42\nAnother finding of this study is that hospitals in the highest risk-standardized NIV % use quartiles had longer hospital length of stay and received more rural patients, indicating that these hospitals delay discharging rural patients (perhaps because of a lack of skilled nursing and rehabilitation facilities in the area).\nOur study has several limitations. It was conducted in a single health-care system, with most of the patients being male. Therefore, we should generalize our findings with caution. Because we did not have smoking exposure data or pulmonary function data, we cannot confirm the diagnosis of COPD. We cannot exclude the misdiagnosis of hypercapnic respiratory failure due to obesity hypoventilation as COPD-related respiratory failure. Nevertheless, we included only patients who received oral steroids and we took obstructive sleep apnea into account to calculate our risk-standardized NIV %. In addition, our previous work, which used less stringent ICD code criteria, showed an accuracy in identifying AECOPD of between 80% and 90%.10 We did not exclude patients with obstructive sleep apnea, as obstructive sleep apnea–COPD overlap is common, in particular among patients with advanced disease.43 Excluding those patients would have resulted in a sample that was not a good representative of the true population. We have no data regarding NIV modes used (eg, BIPAP). Physiological parameters such as partial pressure of CO2 in the arterial blood, which are indicators of patient acuity, were not available. Moreover, we did not have data from civilian hospitals. Veterans may have been hospitalized at civilian hospitals owing to ease of access or convenience. There were no available data regarding the staffing levels of hospitals, including whether a board-certified critical care physician or ICU telemedicine was available, which are associated with improved outcomes. The above limitations do not undermine the strengths of our study, which are the large sample size, the strict exclusion criteria, and the fact that we used an illness severity scoring system to calculate the risk-standardized NIV %.", "Among patients admitted with AECOPD in VA hospitals, NIV use varied significantly across hospitals, with rural hospitals having higher risk-standardized NIV % than urban ones. Hospitals with the highest NIV rates cared for more rural patients and had longer hospital length of stay than hospitals with the lowest NIV rates. Further research should investigate the exact mechanism of variation in NIV use between rural and urban hospitals." ]

[ "intro", null, null, null, null, null, null, null ]

[ "pulmonary disease", "chronic obstructive", "epidemiology", "non-invasive ventilation" ]

[ 2925, 79, 834, 493, 1424, 1412, 74 ]

[ "niv", "hospital", "hospitals", "codes", "10", "icd", "standardized", "use", "risk", "risk standardized" ]

[ "respiratory failure aecopd26", "pulmonary disease copd", "hospitalizations aecopd", "chronic obstructive pulmonary", "utilization copd patients" ]

[ "Graft Rejection", "Graft Survival", "HLA Antigens", "Histocompatibility Testing", "Humans", "Isoantibodies", "Kidney Transplantation", "Lung Transplantation", "Retrospective Studies" ]

[ "Lung transplant cohort", "HLA crossmatching and other immunologic evaluation", "Clinical outcomes", "Statistical analysis", "Ethics statement", "Baseline characteristics", "Operative details and postoperative outcomes", "Survival analysis" ]

[ "Clinical lung transplantation data were derived from patients who received lung transplantation from deceased donors at one of five tertiary centers which performed more than 10 cases annually between 2010 to 2012 in South Korea via the Korean Organ Transplant Registry (KOTRY). The KOTRY was established in 2014 and began to organize the lung transplantation registry in 2015.13 We analyzed the data on registered lung transplantations performed between March 2015 and December 2019. Among these patients, ten patients who did not undergo crossmatching were excluded. Finally, 210 patients were included in the study.\nClinical data, including the general demographic characteristics, primary diagnosis, and pretransplant status of the recipients and donors, were prospectively collected. The details of desensitization protocols, transplant operations, and postoperative follow-up results were also prospectively collected. All clinical data were collected and registered using a web-based report form by the attending physician.\nBecause of donor shortage, transplantation was performed regardless of the status of DSA screening. Moreover, according to a medical urgency-based allocation system in Korea, the results of human leukocyte antigen (HLA) crossmatching were not regarded as mandatory considerations.14 Most patients received induction therapy with high-dose steroids (methylprednisolone, 500 mg) or interleukin-2 antagonist followed by a standard triple immunosuppressant regimen consisting of prednisolone, mycophenolate, and tacrolimus, except when there were contraindications to these medications. Pretransplant immunological results did not affect the choice of immunosuppressant regimen. Desensitization protocols, including plasma exchange and intravenous infusion of immunoglobulin, were considered after transplantation in patients with pretransplant DSA and high mean fluorescence intensity (MFI).", "The Korean Organ Donation Agency (KODA) laboratory performed crossmatching of registered lung transplantations. Both CDC crossmatch (CDC-XM) and flow cytometric crossmatch (flow-XM) were performed. Although virtual crossmatch techniques have gained influence in many countries, they are currently unavailable in Korea.\nFor the CDC crossmatch, both T and B lymphocytes were isolated by negative selection methods using the EasySep HLA Total Lymphocyte Enrichment kit (STEMCELL Technologies Inc., Tukwila, WA, USA). Both the National Institutes of Health and antihuman globulin augmented methods were performed using standard protocols with some minor modifications.11 Cells and duplicate serum dilutions of 1:1 to 1:4, respectively, were incubated at 25°C for 30 minutes for complement reaction. Cells were stained with commercial staining reagent (FluoroQuench Stain; One Lambda, Canoga Park, CA, USA) and observed under an inverted fluorescent microscope. The positive crossmatch results were recorded when the cytotoxic reaction resulted in more than 11% cell lysis.\nFor flow-XM, both T and B lymphocytes were stained using three-color immunofluorescence staining in a single tube as previously described,1415 with minor modifications. After the incubation of cells and serum at 25°C for 15 minutes, a fluorescent conjugate reaction was performed at 25°C for 20 minutes using titrated goat F(ab’)2 antihuman immunoglobulin G fluorescein isothiocyanate (Jackson Immunoresearch Laboratories, West Grove, PA, USA), anti-CD3 PerCP (Becton Dickinson, San Jose, CA, USA), and anti-CD19 allophycocyanin conjugates (Becton Dickinson). Fluorescence was analyzed using a FACSCalibur Flow Cytometer with an HTS microplate acquisition system (Becton Dickinson). Fluorescence was considered positive when the MFI ratio to negative reference test was over 2.0 for both T and B lymphocytes.\nPanel reactive antibody (PRA) class I and II identifications were performed before transplantation with the identification kit (One Lambda, Inc., West Hills, CA, USA or Gen-Probe Inc., San Diego, CA, USA). PRA over 10% was considered positive, and over 50% was considered highly sensitized.16 Antibodies against donor HLA-A, B, DR, and DQ were defined DSAs, and the strength of each DSA was quantified based on MFI.", "Clinical outcomes, including acute rejection, primary graft dysfunction (PGD), chronic lung allograft dysfunction (CLAD), and mortality, were analyzed. Acute cellular rejection (ACR) was diagnosed according to the ISHLT grading system with a transbronchial biopsy specimen.17 However, not all treated recipients with acute rejection were available for transbronchial biopsies for histopathologic confirmation. As such, clinical diagnosis of acute rejection was also assumed when allograft dysfunction without definite entities was responsive to steroid pulse therapy. Though there is no distinct definition of AMR in lung transplantation, AMR was diagnosed as per a proposal published by the ISHLT in 2016, including allograft dysfunction, lung histology, C4d+, and DSA without other explainable causes.818\nPGD in lung transplantation was defined as an allograft dysfunction of the transplanted lung within the first 72 hours after the procedure. PGD was diagnosed and graded according to the ISHLT criteria.19 Bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS) were the two main phenotypes of CLAD, which represented the irreversible loss of lung function and a major cause of limiting long-term survival. BOS was identified by the sustained and irreversible reduction in forced expiratory volume in 1 second (FEV1) compared with the post-lung transplant baseline FEV1 in the absence of any other etiologies.20 RAS is defined by restrictive physiology according to a pulmonary function test, positive findings of a radiologic study, the presence of ground-glass opacity, and interstitial fibrosis.21", "Descriptive statistics were used to demonstrate baseline clinical characteristics of the study cohort. For categorical variables, data are shown as numbers and percentages, and chi-squared or Fisher’s exact tests were used where appropriate. For continuous variables, data were analyzed using Student’s t-test or Mann-Whitney U test and presented as the mean ± standard deviation or the median, interquartile range (IQR). Univariable and multivariable regression analysis was used to evaluate the factors associated with clinical outcomes. To confirm the independent association between variables, linear regression was applied for confounders. Survival analysis was performed by the Kaplan-Meier method with log-rank tests. All statistical analysis was performed with SPSS (version 25.0; SPSS Inc., Chicago, IL, USA), and a P value < 0.05 was considered statistically significant.", "This study was approved by the Institutional Review Board (IRB) of the Severance Hospital of Yonsei University (IRB No. 4-2021-0673). The IRB waived the requirement for obtaining informed consent from the patients.", "The baseline characteristics of the patients according to the results of crossmatches are shown in Table 1. Among the included 210 recipients, nine patients showed positive crossmatches. The median age was 56.4 years, and 134 patients (63.8%) were male. Only one patient was male in the positive crossmatch group. There was no RH− blood type within the study cohort. The most common primary diagnosis was idiopathic pulmonary fibrosis (114 patients, 54.3%), followed by connective tissue-related interstitial lung disease (CTD-ILD) (34 patients, 16.2%). The median number of days on the waiting list was 71.0, and 63 patients (30.0%) had undergone pretransplant extracorporeal membrane oxygenation support. The baseline characteristics were not significantly different between the positive and negative crossmatch groups.\nValues are presented as mean ± standard deviation or number (%).\nXM = crossmatch, BMI = body mass index, COPD = chronic obstructive pulmonary disease, CTD-ILD = connective tissue-related interstitial lung disease, BOS = bronchiolitis obliterans syndrome, ECMO = extracorporeal membrane oxygenation, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, HSCT = hematopoietic stem cell transplantation.\n\nTable 2 shows the results of immunological evaluations, including crossmatching. CDC-XM was performed in 208 patients (99.0%) and flow-XM was performed in 125 patients (59.5%). PRA was identified in 208 patients (99.0%). Among these patients, high levels of class I or class II PRA (> 50%) were detected in 18 patients (8.7%). The proportion of patients with high PRA was higher in the positive crossmatch group than in the negative crossmatch group (37.5% vs. 10.7%, P = 0.026).\nValues are presented as number (%).\nXM = crossmatch, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibody, HLA = human leukocyte antigen.\nAt the time of lung transplantation, most patients (201 patients, 95.7%) had undergone the DSA screening test. Twenty-seven patients (13.4%) were revealed to have DSA. A higher proportion of the patients in the positive crossmatch group had DSA (7 patients, 87.5%, vs. 20 patients, 10.4%, P < 0.001) compared with that in the negative crossmatch group. Desensitization was performed in 35 patients (16.7%), and four patients in the positive crossmatch group underwent desensitization.\nThe characteristics of nine patients with positive crossmatch are shown in Table 3. Among these patients, the most common etiology for lung transplantation was CTD-ILD. CDC-XM and flow-XM revealed positive crossmatches in five and six patients, respectively. DSA was detected in six patients, and three patients (patient numbers 5, 6, and 7) had an MFI higher than 5,000. Five patients underwent desensitization before lung transplantation. Acute rejection was diagnosed in three patients (patient numbers 3, 4, and 6), and two patients underwent transbronchial lung biopsy (patient numbers 3 and 4). Patient number 6 was positively crossmatched to the donor as observed using CDC-XM with both T land B lymphocytes, and class I DSA was detected with MFI over 5,000. The patient was clinically diagnosed with AMR and received aggressive treatments for rejection, including steroid pulse, plasmapheresis, and intravenous infusion of immunoglobulin. However, the patient received retransplantation on postoperative day 15 due to rejection.\nXM = crossmatch, CTD-ILD = connective tissue-related interstitial lung disease, ECMO = extracorporeal membrane oxygenation, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibodies, PGD = primary graft dysfunction, MFI = mean fluorescence intensity, AMR = antibody-mediated rejection, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, IVIG = intravenous immunoglobulin.", "Most patients underwent bilateral lung transplantation (202 patients, 96.2%). According to the PGD grade, patients presenting with grade 3 at postoperative 48 hours or 72 hours were regarded as high-grade PGD. Forty-four patients showed high-grade PGD, and no statistical difference was observed between the groups. The incidences of other postoperative complications, including postoperative bleeding, anastomosis dehiscence, pulmonary thromboembolism, pneumonia, and acute kidney injury, were not statistically distinguishable between the groups (Table 4).\nValues are presented as number (%), mean ± standard deviation or number (interquartile range).\nXM = crossmatch, PGD = primary graft dysfunction, ECMO = extracorporeal membrane oxygenation, ICU = intensive care unit, AKI = acute kidney injury.\nAcute rejection was diagnosed in 65 patients. Transbronchial lung biopsy-proven ACR was diagnosed in 37 patients, and other patients were diagnosed clinically. Though the incidence of acute rejection with regard to crossmatching results was not statistically different between the groups, the positive crossmatch group showed shorter intervals between transplantation and diagnosis of rejection. Five patients had clinical presentations that were compatible with AMR. However, none of these patients were diagnosed using the biopsy specimen or capillary C4d composition. Seven patients (3.3%) showed physiologic changes that were considered as CLAD. BOS was diagnosed in five patients (2.4%), and RAS was diagnosed in three patients (1.4%). The median number of days from transplant to CLAD diagnosis was 648 (IQR, 488.0–759.0). All of the patients showed CLAD were in the negative crossmatch group.", "As shown in Table 4, 55 patients (26.2%) showed 1-year mortality. The 1-year and overall mortality rates were not statistically different between the groups (Table 4). Fig. 1 shows the Kaplan-Meier curves of 1-year and overall mortality rates of the patients. According to Kaplan-Meier analysis, there were no significant differences in the survival rates, regardless of crossmatching status.\nWe also performed a regression analysis to identify the risk factors for poor 1-year survival. Positive CDC crossmatching was the only significant risk factor for poor 1-year survival in the univariable analysis (odds ratio, 11.922, 95% confidence interval, 1.302–19.125, P = 0.018).\nWe further divided the patients based on crossmatching methods and performed survival analysis for the subgroups. When positive crossmatching was confirmed by CDC-XM, the post-transplant 1-year and overall mortality rates were poorer than the negative crossmatching results (P < 0.001 and P < 0.001, respectively) (Fig. 2A and B). According to the Kaplan-Meier method, positive crossmatching by the flow-XM method did not significantly differ in the 1-year and overall morality (Fig. 2C and D). When considering the detection of T and B lymphocytes individually, only positive crossmatching by T lymphocytes was related to mortality within 1-year after transplantation (P = 0.002) and overall mortality (P = 0.006) (Fig. 3).\nCDC = complement-dependent cytotoxicity." ]

[ null, null, null, null, null, null, null, null ]

[ "INTRODUCTION", "METHODS", "Lung transplant cohort", "HLA crossmatching and other immunologic evaluation", "Clinical outcomes", "Statistical analysis", "Ethics statement", "RESULTS", "Baseline characteristics", "Operative details and postoperative outcomes", "Survival analysis", "DISCUSSION" ]

[ "Crossmatching is the assessment of the immune compatibility of a particular donor and the recipient. The association between positive crossmatches and hyperacute rejection was first demonstrated in the 1960s with renal transplantation.1 Over the following decades, a positive crossmatch result began to be considered a contraindication to transplantation because of its devastating postoperative effects.23 In particular, complement-dependent cytotoxicity (CDC) results against T lymphocytes are considered an absolute contraindication.34 However, the meaning of positive crossmatches has changed with the development of crossmatching methodology and immunosuppression strategies.56 Early research suggested that it was crucial to avoid the impact of positive T cell crossmatches on renal transplantation; however, because of their low specificity and sensitivity, recent analyses have found that avoidance is not mandatory.2 A combination of different crossmatching techniques has recently been recommended for solid organ transplantations.7\nIn lung transplantation, considering the limited number of available donors, persistent demand for required organs, and risk of increasing incidences of wait-list mortality and morbidity, immunological “mismatches” have to be accepted despite the potential for the development of donor-specific antibodies (DSAs), which can trigger antibody-mediated rejection (AMR).8910 A pretransplant crossmatch is not mandatory in the lung allocation system suggested by the International Society of Heart and Lung Transplantation (ISHLT); interpretation and decisions are left to local protocols of each center and no definitive guidelines are available.7810\nAccording to the Korea donor allocation system, pretransplant crossmatching is only considered mandatory for renal and pancreas transplantation; the system does not play a role in nonrenal transplantation, including lung transplantation. According to a report from Korean Network for Organ Sharing, there was no lung transplantation with positive crossmatching between March 2014 to February 2015.11 However, the total number of nationwide lung transplantations has almost tripled since 2014 (55 cases in 2014; 64 cases in 2015; 89 cases in 2016; 93 cases in 2017; 92 cases in 2018; 157 cases in 2019)12; thus, the incidences and outcomes of positive crossmatching are worthy of analysis. In this study, we aimed to investigate the positive crossmatch rate in lung transplantation and their posttransplant outcomes using a multicenter nationwide cohort. In addition, we analyzed the impact of each crossmatch technique on the crossmatching results to clarify the meaning of positive crossmatching.", "Lung transplant cohort Clinical lung transplantation data were derived from patients who received lung transplantation from deceased donors at one of five tertiary centers which performed more than 10 cases annually between 2010 to 2012 in South Korea via the Korean Organ Transplant Registry (KOTRY). The KOTRY was established in 2014 and began to organize the lung transplantation registry in 2015.13 We analyzed the data on registered lung transplantations performed between March 2015 and December 2019. Among these patients, ten patients who did not undergo crossmatching were excluded. Finally, 210 patients were included in the study.\nClinical data, including the general demographic characteristics, primary diagnosis, and pretransplant status of the recipients and donors, were prospectively collected. The details of desensitization protocols, transplant operations, and postoperative follow-up results were also prospectively collected. All clinical data were collected and registered using a web-based report form by the attending physician.\nBecause of donor shortage, transplantation was performed regardless of the status of DSA screening. Moreover, according to a medical urgency-based allocation system in Korea, the results of human leukocyte antigen (HLA) crossmatching were not regarded as mandatory considerations.14 Most patients received induction therapy with high-dose steroids (methylprednisolone, 500 mg) or interleukin-2 antagonist followed by a standard triple immunosuppressant regimen consisting of prednisolone, mycophenolate, and tacrolimus, except when there were contraindications to these medications. Pretransplant immunological results did not affect the choice of immunosuppressant regimen. Desensitization protocols, including plasma exchange and intravenous infusion of immunoglobulin, were considered after transplantation in patients with pretransplant DSA and high mean fluorescence intensity (MFI).\nClinical lung transplantation data were derived from patients who received lung transplantation from deceased donors at one of five tertiary centers which performed more than 10 cases annually between 2010 to 2012 in South Korea via the Korean Organ Transplant Registry (KOTRY). The KOTRY was established in 2014 and began to organize the lung transplantation registry in 2015.13 We analyzed the data on registered lung transplantations performed between March 2015 and December 2019. Among these patients, ten patients who did not undergo crossmatching were excluded. Finally, 210 patients were included in the study.\nClinical data, including the general demographic characteristics, primary diagnosis, and pretransplant status of the recipients and donors, were prospectively collected. The details of desensitization protocols, transplant operations, and postoperative follow-up results were also prospectively collected. All clinical data were collected and registered using a web-based report form by the attending physician.\nBecause of donor shortage, transplantation was performed regardless of the status of DSA screening. Moreover, according to a medical urgency-based allocation system in Korea, the results of human leukocyte antigen (HLA) crossmatching were not regarded as mandatory considerations.14 Most patients received induction therapy with high-dose steroids (methylprednisolone, 500 mg) or interleukin-2 antagonist followed by a standard triple immunosuppressant regimen consisting of prednisolone, mycophenolate, and tacrolimus, except when there were contraindications to these medications. Pretransplant immunological results did not affect the choice of immunosuppressant regimen. Desensitization protocols, including plasma exchange and intravenous infusion of immunoglobulin, were considered after transplantation in patients with pretransplant DSA and high mean fluorescence intensity (MFI).\nHLA crossmatching and other immunologic evaluation The Korean Organ Donation Agency (KODA) laboratory performed crossmatching of registered lung transplantations. Both CDC crossmatch (CDC-XM) and flow cytometric crossmatch (flow-XM) were performed. Although virtual crossmatch techniques have gained influence in many countries, they are currently unavailable in Korea.\nFor the CDC crossmatch, both T and B lymphocytes were isolated by negative selection methods using the EasySep HLA Total Lymphocyte Enrichment kit (STEMCELL Technologies Inc., Tukwila, WA, USA). Both the National Institutes of Health and antihuman globulin augmented methods were performed using standard protocols with some minor modifications.11 Cells and duplicate serum dilutions of 1:1 to 1:4, respectively, were incubated at 25°C for 30 minutes for complement reaction. Cells were stained with commercial staining reagent (FluoroQuench Stain; One Lambda, Canoga Park, CA, USA) and observed under an inverted fluorescent microscope. The positive crossmatch results were recorded when the cytotoxic reaction resulted in more than 11% cell lysis.\nFor flow-XM, both T and B lymphocytes were stained using three-color immunofluorescence staining in a single tube as previously described,1415 with minor modifications. After the incubation of cells and serum at 25°C for 15 minutes, a fluorescent conjugate reaction was performed at 25°C for 20 minutes using titrated goat F(ab’)2 antihuman immunoglobulin G fluorescein isothiocyanate (Jackson Immunoresearch Laboratories, West Grove, PA, USA), anti-CD3 PerCP (Becton Dickinson, San Jose, CA, USA), and anti-CD19 allophycocyanin conjugates (Becton Dickinson). Fluorescence was analyzed using a FACSCalibur Flow Cytometer with an HTS microplate acquisition system (Becton Dickinson). Fluorescence was considered positive when the MFI ratio to negative reference test was over 2.0 for both T and B lymphocytes.\nPanel reactive antibody (PRA) class I and II identifications were performed before transplantation with the identification kit (One Lambda, Inc., West Hills, CA, USA or Gen-Probe Inc., San Diego, CA, USA). PRA over 10% was considered positive, and over 50% was considered highly sensitized.16 Antibodies against donor HLA-A, B, DR, and DQ were defined DSAs, and the strength of each DSA was quantified based on MFI.\nThe Korean Organ Donation Agency (KODA) laboratory performed crossmatching of registered lung transplantations. Both CDC crossmatch (CDC-XM) and flow cytometric crossmatch (flow-XM) were performed. Although virtual crossmatch techniques have gained influence in many countries, they are currently unavailable in Korea.\nFor the CDC crossmatch, both T and B lymphocytes were isolated by negative selection methods using the EasySep HLA Total Lymphocyte Enrichment kit (STEMCELL Technologies Inc., Tukwila, WA, USA). Both the National Institutes of Health and antihuman globulin augmented methods were performed using standard protocols with some minor modifications.11 Cells and duplicate serum dilutions of 1:1 to 1:4, respectively, were incubated at 25°C for 30 minutes for complement reaction. Cells were stained with commercial staining reagent (FluoroQuench Stain; One Lambda, Canoga Park, CA, USA) and observed under an inverted fluorescent microscope. The positive crossmatch results were recorded when the cytotoxic reaction resulted in more than 11% cell lysis.\nFor flow-XM, both T and B lymphocytes were stained using three-color immunofluorescence staining in a single tube as previously described,1415 with minor modifications. After the incubation of cells and serum at 25°C for 15 minutes, a fluorescent conjugate reaction was performed at 25°C for 20 minutes using titrated goat F(ab’)2 antihuman immunoglobulin G fluorescein isothiocyanate (Jackson Immunoresearch Laboratories, West Grove, PA, USA), anti-CD3 PerCP (Becton Dickinson, San Jose, CA, USA), and anti-CD19 allophycocyanin conjugates (Becton Dickinson). Fluorescence was analyzed using a FACSCalibur Flow Cytometer with an HTS microplate acquisition system (Becton Dickinson). Fluorescence was considered positive when the MFI ratio to negative reference test was over 2.0 for both T and B lymphocytes.\nPanel reactive antibody (PRA) class I and II identifications were performed before transplantation with the identification kit (One Lambda, Inc., West Hills, CA, USA or Gen-Probe Inc., San Diego, CA, USA). PRA over 10% was considered positive, and over 50% was considered highly sensitized.16 Antibodies against donor HLA-A, B, DR, and DQ were defined DSAs, and the strength of each DSA was quantified based on MFI.\nClinical outcomes Clinical outcomes, including acute rejection, primary graft dysfunction (PGD), chronic lung allograft dysfunction (CLAD), and mortality, were analyzed. Acute cellular rejection (ACR) was diagnosed according to the ISHLT grading system with a transbronchial biopsy specimen.17 However, not all treated recipients with acute rejection were available for transbronchial biopsies for histopathologic confirmation. As such, clinical diagnosis of acute rejection was also assumed when allograft dysfunction without definite entities was responsive to steroid pulse therapy. Though there is no distinct definition of AMR in lung transplantation, AMR was diagnosed as per a proposal published by the ISHLT in 2016, including allograft dysfunction, lung histology, C4d+, and DSA without other explainable causes.818\nPGD in lung transplantation was defined as an allograft dysfunction of the transplanted lung within the first 72 hours after the procedure. PGD was diagnosed and graded according to the ISHLT criteria.19 Bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS) were the two main phenotypes of CLAD, which represented the irreversible loss of lung function and a major cause of limiting long-term survival. BOS was identified by the sustained and irreversible reduction in forced expiratory volume in 1 second (FEV1) compared with the post-lung transplant baseline FEV1 in the absence of any other etiologies.20 RAS is defined by restrictive physiology according to a pulmonary function test, positive findings of a radiologic study, the presence of ground-glass opacity, and interstitial fibrosis.21\nClinical outcomes, including acute rejection, primary graft dysfunction (PGD), chronic lung allograft dysfunction (CLAD), and mortality, were analyzed. Acute cellular rejection (ACR) was diagnosed according to the ISHLT grading system with a transbronchial biopsy specimen.17 However, not all treated recipients with acute rejection were available for transbronchial biopsies for histopathologic confirmation. As such, clinical diagnosis of acute rejection was also assumed when allograft dysfunction without definite entities was responsive to steroid pulse therapy. Though there is no distinct definition of AMR in lung transplantation, AMR was diagnosed as per a proposal published by the ISHLT in 2016, including allograft dysfunction, lung histology, C4d+, and DSA without other explainable causes.818\nPGD in lung transplantation was defined as an allograft dysfunction of the transplanted lung within the first 72 hours after the procedure. PGD was diagnosed and graded according to the ISHLT criteria.19 Bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS) were the two main phenotypes of CLAD, which represented the irreversible loss of lung function and a major cause of limiting long-term survival. BOS was identified by the sustained and irreversible reduction in forced expiratory volume in 1 second (FEV1) compared with the post-lung transplant baseline FEV1 in the absence of any other etiologies.20 RAS is defined by restrictive physiology according to a pulmonary function test, positive findings of a radiologic study, the presence of ground-glass opacity, and interstitial fibrosis.21\nStatistical analysis Descriptive statistics were used to demonstrate baseline clinical characteristics of the study cohort. For categorical variables, data are shown as numbers and percentages, and chi-squared or Fisher’s exact tests were used where appropriate. For continuous variables, data were analyzed using Student’s t-test or Mann-Whitney U test and presented as the mean ± standard deviation or the median, interquartile range (IQR). Univariable and multivariable regression analysis was used to evaluate the factors associated with clinical outcomes. To confirm the independent association between variables, linear regression was applied for confounders. Survival analysis was performed by the Kaplan-Meier method with log-rank tests. All statistical analysis was performed with SPSS (version 25.0; SPSS Inc., Chicago, IL, USA), and a P value < 0.05 was considered statistically significant.\nDescriptive statistics were used to demonstrate baseline clinical characteristics of the study cohort. For categorical variables, data are shown as numbers and percentages, and chi-squared or Fisher’s exact tests were used where appropriate. For continuous variables, data were analyzed using Student’s t-test or Mann-Whitney U test and presented as the mean ± standard deviation or the median, interquartile range (IQR). Univariable and multivariable regression analysis was used to evaluate the factors associated with clinical outcomes. To confirm the independent association between variables, linear regression was applied for confounders. Survival analysis was performed by the Kaplan-Meier method with log-rank tests. All statistical analysis was performed with SPSS (version 25.0; SPSS Inc., Chicago, IL, USA), and a P value < 0.05 was considered statistically significant.\nEthics statement This study was approved by the Institutional Review Board (IRB) of the Severance Hospital of Yonsei University (IRB No. 4-2021-0673). The IRB waived the requirement for obtaining informed consent from the patients.\nThis study was approved by the Institutional Review Board (IRB) of the Severance Hospital of Yonsei University (IRB No. 4-2021-0673). The IRB waived the requirement for obtaining informed consent from the patients.", "Clinical lung transplantation data were derived from patients who received lung transplantation from deceased donors at one of five tertiary centers which performed more than 10 cases annually between 2010 to 2012 in South Korea via the Korean Organ Transplant Registry (KOTRY). The KOTRY was established in 2014 and began to organize the lung transplantation registry in 2015.13 We analyzed the data on registered lung transplantations performed between March 2015 and December 2019. Among these patients, ten patients who did not undergo crossmatching were excluded. Finally, 210 patients were included in the study.\nClinical data, including the general demographic characteristics, primary diagnosis, and pretransplant status of the recipients and donors, were prospectively collected. The details of desensitization protocols, transplant operations, and postoperative follow-up results were also prospectively collected. All clinical data were collected and registered using a web-based report form by the attending physician.\nBecause of donor shortage, transplantation was performed regardless of the status of DSA screening. Moreover, according to a medical urgency-based allocation system in Korea, the results of human leukocyte antigen (HLA) crossmatching were not regarded as mandatory considerations.14 Most patients received induction therapy with high-dose steroids (methylprednisolone, 500 mg) or interleukin-2 antagonist followed by a standard triple immunosuppressant regimen consisting of prednisolone, mycophenolate, and tacrolimus, except when there were contraindications to these medications. Pretransplant immunological results did not affect the choice of immunosuppressant regimen. Desensitization protocols, including plasma exchange and intravenous infusion of immunoglobulin, were considered after transplantation in patients with pretransplant DSA and high mean fluorescence intensity (MFI).", "The Korean Organ Donation Agency (KODA) laboratory performed crossmatching of registered lung transplantations. Both CDC crossmatch (CDC-XM) and flow cytometric crossmatch (flow-XM) were performed. Although virtual crossmatch techniques have gained influence in many countries, they are currently unavailable in Korea.\nFor the CDC crossmatch, both T and B lymphocytes were isolated by negative selection methods using the EasySep HLA Total Lymphocyte Enrichment kit (STEMCELL Technologies Inc., Tukwila, WA, USA). Both the National Institutes of Health and antihuman globulin augmented methods were performed using standard protocols with some minor modifications.11 Cells and duplicate serum dilutions of 1:1 to 1:4, respectively, were incubated at 25°C for 30 minutes for complement reaction. Cells were stained with commercial staining reagent (FluoroQuench Stain; One Lambda, Canoga Park, CA, USA) and observed under an inverted fluorescent microscope. The positive crossmatch results were recorded when the cytotoxic reaction resulted in more than 11% cell lysis.\nFor flow-XM, both T and B lymphocytes were stained using three-color immunofluorescence staining in a single tube as previously described,1415 with minor modifications. After the incubation of cells and serum at 25°C for 15 minutes, a fluorescent conjugate reaction was performed at 25°C for 20 minutes using titrated goat F(ab’)2 antihuman immunoglobulin G fluorescein isothiocyanate (Jackson Immunoresearch Laboratories, West Grove, PA, USA), anti-CD3 PerCP (Becton Dickinson, San Jose, CA, USA), and anti-CD19 allophycocyanin conjugates (Becton Dickinson). Fluorescence was analyzed using a FACSCalibur Flow Cytometer with an HTS microplate acquisition system (Becton Dickinson). Fluorescence was considered positive when the MFI ratio to negative reference test was over 2.0 for both T and B lymphocytes.\nPanel reactive antibody (PRA) class I and II identifications were performed before transplantation with the identification kit (One Lambda, Inc., West Hills, CA, USA or Gen-Probe Inc., San Diego, CA, USA). PRA over 10% was considered positive, and over 50% was considered highly sensitized.16 Antibodies against donor HLA-A, B, DR, and DQ were defined DSAs, and the strength of each DSA was quantified based on MFI.", "Clinical outcomes, including acute rejection, primary graft dysfunction (PGD), chronic lung allograft dysfunction (CLAD), and mortality, were analyzed. Acute cellular rejection (ACR) was diagnosed according to the ISHLT grading system with a transbronchial biopsy specimen.17 However, not all treated recipients with acute rejection were available for transbronchial biopsies for histopathologic confirmation. As such, clinical diagnosis of acute rejection was also assumed when allograft dysfunction without definite entities was responsive to steroid pulse therapy. Though there is no distinct definition of AMR in lung transplantation, AMR was diagnosed as per a proposal published by the ISHLT in 2016, including allograft dysfunction, lung histology, C4d+, and DSA without other explainable causes.818\nPGD in lung transplantation was defined as an allograft dysfunction of the transplanted lung within the first 72 hours after the procedure. PGD was diagnosed and graded according to the ISHLT criteria.19 Bronchiolitis obliterans syndrome (BOS) and restrictive allograft syndrome (RAS) were the two main phenotypes of CLAD, which represented the irreversible loss of lung function and a major cause of limiting long-term survival. BOS was identified by the sustained and irreversible reduction in forced expiratory volume in 1 second (FEV1) compared with the post-lung transplant baseline FEV1 in the absence of any other etiologies.20 RAS is defined by restrictive physiology according to a pulmonary function test, positive findings of a radiologic study, the presence of ground-glass opacity, and interstitial fibrosis.21", "Descriptive statistics were used to demonstrate baseline clinical characteristics of the study cohort. For categorical variables, data are shown as numbers and percentages, and chi-squared or Fisher’s exact tests were used where appropriate. For continuous variables, data were analyzed using Student’s t-test or Mann-Whitney U test and presented as the mean ± standard deviation or the median, interquartile range (IQR). Univariable and multivariable regression analysis was used to evaluate the factors associated with clinical outcomes. To confirm the independent association between variables, linear regression was applied for confounders. Survival analysis was performed by the Kaplan-Meier method with log-rank tests. All statistical analysis was performed with SPSS (version 25.0; SPSS Inc., Chicago, IL, USA), and a P value < 0.05 was considered statistically significant.", "This study was approved by the Institutional Review Board (IRB) of the Severance Hospital of Yonsei University (IRB No. 4-2021-0673). The IRB waived the requirement for obtaining informed consent from the patients.", "Baseline characteristics The baseline characteristics of the patients according to the results of crossmatches are shown in Table 1. Among the included 210 recipients, nine patients showed positive crossmatches. The median age was 56.4 years, and 134 patients (63.8%) were male. Only one patient was male in the positive crossmatch group. There was no RH− blood type within the study cohort. The most common primary diagnosis was idiopathic pulmonary fibrosis (114 patients, 54.3%), followed by connective tissue-related interstitial lung disease (CTD-ILD) (34 patients, 16.2%). The median number of days on the waiting list was 71.0, and 63 patients (30.0%) had undergone pretransplant extracorporeal membrane oxygenation support. The baseline characteristics were not significantly different between the positive and negative crossmatch groups.\nValues are presented as mean ± standard deviation or number (%).\nXM = crossmatch, BMI = body mass index, COPD = chronic obstructive pulmonary disease, CTD-ILD = connective tissue-related interstitial lung disease, BOS = bronchiolitis obliterans syndrome, ECMO = extracorporeal membrane oxygenation, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, HSCT = hematopoietic stem cell transplantation.\n\nTable 2 shows the results of immunological evaluations, including crossmatching. CDC-XM was performed in 208 patients (99.0%) and flow-XM was performed in 125 patients (59.5%). PRA was identified in 208 patients (99.0%). Among these patients, high levels of class I or class II PRA (> 50%) were detected in 18 patients (8.7%). The proportion of patients with high PRA was higher in the positive crossmatch group than in the negative crossmatch group (37.5% vs. 10.7%, P = 0.026).\nValues are presented as number (%).\nXM = crossmatch, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibody, HLA = human leukocyte antigen.\nAt the time of lung transplantation, most patients (201 patients, 95.7%) had undergone the DSA screening test. Twenty-seven patients (13.4%) were revealed to have DSA. A higher proportion of the patients in the positive crossmatch group had DSA (7 patients, 87.5%, vs. 20 patients, 10.4%, P < 0.001) compared with that in the negative crossmatch group. Desensitization was performed in 35 patients (16.7%), and four patients in the positive crossmatch group underwent desensitization.\nThe characteristics of nine patients with positive crossmatch are shown in Table 3. Among these patients, the most common etiology for lung transplantation was CTD-ILD. CDC-XM and flow-XM revealed positive crossmatches in five and six patients, respectively. DSA was detected in six patients, and three patients (patient numbers 5, 6, and 7) had an MFI higher than 5,000. Five patients underwent desensitization before lung transplantation. Acute rejection was diagnosed in three patients (patient numbers 3, 4, and 6), and two patients underwent transbronchial lung biopsy (patient numbers 3 and 4). Patient number 6 was positively crossmatched to the donor as observed using CDC-XM with both T land B lymphocytes, and class I DSA was detected with MFI over 5,000. The patient was clinically diagnosed with AMR and received aggressive treatments for rejection, including steroid pulse, plasmapheresis, and intravenous infusion of immunoglobulin. However, the patient received retransplantation on postoperative day 15 due to rejection.\nXM = crossmatch, CTD-ILD = connective tissue-related interstitial lung disease, ECMO = extracorporeal membrane oxygenation, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibodies, PGD = primary graft dysfunction, MFI = mean fluorescence intensity, AMR = antibody-mediated rejection, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, IVIG = intravenous immunoglobulin.\nThe baseline characteristics of the patients according to the results of crossmatches are shown in Table 1. Among the included 210 recipients, nine patients showed positive crossmatches. The median age was 56.4 years, and 134 patients (63.8%) were male. Only one patient was male in the positive crossmatch group. There was no RH− blood type within the study cohort. The most common primary diagnosis was idiopathic pulmonary fibrosis (114 patients, 54.3%), followed by connective tissue-related interstitial lung disease (CTD-ILD) (34 patients, 16.2%). The median number of days on the waiting list was 71.0, and 63 patients (30.0%) had undergone pretransplant extracorporeal membrane oxygenation support. The baseline characteristics were not significantly different between the positive and negative crossmatch groups.\nValues are presented as mean ± standard deviation or number (%).\nXM = crossmatch, BMI = body mass index, COPD = chronic obstructive pulmonary disease, CTD-ILD = connective tissue-related interstitial lung disease, BOS = bronchiolitis obliterans syndrome, ECMO = extracorporeal membrane oxygenation, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, HSCT = hematopoietic stem cell transplantation.\n\nTable 2 shows the results of immunological evaluations, including crossmatching. CDC-XM was performed in 208 patients (99.0%) and flow-XM was performed in 125 patients (59.5%). PRA was identified in 208 patients (99.0%). Among these patients, high levels of class I or class II PRA (> 50%) were detected in 18 patients (8.7%). The proportion of patients with high PRA was higher in the positive crossmatch group than in the negative crossmatch group (37.5% vs. 10.7%, P = 0.026).\nValues are presented as number (%).\nXM = crossmatch, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibody, HLA = human leukocyte antigen.\nAt the time of lung transplantation, most patients (201 patients, 95.7%) had undergone the DSA screening test. Twenty-seven patients (13.4%) were revealed to have DSA. A higher proportion of the patients in the positive crossmatch group had DSA (7 patients, 87.5%, vs. 20 patients, 10.4%, P < 0.001) compared with that in the negative crossmatch group. Desensitization was performed in 35 patients (16.7%), and four patients in the positive crossmatch group underwent desensitization.\nThe characteristics of nine patients with positive crossmatch are shown in Table 3. Among these patients, the most common etiology for lung transplantation was CTD-ILD. CDC-XM and flow-XM revealed positive crossmatches in five and six patients, respectively. DSA was detected in six patients, and three patients (patient numbers 5, 6, and 7) had an MFI higher than 5,000. Five patients underwent desensitization before lung transplantation. Acute rejection was diagnosed in three patients (patient numbers 3, 4, and 6), and two patients underwent transbronchial lung biopsy (patient numbers 3 and 4). Patient number 6 was positively crossmatched to the donor as observed using CDC-XM with both T land B lymphocytes, and class I DSA was detected with MFI over 5,000. The patient was clinically diagnosed with AMR and received aggressive treatments for rejection, including steroid pulse, plasmapheresis, and intravenous infusion of immunoglobulin. However, the patient received retransplantation on postoperative day 15 due to rejection.\nXM = crossmatch, CTD-ILD = connective tissue-related interstitial lung disease, ECMO = extracorporeal membrane oxygenation, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibodies, PGD = primary graft dysfunction, MFI = mean fluorescence intensity, AMR = antibody-mediated rejection, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, IVIG = intravenous immunoglobulin.\nOperative details and postoperative outcomes Most patients underwent bilateral lung transplantation (202 patients, 96.2%). According to the PGD grade, patients presenting with grade 3 at postoperative 48 hours or 72 hours were regarded as high-grade PGD. Forty-four patients showed high-grade PGD, and no statistical difference was observed between the groups. The incidences of other postoperative complications, including postoperative bleeding, anastomosis dehiscence, pulmonary thromboembolism, pneumonia, and acute kidney injury, were not statistically distinguishable between the groups (Table 4).\nValues are presented as number (%), mean ± standard deviation or number (interquartile range).\nXM = crossmatch, PGD = primary graft dysfunction, ECMO = extracorporeal membrane oxygenation, ICU = intensive care unit, AKI = acute kidney injury.\nAcute rejection was diagnosed in 65 patients. Transbronchial lung biopsy-proven ACR was diagnosed in 37 patients, and other patients were diagnosed clinically. Though the incidence of acute rejection with regard to crossmatching results was not statistically different between the groups, the positive crossmatch group showed shorter intervals between transplantation and diagnosis of rejection. Five patients had clinical presentations that were compatible with AMR. However, none of these patients were diagnosed using the biopsy specimen or capillary C4d composition. Seven patients (3.3%) showed physiologic changes that were considered as CLAD. BOS was diagnosed in five patients (2.4%), and RAS was diagnosed in three patients (1.4%). The median number of days from transplant to CLAD diagnosis was 648 (IQR, 488.0–759.0). All of the patients showed CLAD were in the negative crossmatch group.\nMost patients underwent bilateral lung transplantation (202 patients, 96.2%). According to the PGD grade, patients presenting with grade 3 at postoperative 48 hours or 72 hours were regarded as high-grade PGD. Forty-four patients showed high-grade PGD, and no statistical difference was observed between the groups. The incidences of other postoperative complications, including postoperative bleeding, anastomosis dehiscence, pulmonary thromboembolism, pneumonia, and acute kidney injury, were not statistically distinguishable between the groups (Table 4).\nValues are presented as number (%), mean ± standard deviation or number (interquartile range).\nXM = crossmatch, PGD = primary graft dysfunction, ECMO = extracorporeal membrane oxygenation, ICU = intensive care unit, AKI = acute kidney injury.\nAcute rejection was diagnosed in 65 patients. Transbronchial lung biopsy-proven ACR was diagnosed in 37 patients, and other patients were diagnosed clinically. Though the incidence of acute rejection with regard to crossmatching results was not statistically different between the groups, the positive crossmatch group showed shorter intervals between transplantation and diagnosis of rejection. Five patients had clinical presentations that were compatible with AMR. However, none of these patients were diagnosed using the biopsy specimen or capillary C4d composition. Seven patients (3.3%) showed physiologic changes that were considered as CLAD. BOS was diagnosed in five patients (2.4%), and RAS was diagnosed in three patients (1.4%). The median number of days from transplant to CLAD diagnosis was 648 (IQR, 488.0–759.0). All of the patients showed CLAD were in the negative crossmatch group.\nSurvival analysis As shown in Table 4, 55 patients (26.2%) showed 1-year mortality. The 1-year and overall mortality rates were not statistically different between the groups (Table 4). Fig. 1 shows the Kaplan-Meier curves of 1-year and overall mortality rates of the patients. According to Kaplan-Meier analysis, there were no significant differences in the survival rates, regardless of crossmatching status.\nWe also performed a regression analysis to identify the risk factors for poor 1-year survival. Positive CDC crossmatching was the only significant risk factor for poor 1-year survival in the univariable analysis (odds ratio, 11.922, 95% confidence interval, 1.302–19.125, P = 0.018).\nWe further divided the patients based on crossmatching methods and performed survival analysis for the subgroups. When positive crossmatching was confirmed by CDC-XM, the post-transplant 1-year and overall mortality rates were poorer than the negative crossmatching results (P < 0.001 and P < 0.001, respectively) (Fig. 2A and B). According to the Kaplan-Meier method, positive crossmatching by the flow-XM method did not significantly differ in the 1-year and overall morality (Fig. 2C and D). When considering the detection of T and B lymphocytes individually, only positive crossmatching by T lymphocytes was related to mortality within 1-year after transplantation (P = 0.002) and overall mortality (P = 0.006) (Fig. 3).\nCDC = complement-dependent cytotoxicity.\nAs shown in Table 4, 55 patients (26.2%) showed 1-year mortality. The 1-year and overall mortality rates were not statistically different between the groups (Table 4). Fig. 1 shows the Kaplan-Meier curves of 1-year and overall mortality rates of the patients. According to Kaplan-Meier analysis, there were no significant differences in the survival rates, regardless of crossmatching status.\nWe also performed a regression analysis to identify the risk factors for poor 1-year survival. Positive CDC crossmatching was the only significant risk factor for poor 1-year survival in the univariable analysis (odds ratio, 11.922, 95% confidence interval, 1.302–19.125, P = 0.018).\nWe further divided the patients based on crossmatching methods and performed survival analysis for the subgroups. When positive crossmatching was confirmed by CDC-XM, the post-transplant 1-year and overall mortality rates were poorer than the negative crossmatching results (P < 0.001 and P < 0.001, respectively) (Fig. 2A and B). According to the Kaplan-Meier method, positive crossmatching by the flow-XM method did not significantly differ in the 1-year and overall morality (Fig. 2C and D). When considering the detection of T and B lymphocytes individually, only positive crossmatching by T lymphocytes was related to mortality within 1-year after transplantation (P = 0.002) and overall mortality (P = 0.006) (Fig. 3).\nCDC = complement-dependent cytotoxicity.", "The baseline characteristics of the patients according to the results of crossmatches are shown in Table 1. Among the included 210 recipients, nine patients showed positive crossmatches. The median age was 56.4 years, and 134 patients (63.8%) were male. Only one patient was male in the positive crossmatch group. There was no RH− blood type within the study cohort. The most common primary diagnosis was idiopathic pulmonary fibrosis (114 patients, 54.3%), followed by connective tissue-related interstitial lung disease (CTD-ILD) (34 patients, 16.2%). The median number of days on the waiting list was 71.0, and 63 patients (30.0%) had undergone pretransplant extracorporeal membrane oxygenation support. The baseline characteristics were not significantly different between the positive and negative crossmatch groups.\nValues are presented as mean ± standard deviation or number (%).\nXM = crossmatch, BMI = body mass index, COPD = chronic obstructive pulmonary disease, CTD-ILD = connective tissue-related interstitial lung disease, BOS = bronchiolitis obliterans syndrome, ECMO = extracorporeal membrane oxygenation, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, HSCT = hematopoietic stem cell transplantation.\n\nTable 2 shows the results of immunological evaluations, including crossmatching. CDC-XM was performed in 208 patients (99.0%) and flow-XM was performed in 125 patients (59.5%). PRA was identified in 208 patients (99.0%). Among these patients, high levels of class I or class II PRA (> 50%) were detected in 18 patients (8.7%). The proportion of patients with high PRA was higher in the positive crossmatch group than in the negative crossmatch group (37.5% vs. 10.7%, P = 0.026).\nValues are presented as number (%).\nXM = crossmatch, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibody, HLA = human leukocyte antigen.\nAt the time of lung transplantation, most patients (201 patients, 95.7%) had undergone the DSA screening test. Twenty-seven patients (13.4%) were revealed to have DSA. A higher proportion of the patients in the positive crossmatch group had DSA (7 patients, 87.5%, vs. 20 patients, 10.4%, P < 0.001) compared with that in the negative crossmatch group. Desensitization was performed in 35 patients (16.7%), and four patients in the positive crossmatch group underwent desensitization.\nThe characteristics of nine patients with positive crossmatch are shown in Table 3. Among these patients, the most common etiology for lung transplantation was CTD-ILD. CDC-XM and flow-XM revealed positive crossmatches in five and six patients, respectively. DSA was detected in six patients, and three patients (patient numbers 5, 6, and 7) had an MFI higher than 5,000. Five patients underwent desensitization before lung transplantation. Acute rejection was diagnosed in three patients (patient numbers 3, 4, and 6), and two patients underwent transbronchial lung biopsy (patient numbers 3 and 4). Patient number 6 was positively crossmatched to the donor as observed using CDC-XM with both T land B lymphocytes, and class I DSA was detected with MFI over 5,000. The patient was clinically diagnosed with AMR and received aggressive treatments for rejection, including steroid pulse, plasmapheresis, and intravenous infusion of immunoglobulin. However, the patient received retransplantation on postoperative day 15 due to rejection.\nXM = crossmatch, CTD-ILD = connective tissue-related interstitial lung disease, ECMO = extracorporeal membrane oxygenation, CDC = complement-dependent cytotoxicity, PRA = panel-reactive antibody, DSA = donor specific antibodies, PGD = primary graft dysfunction, MFI = mean fluorescence intensity, AMR = antibody-mediated rejection, ARDS = acute respiratory distress syndrome, IPF = idiopathic pulmonary fibrosis, IVIG = intravenous immunoglobulin.", "Most patients underwent bilateral lung transplantation (202 patients, 96.2%). According to the PGD grade, patients presenting with grade 3 at postoperative 48 hours or 72 hours were regarded as high-grade PGD. Forty-four patients showed high-grade PGD, and no statistical difference was observed between the groups. The incidences of other postoperative complications, including postoperative bleeding, anastomosis dehiscence, pulmonary thromboembolism, pneumonia, and acute kidney injury, were not statistically distinguishable between the groups (Table 4).\nValues are presented as number (%), mean ± standard deviation or number (interquartile range).\nXM = crossmatch, PGD = primary graft dysfunction, ECMO = extracorporeal membrane oxygenation, ICU = intensive care unit, AKI = acute kidney injury.\nAcute rejection was diagnosed in 65 patients. Transbronchial lung biopsy-proven ACR was diagnosed in 37 patients, and other patients were diagnosed clinically. Though the incidence of acute rejection with regard to crossmatching results was not statistically different between the groups, the positive crossmatch group showed shorter intervals between transplantation and diagnosis of rejection. Five patients had clinical presentations that were compatible with AMR. However, none of these patients were diagnosed using the biopsy specimen or capillary C4d composition. Seven patients (3.3%) showed physiologic changes that were considered as CLAD. BOS was diagnosed in five patients (2.4%), and RAS was diagnosed in three patients (1.4%). The median number of days from transplant to CLAD diagnosis was 648 (IQR, 488.0–759.0). All of the patients showed CLAD were in the negative crossmatch group.", "As shown in Table 4, 55 patients (26.2%) showed 1-year mortality. The 1-year and overall mortality rates were not statistically different between the groups (Table 4). Fig. 1 shows the Kaplan-Meier curves of 1-year and overall mortality rates of the patients. According to Kaplan-Meier analysis, there were no significant differences in the survival rates, regardless of crossmatching status.\nWe also performed a regression analysis to identify the risk factors for poor 1-year survival. Positive CDC crossmatching was the only significant risk factor for poor 1-year survival in the univariable analysis (odds ratio, 11.922, 95% confidence interval, 1.302–19.125, P = 0.018).\nWe further divided the patients based on crossmatching methods and performed survival analysis for the subgroups. When positive crossmatching was confirmed by CDC-XM, the post-transplant 1-year and overall mortality rates were poorer than the negative crossmatching results (P < 0.001 and P < 0.001, respectively) (Fig. 2A and B). According to the Kaplan-Meier method, positive crossmatching by the flow-XM method did not significantly differ in the 1-year and overall morality (Fig. 2C and D). When considering the detection of T and B lymphocytes individually, only positive crossmatching by T lymphocytes was related to mortality within 1-year after transplantation (P = 0.002) and overall mortality (P = 0.006) (Fig. 3).\nCDC = complement-dependent cytotoxicity.", "We conducted a multicenter nationwide study to analyze the prevalence of positive crossmatching and the outcomes of positively crossmatched patients in lung transplantation. Although the incidence of positive crossmatching was low (4.3%), positive CDC-XM and T lymphocyte crossmatching results were related to poor 1-year and overall survival.\nIt is controversial to consider HLA matching as an allocation factor. ISHLT introduced the lung allocation score (LAS) in 2005 and revised it in 2015 and 2020.22 The LAS includes factors related to waiting list mortality and post-transplant 1-year survival. HLA matching is not considered in the LAS system for several reasons. First, as lung transplantation is mostly performed under emergency settings, it is not always possible to conduct donor HLA typing prior to allocation. Furthermore, there is also a possibility of prolonging allograft ischemic time while waiting for crossmatching results.810 Brugière et al.23 analyzed the relative impact of mismatching and graft ischemic time; an allograft ischemic time of over 330 minutes worsened patient survival, even with well-matched donor and recipient pairs. Second, because of the shortage of possible donor organs and the extensive complexity of the HLA system, the probability of finding a matched recipient is extremely low.10 Furthermore, nonimmunologic factors are considered more important for 1-year survival. Early mortality resulting from the ischemia and reperfusion injury has been shown to decrease after introducing low-potassium dextran lung preservation solution.24 Advances in immunosuppressive regimens and enhanced combinations of antibiotics may also be responsible for improved unstable hemodynamics during the early post-transplant period.25 After alleviating these nonimmunological factors for early graft dysfunction and mortality in lung transplantation, the effect of HLA compatibility between the donor and recipient becomes more important for early outcomes. In addition, an improved methodology for HLA crossmatching has facilitated more accurate and rapid access to crossmatching results, leading to increased awareness of the importance of HLA crossmatching in lung transplantation.\nSeveral studies analyzed the impact of HLA mismatching on lung transplantation. It is well known that HLA mismatching can trigger graft damage or even mortality.10 Donor HLA molecules are recognized by the immune system of the recipient. Immunogenetic discrepancies can lead to ACR and AMR, which play major roles in graft dysfunction and loss.\nIn 2003, the Eurotransplant Foundation analyzed 590 patients who underwent cadaveric lung transplantation between January 1997 and December 1999. In this study, patients with more than four mismatches of HLA loci showed poorer 1-year survival than the others.26 A retrospective study by the United Network for Organ Sharing/ISHLT also confirmed HLA mismatches as a risk factor for 1-year mortality in lung transplantation.27 As routine pretransplant crossmatching is not considered feasible in lung transplantation; these studies did not analyze prospective crossmatching; however, the results confirmed that HLA mismatching is related to graft dysfunction and survival.\nIn our study, the incidence of rejection or 1-year survival was not statistically different according to the crossmatching results. This result may be because of the low incidence of positive crossmatching in the cohort (4.3%) and the relatively short-term follow-up period of the positive group (median 129.0 vs. 386.5 days). Moreover, most centers involved in the registry tended to decline the allocation when positive crossmatching had been verified not only by the KODA lab but also by the center’s study. However, the relationship between 1-year survival and positive crossmatching was observed when the cohort was subdivided according to the crossmatching method.\nFlow-XM is reportedly more sensitive for detecting anti-HLA antibodies than CDC-XM.28 Several studies have shown the relationship between positive flow-XM results and a higher risk of graft dysfunction and poor survival.237 However, as flow-XM is sometimes too sensitive, denying the allocation because of a positive flow crossmatch is still controversial.29 Moreover, flow-XM is not a functional test, and the binding of antibodies to lymphocytes may not always reflect complement system activation.6 Thus, transplantation across a positive flow crossmatch and negative CDC crossmatch is acceptable, whereas transplantation across a positive CDC result is not typically recommended. In our study, the Kaplan-Meier analysis also supported this principle. The patients with positive CDC crossmatch showed significantly lower 1-year and overall survival, whereas positive flow crossmatch did not significantly affect the outcome.\nThe impact of positive T cell crossmatches on poor graft outcomes was apparent in renal, cardiac, and liver transplantation.230 A negative lymphocytotoxic T cell result is generally considered as grounds to proceed with transplantation. Here, the results of Kaplan-Meier analysis also demonstrated significantly poor 1-year and overall survival in positive T-cell crossmatched patients, whereas positive B-cell crossmatching did not show a significant difference in graft survival. Positive T-cell crossmatching indicates the presence of DSAs against class I antigens, which can lead to antibody-mediated damages to the graft.378 This damage can cause hyperacute rejection or graft loss.\nThis study has certain limitations. First, although this study analyzed a nationwide cohort, this is a retrospective study with a relatively small number of patients and a short follow-up period. CLAD was not observed in the positively crossmatched patients, probably because of the short follow-up time (median 129.0 days). Thus, further follow-up for the CLAD is needed for the positive crossmath group. Second, the incidence of positive crossmatching was too low to perform further statistical analyses. As mentioned above, we performed regression analysis to identify the risk factors of poor survival, and positive CDC crossmatching was the only meaningful risk factor for poor 1-year survival. Third, a nationwide multicenter study with registry data does not reflect detailed information on each case. In particular, it is difficult to determine the actual immunologic consequences on graft survival.\nIn conclusion, this study analyzed the outcomes of positively crossmatched patients in lung transplantation and revealed the importance of crossmatching methods in lung transplantation using nationwide data. Positive results of CDC and T lymphocyte crossmatching may lead to devastating outcomes in lung transplantation. Although crossmatching is not part of the allocation system, the results of crossmatching should be considered with caution during postoperative management after lung transplantation." ]

[ "intro", "methods", null, null, null, null, null, "results", null, null, null, "discussion" ]

[ "Lung Transplantation", "Crossmatching", "Allocation", "Histocompatibility" ]

[ 299, 423, 270, 156, 43, 748, 304, 292 ]

[ "patients", "lung", "positive", "transplantation", "crossmatching", "crossmatch", "xm", "performed", "lung transplantation", "cdc" ]

[ "immunologic consequences graft", "crossmatching lung transplantation", "crossmatching methodology immunosuppression", "crossmatching immunologic evaluation", "crossmatches renal transplantation" ]

[ "Absenteeism", "Adult", "Aged", "Employment", "Female", "Humans", "Japan", "Job Satisfaction", "Longitudinal Studies", "Male", "Middle Aged", "Occupational Health", "Prospective Studies", "Risk Factors", "Sick Leave", "Social Environment", "Social Support", "Stress, Psychological", "Surveys and Questionnaires", "Workload", "Workplace", "Young Adult" ]

[ "Participants", "Measures", "Job dissatisfaction", "Long-term sickness absence", "Psychosocial work environment", "Covariates", "Statistical analysis" ]

[ "A 1-year prospective study of employees from a financial service company listed on the major stock exchanges was conducted from July 2015 to July 2016. Information was gathered using a self-administered questionnaire and the personnel records of the surveyed company. At baseline (July–August 2015), all employees, except for board members; temporary transferred, overseas, and dispatched employees; and absentees (N = 15,615) were invited to participate in this study; a total of 14,711 employees completed the baseline questionnaire (response rate: 94.2%). After excluding 24 employees who had histories of long-term sickness absence in the past 3 years, 14,687 employees (7,343 men and 7,344 women) aged 20–66 years were followed for 1 year (until July 31st, 2016) (Figure 1). Informed consent was obtained from participants using the opt-out method for the secondary analysis of existing anonymous data. The study procedure was reviewed and approved by the Kitasato University Medical Ethics Organization (No. B15-113).", " Job dissatisfaction Job dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\nJob dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\n Long-term sickness absence Information on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\nInformation on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\n Psychosocial work environment For psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\nFor psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\n Covariates Covariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.\nCovariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.", "Job dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.", "Information on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.", "For psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.", "Covariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.", "We first conducted a descriptive analysis using Student’s t test or Fisher’s exact test to compare the demographic and occupational characteristics and the scale scores between the satisfied and dissatisfied groups. Afterwards, the cumulative hazard of long-term sickness absence was plotted as Kaplan-Meier curves and the log-rank test was conducted to compare the hazard functions between the satisfied and dissatisfied groups. Finally, using the satisfied group as a reference, Cox’s proportional hazard regression analysis was conducted to estimate the hazard ratio (HR) and its 95% confidence interval (CI) of the incidence of long-term sickness absence during the follow-up period in the dissatisfied group. In the series of analyses, we first adjusted for demographic characteristics (ie, age and gender) (model 1). Subsequently, we incrementally adjusted for occupational characteristics (ie, length of service, job type, and employment position) (model 2) and psychosocial work environment (ie, quantitative job overload, job control, and workplace social support) (model 3). For model 3, overcontrol bias due to common method variance might occur since the present study measured job dissatisfaction and psychosocial work environment simultaneously with the same self-administered questionnaire (ie, the BJSQ). Therefore, to test the presence of overcontrol bias due to common method variance, Harman’s single-factor test40 was conducted by entering items for job dissatisfaction, quantitative job overload, job control, and workplace social support (ie, a total of 13 items) into the unrotated principal component analysis. Furthermore, as sub-analyses, the log-rank test and the Cox’s proportional hazard regression analysis were conducted by gender because men and women are exposed to different work environment in Japan. The level of significance was 0.05 (two-tailed). The statistical analyses were conducted using IBM® SPSS® Statistics Version 23.0 for Windows (IBM Corp., Armonk, NY, USA)." ]

[ null, null, null, null, null, null, null ]

[ "INTRODUCTION", "MATERIAL AND METHODS", "Participants", "Measures", "Job dissatisfaction", "Long-term sickness absence", "Psychosocial work environment", "Covariates", "Statistical analysis", "RESULTS", "DISCUSSION" ]

[ "Sickness absence is a major public health and economic problem in many countries.1,2 Among others, long-term sickness absence, often defined as sickness absence lasting 4 weeks/1 month or more,3 bears high costs for a variety of stakeholders, including employees, employers, insurance agencies, and society at large.4,5 The Organization for Economic Co-operation and Development (OECD) has reported that OECD member countries spend, on average, approximately 1.9% of the gross domestic product (GDP) on sickness absence benefits,6 most of which are accounted for by long-term sickness absence.2 Furthermore, long-term sickness absence has various adverse effects on employees, such as lower probability of returning to work,7,8 a higher risk of social exclusion,9 and mortality.10–12 Therefore, identifying predictors of long-term sickness absence and preventing it are beneficial for both employees and society.\nIn the occupational health research field, job dissatisfaction (ie, an unpleasant emotion when one’s work is frustrating and blocking the affirmation of their values)13 has been attracting attention as a predictor of sickness absence, as well as of poor mental health (ie, anxiety, burnout, depression, and low self-esteem) and physical health (ie, cardiovascular disease and musculoskeletal disorders).14 Several prospective studies in European countries have examined the association of job dissatisfaction with sickness absence15–24; the results have been inconsistent, and most of these studies focused mainly on short-term sickness absence lasting from a few days to a few weeks. To date, only three studies focused on long-term sickness absence16,21,22; two, however, relied on self-reports rather than on personnel records or national register data for measuring sickness absence duration.21,22 This may have led to a less accurate association with job dissatisfaction.25 Furthermore, only one study conducted a survival analysis.23\nIn addition to the above, psychosocial work environment may explain the association of job dissatisfaction with sickness absence.26 In fact, major psychosocial work environment, such as described in the job demands-control (JD-C) or demand-control-support (DCS) model,27,28 has been associated with job dissatisfaction.29,30 It is also known that poor psychosocial wok environment causes sickness absence.31 It might be interesting to know how much unique impact job dissatisfaction has on long-term sickness absence independent of psychosocial work environment, because it would be relevant for developing an effective strategy to prevent long-term sickness absence whether targeting on job dissatisfaction per se or psychosocial work environment.\nContrary to European countries, the association between psychosocial work environment, job dissatisfaction, and long-term sickness absence has not been fully examined among Japanese employees. In Japan, approximately 60% of employees reported job-related distress due to psychosocial work environment such as job overload and workplace human relations.32 Furthermore, compared to European countries, Japanese employees have been found to have lower levels of job satisfaction,33 as well as positive work-related state of mind, such as work engagement.34 On the other hand, because the social notion that “not taking time off and working hard are virtues” is still strongly rooted in the Japanese psyche,35 taking long-term sickness absence is a serious event for Japanese employees. Therefore, it is extremely valuable to clarify the association of job dissatisfaction with long-term sickness absence and the role of psychosocial work environment in this association among Japanese employees. To date, two cross-sectional studies have reported the association of job dissatisfaction with sickness absence among Japanese employees,36,37 while prospective evidence is lacking and the role of psychosocial work environment in the association is still unclear.\nThe purpose of the present study was twofold. The first purpose was to examine the prospective association of job dissatisfaction with long-term sickness absence obtained from personnel records in a large sample of Japanese employees, conducting survival analysis. The second purpose was to examine whether psychosocial work environment explains the association of job dissatisfaction with long-term sickness absence. In the present study, we focused especially on financial service employees because they experience increased stress and worries due to greater time pressures, problems with ergonomics, conflicting roles, work demands, and difficult relationships with customers.38", " Participants A 1-year prospective study of employees from a financial service company listed on the major stock exchanges was conducted from July 2015 to July 2016. Information was gathered using a self-administered questionnaire and the personnel records of the surveyed company. At baseline (July–August 2015), all employees, except for board members; temporary transferred, overseas, and dispatched employees; and absentees (N = 15,615) were invited to participate in this study; a total of 14,711 employees completed the baseline questionnaire (response rate: 94.2%). After excluding 24 employees who had histories of long-term sickness absence in the past 3 years, 14,687 employees (7,343 men and 7,344 women) aged 20–66 years were followed for 1 year (until July 31st, 2016) (Figure 1). Informed consent was obtained from participants using the opt-out method for the secondary analysis of existing anonymous data. The study procedure was reviewed and approved by the Kitasato University Medical Ethics Organization (No. B15-113).\nA 1-year prospective study of employees from a financial service company listed on the major stock exchanges was conducted from July 2015 to July 2016. Information was gathered using a self-administered questionnaire and the personnel records of the surveyed company. At baseline (July–August 2015), all employees, except for board members; temporary transferred, overseas, and dispatched employees; and absentees (N = 15,615) were invited to participate in this study; a total of 14,711 employees completed the baseline questionnaire (response rate: 94.2%). After excluding 24 employees who had histories of long-term sickness absence in the past 3 years, 14,687 employees (7,343 men and 7,344 women) aged 20–66 years were followed for 1 year (until July 31st, 2016) (Figure 1). Informed consent was obtained from participants using the opt-out method for the secondary analysis of existing anonymous data. The study procedure was reviewed and approved by the Kitasato University Medical Ethics Organization (No. B15-113).\n Measures Job dissatisfaction Job dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\nJob dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\n Long-term sickness absence Information on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\nInformation on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\n Psychosocial work environment For psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\nFor psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\n Covariates Covariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.\nCovariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.\n Job dissatisfaction Job dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\nJob dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\n Long-term sickness absence Information on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\nInformation on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\n Psychosocial work environment For psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\nFor psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\n Covariates Covariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.\nCovariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.\n Statistical analysis We first conducted a descriptive analysis using Student’s t test or Fisher’s exact test to compare the demographic and occupational characteristics and the scale scores between the satisfied and dissatisfied groups. Afterwards, the cumulative hazard of long-term sickness absence was plotted as Kaplan-Meier curves and the log-rank test was conducted to compare the hazard functions between the satisfied and dissatisfied groups. Finally, using the satisfied group as a reference, Cox’s proportional hazard regression analysis was conducted to estimate the hazard ratio (HR) and its 95% confidence interval (CI) of the incidence of long-term sickness absence during the follow-up period in the dissatisfied group. In the series of analyses, we first adjusted for demographic characteristics (ie, age and gender) (model 1). Subsequently, we incrementally adjusted for occupational characteristics (ie, length of service, job type, and employment position) (model 2) and psychosocial work environment (ie, quantitative job overload, job control, and workplace social support) (model 3). For model 3, overcontrol bias due to common method variance might occur since the present study measured job dissatisfaction and psychosocial work environment simultaneously with the same self-administered questionnaire (ie, the BJSQ). Therefore, to test the presence of overcontrol bias due to common method variance, Harman’s single-factor test40 was conducted by entering items for job dissatisfaction, quantitative job overload, job control, and workplace social support (ie, a total of 13 items) into the unrotated principal component analysis. Furthermore, as sub-analyses, the log-rank test and the Cox’s proportional hazard regression analysis were conducted by gender because men and women are exposed to different work environment in Japan. The level of significance was 0.05 (two-tailed). The statistical analyses were conducted using IBM® SPSS® Statistics Version 23.0 for Windows (IBM Corp., Armonk, NY, USA).\nWe first conducted a descriptive analysis using Student’s t test or Fisher’s exact test to compare the demographic and occupational characteristics and the scale scores between the satisfied and dissatisfied groups. Afterwards, the cumulative hazard of long-term sickness absence was plotted as Kaplan-Meier curves and the log-rank test was conducted to compare the hazard functions between the satisfied and dissatisfied groups. Finally, using the satisfied group as a reference, Cox’s proportional hazard regression analysis was conducted to estimate the hazard ratio (HR) and its 95% confidence interval (CI) of the incidence of long-term sickness absence during the follow-up period in the dissatisfied group. In the series of analyses, we first adjusted for demographic characteristics (ie, age and gender) (model 1). Subsequently, we incrementally adjusted for occupational characteristics (ie, length of service, job type, and employment position) (model 2) and psychosocial work environment (ie, quantitative job overload, job control, and workplace social support) (model 3). For model 3, overcontrol bias due to common method variance might occur since the present study measured job dissatisfaction and psychosocial work environment simultaneously with the same self-administered questionnaire (ie, the BJSQ). Therefore, to test the presence of overcontrol bias due to common method variance, Harman’s single-factor test40 was conducted by entering items for job dissatisfaction, quantitative job overload, job control, and workplace social support (ie, a total of 13 items) into the unrotated principal component analysis. Furthermore, as sub-analyses, the log-rank test and the Cox’s proportional hazard regression analysis were conducted by gender because men and women are exposed to different work environment in Japan. The level of significance was 0.05 (two-tailed). The statistical analyses were conducted using IBM® SPSS® Statistics Version 23.0 for Windows (IBM Corp., Armonk, NY, USA).", "A 1-year prospective study of employees from a financial service company listed on the major stock exchanges was conducted from July 2015 to July 2016. Information was gathered using a self-administered questionnaire and the personnel records of the surveyed company. At baseline (July–August 2015), all employees, except for board members; temporary transferred, overseas, and dispatched employees; and absentees (N = 15,615) were invited to participate in this study; a total of 14,711 employees completed the baseline questionnaire (response rate: 94.2%). After excluding 24 employees who had histories of long-term sickness absence in the past 3 years, 14,687 employees (7,343 men and 7,344 women) aged 20–66 years were followed for 1 year (until July 31st, 2016) (Figure 1). Informed consent was obtained from participants using the opt-out method for the secondary analysis of existing anonymous data. The study procedure was reviewed and approved by the Kitasato University Medical Ethics Organization (No. B15-113).", " Job dissatisfaction Job dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\nJob dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.\n Long-term sickness absence Information on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\nInformation on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.\n Psychosocial work environment For psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\nFor psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.\n Covariates Covariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.\nCovariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.", "Job dissatisfaction was measured using the Brief Job Stress Questionnaire (BJSQ). The BJSQ has high levels of internal consistency reliability and factor-based validity39 and includes a single-item summary measure of job satisfaction (“I am satisfied with my job”). Responses are provided on a four-point Likert scale (1 = Dissatisfied, 2 = Somewhat dissatisfied, 3 = Somewhat satisfied, and 4 = Satisfied). Participants were dichotomized into “dissatisfied” (those who answered 1 or 2) and “satisfied” (those who answered 3 or 4) groups.", "Information on dates of application for invalidity benefits with medical certification for long-term sickness absence lasting 1 month or more was obtained from the personnel records of the surveyed company. In the surveyed company, it was mandatory for employees to submit medical certification from his/her attending physician to the human resource department when applying for invalidity benefits. Furthermore, the personnel records included information on resignation/retirement date. Based on this information, those who resigned/retired from the surveyed company during the follow-up period were treated as censored cases. The follow-up began on the date of response to the BJSQ and ended at the start date of long-term sickness absence (ie, the date of application for invalidity benefits), the resignation/retirement date, or July 31st, 2016, whichever came first.", "For psychosocial work environment, we examined quantitative job overload, job control, and workplace social support, based on the JD-C or DCS model.27,28 These were measured using the BJSQ introduced above. The BJSQ includes three-item quantitative job overload, job control, supervisor support, and coworker support scales. The answers are provided on a four-point Likert scale (1 = Not at all, 2 = Somewhat, 3 = Moderately so, and 4 = Very much so for quantitative job overload and job control; 1 = Not at all, 2 = Somewhat, 3 = Very much, and 4 = Extremely for supervisor support and coworker support), with the scores of each scale ranging from 3–12. For workplace social support, total scores for supervisor support and coworker support were calculated (score range: 6–24). In this sample, the Cronbach’s alpha coefficients were 0.78, 0.70, and 0.88 for quantitative job overload, job control, and workplace social support, respectively.", "Covariates included demographic and occupational characteristics, all of which were obtained from the personnel records of the surveyed company. Demographic characteristics included age and gender. Age was used as a continuous variable. Occupational characteristics included length of service, job type, and employment position. Length of service was used as a continuous variable. Job type was classified into four groups: sales, claims service, administrative, and others. Employment position was classified into five groups: manager, staff, senior employee, temporary employee, and others.", "We first conducted a descriptive analysis using Student’s t test or Fisher’s exact test to compare the demographic and occupational characteristics and the scale scores between the satisfied and dissatisfied groups. Afterwards, the cumulative hazard of long-term sickness absence was plotted as Kaplan-Meier curves and the log-rank test was conducted to compare the hazard functions between the satisfied and dissatisfied groups. Finally, using the satisfied group as a reference, Cox’s proportional hazard regression analysis was conducted to estimate the hazard ratio (HR) and its 95% confidence interval (CI) of the incidence of long-term sickness absence during the follow-up period in the dissatisfied group. In the series of analyses, we first adjusted for demographic characteristics (ie, age and gender) (model 1). Subsequently, we incrementally adjusted for occupational characteristics (ie, length of service, job type, and employment position) (model 2) and psychosocial work environment (ie, quantitative job overload, job control, and workplace social support) (model 3). For model 3, overcontrol bias due to common method variance might occur since the present study measured job dissatisfaction and psychosocial work environment simultaneously with the same self-administered questionnaire (ie, the BJSQ). Therefore, to test the presence of overcontrol bias due to common method variance, Harman’s single-factor test40 was conducted by entering items for job dissatisfaction, quantitative job overload, job control, and workplace social support (ie, a total of 13 items) into the unrotated principal component analysis. Furthermore, as sub-analyses, the log-rank test and the Cox’s proportional hazard regression analysis were conducted by gender because men and women are exposed to different work environment in Japan. The level of significance was 0.05 (two-tailed). The statistical analyses were conducted using IBM® SPSS® Statistics Version 23.0 for Windows (IBM Corp., Armonk, NY, USA).", "Table 1 shows the detailed characteristics of the participants in the satisfied and dissatisfied groups. Compared to the satisfied group, the dissatisfied group was significantly younger, had a greater proportion of women, claims service, staff, and temporary employees, and perceived significantly higher levels of quantitative job overload and lower levels of job control and workplace social support.\nSD, standard deviation.\naStudent’s t test and Fisher’s exact test were used for the continuous and categorical variables, respectively.\nFigure 2 shows the Kaplan-Meier curves for the cumulative hazard of long-term sickness absence among the dissatisfied group compared to the satisfied group. The log-rank test showed that the dissatisfied group had a significantly higher incidence rate of long-term sickness absence compared to the satisfied group (P < 0.001).\nTable 2 shows the results of the Cox’s proportional hazard regression analysis. During 5,258,910 person-days (mean: 358 days, range: 3–373 days), 62 employees (32 men and 30 women) took long-term sickness absence (mental disorders: 51 cases, musculoskeletal disorders: 6 cases, cerebrovascular disease: 3 cases, and cardiovascular disease: 2 cases). After adjusting for demographic and occupational characteristics (models 1 and 2), the dissatisfied group had a significantly higher HR of long-term sickness absence than the satisfied group (HR 3.00; 95% CI, 1.80–5.00 and HR 2.91; 95% CI, 1.74–4.87 for models 1 and 2, respectively). However, after additionally adjusting for psychosocial work environment (model 3), this association was weakened and no longer significant (HR 1.55; 95% CI, 0.86–2.80).\naAdjusted for age (and gender).\nbAdditionally adjusted for length of service, job type, and employment position.\ncAdditionally adjusted for quantitative job overload, job control, and workplace social support.\nFor the Harman’s single-factor test, three factors with eigenvalues greater than 1.0 were extracted and the first (largest) factor did not account for a majority of the variance (32.7%), indicating that overcontrol bias due to common method variance was not of great concern.\nWhen we conducted the gender-stratified analysis, similar tendency to the main analysis was observed among both genders while statistical significance was marginal for the log-rank test (P = 0.063) and for models 1 and 2 of the Cox’s proportional hazard regression analysis among women (Table 2).", "The present study demonstrated that after adjusting for demographic and occupational characteristics, those who perceived job dissatisfaction had a significantly higher risk of long-term sickness absence during the 1-year follow-up period than those who perceived job satisfaction. After additionally adjusting for psychosocial work environment based on the JD-C or DCS model, the risk was no longer significant.\nJob dissatisfaction was significantly associated with a higher risk of long-term sickness absence after adjusting for demographic and occupational characteristics. This finding is consistent with previous prospective studies in European countries (ie, Norway and the Netherlands) that have reported a significant association of job dissatisfaction with long-term sickness absence in the crude model,22 as well as after adjusting for demographic and occupational characteristics (eg, age, gender, education, and affiliation).16,21 Using personnel records to measure long-term sickness absence and conducting a survival analysis, the present study expanded this evidence into other than European countries.\nAfter additionally adjusting for psychosocial work environment based on the JD-C or DCS model, the association of job dissatisfaction with long-term sickness absence was weakened and no longer significant. This is consistent with previous studies in that a significant association of job dissatisfaction with sickness absence (including both short-term and long-term ones) was not observed when psychosocial work environment was included in the model.16,17,20 Our findings suggest that the association of job dissatisfaction with long-term sickness absence is explained mainly by psychosocial work environment and that improving psychosocial work environment is effective for the prevention of long-term sickness absence. However, although not statistically significant, the fully adjusted HR of job dissatisfaction was still approximately 1.5; therefore, there may be a unique effect of job dissatisfaction on long-term sickness absence independently of psychosocial work environment. Future research should examine more precisely the association between psychosocial work environment, job dissatisfaction, and sickness absence.\nPossible limitations of the present study should be considered. First, our sample was recruited from one financial service company in Japan; therefore, our findings should be interpreted with caution in light of limited generalizability. Second, job dissatisfaction was measured using a single-item question, which may limit its measurement validity; however, some researchers have argued that single-item questions are preferred to measure overall job dissatisfaction because differences in individual scores are lost in the total mean scores of multi-item questions.41,42 Third, some employees may have transferred to another department in the surveyed company, which may have influenced job dissatisfaction and masked the true association; nevertheless, the frequency of transfer may not have been so high at 1-year follow-up. Finally, although some previous studies focused on workplace-level (in addition to individual-level) job dissatisfaction to examine its association with sickness absence,19 the present study could not take workplace-level job dissatisfaction into account due to a lack of information on the departments to which the individual participants belonged.\nIn conclusion, the present study provided evidence that the association of job dissatisfaction with long-term sickness absence lasting 1 month or more is spurious and explained mainly via adverse psychosocial work environment. More detailed underlying mechanisms in the association between psychosocial work environment, job dissatisfaction, and sickness absence can be explored using mediation analysis." ]

[ "intro", "materials|methods", null, null, null, null, null, null, null, "results", "discussion" ]

[ "absenteeism", "job satisfaction", "longitudinal studies", "psychosocial job characteristics", "survival analysis" ]

[ 195, 1131, 110, 156, 189, 99, 372 ]

[ "job", "support", "sickness", "absence", "sickness absence", "term", "term sickness", "long term", "long", "term sickness absence" ]

[ "sickness absence ie", "dissatisfaction sickness absence15", "sickness absence compared", "job dissatisfaction sickness", "sickness absence benefits" ]

[ "Adolescent", "Adult", "Aged", "Aged, 80 and over", "Child", "Cluster Analysis", "Female", "Humans", "Japan", "Kaplan-Meier Estimate", "Male", "Middle Aged", "Myasthenia Gravis", "Reproducibility of Results", "Severity of Illness Index", "Thymoma", "Thymus Neoplasms", "Young Adult" ]

[ "Patients and clinical factors", "Two-step cluster analysis", "Early-stage response to treatment and stability of improved status in each of the five subtypes", "Early-stage response to treatment", "Stability of improved status of MM-or-better ≥1 M", "Statistical analysis", "Two-step cluster analysis", "Clinical characteristics of each subtype", "Onset age histograms of the five subtypes", "Early-stage response to treatment and stability of improved status among the five subtypes", "Early-stage response to treatment (first achievement of MM-or-better ≥1 M)", "Stability of improved status" ]

[ "This survey was conducted by the Japan MG Registry Study Group, which comprises 13 neurological centers (Table 1). We evaluated patients with established MG between April and July 2015. To avoid potential bias, we enrolled consecutive patients over a short duration (4 months). All 1088 of these MG patients visited our hospitals, provided written informed consent, and underwent analysis. Among these 1088 patients, 331 (30.4%) were included in our previous survey in 2012 [8].Table 1Institutions participating in the Japan MG Registry Study 2015Department of Neurology, Sapporo Medical University Hospital, SapporoDepartment of Neurology, Hokkaido Medical Center, SapporoDepartment of Neurology, Hanamaki General Hospital, HanamakiDepartment of Neurology, Sendai Medical Center, SendaiDepartment of Neurology, Tohoku University Graduate School of Medicine, SendaiChiba Neurology Clinic, ChibaDepartment of Neurology, Chiba University School of Medicine, ChibaDepartment of Neurology, Tokyo Medical University, TokyoDepartment of Neurology, Toho University Medical Center Oh-hashi Hospital, TokyoDepartment of Neurology, Tokyo Women's Medical University, TokyoDepartment of Neurology, Kinki University School of Medicine, OsakaDepartment of Neurology, Graduate School of Medical Sciences, Kyushu University, FukuokaDepartment of Neurology and Strokology, Nagasaki University Hospital, Nagasaki\nAbbreviation: MG myasthenia gravis\n\nInstitutions participating in the Japan MG Registry Study 2015\n\nAbbreviation: MG myasthenia gravis\nThe following clinical parameters were obtained for all patients: sex; age; age at disease onset; duration of disease; duration of immunotherapy; history of bulbar symptoms; presence of thymoma or thymic hyperplasia in thymectomized patients; presence of serum AChR-Ab or MuSK-Ab; and presence of other non-MG-specific autoantibodies, such as anti-nuclear antibody, SS-A/SS-B antibody, TSH-receptor antibody, anti-thyroglobulin/thyroperoxidase antibody, and rheumatoid factor. In addition, the current and past disease status and details of treatment were surveyed for all patients. Clinical severity at the worst condition was determined according to the classification of the MG Foundation of America (MGFA) [9], and, in some patients, the MGFA quantitative MG score (QMG) [9, 10] from medical records. Clinical severity at the current condition was determined according to QMG and MG Composite (MGC) scores [11]. Furthermore, all patients completed the Japanese version of the 15-item Myasthenia Gravis Quality-of-Life Scale (MG-QOL-15), [12, 13] upon study entry.\nPrednisone and prednisolone are the global standard oral corticosteroids used to treat MG, and prednisolone is generally used in Japan. Therefore, the current use, peak dose [mg/day], and duration of prednisolone ≥20 mg/day were recorded for all patients, as was the use of calcineurin inhibitors, azathioprine, plasmapheresis, and intravenous immunoglobulin.\nFinally, the courses of current and past MGFA post-intervention statuses, particularly the time required to achieve first minimal manifestations (MM) or better status lasting more than one month (MM-or-better ≥1 M) [9], were determined as benchmarks for evaluating response to treatment in each patient. These clinical data were fully collected from 923 (84.8%) of the 1088 patients.", "To examine the reproducibility of the five-subtype classification in the same manner as reported elsewhere [8], we conducted two-step cluster analysis of the 923 patients using SPSS Statistics Base 22 software (IBM, Armonk, NY, USA). To avoid bias beset by the problem of multicollinearity, current or worst disease status was handled as a single variable (Table 2). The other variables evaluated were: sex; age of onset; disease duration; presence of thymoma; presence of thymic hyperplasia in thymectomized cases; positivity for AChR-Ab or MuSK-Ab; and positivity for other concurrent autoantibodies (Table 2).Table 2Set of variables used in the cluster analysesPatients’ backgroundsAutoantibody statusDisease status during the worst conditionCurrent disease statusSexAge of onsetDisease durationPresence of thymomaPresence of thymic hyperplasiaAChR-AbMuSK-AbNon-MG-specific antibodies\nOne of the following:\nThe worst MGFA classificationThe worst QMG\nOne of the following:\nCurrent PISCurrent QMGMG-QOL-15 score\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status\n\nSet of variables used in the cluster analyses\n\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status", " Early-stage response to treatment The time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\nThe time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\n Stability of improved status of MM-or-better ≥1 M As an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.\nAs an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.", "The time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.", "As an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.", "All statistical analyses were performed using SPSS Statistics Base 22 software (IBM) and MATLAB R2015a (MathWorks, Natick, MA, USA). All continuous data are expressed as the mean ± standard deviation (SD) and the median.", "Based on the results of two-step cluster analyses, all 923 MG patients could be classified into the same five subtypes described elsewhere [8]: ocular MG; thymoma-associated MG; MG with thymic hyperplasia; AChR-Ab-negative MG; and other (in order of predicted importance). Among these five subtypes, the residual patients group “other” was the largest, and could be defined as generalized AChR-Ab-positive MG without thymic abnormalities. These results were demonstrated repeatedly with several sets of variables, as shown in Table 2, which confirmed the high reliability and reproducibility of the classification system. Although the order among thymoma-associated MG, MG with thymic hyperplasia, and AChR-Ab-negative MG was unstable depending on the variable sets used, the differences in terms of predicted importance were not large. These results were almost identical to those reported elsewhere [8], with only minor discrepancies in regard to the order of selection priority (the order in the previous study was as follows: ocular MG; MG with thymic hyperplasia; AChR-Ab-negative MG; thymoma-associated MG; and AChR-Ab-positive MG without thymic abnormalities). In the present study, the quality of clusterization under each set of variables, which was estimated using a previously reported interpretation model [14], was indicated as “fair” to “good” for all clusters, suggesting that the results were reasonable.\nA total of 111 patients (10.2%) fit two of the five subtypes (Table 3). These patients were allocated to sole subtypes according to the separation priority in the two-step cluster analysis. For example, an ocular MG patient with thymoma was allocated into ocular MG. Under this criterion, the percentage of patients assigned to the five subtypes was as follows: ocular MG, 23.0%; thymoma-associated MG, 21.5%; MG with thymic hyperplasia, 12.9%; AChR-Ab-negative MG, 12.1%; and AChR-Ab-positive MG without thymic abnormalities, 30.5% (Table 4).Table 3Number of patients fitting two categoriesNumber of patientsFinal assignmentOcular MG and thymoma-associated MG32 (2.9%)Ocular MGOcular MG and AChR-Ab-negative MG56 (5.1%)Ocular MGOcular MG and MG with thymic hyperplasia8 (0.7%)Ocular MGMG with thymic hyperplasia and AChR-Ab-negative MG14 (1.3%)THMGThymoma-associated MG and AChR-Ab-negative MG1 (0.09%)TAMGValues within the parentheses show the percentages of the total of 1,088 patients\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nTable 4Characteristics and severity for each of the five MG subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPatients (n)250234140132(22)3321088Female,%52.067.581.4*81.1*(81.8)61.165.4Onset age, y51.0 ± 20.0, 53.0a\n51.0 ± 12.8, 51.033.3 ± 13.9, 31.0a\n39.9 ± 16.2, 41.5a\n(38.6 ± 15.3, 42.0)50.7 ± 20.7, 55.0a\n47.3 ± 18.8, 48.1Duration of disease, y10.4 ± 11.4, 6.49.5 ± 7.7, 8.017.4 ± 11.8, 14.5a\n10.8 ± 9.3, 8.0(11.0 ± 8.1, 9.8)11.6 ± 10.5, 8.211.6 ± 10.6, 8.2AChR-Ab-positivity,%77.299.689.40.0(0.0)100.081.3MuSK-Ab-positivity,%0.0%0.0%0.0%20.6%(100.0%)0.0%2.1%Thymectomy,%23.6%*97.4%*100.0%*12.1%*(9.1%)35.8%*51.7%The worst condition of the disease MGFA classification (n = 1088) I,%100%0.0%0.0%0.0%(0.0%)0.0%23.0% II,%0.0%44.0%52.9%59.1%(45.5%)64.8%43.2% III,%0.0%27.8%30.7%28.0%(13.6%)20.5%19.6% IV,%0.0%7.7%7.9%4.5%(9.1%)4.2%4.5% V,%0.0%20.5%8.6%8.3%(31.8%)10.5%9.7% Rate of MGFA > III,%0.0%*56.0%*47.1%40.9%(54.5%)35.2%33.8% QMG score (n = 922)6.6 ± 2.6, 6.0a\n(n = 225)17.1 ± 8.0,15.5a\n(n = 194)15.8 ± 5.8, 15.0a\n(n = 107)14.7 ± 7.2, 13.0(n = 114)18.1 ± 9.7, 15.5(n = 20)14.7 ± 7.0, 13.0(n = 282)13.4 ± 7.5, 12.0Current disease condition (mean ± SD, median) QMG score (n = 923)4.2 ± 2.8, 4.0a\n(n = 208)6.8 ± 4.8, 6.0(n = 198)7.8 ± 5.5, 7.0(n = 125)8.4 ± 5.4, 8.0(n = 106)(8.3 ± 6.4, 7.0)(n = 20)7.2 ± 4.8, 6.0(n = 286)6.6 ± 4.8, 6.0 MGC score (n = 923)1.9 ± 2.5, 1.0a\n(n = 208)4.5 ± 5.4, 3.0(n = 198)5.4 ± 5.7, 3.0(n = 125)6.5 ± 6.2, 5.0a\n(n = 106)(6.2 ± 7.0, 4.5)(n = 20)4.2 ± 4.6, 3.0(n = 286)4.1 ± 5.0, 3.0 MG-QOL-15 (n = 923)8.1 ± 9.0, 5.0a\n(n = 208)14.7 ± 13.6, 11.0(n = 198)16.2 ± 13.7, 13.5a\n(n = 125)14.6 ± 12.6, 12.0(n = 106)(11.6 ± 8.8, 11.0)(n = 20)14.1 ± 13.9, 9.0(n = 286)13.2 ± 13.0, 9.0All continuous data are expressed as the mean ± standard deviation (SD) and the median\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\n\nNumber of patients fitting two categories\nValues within the parentheses show the percentages of the total of 1,088 patients\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nCharacteristics and severity for each of the five MG subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation\n*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\nMG with thymic hyperplasia is only diagnosed for thymectomized patients; therefore, some non-thymectomized patients with thymic hyperplasia may be assigned as other subtypes, particularly AChR-Ab-positive MG patients without thymic abnormalities.", "The clinical characteristics, including current and worst severity, for each of the five subtypes are shown in Table 4. The patients with MuSK-Ab were not separated by two-step cluster analysis because the number is not great enough for statistical evaluation. However, MG patients with MuSK-Ab showed a distinct clinical manifestation and therapy responsiveness reflecting the unique pathological mechanism [15]. Therefore, details of MG patients with MuSK-Ab (n = 22) are individually described next to SNMG patients in Table 4. The percentage of females was significantly higher among MG with thymic hyperplasia and AChR-Ab-negative MG patients compared with the other three subtypes (p < 0.0001, chi-square test). Onset age was significantly younger in MG with thymic hyperplasia and AChR-Ab-negative MG patients (p < 0.0001, Mann–Whitney U test) and older in ocular MG and AChR-Ab-positive MG patients without thymic abnormalities (p < 0.0001, Mann–Whitney U test).\nSeverity at the worst condition (MGFA classification and QMG) was significantly higher in thymoma-associated MG patients (p < 0.0001, Mann–Whitney U test). Patients with MuSK-Ab also showed worst severity at the same level as thymoma-associated MG patients, although this result was not statistically significant because of the small number of patients. The severity scales (QMG and MGC), and a QOL scale (MG-QOL-15) scores in the present survey were generally worse, although not statistically significant, in MG with thymic hyperplasia and AChR-Ab-negative MG patients, both of which primarily comprise females with younger onset ages. On the other hand, as a matter of course, ocular MG patients showed much lower clinical severity in all batteries at both current and worst conditions (p < 0.0001, Mann–Whitney U test).", "Histograms of the onset age for each of the five subtypes are shown in Fig. 1a. These histograms were converted into approximate curves (sixth-order polynomial approximations) and superimposed in Fig. 1b. The peak ages of the histogram were 60–64 years in ocular MG, 25–29 years in MG with thymic hyperplasia, 35–39 years in AChR-Ab-negative MG, 50–54 years in thymoma-associated MG, and 65–69 years in AChR-Ab-positive MG without thymic abnormalities. The histogram was skewed toward younger onset age in MG with thymic hyperplasia and toward older onset age in ocular MG and AChR-Ab-positive MG without thymic abnormalities. Regarding patients with MuSK-Ab (n = 22), the mean ± SD onset age was 38.6 ± 15.3 (median, 42.0 years), and the ratio of females was 81.8%; however, neither of these findings was significantly different from other AChR-Ab-negative MG patients.Fig. 1Histograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis\n\nHistograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis", "Details of past immunotherapy for each of the five subtypes are shown at the top of Table 5.Table 5Details of past treatment and response to treatment for each of the five subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPast immunotherapy (n = 923) Peak dose of oral PSL, mg/day9.2 ± 12.2, 5.0†\n28.5 ± 18.8, 30.0†\n29.7 ± 19.4, 30.0†\n18.8 ± 17.2, 15.032.6 ± 20.6, 30.023.7 ± 20.2, 20.021.5 ± 19.3, 15.0 Duration of PSL ≥20 mg/day, M0.0 ± 0.0, 0.0†\n12.0 ± 25.2, 5.0†\n13.0 ± 27.3, 6.0†\n3.8 ± 7.0, 0.07.2 ± 9.5, 4.08.2 ± 17.0, 2.07.9 ± 19.3, 1.0 CNIs,%24.0%*68.2%*54.0%67.4%(72.7%)58.1%52.9% PP,%2.0%*48.1%*22.1%46.0%*(54.5%)37.2%27.3% IVIG,%6.1%*36.1%29.9%42.5%*(27.3%)24.7%15.0%Initial response to treatment (n = 923, see Fig. 2a) Achievement of MM-or-better once,%79.8%*73.5%66.1%56.2%*(75.0%)67.8%70.2% Months to achieve MM-or-better in 50% of patients4.0‡\n8.018.0‡\n31.0‡\n(7.0)6.08.0Stability of improved status (n = 923) MM-or-better at present,%74.0%*58.1%49.6%39.6%*(55.0%)55.4%57.6% Maintaining rate of MM-or-better, %92.7%*79.0%75.0%70.5%(73.3%)81.7%82.1%All continuous data are expressed as the mean ± standard deviation (SD) and the median\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n\nDetails of past treatment and response to treatment for each of the five subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation\n*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n Early-stage response to treatment (first achievement of MM-or-better ≥1 M) As shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\nAs shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\n Stability of improved status The rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).\nThe rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).", "As shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).", "The rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test)." ]

[ null, null, null, null, null, null, null, null, null, null, null, null ]

[ "Background", "Methods", "Patients and clinical factors", "Two-step cluster analysis", "Early-stage response to treatment and stability of improved status in each of the five subtypes", "Early-stage response to treatment", "Stability of improved status of MM-or-better ≥1 M", "Statistical analysis", "Results", "Two-step cluster analysis", "Clinical characteristics of each subtype", "Onset age histograms of the five subtypes", "Early-stage response to treatment and stability of improved status among the five subtypes", "Early-stage response to treatment (first achievement of MM-or-better ≥1 M)", "Stability of improved status", "Discussion", "Conclusion" ]

[ "Myasthenia gravis (MG) is a neurological disorder that manifests as fatigable and fluctuating weakness of voluntary muscles, which are mediated by autoantibodies against neuromuscular junction proteins in skeletal muscle that impair neuromuscular transmission [1]. MG typically involves the ocular, bulbar, and extremity muscles, and, in severe cases, respiratory muscles. The clinical course and outcome in MG are affected by several different autoantibodies, thymic abnormalities, onset age and disease severity, as well as response to treatment [2–4]. MG is distinguished according to the production of pathogenic autoantibodies such as anti-acetylcholine receptor antibody (AChR-Ab) and anti–muscle-specific tyrosine kinase antibody (MuSK-Ab) [1, 5, 6]. Clinically, MG is often classified into the following three subtypes based on thymic abnormalities and onset age: thymoma-associated MG; early-onset MG (onset age <50 years); and late-onset MG (onset age ≥50 years) [7]. Furthermore, discrimination is observed in the clinical setting—for example, between ocular and generalized MG—based on the distribution of symptoms.\nPreviously, we reported classifying MG into the following five subtypes using two-step cluster analysis of a detailed cross-sectional data set of 640 consecutive patients (Japan MG Registry Study 2012): ocular MG; generalized thymoma-associated MG; generalized MG with thymic hyperplasia; generalized AChR-Ab-negative MG; and generalized AChR-Ab-positive MG without thymic abnormalities [8]. However, this five-subtype classification approach requires further confirmation, and its clinical relevance remains to be established.\nTherefore, in 2015, we conducted a larger cross-sectional survey to obtain clinical parameters from 1,088 consecutive MG patients. In the present study, using this new data set, we attempted to confirm the reproducibility of our five-subtype classification approach and to specify additional characteristics of these five subtypes with a particular focus on response to treatment in the clinical setting.", " Patients and clinical factors This survey was conducted by the Japan MG Registry Study Group, which comprises 13 neurological centers (Table 1). We evaluated patients with established MG between April and July 2015. To avoid potential bias, we enrolled consecutive patients over a short duration (4 months). All 1088 of these MG patients visited our hospitals, provided written informed consent, and underwent analysis. Among these 1088 patients, 331 (30.4%) were included in our previous survey in 2012 [8].Table 1Institutions participating in the Japan MG Registry Study 2015Department of Neurology, Sapporo Medical University Hospital, SapporoDepartment of Neurology, Hokkaido Medical Center, SapporoDepartment of Neurology, Hanamaki General Hospital, HanamakiDepartment of Neurology, Sendai Medical Center, SendaiDepartment of Neurology, Tohoku University Graduate School of Medicine, SendaiChiba Neurology Clinic, ChibaDepartment of Neurology, Chiba University School of Medicine, ChibaDepartment of Neurology, Tokyo Medical University, TokyoDepartment of Neurology, Toho University Medical Center Oh-hashi Hospital, TokyoDepartment of Neurology, Tokyo Women's Medical University, TokyoDepartment of Neurology, Kinki University School of Medicine, OsakaDepartment of Neurology, Graduate School of Medical Sciences, Kyushu University, FukuokaDepartment of Neurology and Strokology, Nagasaki University Hospital, Nagasaki\nAbbreviation: MG myasthenia gravis\n\nInstitutions participating in the Japan MG Registry Study 2015\n\nAbbreviation: MG myasthenia gravis\nThe following clinical parameters were obtained for all patients: sex; age; age at disease onset; duration of disease; duration of immunotherapy; history of bulbar symptoms; presence of thymoma or thymic hyperplasia in thymectomized patients; presence of serum AChR-Ab or MuSK-Ab; and presence of other non-MG-specific autoantibodies, such as anti-nuclear antibody, SS-A/SS-B antibody, TSH-receptor antibody, anti-thyroglobulin/thyroperoxidase antibody, and rheumatoid factor. In addition, the current and past disease status and details of treatment were surveyed for all patients. Clinical severity at the worst condition was determined according to the classification of the MG Foundation of America (MGFA) [9], and, in some patients, the MGFA quantitative MG score (QMG) [9, 10] from medical records. Clinical severity at the current condition was determined according to QMG and MG Composite (MGC) scores [11]. Furthermore, all patients completed the Japanese version of the 15-item Myasthenia Gravis Quality-of-Life Scale (MG-QOL-15), [12, 13] upon study entry.\nPrednisone and prednisolone are the global standard oral corticosteroids used to treat MG, and prednisolone is generally used in Japan. Therefore, the current use, peak dose [mg/day], and duration of prednisolone ≥20 mg/day were recorded for all patients, as was the use of calcineurin inhibitors, azathioprine, plasmapheresis, and intravenous immunoglobulin.\nFinally, the courses of current and past MGFA post-intervention statuses, particularly the time required to achieve first minimal manifestations (MM) or better status lasting more than one month (MM-or-better ≥1 M) [9], were determined as benchmarks for evaluating response to treatment in each patient. These clinical data were fully collected from 923 (84.8%) of the 1088 patients.\nThis survey was conducted by the Japan MG Registry Study Group, which comprises 13 neurological centers (Table 1). We evaluated patients with established MG between April and July 2015. To avoid potential bias, we enrolled consecutive patients over a short duration (4 months). All 1088 of these MG patients visited our hospitals, provided written informed consent, and underwent analysis. Among these 1088 patients, 331 (30.4%) were included in our previous survey in 2012 [8].Table 1Institutions participating in the Japan MG Registry Study 2015Department of Neurology, Sapporo Medical University Hospital, SapporoDepartment of Neurology, Hokkaido Medical Center, SapporoDepartment of Neurology, Hanamaki General Hospital, HanamakiDepartment of Neurology, Sendai Medical Center, SendaiDepartment of Neurology, Tohoku University Graduate School of Medicine, SendaiChiba Neurology Clinic, ChibaDepartment of Neurology, Chiba University School of Medicine, ChibaDepartment of Neurology, Tokyo Medical University, TokyoDepartment of Neurology, Toho University Medical Center Oh-hashi Hospital, TokyoDepartment of Neurology, Tokyo Women's Medical University, TokyoDepartment of Neurology, Kinki University School of Medicine, OsakaDepartment of Neurology, Graduate School of Medical Sciences, Kyushu University, FukuokaDepartment of Neurology and Strokology, Nagasaki University Hospital, Nagasaki\nAbbreviation: MG myasthenia gravis\n\nInstitutions participating in the Japan MG Registry Study 2015\n\nAbbreviation: MG myasthenia gravis\nThe following clinical parameters were obtained for all patients: sex; age; age at disease onset; duration of disease; duration of immunotherapy; history of bulbar symptoms; presence of thymoma or thymic hyperplasia in thymectomized patients; presence of serum AChR-Ab or MuSK-Ab; and presence of other non-MG-specific autoantibodies, such as anti-nuclear antibody, SS-A/SS-B antibody, TSH-receptor antibody, anti-thyroglobulin/thyroperoxidase antibody, and rheumatoid factor. In addition, the current and past disease status and details of treatment were surveyed for all patients. Clinical severity at the worst condition was determined according to the classification of the MG Foundation of America (MGFA) [9], and, in some patients, the MGFA quantitative MG score (QMG) [9, 10] from medical records. Clinical severity at the current condition was determined according to QMG and MG Composite (MGC) scores [11]. Furthermore, all patients completed the Japanese version of the 15-item Myasthenia Gravis Quality-of-Life Scale (MG-QOL-15), [12, 13] upon study entry.\nPrednisone and prednisolone are the global standard oral corticosteroids used to treat MG, and prednisolone is generally used in Japan. Therefore, the current use, peak dose [mg/day], and duration of prednisolone ≥20 mg/day were recorded for all patients, as was the use of calcineurin inhibitors, azathioprine, plasmapheresis, and intravenous immunoglobulin.\nFinally, the courses of current and past MGFA post-intervention statuses, particularly the time required to achieve first minimal manifestations (MM) or better status lasting more than one month (MM-or-better ≥1 M) [9], were determined as benchmarks for evaluating response to treatment in each patient. These clinical data were fully collected from 923 (84.8%) of the 1088 patients.\n Two-step cluster analysis To examine the reproducibility of the five-subtype classification in the same manner as reported elsewhere [8], we conducted two-step cluster analysis of the 923 patients using SPSS Statistics Base 22 software (IBM, Armonk, NY, USA). To avoid bias beset by the problem of multicollinearity, current or worst disease status was handled as a single variable (Table 2). The other variables evaluated were: sex; age of onset; disease duration; presence of thymoma; presence of thymic hyperplasia in thymectomized cases; positivity for AChR-Ab or MuSK-Ab; and positivity for other concurrent autoantibodies (Table 2).Table 2Set of variables used in the cluster analysesPatients’ backgroundsAutoantibody statusDisease status during the worst conditionCurrent disease statusSexAge of onsetDisease durationPresence of thymomaPresence of thymic hyperplasiaAChR-AbMuSK-AbNon-MG-specific antibodies\nOne of the following:\nThe worst MGFA classificationThe worst QMG\nOne of the following:\nCurrent PISCurrent QMGMG-QOL-15 score\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status\n\nSet of variables used in the cluster analyses\n\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status\nTo examine the reproducibility of the five-subtype classification in the same manner as reported elsewhere [8], we conducted two-step cluster analysis of the 923 patients using SPSS Statistics Base 22 software (IBM, Armonk, NY, USA). To avoid bias beset by the problem of multicollinearity, current or worst disease status was handled as a single variable (Table 2). The other variables evaluated were: sex; age of onset; disease duration; presence of thymoma; presence of thymic hyperplasia in thymectomized cases; positivity for AChR-Ab or MuSK-Ab; and positivity for other concurrent autoantibodies (Table 2).Table 2Set of variables used in the cluster analysesPatients’ backgroundsAutoantibody statusDisease status during the worst conditionCurrent disease statusSexAge of onsetDisease durationPresence of thymomaPresence of thymic hyperplasiaAChR-AbMuSK-AbNon-MG-specific antibodies\nOne of the following:\nThe worst MGFA classificationThe worst QMG\nOne of the following:\nCurrent PISCurrent QMGMG-QOL-15 score\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status\n\nSet of variables used in the cluster analyses\n\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status\n Early-stage response to treatment and stability of improved status in each of the five subtypes Early-stage response to treatment The time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\nThe time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\n Stability of improved status of MM-or-better ≥1 M As an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.\nAs an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.\n Early-stage response to treatment The time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\nThe time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\n Stability of improved status of MM-or-better ≥1 M As an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.\nAs an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.\n Statistical analysis All statistical analyses were performed using SPSS Statistics Base 22 software (IBM) and MATLAB R2015a (MathWorks, Natick, MA, USA). All continuous data are expressed as the mean ± standard deviation (SD) and the median.\nAll statistical analyses were performed using SPSS Statistics Base 22 software (IBM) and MATLAB R2015a (MathWorks, Natick, MA, USA). All continuous data are expressed as the mean ± standard deviation (SD) and the median.", "This survey was conducted by the Japan MG Registry Study Group, which comprises 13 neurological centers (Table 1). We evaluated patients with established MG between April and July 2015. To avoid potential bias, we enrolled consecutive patients over a short duration (4 months). All 1088 of these MG patients visited our hospitals, provided written informed consent, and underwent analysis. Among these 1088 patients, 331 (30.4%) were included in our previous survey in 2012 [8].Table 1Institutions participating in the Japan MG Registry Study 2015Department of Neurology, Sapporo Medical University Hospital, SapporoDepartment of Neurology, Hokkaido Medical Center, SapporoDepartment of Neurology, Hanamaki General Hospital, HanamakiDepartment of Neurology, Sendai Medical Center, SendaiDepartment of Neurology, Tohoku University Graduate School of Medicine, SendaiChiba Neurology Clinic, ChibaDepartment of Neurology, Chiba University School of Medicine, ChibaDepartment of Neurology, Tokyo Medical University, TokyoDepartment of Neurology, Toho University Medical Center Oh-hashi Hospital, TokyoDepartment of Neurology, Tokyo Women's Medical University, TokyoDepartment of Neurology, Kinki University School of Medicine, OsakaDepartment of Neurology, Graduate School of Medical Sciences, Kyushu University, FukuokaDepartment of Neurology and Strokology, Nagasaki University Hospital, Nagasaki\nAbbreviation: MG myasthenia gravis\n\nInstitutions participating in the Japan MG Registry Study 2015\n\nAbbreviation: MG myasthenia gravis\nThe following clinical parameters were obtained for all patients: sex; age; age at disease onset; duration of disease; duration of immunotherapy; history of bulbar symptoms; presence of thymoma or thymic hyperplasia in thymectomized patients; presence of serum AChR-Ab or MuSK-Ab; and presence of other non-MG-specific autoantibodies, such as anti-nuclear antibody, SS-A/SS-B antibody, TSH-receptor antibody, anti-thyroglobulin/thyroperoxidase antibody, and rheumatoid factor. In addition, the current and past disease status and details of treatment were surveyed for all patients. Clinical severity at the worst condition was determined according to the classification of the MG Foundation of America (MGFA) [9], and, in some patients, the MGFA quantitative MG score (QMG) [9, 10] from medical records. Clinical severity at the current condition was determined according to QMG and MG Composite (MGC) scores [11]. Furthermore, all patients completed the Japanese version of the 15-item Myasthenia Gravis Quality-of-Life Scale (MG-QOL-15), [12, 13] upon study entry.\nPrednisone and prednisolone are the global standard oral corticosteroids used to treat MG, and prednisolone is generally used in Japan. Therefore, the current use, peak dose [mg/day], and duration of prednisolone ≥20 mg/day were recorded for all patients, as was the use of calcineurin inhibitors, azathioprine, plasmapheresis, and intravenous immunoglobulin.\nFinally, the courses of current and past MGFA post-intervention statuses, particularly the time required to achieve first minimal manifestations (MM) or better status lasting more than one month (MM-or-better ≥1 M) [9], were determined as benchmarks for evaluating response to treatment in each patient. These clinical data were fully collected from 923 (84.8%) of the 1088 patients.", "To examine the reproducibility of the five-subtype classification in the same manner as reported elsewhere [8], we conducted two-step cluster analysis of the 923 patients using SPSS Statistics Base 22 software (IBM, Armonk, NY, USA). To avoid bias beset by the problem of multicollinearity, current or worst disease status was handled as a single variable (Table 2). The other variables evaluated were: sex; age of onset; disease duration; presence of thymoma; presence of thymic hyperplasia in thymectomized cases; positivity for AChR-Ab or MuSK-Ab; and positivity for other concurrent autoantibodies (Table 2).Table 2Set of variables used in the cluster analysesPatients’ backgroundsAutoantibody statusDisease status during the worst conditionCurrent disease statusSexAge of onsetDisease durationPresence of thymomaPresence of thymic hyperplasiaAChR-AbMuSK-AbNon-MG-specific antibodies\nOne of the following:\nThe worst MGFA classificationThe worst QMG\nOne of the following:\nCurrent PISCurrent QMGMG-QOL-15 score\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status\n\nSet of variables used in the cluster analyses\n\nAChR-Ab anti-acetylcholine receptor antibody, MuSK-Ab anti-muscle specific kinase antibody, MG myasthenia gravis, MGFA MG Foundation of America, QMG quantitative MG, MG-QOL-15 15-item MG-specific quality of life scale, PIS post-intervention status", " Early-stage response to treatment The time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\nThe time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.\n Stability of improved status of MM-or-better ≥1 M As an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.\nAs an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.", "The time (months) from the start of the immunotherapy until achieving first MM-or-better ≥1 M was determined from medical records and compared between the five subtypes using Kaplan-Meier analysis and the log-rank test with the Cochran-Mantel-Haenszel procedure. The time required to achieve first MM-or-better ≥1 M in 50% of patients was also compared among subtypes.", "As an indicator of stability of improved status, the rate of the number of patients who maintained minimal manifestations in the 2015 survey/that of patients who achieved the status at least once was calculated and compared among the five subtypes.", "All statistical analyses were performed using SPSS Statistics Base 22 software (IBM) and MATLAB R2015a (MathWorks, Natick, MA, USA). All continuous data are expressed as the mean ± standard deviation (SD) and the median.", " Two-step cluster analysis Based on the results of two-step cluster analyses, all 923 MG patients could be classified into the same five subtypes described elsewhere [8]: ocular MG; thymoma-associated MG; MG with thymic hyperplasia; AChR-Ab-negative MG; and other (in order of predicted importance). Among these five subtypes, the residual patients group “other” was the largest, and could be defined as generalized AChR-Ab-positive MG without thymic abnormalities. These results were demonstrated repeatedly with several sets of variables, as shown in Table 2, which confirmed the high reliability and reproducibility of the classification system. Although the order among thymoma-associated MG, MG with thymic hyperplasia, and AChR-Ab-negative MG was unstable depending on the variable sets used, the differences in terms of predicted importance were not large. These results were almost identical to those reported elsewhere [8], with only minor discrepancies in regard to the order of selection priority (the order in the previous study was as follows: ocular MG; MG with thymic hyperplasia; AChR-Ab-negative MG; thymoma-associated MG; and AChR-Ab-positive MG without thymic abnormalities). In the present study, the quality of clusterization under each set of variables, which was estimated using a previously reported interpretation model [14], was indicated as “fair” to “good” for all clusters, suggesting that the results were reasonable.\nA total of 111 patients (10.2%) fit two of the five subtypes (Table 3). These patients were allocated to sole subtypes according to the separation priority in the two-step cluster analysis. For example, an ocular MG patient with thymoma was allocated into ocular MG. Under this criterion, the percentage of patients assigned to the five subtypes was as follows: ocular MG, 23.0%; thymoma-associated MG, 21.5%; MG with thymic hyperplasia, 12.9%; AChR-Ab-negative MG, 12.1%; and AChR-Ab-positive MG without thymic abnormalities, 30.5% (Table 4).Table 3Number of patients fitting two categoriesNumber of patientsFinal assignmentOcular MG and thymoma-associated MG32 (2.9%)Ocular MGOcular MG and AChR-Ab-negative MG56 (5.1%)Ocular MGOcular MG and MG with thymic hyperplasia8 (0.7%)Ocular MGMG with thymic hyperplasia and AChR-Ab-negative MG14 (1.3%)THMGThymoma-associated MG and AChR-Ab-negative MG1 (0.09%)TAMGValues within the parentheses show the percentages of the total of 1,088 patients\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nTable 4Characteristics and severity for each of the five MG subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPatients (n)250234140132(22)3321088Female,%52.067.581.4*81.1*(81.8)61.165.4Onset age, y51.0 ± 20.0, 53.0a\n51.0 ± 12.8, 51.033.3 ± 13.9, 31.0a\n39.9 ± 16.2, 41.5a\n(38.6 ± 15.3, 42.0)50.7 ± 20.7, 55.0a\n47.3 ± 18.8, 48.1Duration of disease, y10.4 ± 11.4, 6.49.5 ± 7.7, 8.017.4 ± 11.8, 14.5a\n10.8 ± 9.3, 8.0(11.0 ± 8.1, 9.8)11.6 ± 10.5, 8.211.6 ± 10.6, 8.2AChR-Ab-positivity,%77.299.689.40.0(0.0)100.081.3MuSK-Ab-positivity,%0.0%0.0%0.0%20.6%(100.0%)0.0%2.1%Thymectomy,%23.6%*97.4%*100.0%*12.1%*(9.1%)35.8%*51.7%The worst condition of the disease MGFA classification (n = 1088) I,%100%0.0%0.0%0.0%(0.0%)0.0%23.0% II,%0.0%44.0%52.9%59.1%(45.5%)64.8%43.2% III,%0.0%27.8%30.7%28.0%(13.6%)20.5%19.6% IV,%0.0%7.7%7.9%4.5%(9.1%)4.2%4.5% V,%0.0%20.5%8.6%8.3%(31.8%)10.5%9.7% Rate of MGFA > III,%0.0%*56.0%*47.1%40.9%(54.5%)35.2%33.8% QMG score (n = 922)6.6 ± 2.6, 6.0a\n(n = 225)17.1 ± 8.0,15.5a\n(n = 194)15.8 ± 5.8, 15.0a\n(n = 107)14.7 ± 7.2, 13.0(n = 114)18.1 ± 9.7, 15.5(n = 20)14.7 ± 7.0, 13.0(n = 282)13.4 ± 7.5, 12.0Current disease condition (mean ± SD, median) QMG score (n = 923)4.2 ± 2.8, 4.0a\n(n = 208)6.8 ± 4.8, 6.0(n = 198)7.8 ± 5.5, 7.0(n = 125)8.4 ± 5.4, 8.0(n = 106)(8.3 ± 6.4, 7.0)(n = 20)7.2 ± 4.8, 6.0(n = 286)6.6 ± 4.8, 6.0 MGC score (n = 923)1.9 ± 2.5, 1.0a\n(n = 208)4.5 ± 5.4, 3.0(n = 198)5.4 ± 5.7, 3.0(n = 125)6.5 ± 6.2, 5.0a\n(n = 106)(6.2 ± 7.0, 4.5)(n = 20)4.2 ± 4.6, 3.0(n = 286)4.1 ± 5.0, 3.0 MG-QOL-15 (n = 923)8.1 ± 9.0, 5.0a\n(n = 208)14.7 ± 13.6, 11.0(n = 198)16.2 ± 13.7, 13.5a\n(n = 125)14.6 ± 12.6, 12.0(n = 106)(11.6 ± 8.8, 11.0)(n = 20)14.1 ± 13.9, 9.0(n = 286)13.2 ± 13.0, 9.0All continuous data are expressed as the mean ± standard deviation (SD) and the median\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\n\nNumber of patients fitting two categories\nValues within the parentheses show the percentages of the total of 1,088 patients\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nCharacteristics and severity for each of the five MG subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation\n*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\nMG with thymic hyperplasia is only diagnosed for thymectomized patients; therefore, some non-thymectomized patients with thymic hyperplasia may be assigned as other subtypes, particularly AChR-Ab-positive MG patients without thymic abnormalities.\nBased on the results of two-step cluster analyses, all 923 MG patients could be classified into the same five subtypes described elsewhere [8]: ocular MG; thymoma-associated MG; MG with thymic hyperplasia; AChR-Ab-negative MG; and other (in order of predicted importance). Among these five subtypes, the residual patients group “other” was the largest, and could be defined as generalized AChR-Ab-positive MG without thymic abnormalities. These results were demonstrated repeatedly with several sets of variables, as shown in Table 2, which confirmed the high reliability and reproducibility of the classification system. Although the order among thymoma-associated MG, MG with thymic hyperplasia, and AChR-Ab-negative MG was unstable depending on the variable sets used, the differences in terms of predicted importance were not large. These results were almost identical to those reported elsewhere [8], with only minor discrepancies in regard to the order of selection priority (the order in the previous study was as follows: ocular MG; MG with thymic hyperplasia; AChR-Ab-negative MG; thymoma-associated MG; and AChR-Ab-positive MG without thymic abnormalities). In the present study, the quality of clusterization under each set of variables, which was estimated using a previously reported interpretation model [14], was indicated as “fair” to “good” for all clusters, suggesting that the results were reasonable.\nA total of 111 patients (10.2%) fit two of the five subtypes (Table 3). These patients were allocated to sole subtypes according to the separation priority in the two-step cluster analysis. For example, an ocular MG patient with thymoma was allocated into ocular MG. Under this criterion, the percentage of patients assigned to the five subtypes was as follows: ocular MG, 23.0%; thymoma-associated MG, 21.5%; MG with thymic hyperplasia, 12.9%; AChR-Ab-negative MG, 12.1%; and AChR-Ab-positive MG without thymic abnormalities, 30.5% (Table 4).Table 3Number of patients fitting two categoriesNumber of patientsFinal assignmentOcular MG and thymoma-associated MG32 (2.9%)Ocular MGOcular MG and AChR-Ab-negative MG56 (5.1%)Ocular MGOcular MG and MG with thymic hyperplasia8 (0.7%)Ocular MGMG with thymic hyperplasia and AChR-Ab-negative MG14 (1.3%)THMGThymoma-associated MG and AChR-Ab-negative MG1 (0.09%)TAMGValues within the parentheses show the percentages of the total of 1,088 patients\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nTable 4Characteristics and severity for each of the five MG subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPatients (n)250234140132(22)3321088Female,%52.067.581.4*81.1*(81.8)61.165.4Onset age, y51.0 ± 20.0, 53.0a\n51.0 ± 12.8, 51.033.3 ± 13.9, 31.0a\n39.9 ± 16.2, 41.5a\n(38.6 ± 15.3, 42.0)50.7 ± 20.7, 55.0a\n47.3 ± 18.8, 48.1Duration of disease, y10.4 ± 11.4, 6.49.5 ± 7.7, 8.017.4 ± 11.8, 14.5a\n10.8 ± 9.3, 8.0(11.0 ± 8.1, 9.8)11.6 ± 10.5, 8.211.6 ± 10.6, 8.2AChR-Ab-positivity,%77.299.689.40.0(0.0)100.081.3MuSK-Ab-positivity,%0.0%0.0%0.0%20.6%(100.0%)0.0%2.1%Thymectomy,%23.6%*97.4%*100.0%*12.1%*(9.1%)35.8%*51.7%The worst condition of the disease MGFA classification (n = 1088) I,%100%0.0%0.0%0.0%(0.0%)0.0%23.0% II,%0.0%44.0%52.9%59.1%(45.5%)64.8%43.2% III,%0.0%27.8%30.7%28.0%(13.6%)20.5%19.6% IV,%0.0%7.7%7.9%4.5%(9.1%)4.2%4.5% V,%0.0%20.5%8.6%8.3%(31.8%)10.5%9.7% Rate of MGFA > III,%0.0%*56.0%*47.1%40.9%(54.5%)35.2%33.8% QMG score (n = 922)6.6 ± 2.6, 6.0a\n(n = 225)17.1 ± 8.0,15.5a\n(n = 194)15.8 ± 5.8, 15.0a\n(n = 107)14.7 ± 7.2, 13.0(n = 114)18.1 ± 9.7, 15.5(n = 20)14.7 ± 7.0, 13.0(n = 282)13.4 ± 7.5, 12.0Current disease condition (mean ± SD, median) QMG score (n = 923)4.2 ± 2.8, 4.0a\n(n = 208)6.8 ± 4.8, 6.0(n = 198)7.8 ± 5.5, 7.0(n = 125)8.4 ± 5.4, 8.0(n = 106)(8.3 ± 6.4, 7.0)(n = 20)7.2 ± 4.8, 6.0(n = 286)6.6 ± 4.8, 6.0 MGC score (n = 923)1.9 ± 2.5, 1.0a\n(n = 208)4.5 ± 5.4, 3.0(n = 198)5.4 ± 5.7, 3.0(n = 125)6.5 ± 6.2, 5.0a\n(n = 106)(6.2 ± 7.0, 4.5)(n = 20)4.2 ± 4.6, 3.0(n = 286)4.1 ± 5.0, 3.0 MG-QOL-15 (n = 923)8.1 ± 9.0, 5.0a\n(n = 208)14.7 ± 13.6, 11.0(n = 198)16.2 ± 13.7, 13.5a\n(n = 125)14.6 ± 12.6, 12.0(n = 106)(11.6 ± 8.8, 11.0)(n = 20)14.1 ± 13.9, 9.0(n = 286)13.2 ± 13.0, 9.0All continuous data are expressed as the mean ± standard deviation (SD) and the median\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\n\nNumber of patients fitting two categories\nValues within the parentheses show the percentages of the total of 1,088 patients\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nCharacteristics and severity for each of the five MG subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation\n*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\nMG with thymic hyperplasia is only diagnosed for thymectomized patients; therefore, some non-thymectomized patients with thymic hyperplasia may be assigned as other subtypes, particularly AChR-Ab-positive MG patients without thymic abnormalities.\n Clinical characteristics of each subtype The clinical characteristics, including current and worst severity, for each of the five subtypes are shown in Table 4. The patients with MuSK-Ab were not separated by two-step cluster analysis because the number is not great enough for statistical evaluation. However, MG patients with MuSK-Ab showed a distinct clinical manifestation and therapy responsiveness reflecting the unique pathological mechanism [15]. Therefore, details of MG patients with MuSK-Ab (n = 22) are individually described next to SNMG patients in Table 4. The percentage of females was significantly higher among MG with thymic hyperplasia and AChR-Ab-negative MG patients compared with the other three subtypes (p < 0.0001, chi-square test). Onset age was significantly younger in MG with thymic hyperplasia and AChR-Ab-negative MG patients (p < 0.0001, Mann–Whitney U test) and older in ocular MG and AChR-Ab-positive MG patients without thymic abnormalities (p < 0.0001, Mann–Whitney U test).\nSeverity at the worst condition (MGFA classification and QMG) was significantly higher in thymoma-associated MG patients (p < 0.0001, Mann–Whitney U test). Patients with MuSK-Ab also showed worst severity at the same level as thymoma-associated MG patients, although this result was not statistically significant because of the small number of patients. The severity scales (QMG and MGC), and a QOL scale (MG-QOL-15) scores in the present survey were generally worse, although not statistically significant, in MG with thymic hyperplasia and AChR-Ab-negative MG patients, both of which primarily comprise females with younger onset ages. On the other hand, as a matter of course, ocular MG patients showed much lower clinical severity in all batteries at both current and worst conditions (p < 0.0001, Mann–Whitney U test).\nThe clinical characteristics, including current and worst severity, for each of the five subtypes are shown in Table 4. The patients with MuSK-Ab were not separated by two-step cluster analysis because the number is not great enough for statistical evaluation. However, MG patients with MuSK-Ab showed a distinct clinical manifestation and therapy responsiveness reflecting the unique pathological mechanism [15]. Therefore, details of MG patients with MuSK-Ab (n = 22) are individually described next to SNMG patients in Table 4. The percentage of females was significantly higher among MG with thymic hyperplasia and AChR-Ab-negative MG patients compared with the other three subtypes (p < 0.0001, chi-square test). Onset age was significantly younger in MG with thymic hyperplasia and AChR-Ab-negative MG patients (p < 0.0001, Mann–Whitney U test) and older in ocular MG and AChR-Ab-positive MG patients without thymic abnormalities (p < 0.0001, Mann–Whitney U test).\nSeverity at the worst condition (MGFA classification and QMG) was significantly higher in thymoma-associated MG patients (p < 0.0001, Mann–Whitney U test). Patients with MuSK-Ab also showed worst severity at the same level as thymoma-associated MG patients, although this result was not statistically significant because of the small number of patients. The severity scales (QMG and MGC), and a QOL scale (MG-QOL-15) scores in the present survey were generally worse, although not statistically significant, in MG with thymic hyperplasia and AChR-Ab-negative MG patients, both of which primarily comprise females with younger onset ages. On the other hand, as a matter of course, ocular MG patients showed much lower clinical severity in all batteries at both current and worst conditions (p < 0.0001, Mann–Whitney U test).\n Onset age histograms of the five subtypes Histograms of the onset age for each of the five subtypes are shown in Fig. 1a. These histograms were converted into approximate curves (sixth-order polynomial approximations) and superimposed in Fig. 1b. The peak ages of the histogram were 60–64 years in ocular MG, 25–29 years in MG with thymic hyperplasia, 35–39 years in AChR-Ab-negative MG, 50–54 years in thymoma-associated MG, and 65–69 years in AChR-Ab-positive MG without thymic abnormalities. The histogram was skewed toward younger onset age in MG with thymic hyperplasia and toward older onset age in ocular MG and AChR-Ab-positive MG without thymic abnormalities. Regarding patients with MuSK-Ab (n = 22), the mean ± SD onset age was 38.6 ± 15.3 (median, 42.0 years), and the ratio of females was 81.8%; however, neither of these findings was significantly different from other AChR-Ab-negative MG patients.Fig. 1Histograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis\n\nHistograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis\nHistograms of the onset age for each of the five subtypes are shown in Fig. 1a. These histograms were converted into approximate curves (sixth-order polynomial approximations) and superimposed in Fig. 1b. The peak ages of the histogram were 60–64 years in ocular MG, 25–29 years in MG with thymic hyperplasia, 35–39 years in AChR-Ab-negative MG, 50–54 years in thymoma-associated MG, and 65–69 years in AChR-Ab-positive MG without thymic abnormalities. The histogram was skewed toward younger onset age in MG with thymic hyperplasia and toward older onset age in ocular MG and AChR-Ab-positive MG without thymic abnormalities. Regarding patients with MuSK-Ab (n = 22), the mean ± SD onset age was 38.6 ± 15.3 (median, 42.0 years), and the ratio of females was 81.8%; however, neither of these findings was significantly different from other AChR-Ab-negative MG patients.Fig. 1Histograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis\n\nHistograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis\n Early-stage response to treatment and stability of improved status among the five subtypes Details of past immunotherapy for each of the five subtypes are shown at the top of Table 5.Table 5Details of past treatment and response to treatment for each of the five subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPast immunotherapy (n = 923) Peak dose of oral PSL, mg/day9.2 ± 12.2, 5.0†\n28.5 ± 18.8, 30.0†\n29.7 ± 19.4, 30.0†\n18.8 ± 17.2, 15.032.6 ± 20.6, 30.023.7 ± 20.2, 20.021.5 ± 19.3, 15.0 Duration of PSL ≥20 mg/day, M0.0 ± 0.0, 0.0†\n12.0 ± 25.2, 5.0†\n13.0 ± 27.3, 6.0†\n3.8 ± 7.0, 0.07.2 ± 9.5, 4.08.2 ± 17.0, 2.07.9 ± 19.3, 1.0 CNIs,%24.0%*68.2%*54.0%67.4%(72.7%)58.1%52.9% PP,%2.0%*48.1%*22.1%46.0%*(54.5%)37.2%27.3% IVIG,%6.1%*36.1%29.9%42.5%*(27.3%)24.7%15.0%Initial response to treatment (n = 923, see Fig. 2a) Achievement of MM-or-better once,%79.8%*73.5%66.1%56.2%*(75.0%)67.8%70.2% Months to achieve MM-or-better in 50% of patients4.0‡\n8.018.0‡\n31.0‡\n(7.0)6.08.0Stability of improved status (n = 923) MM-or-better at present,%74.0%*58.1%49.6%39.6%*(55.0%)55.4%57.6% Maintaining rate of MM-or-better, %92.7%*79.0%75.0%70.5%(73.3%)81.7%82.1%All continuous data are expressed as the mean ± standard deviation (SD) and the median\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n\nDetails of past treatment and response to treatment for each of the five subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation\n*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n Early-stage response to treatment (first achievement of MM-or-better ≥1 M) As shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\nAs shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\n Stability of improved status The rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).\nThe rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).\nDetails of past immunotherapy for each of the five subtypes are shown at the top of Table 5.Table 5Details of past treatment and response to treatment for each of the five subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPast immunotherapy (n = 923) Peak dose of oral PSL, mg/day9.2 ± 12.2, 5.0†\n28.5 ± 18.8, 30.0†\n29.7 ± 19.4, 30.0†\n18.8 ± 17.2, 15.032.6 ± 20.6, 30.023.7 ± 20.2, 20.021.5 ± 19.3, 15.0 Duration of PSL ≥20 mg/day, M0.0 ± 0.0, 0.0†\n12.0 ± 25.2, 5.0†\n13.0 ± 27.3, 6.0†\n3.8 ± 7.0, 0.07.2 ± 9.5, 4.08.2 ± 17.0, 2.07.9 ± 19.3, 1.0 CNIs,%24.0%*68.2%*54.0%67.4%(72.7%)58.1%52.9% PP,%2.0%*48.1%*22.1%46.0%*(54.5%)37.2%27.3% IVIG,%6.1%*36.1%29.9%42.5%*(27.3%)24.7%15.0%Initial response to treatment (n = 923, see Fig. 2a) Achievement of MM-or-better once,%79.8%*73.5%66.1%56.2%*(75.0%)67.8%70.2% Months to achieve MM-or-better in 50% of patients4.0‡\n8.018.0‡\n31.0‡\n(7.0)6.08.0Stability of improved status (n = 923) MM-or-better at present,%74.0%*58.1%49.6%39.6%*(55.0%)55.4%57.6% Maintaining rate of MM-or-better, %92.7%*79.0%75.0%70.5%(73.3%)81.7%82.1%All continuous data are expressed as the mean ± standard deviation (SD) and the median\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n\nDetails of past treatment and response to treatment for each of the five subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation\n*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n Early-stage response to treatment (first achievement of MM-or-better ≥1 M) As shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\nAs shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\n Stability of improved status The rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).\nThe rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).", "Based on the results of two-step cluster analyses, all 923 MG patients could be classified into the same five subtypes described elsewhere [8]: ocular MG; thymoma-associated MG; MG with thymic hyperplasia; AChR-Ab-negative MG; and other (in order of predicted importance). Among these five subtypes, the residual patients group “other” was the largest, and could be defined as generalized AChR-Ab-positive MG without thymic abnormalities. These results were demonstrated repeatedly with several sets of variables, as shown in Table 2, which confirmed the high reliability and reproducibility of the classification system. Although the order among thymoma-associated MG, MG with thymic hyperplasia, and AChR-Ab-negative MG was unstable depending on the variable sets used, the differences in terms of predicted importance were not large. These results were almost identical to those reported elsewhere [8], with only minor discrepancies in regard to the order of selection priority (the order in the previous study was as follows: ocular MG; MG with thymic hyperplasia; AChR-Ab-negative MG; thymoma-associated MG; and AChR-Ab-positive MG without thymic abnormalities). In the present study, the quality of clusterization under each set of variables, which was estimated using a previously reported interpretation model [14], was indicated as “fair” to “good” for all clusters, suggesting that the results were reasonable.\nA total of 111 patients (10.2%) fit two of the five subtypes (Table 3). These patients were allocated to sole subtypes according to the separation priority in the two-step cluster analysis. For example, an ocular MG patient with thymoma was allocated into ocular MG. Under this criterion, the percentage of patients assigned to the five subtypes was as follows: ocular MG, 23.0%; thymoma-associated MG, 21.5%; MG with thymic hyperplasia, 12.9%; AChR-Ab-negative MG, 12.1%; and AChR-Ab-positive MG without thymic abnormalities, 30.5% (Table 4).Table 3Number of patients fitting two categoriesNumber of patientsFinal assignmentOcular MG and thymoma-associated MG32 (2.9%)Ocular MGOcular MG and AChR-Ab-negative MG56 (5.1%)Ocular MGOcular MG and MG with thymic hyperplasia8 (0.7%)Ocular MGMG with thymic hyperplasia and AChR-Ab-negative MG14 (1.3%)THMGThymoma-associated MG and AChR-Ab-negative MG1 (0.09%)TAMGValues within the parentheses show the percentages of the total of 1,088 patients\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nTable 4Characteristics and severity for each of the five MG subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPatients (n)250234140132(22)3321088Female,%52.067.581.4*81.1*(81.8)61.165.4Onset age, y51.0 ± 20.0, 53.0a\n51.0 ± 12.8, 51.033.3 ± 13.9, 31.0a\n39.9 ± 16.2, 41.5a\n(38.6 ± 15.3, 42.0)50.7 ± 20.7, 55.0a\n47.3 ± 18.8, 48.1Duration of disease, y10.4 ± 11.4, 6.49.5 ± 7.7, 8.017.4 ± 11.8, 14.5a\n10.8 ± 9.3, 8.0(11.0 ± 8.1, 9.8)11.6 ± 10.5, 8.211.6 ± 10.6, 8.2AChR-Ab-positivity,%77.299.689.40.0(0.0)100.081.3MuSK-Ab-positivity,%0.0%0.0%0.0%20.6%(100.0%)0.0%2.1%Thymectomy,%23.6%*97.4%*100.0%*12.1%*(9.1%)35.8%*51.7%The worst condition of the disease MGFA classification (n = 1088) I,%100%0.0%0.0%0.0%(0.0%)0.0%23.0% II,%0.0%44.0%52.9%59.1%(45.5%)64.8%43.2% III,%0.0%27.8%30.7%28.0%(13.6%)20.5%19.6% IV,%0.0%7.7%7.9%4.5%(9.1%)4.2%4.5% V,%0.0%20.5%8.6%8.3%(31.8%)10.5%9.7% Rate of MGFA > III,%0.0%*56.0%*47.1%40.9%(54.5%)35.2%33.8% QMG score (n = 922)6.6 ± 2.6, 6.0a\n(n = 225)17.1 ± 8.0,15.5a\n(n = 194)15.8 ± 5.8, 15.0a\n(n = 107)14.7 ± 7.2, 13.0(n = 114)18.1 ± 9.7, 15.5(n = 20)14.7 ± 7.0, 13.0(n = 282)13.4 ± 7.5, 12.0Current disease condition (mean ± SD, median) QMG score (n = 923)4.2 ± 2.8, 4.0a\n(n = 208)6.8 ± 4.8, 6.0(n = 198)7.8 ± 5.5, 7.0(n = 125)8.4 ± 5.4, 8.0(n = 106)(8.3 ± 6.4, 7.0)(n = 20)7.2 ± 4.8, 6.0(n = 286)6.6 ± 4.8, 6.0 MGC score (n = 923)1.9 ± 2.5, 1.0a\n(n = 208)4.5 ± 5.4, 3.0(n = 198)5.4 ± 5.7, 3.0(n = 125)6.5 ± 6.2, 5.0a\n(n = 106)(6.2 ± 7.0, 4.5)(n = 20)4.2 ± 4.6, 3.0(n = 286)4.1 ± 5.0, 3.0 MG-QOL-15 (n = 923)8.1 ± 9.0, 5.0a\n(n = 208)14.7 ± 13.6, 11.0(n = 198)16.2 ± 13.7, 13.5a\n(n = 125)14.6 ± 12.6, 12.0(n = 106)(11.6 ± 8.8, 11.0)(n = 20)14.1 ± 13.9, 9.0(n = 286)13.2 ± 13.0, 9.0All continuous data are expressed as the mean ± standard deviation (SD) and the median\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\n\nNumber of patients fitting two categories\nValues within the parentheses show the percentages of the total of 1,088 patients\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis\nCharacteristics and severity for each of the five MG subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nAChR-Ab anti-acetylcholine receptor antibody, MG myasthenia gravis, MGC MG composite scale, MGFA MG Foundation of America, MG-QOL-15 15-item MG-specific quality of life scale, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, QMG quantitative MG score, SD standard deviation\n*p < 0.0001, chi-square test (compared to the others), †\np < 0.0001, Mann–Whitney U test\nMG with thymic hyperplasia is only diagnosed for thymectomized patients; therefore, some non-thymectomized patients with thymic hyperplasia may be assigned as other subtypes, particularly AChR-Ab-positive MG patients without thymic abnormalities.", "The clinical characteristics, including current and worst severity, for each of the five subtypes are shown in Table 4. The patients with MuSK-Ab were not separated by two-step cluster analysis because the number is not great enough for statistical evaluation. However, MG patients with MuSK-Ab showed a distinct clinical manifestation and therapy responsiveness reflecting the unique pathological mechanism [15]. Therefore, details of MG patients with MuSK-Ab (n = 22) are individually described next to SNMG patients in Table 4. The percentage of females was significantly higher among MG with thymic hyperplasia and AChR-Ab-negative MG patients compared with the other three subtypes (p < 0.0001, chi-square test). Onset age was significantly younger in MG with thymic hyperplasia and AChR-Ab-negative MG patients (p < 0.0001, Mann–Whitney U test) and older in ocular MG and AChR-Ab-positive MG patients without thymic abnormalities (p < 0.0001, Mann–Whitney U test).\nSeverity at the worst condition (MGFA classification and QMG) was significantly higher in thymoma-associated MG patients (p < 0.0001, Mann–Whitney U test). Patients with MuSK-Ab also showed worst severity at the same level as thymoma-associated MG patients, although this result was not statistically significant because of the small number of patients. The severity scales (QMG and MGC), and a QOL scale (MG-QOL-15) scores in the present survey were generally worse, although not statistically significant, in MG with thymic hyperplasia and AChR-Ab-negative MG patients, both of which primarily comprise females with younger onset ages. On the other hand, as a matter of course, ocular MG patients showed much lower clinical severity in all batteries at both current and worst conditions (p < 0.0001, Mann–Whitney U test).", "Histograms of the onset age for each of the five subtypes are shown in Fig. 1a. These histograms were converted into approximate curves (sixth-order polynomial approximations) and superimposed in Fig. 1b. The peak ages of the histogram were 60–64 years in ocular MG, 25–29 years in MG with thymic hyperplasia, 35–39 years in AChR-Ab-negative MG, 50–54 years in thymoma-associated MG, and 65–69 years in AChR-Ab-positive MG without thymic abnormalities. The histogram was skewed toward younger onset age in MG with thymic hyperplasia and toward older onset age in ocular MG and AChR-Ab-positive MG without thymic abnormalities. Regarding patients with MuSK-Ab (n = 22), the mean ± SD onset age was 38.6 ± 15.3 (median, 42.0 years), and the ratio of females was 81.8%; however, neither of these findings was significantly different from other AChR-Ab-negative MG patients.Fig. 1Histograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis\n\nHistograms and approximate curves for onset age in the five MG subtypes. a Histograms for ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG). b Superimposed approximate curves for the five subtypes regarding the distribution of onset age. The vertical broken line indicates the cutoff onset age of 50 years between early- and late-onset MG. MG, myasthenia gravis", "Details of past immunotherapy for each of the five subtypes are shown at the top of Table 5.Table 5Details of past treatment and response to treatment for each of the five subtypesOcular MGThymoma-associated MGMG with thymic hyperplasiaAChR-Ab-negative MG(MuSK-Ab-positive)AChR-Ab-positive MG without thymic abnormalitiesTotalPast immunotherapy (n = 923) Peak dose of oral PSL, mg/day9.2 ± 12.2, 5.0†\n28.5 ± 18.8, 30.0†\n29.7 ± 19.4, 30.0†\n18.8 ± 17.2, 15.032.6 ± 20.6, 30.023.7 ± 20.2, 20.021.5 ± 19.3, 15.0 Duration of PSL ≥20 mg/day, M0.0 ± 0.0, 0.0†\n12.0 ± 25.2, 5.0†\n13.0 ± 27.3, 6.0†\n3.8 ± 7.0, 0.07.2 ± 9.5, 4.08.2 ± 17.0, 2.07.9 ± 19.3, 1.0 CNIs,%24.0%*68.2%*54.0%67.4%(72.7%)58.1%52.9% PP,%2.0%*48.1%*22.1%46.0%*(54.5%)37.2%27.3% IVIG,%6.1%*36.1%29.9%42.5%*(27.3%)24.7%15.0%Initial response to treatment (n = 923, see Fig. 2a) Achievement of MM-or-better once,%79.8%*73.5%66.1%56.2%*(75.0%)67.8%70.2% Months to achieve MM-or-better in 50% of patients4.0‡\n8.018.0‡\n31.0‡\n(7.0)6.08.0Stability of improved status (n = 923) MM-or-better at present,%74.0%*58.1%49.6%39.6%*(55.0%)55.4%57.6% Maintaining rate of MM-or-better, %92.7%*79.0%75.0%70.5%(73.3%)81.7%82.1%All continuous data are expressed as the mean ± standard deviation (SD) and the median\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n\nDetails of past treatment and response to treatment for each of the five subtypes\nAll continuous data are expressed as the mean ± standard deviation (SD) and the median\n\nCNIs calcineurin inhibitors, EAT early aggressive therapy, IVIG intravenous immunoglobulin, M months, MG myasthenia gravis, MM-or-better ≥1 M minimal manifestation or better status lasting more than one month, MuSK-Ab-positive MG patients with serum anti-muscle specific kinase (MuSK) autoantibody in AChR-Ab-negative patients, PP plasmapheresis, PSL prednisolone, SD standard deviation\n*p < 0.0001, chi-square test,†\np < 0.0001, Mann–Whitney U test,‡\np < 0.0001, log-rank test\n Early-stage response to treatment (first achievement of MM-or-better ≥1 M) As shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\nAs shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).\n Stability of improved status The rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).\nThe rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).", "As shown in the middle of Table 5, the rate of the patients achieving MM-or-better ≥1 M at least once was significantly higher for ocular MG (p < 0.001, chi-square test) and significantly lower for AChR-Ab-negative MG (p < 0.001, chi-square test).\nKaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in each of the five subtypes up to 10 years from initiating immunotherapy are shown in Fig. 2a. The time to first achieve MM-or-better ≥1 M was significantly different among the five subtypes (p < 0.0001; generalized Wilcoxon test and log-rank test). Significant differences were observed between all pairs of two subtypes (p < 0.01 for all pairs; generalized Wilcoxon test) except MG with thymic hyperplasia and AChR-Ab-negative MG (p ≥ 0.10). Patients with ocular MG showed the best early-stage response to treatment compared with others (p < 0.0001; log-rank test, p < 0.001; chi-square test). The time required to achieve MM-or-better ≥1 M in 50% of the patients was significantly longer in MG with thymic hyperplasia and AChR-Ab-negative MG compared with ocular MG, thymoma-associated MG, and AChR-Ab-positive MG without thymic abnormalities (p < 0.0001, log-rank test; middle of Table 5).Fig. 2Kaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\n\nKaplan-Meier curves for the first achievement of MM-or-better ≥1 M in the five subtypes and those in the three subtypes of early-onset, late-onset, and thymoma-associated MG. a Kaplan-Meier curves for the five subtypes [ocular MG, generalized thymoma-associated MG (TAMG), generalized MG with thymic hyperplasia (THMG), generalized AChR-Ab-negative MG (SNMG) and generalized AChR-Ab-positive MG without thymic abnormalities (SPMG)]. b Kaplan-Meier curves for the three subtypes of early-onset, late-onset, and thymoma-associated MG. MM, minimal manifestations; MG, myasthenia gravis\nFor comparison, Kaplan-Meier curves for the time to first achieve MM-or-better ≥1 M in early-onset, late-onset, and thymoma-associated MG (three-type classification) are shown in Fig. 2b. Significant differences were observed between early- and late-onset MG (p < 0.01) and between early-onset and thymoma-associated MG (p < 0.01); however, no significant differences were found between late-onset and thymoma-associated MG (p ≥ 0.10).", "The rates of patients with MM-or-better status during the survey and stability of improved status are shown in the bottom of Table 5. Stability of improved status was significantly better in ocular MG compared with other subtypes (p < 0.0001; chi-square test); however, no significant differences were observed among subtypes other than ocular MG (p ≥ 0.10 for all pairs, excluding ocular MG; chi-square test).", "The present analyses based on several sets of variables classified 923 MG patients into the same five following subtypes with the same characteristics of the onset-age histograms as reported in our previous study [8]: ocular MG (AChR-Ab-positivity, 77%; histogram of onset age, skewed to older age); thymoma-associated MG (100%; normal distribution); MG with thymic hyperplasia (89%; skewed to younger age); AChR-Ab-negative MG (0%; normal distribution); and AChR-Ab-positive MG without thymic abnormalities (100%, skewed to older age). The results from the two different samples demonstrated high reproducibility, which suggests the reliability of our five-subtype classification method. In the process of analyses, two points were suggested. First, discrimination between ocular and generalized MG is more principal than that according to onset age, thymus pathology or AChR-Ab-positivity. Second, AChR-Ab-negative MG shows normal distribution of onset age not fitting discrimination based on onset age. Therefore, it is probably better to adopt the often-used three-type classification (early-onset, late-onset and thymoma-associated MG) for generalized and AChR-Ab-positive phenotypes. Consistently, in our five-subtype classification, MG with thymic hyperplasia (with early-onset age), AChR-Ab-positive MG without thymic abnormalities (with late-onset age) and thymoma-associated MG were generalized and AChR-Ab-positive phenotypes.\nIn fact, these results of our classification statistically performed are consistent with a recently reported classification of MG by Gilhus et al. [16, 17], which included the following classifications: early-onset MG; late-onset MG; thymoma-associated MG; MuSK-Ab positive MG; lipoprotein-related protein 4 (LRP4)-Ab positive MG; seronegative MG; and ocular MG. In addition, they commented that early- and late-onset MG should be distinguished according to onset age only for patients having generalized symptoms and AChR-Ab. In the present study, because of their small numbers, MuSK-Ab-positive MG patients were not separated, and LRP4-Ab positivity was not systematically determined; though MG patients with MuSK-Ab or LRP 4-Ab have a distinct clinical manifestation and a unique pathological mechanism [15].\nOcular MG was found to have unique characteristics such as having a higher onset age, predominantly affecting males, and having an ocular muscle-specific pathogenesis [18], which may be related to the aging-associated susceptibility of ocular muscles to antibodies against the neuromuscular junction. Given that response to treatment and stability of improved status were substantially better in ocular MG compared with the other four subtypes, it seems reasonable to conclude that ocular MG should be treated as a distinct subgroup of MG in the clinical setting.\nAmong the four generalized subtypes in the present classification method, both early-stage response to treatment and stability of improved status were worst in AChR-Ab-negative MG, although symptoms at the time the condition was at its worst were not particularly severe. Patients with MuSK-Ab-positive MG showed better results despite having more severe worst conditions, which suggests that AChR-Ab-negative MG, excluding MuSK-Ab-positive MG, is distinct from other generalized MG subtypes from the perspective of response to therapy. Overall, as shown in Fig. 2a, each of the five present subtypes showed different levels of response to treatment, whereas such differences in the three commonly used subtypes (early-onset, late-onset, and thymoma-associated MG) remain somewhat unclear (Fig. 2b). It would be more helpful in the clinical setting to elucidate the levels of response to some types of medication or therapy (e.g. corticosteroids, non-steroid immunosuppressants, intravenous immunoglobulin and plasmapheresis) in the five subtypes. However, it was difficult to analyze such response levels, as plural treatment agents and methods were employed simultaneously in most of individual patients. We are now analyzing the response levels according to patterns of immune treatment (treatment strategies) in generalized MG patients [19]. Such analysis should be performed also for the present five subtypes, but could not be addressed in the present report.\nThe present study did have some limitations. First, 331 (30.4%) of the 1,088 patients were included in our previous survey in 2012, which might have affected the reproducibility of the present five-subtype classification. Second, almost all of the MG patients in our database are Japanese; therefore, a race/ethnicity bias may have affected the results. Finally, some MG patients with thymic hyperplasia might have been classified as AChR-Ab-positive MG without thymic abnormalities because the diagnosis of thymic hyperplasia is made based on the results of pathological examinations after thymectomy. However, the frequency of thymectomy for MG patients without thymoma has been decreasing [20]; therefore, some of the AChR-Ab-positive MG patients with younger onset age who had not undergone thymectomy could have had thymic hyperplasia.", "The results of the present study suggest that MG patients can be classified into the following five subtypes in order of priority: ocular MG; thymoma-associated MG; MG with thymic hyperplasia; AChR-Ab-negative MG; and AChR-Ab-positive MG without thymic abnormalities. All MG patients can be allocated to one of the subtypes based on the results of routine examinations. These five subtypes were shown to have characteristic demographic characteristics, clinical severity, and therapeutic responses. Therefore, our five-subtype classification method is expected to be beneficial not only for elucidating disease types, but also for planning proper treatment for individual patients." ]

[ "introduction", "materials|methods", null, null, null, null, null, null, "results", null, null, null, null, null, null, "discussion", "conclusion" ]

[ "Classification", "Cluster analysis", "Myasthenia", "Onset age", "Treatment" ]

[ 615, 298, 269, 78, 44, 47, 1348, 372, 397, 2099, 677, 89 ]

[ "mg", "ab", "patients", "achr", "achr ab", "subtypes", "onset", "thymic", "mg thymic", "test" ]

[ "autoantibodies neuromuscular junction", "myasthenia gravis mgc", "myasthenia gravis mg", "muscles antibodies", "mg specific autoantibodies" ]

[ "Adult", "Carcinoma, Papillary", "Catheter Ablation", "Comorbidity", "Endoscopic Ultrasound-Guided Fine Needle Aspiration", "Female", "Hashimoto Disease", "Humans", "Male", "Neoplasm Recurrence, Local", "Survival Analysis", "Thyroid Neoplasms", "Treatment Outcome" ]

[ "INTRODUCTION", "Patients", "Instrument and equipment", "Preablation assessment", "Ablation procedure", "Follow‐up", "Statistical analysis", "Preoperative patient and tumor characteristics", "Ablation power and time in the PTMC and PTMC+CLT groups", "Tumor volume and VRR after RFA", "Complications", "AUTHOR CONTRIBUTIONS" ]

[ "Since the mid‐1990s, the incidence of thyroid cancer has increased worldwide and is reportedly the fastest‐growing cancer. In the United States, the overall incidence of thyroid cancer increased by 3% annually from 1974 to 20131 and the number of new cases likely reached 53 900 in 2018.2 In China, the incidence of thyroid cancer is also increasing, and in 2014, it was among the top four cancers in terms of incidence among women.3 Thyroid cancer includes several pathological types, of which papillary thyroid carcinoma (PTC) is the most common. Tumors ≤10 mm in diameter are defined as papillary thyroid microcarcinomas (PTMCs). These have been observed in 15.5% of autopsies in which whole‐gland examination was performed and are typically associated with good prognosis.4\n\nChronic lymphocytic thyroiditis (CLT), also known as Hashimoto's thyroiditis, is an autoimmune disease characterized by widespread lymphocyte infiltration, fibrosis and parenchymal atrophy of the thyroid tissue. The male‐to‐female incidence ratio of CLT is 1:5‐20. CLT is commonly associated with PTC, and is characterized by a smaller primary tumor size at presentation. Leni et al5 reported that one‐third of PTC cases (33.3%, 168/505) have CLT coexistence. No association has been found between CLT and follicular, medullary, or anaplastic thyroid cancer.6 The coexistence of PTC and CLT is reportedly associated with better prognoses, lower rates of lymph node and distant metastases, and recurrence, particularly in patients aged ≥45 years.7 There is controversy surrounding the treatment of PTC with coexisting CLT. For PTMC, some guidelines recommend the performance of surgery.8, 9 However, traditional surgery results in injuries, prominent neck scarring, and a lowered quality of life, especially among older patients with chronic comorbidities. The Korean Society of Thyroid Radiology consensus statement10 highlights the need for active surveillance rather than immediate surgery in adult patients with low‐risk PTMC. However, a majority of patients experience severe anxiety if no treatment is provided. Therefore, minimally invasive therapy is utilized in many cases. Thermal tumor ablation (using microwave, radiofrequency, or laser ablation, or high‐intensity focused ultrasound [US]) has been applied in clinical practice with good feedback.11, 12, 13 Radiofrequency ablation (RFA) was found to be efficient and safe in the treatment of PTMC in our preliminary study.14 We aimed to investigate the therapeutic effect and safety of RFA in PTMC cases with CLT in this study.", "Patients who fulfilled the following criteria were enrolled: (1) presence of PTC confirmed by US‐guided CNB; (2) maximum diameter less than 1 cm; (3) absence of capsular infiltration and extrathyroidal invasion, and lack of LNM detection; (4) absence of neck irradiation history; and (5) unable or refused to receive surgery. Exclusion criteria were: the presence of (1) multifocal cancer; (2) aggressive histological PTMC, such as tall cell, insular, or columnar cell carcinoma; (3) suspicious cervical LNM; (4) pregnancy and lactation; (5) severe coagulation disorders, respiratory failure, myocardial infarction, systemic infection, or uncontrolled diabetes; (6) neuropsychiatric disturbance or neck extension disorder leading to RFA nontolerance; (7) cardiac pacemaker implantation; (8) contralateral vocal cord paralysis; and (9) allergic to sulfur hexafluoride microbubbles (SonoVue, Bracco International, Milan, Italy).\nBetween February 2013 and March 2017, 60 tumors in 60 patients (12 men and 48 women) treated with US‐guided RFA were included, comprising 30 patients with only PTMC, and 30 with CLT+PTMC. CLT was confirmed by pathologic examination and serological test, and was defined as diffuse lymphocytic and plasma cell infiltrates, lymphoid follicles formation with germinal centers, varying degree of fibrosis, parenchymal atrophy, and the presence of large follicular cells with oxyphilic cell changes,7 as well as positive hyroperoxidase antibody (TPOAb) and thyroglobulin antibody (TgAb). The presence of peritumoral inflammatory reaction was not considered CLT. All patients had complete records and were followed for more than 18 months.", "US and contrast‐enhanced US (CEUS) examinations were performed using a Siemens Acuson Sequoia 512 Ultrasound System (Siemens, Mountain View, CA) with a 15L8W linear array transducer. US‐guided RFA and CNB were performed using a Siemens Acuson Sequoia 512 Ultrasound System with a 6L3 linear array transducer. CNB on each nodule was performed using an 18‐gauge biopsy needle after RFA (Biopty; Bard, Covington, GA).\nA bipolar RFA generator (CelonLabPOWER; Olympus Surgical Technologies Europe, Hamburg, Germany) and an 18‐gauge bipolar radiofrequency (RF) applicator with a 0.9‐cm active tip (CelonProSurge micro 100‐T09; Olympus Surgical Technologies Europe) were used for RFA treatment in this study. During the application of RF energy, the electric impedance of the tissue between the two electrodes at the tip of the RF applicator was measured continuously by the generator. The power is automatically reduced if the temperature at the electrodes reaches 100°C.", "Careful history‐taking and thorough physical examinations were conducted in all patients in our department. All patients who qualified for RFA were subject to thyroid US, CEUS, and determination of the levels of free thyroid hormones T3 (normal reference range: 2.76‐6.30 pmol/L) and T4 (10.42‐24.32 pmol/L), thyroid‐stimulating hormone (TSH) (0.35‐5.5 mU/L), TPOAb (＜60 IU/mL) and TgAb (＜60 IU/mL).\nPatients were supine with the neck extended during the procedure. An intravenous line was introduced into an elbow vein. US appearances were evaluated and recorded according to the multidisciplinary consensus statement for thyroid nodules.15 For each tumor, the size, volume, location, echogenicity, margin, shape (height/width), calcifications, and vascularity were evaluated by US. The volume of each tumor was calculated as V = πabc/6 (V: volume, a: transverse diameter of tumor, b: vertical diameter of tumor, and c: anteroposterior diameter of tumor). CEUS with a low mechanical index (0.19‐0.24) was used to describe the blood supply region of the tumor before and after RFA. The contrast agent used was 59 mg of dry powder SonoVue constituted in 5 mL of normal saline. CEUS was performed after a bolus injection of SonoVue (2.4 mL), followed by a normal saline flush (5 mL). Real‐time microbubble perfusions within the tumor and surrounding tissues were observed for a minimum of 2 minutes and recorded electronically.\nCapsular and extrathyroidal invasion of thyroid cancer were evaluated by both US and CEUS. Extracapsular extension on the US image was defined as cases in which the anterior and posterior hyperechoic thyroid capsules were discontinued. During the real‐time and multi‐angle scanning, the capsular infiltration and extrathyroidal invasion on CEUS were shown as low‐ or nonenhancing areas on the thyroid capsule invaded by malignant nodules.16\n", "Conventional US was performed to evaluate the relationship between the tumor and critical structures in the neck, such as the trachea, esophagus, jugular vein, common carotid artery (CCA), and recurrent laryngeal nerves, in order to decide the best insertion way. A local anesthetic (1% lidocaine) was injected at the subcutaneous puncture site and the thyroid anterior capsule. If the distance between the tumor and critical neck structures was <5 mm, normal saline was injected using another needle (23 gauge) to form at least a 1cm distance between the tumor and critical structure for the prevention of thermal injuries. RFA was performed using the moving‐shot technique (P–Q); 3W was the initial radiofrequency power which was increased to 5W if a transient hyperechoic zone did not form at the electrode tip within 5‐10 s. To prevent residual tumor and recurrence, the RFA area exceeded the tumor margin. The ablation procedure ended when the tumor was completely covered with hyperechoic zone in three‐dimensional space on US. The damage range of ablation was evaluated by CEUS immediately after ablation, and the electrode needle was pulled out of the tissue after ensuring that no residual tumor (completely no enhancement in the ablation area). If the enhancement was showed in some areas of tumor, complementary ablation could be applied promptly to avoid residual cancer cells. During the procedure, special attention was given to the protection of critical neck structures to prevent the significant complications such as hematoma or nerve injury. All complications occurring during and after RFA were carefully assessed according to patients' clinical signs and symptoms. Each patient was observed for 1‐2 hours after RFA.", "Patients were followed‐up using conventional US and CEUS at months 1, 3, 6, and 12 after RFA, and every 6 months thereafter. The ablation area was evaluated by CEUS to detect volume of ablation and provide a baseline to screen for recurrence. The volume reduction ratio (VRR) was calculated as follows: VRR (%) = [(1 − final volume)/initial volume] × 100%. The involvement of cervical lymph nodes was evaluated by US; suspicious lymph nodes (a globular shape, loss of the normal echogenic hilum, presence of peripheral rather than hilar flow, and microcalcifications17) were biopsied. US‐guided CNB was performed at the center and the margin of the ablation area, and in the surrounding thyroid parenchyma 3 months after RFA. Complications during the follow‐up period were assessed according to the reporting standards of the Society of Interventional Radiology.18\n\nAll RFA procedures and preoperative examinations were performed by one experienced physician to exclude bias associated with different operators. Postoperative CNB was also performed by the same physician who performed RFA. Follow‐up conventional US and CEUS examinations were performed by two other experienced physicians who were blinded to the histological and imaging findings. Discrepancies were resolved by the judgment of a third experienced physician who had specialized in thyroid US and CEUS for over 15 years.", "All statistical analyses were performed using SPSS software package, Version 13 for Windows (SPSS Inc, Chicago, IL). A chi‐squared test (χ2) was used to analyze the categorical variables. Continuous data were reported as mean ± standard deviation (range). Volume and VRR of the ablation area before RFA and at each follow‐up were analyzed by the t test. The Wilcoxon signed rank test was used to compare tumor calcification, color Doppler flow imaging (CDFI) blood flow grades, and changes in the number of patients with tumor disappearance at each follow‐up between CLT+PTMC and PTMC groups. The Wilcoxon rank sum test was used to compare Free T3, T4, and TSH values in patients with and without CLT P values <0.05 were considered statistically significant.", "There were no significant differences in terms of age, sex, and tumor volume between the PTMC and PTMC+CLT groups (P > 0.05) (Table 1). All thyroid nodules were hypoechoic; other characteristics are shown in Table 2. There were no significant differences in nodule location, margin, shape, height/width, calcification, and CDFI type between the two groups (P > 0.05). The distance between the nodule and trachea or common carotid artery (CCA) was <2 mm in 13 nodules, including eight near the trachea (0.117 ± 0.024) cm, and five near the CCA (0.094 ± 0.013) cm. There were no significant differences in free T3, T4 and TSH values between the two groups (U = 0.434, P = 0.664; U = 0.452, P = 0.651; and U = 0.886, P = 0.376, respectively). All patients with CLT had positive TgAb and TPOAb. The thyroid function test results of those with PTMC were in the normal range.\nComparison of the preoperative data of the patients and tumor volume between the PTMC+CLT and PTMC groups\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.\nUltrasonic characteristics of the tumors before RFA in the PTMC+CLT and PTMC groups\nAbbreviations: CDFI, color Doppler flow imaging; CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.", "The ablation powers in the PTMC and PTMC+CLT groups were 0.9 ± 0.5 KJ and 0.7 ± 0.5 KJ, respectively (t = 1.453, P = 0.1515); ablation times were 3.8 ± 2.1 min and 2.9 ± 2 0.1 min, respectively (t = 1.801, P = 0.0768).", "In terms of postoperative ablation volume, there was no significant difference between the two groups at 1, 3, 6, 12, and 18 months after RFA (P > 0.05) (Table 3, Figure 1). While there was a significant difference in VRR between the two groups 3 months after ablation (t = 1.28, P = 0.03), no difference was observed at any other time‐point (P > 0.05) (Table 4, Figure 2).\nChanges in the tumor volume between the PTMC + CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nRadiofrequency ablation (RFA) treatment and follow‐up of one case of papillary thyroid microcarcinoma with chronic lymphocytic thyroiditis. (A) A hypoechoic nodule sized 0.4 × 0.5 × 0.4 cm, with irregular margins and microcalcifications was displayed in the right thyroid lobe (arrow). (B) Uneven and irregular hypo‐enhancement in the nodule was observed by contrast‐enhanced ultrasound (CEUS) (arrow, left image). (C) During RFA, the nodule was covered by a hyperechoic area (arrow) on US. (D) Immediately after RFA, the ablation area was showed completely no enhancement by CEUS, and its size (0.7 × 1.1 × 1.0 cm) was larger than the initial nodule size. (E) One month after RFA, the ablation area decreased in size to 0.9 × 0.8 × 0.5 cm. (F) Three months after RFA, the ablation area decreased to 0.6 × 0.5 × 0.6 cm. (G) Six months after RFA, the ablation area decreased to 0.3 × 0.2 × 0.3 cm. (H) The ablation area could not be identified on both US and CEUS. (I) Before RFA, the pathologic examination of this nodule showed the presence of papillary thyroid carcinoma accompanied by chronic lymphocytic thyroiditis. (J) Three months after RFA, pathology showed degenerated and necrotic follicular epithelia, interstitial fibrous tissue hyperplasia, and hyaline degeneration in the ablation lesion, with lymphocyte infiltration and multinucleated giant cell reaction in the adjacent thyroid tissue. No residual cancer was found\nChanges in the tumor volume reduction ratio between the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nChanges in ablation zone volume in PTMC cases with and without CLT at each follow‐up. PTMC, papillary thyroid microcarcinoma; CLT, chronic lymphocytic thyroiditis\nAfter RFA, the times to tumor disappearance were 9.800 ± 5.041 months and 10.0 ± 4.8 months (t = 0.16, P = 0.88) in the PTMC and PTMC + CLT groups, respectively. A total of 43% of the nodules in the PTMC+CLT group resolved in 12 months, and 47% in the PTMC group resolved in 6 months. No significant differences were observed with respect to the number of ablation areas between the groups (u = 0.319, P > 0.05) (Table 5). Calcification was observed in 20 PTMC cases, with time to tumor disappearance of 10.2 ± 5.1 months; no calcification was found in 40 PTMC cases, with time to tumor disappearance of 9.8 ± 4.9 months; no significant difference in time was observed between the PTMC cases with and without calcification (t = 0.28, P = 0.78). No residual cancer cells were found by CNB 3 months after ablation (Figure 1). No recurrent tumors or suspicious metastatic lymph nodes were detected.\nNumber of patients with tumors disappearance in the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation.", "Slight voice hoarseness was observed in one patient (1.7%, 1/60) after RFA; voice recovery occurred without treatment in 1 week. The presence of moderate‐intensity pain was reported by two patients (3.3%, 2/60), and slight fever was noted in one patient (1.7%, 1/60); in all cases, spontaneous recovery was observed.", "Yan Zhang contributed to the collection, analysis, and interpretation of data and writing initial draft. Mingbo Zhang performed the statistical analyses, had full access to all data, and takes responsibility for the accuracy of the data analysis in the study. Ying Zhang contributed to the collection, analysis and interpreted the results. Jie Li: contributed to the pathological figures analysis and interpretation. Jie Tang contributed to writing–review and revisions. Yukun Luo contributed to the study design, writing–initial draft, guarantor of the study and had full access to all data in the study, and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to critical revisions and approved the final version." ]

[ null, null, null, null, null, null, null, null, null, null, null, null ]

[ "INTRODUCTION", "MATERIALS AND METHODS", "Patients", "Instrument and equipment", "Preablation assessment", "Ablation procedure", "Follow‐up", "Statistical analysis", "RESULTS", "Preoperative patient and tumor characteristics", "Ablation power and time in the PTMC and PTMC+CLT groups", "Tumor volume and VRR after RFA", "Complications", "DISCUSSION", "CONFLICT OF INTEREST", "AUTHOR CONTRIBUTIONS" ]

[ "Since the mid‐1990s, the incidence of thyroid cancer has increased worldwide and is reportedly the fastest‐growing cancer. In the United States, the overall incidence of thyroid cancer increased by 3% annually from 1974 to 20131 and the number of new cases likely reached 53 900 in 2018.2 In China, the incidence of thyroid cancer is also increasing, and in 2014, it was among the top four cancers in terms of incidence among women.3 Thyroid cancer includes several pathological types, of which papillary thyroid carcinoma (PTC) is the most common. Tumors ≤10 mm in diameter are defined as papillary thyroid microcarcinomas (PTMCs). These have been observed in 15.5% of autopsies in which whole‐gland examination was performed and are typically associated with good prognosis.4\n\nChronic lymphocytic thyroiditis (CLT), also known as Hashimoto's thyroiditis, is an autoimmune disease characterized by widespread lymphocyte infiltration, fibrosis and parenchymal atrophy of the thyroid tissue. The male‐to‐female incidence ratio of CLT is 1:5‐20. CLT is commonly associated with PTC, and is characterized by a smaller primary tumor size at presentation. Leni et al5 reported that one‐third of PTC cases (33.3%, 168/505) have CLT coexistence. No association has been found between CLT and follicular, medullary, or anaplastic thyroid cancer.6 The coexistence of PTC and CLT is reportedly associated with better prognoses, lower rates of lymph node and distant metastases, and recurrence, particularly in patients aged ≥45 years.7 There is controversy surrounding the treatment of PTC with coexisting CLT. For PTMC, some guidelines recommend the performance of surgery.8, 9 However, traditional surgery results in injuries, prominent neck scarring, and a lowered quality of life, especially among older patients with chronic comorbidities. The Korean Society of Thyroid Radiology consensus statement10 highlights the need for active surveillance rather than immediate surgery in adult patients with low‐risk PTMC. However, a majority of patients experience severe anxiety if no treatment is provided. Therefore, minimally invasive therapy is utilized in many cases. Thermal tumor ablation (using microwave, radiofrequency, or laser ablation, or high‐intensity focused ultrasound [US]) has been applied in clinical practice with good feedback.11, 12, 13 Radiofrequency ablation (RFA) was found to be efficient and safe in the treatment of PTMC in our preliminary study.14 We aimed to investigate the therapeutic effect and safety of RFA in PTMC cases with CLT in this study.", "This study was approved by the ethics committee of our hospital, and informed consent was obtained from each patient before the performance of US‐guided core‐needle biopsy (CNB) and RFA. The informed consent form for RFA emphasized that surgery is the routine treatment procedure recommended by guidelines, and that RFA administration cannot prevent the development of recurrent PTMC and undetectable cervical lymph node metastasis (LNM).\n Patients Patients who fulfilled the following criteria were enrolled: (1) presence of PTC confirmed by US‐guided CNB; (2) maximum diameter less than 1 cm; (3) absence of capsular infiltration and extrathyroidal invasion, and lack of LNM detection; (4) absence of neck irradiation history; and (5) unable or refused to receive surgery. Exclusion criteria were: the presence of (1) multifocal cancer; (2) aggressive histological PTMC, such as tall cell, insular, or columnar cell carcinoma; (3) suspicious cervical LNM; (4) pregnancy and lactation; (5) severe coagulation disorders, respiratory failure, myocardial infarction, systemic infection, or uncontrolled diabetes; (6) neuropsychiatric disturbance or neck extension disorder leading to RFA nontolerance; (7) cardiac pacemaker implantation; (8) contralateral vocal cord paralysis; and (9) allergic to sulfur hexafluoride microbubbles (SonoVue, Bracco International, Milan, Italy).\nBetween February 2013 and March 2017, 60 tumors in 60 patients (12 men and 48 women) treated with US‐guided RFA were included, comprising 30 patients with only PTMC, and 30 with CLT+PTMC. CLT was confirmed by pathologic examination and serological test, and was defined as diffuse lymphocytic and plasma cell infiltrates, lymphoid follicles formation with germinal centers, varying degree of fibrosis, parenchymal atrophy, and the presence of large follicular cells with oxyphilic cell changes,7 as well as positive hyroperoxidase antibody (TPOAb) and thyroglobulin antibody (TgAb). The presence of peritumoral inflammatory reaction was not considered CLT. All patients had complete records and were followed for more than 18 months.\nPatients who fulfilled the following criteria were enrolled: (1) presence of PTC confirmed by US‐guided CNB; (2) maximum diameter less than 1 cm; (3) absence of capsular infiltration and extrathyroidal invasion, and lack of LNM detection; (4) absence of neck irradiation history; and (5) unable or refused to receive surgery. Exclusion criteria were: the presence of (1) multifocal cancer; (2) aggressive histological PTMC, such as tall cell, insular, or columnar cell carcinoma; (3) suspicious cervical LNM; (4) pregnancy and lactation; (5) severe coagulation disorders, respiratory failure, myocardial infarction, systemic infection, or uncontrolled diabetes; (6) neuropsychiatric disturbance or neck extension disorder leading to RFA nontolerance; (7) cardiac pacemaker implantation; (8) contralateral vocal cord paralysis; and (9) allergic to sulfur hexafluoride microbubbles (SonoVue, Bracco International, Milan, Italy).\nBetween February 2013 and March 2017, 60 tumors in 60 patients (12 men and 48 women) treated with US‐guided RFA were included, comprising 30 patients with only PTMC, and 30 with CLT+PTMC. CLT was confirmed by pathologic examination and serological test, and was defined as diffuse lymphocytic and plasma cell infiltrates, lymphoid follicles formation with germinal centers, varying degree of fibrosis, parenchymal atrophy, and the presence of large follicular cells with oxyphilic cell changes,7 as well as positive hyroperoxidase antibody (TPOAb) and thyroglobulin antibody (TgAb). The presence of peritumoral inflammatory reaction was not considered CLT. All patients had complete records and were followed for more than 18 months.\n Instrument and equipment US and contrast‐enhanced US (CEUS) examinations were performed using a Siemens Acuson Sequoia 512 Ultrasound System (Siemens, Mountain View, CA) with a 15L8W linear array transducer. US‐guided RFA and CNB were performed using a Siemens Acuson Sequoia 512 Ultrasound System with a 6L3 linear array transducer. CNB on each nodule was performed using an 18‐gauge biopsy needle after RFA (Biopty; Bard, Covington, GA).\nA bipolar RFA generator (CelonLabPOWER; Olympus Surgical Technologies Europe, Hamburg, Germany) and an 18‐gauge bipolar radiofrequency (RF) applicator with a 0.9‐cm active tip (CelonProSurge micro 100‐T09; Olympus Surgical Technologies Europe) were used for RFA treatment in this study. During the application of RF energy, the electric impedance of the tissue between the two electrodes at the tip of the RF applicator was measured continuously by the generator. The power is automatically reduced if the temperature at the electrodes reaches 100°C.\nUS and contrast‐enhanced US (CEUS) examinations were performed using a Siemens Acuson Sequoia 512 Ultrasound System (Siemens, Mountain View, CA) with a 15L8W linear array transducer. US‐guided RFA and CNB were performed using a Siemens Acuson Sequoia 512 Ultrasound System with a 6L3 linear array transducer. CNB on each nodule was performed using an 18‐gauge biopsy needle after RFA (Biopty; Bard, Covington, GA).\nA bipolar RFA generator (CelonLabPOWER; Olympus Surgical Technologies Europe, Hamburg, Germany) and an 18‐gauge bipolar radiofrequency (RF) applicator with a 0.9‐cm active tip (CelonProSurge micro 100‐T09; Olympus Surgical Technologies Europe) were used for RFA treatment in this study. During the application of RF energy, the electric impedance of the tissue between the two electrodes at the tip of the RF applicator was measured continuously by the generator. The power is automatically reduced if the temperature at the electrodes reaches 100°C.\n Preablation assessment Careful history‐taking and thorough physical examinations were conducted in all patients in our department. All patients who qualified for RFA were subject to thyroid US, CEUS, and determination of the levels of free thyroid hormones T3 (normal reference range: 2.76‐6.30 pmol/L) and T4 (10.42‐24.32 pmol/L), thyroid‐stimulating hormone (TSH) (0.35‐5.5 mU/L), TPOAb (＜60 IU/mL) and TgAb (＜60 IU/mL).\nPatients were supine with the neck extended during the procedure. An intravenous line was introduced into an elbow vein. US appearances were evaluated and recorded according to the multidisciplinary consensus statement for thyroid nodules.15 For each tumor, the size, volume, location, echogenicity, margin, shape (height/width), calcifications, and vascularity were evaluated by US. The volume of each tumor was calculated as V = πabc/6 (V: volume, a: transverse diameter of tumor, b: vertical diameter of tumor, and c: anteroposterior diameter of tumor). CEUS with a low mechanical index (0.19‐0.24) was used to describe the blood supply region of the tumor before and after RFA. The contrast agent used was 59 mg of dry powder SonoVue constituted in 5 mL of normal saline. CEUS was performed after a bolus injection of SonoVue (2.4 mL), followed by a normal saline flush (5 mL). Real‐time microbubble perfusions within the tumor and surrounding tissues were observed for a minimum of 2 minutes and recorded electronically.\nCapsular and extrathyroidal invasion of thyroid cancer were evaluated by both US and CEUS. Extracapsular extension on the US image was defined as cases in which the anterior and posterior hyperechoic thyroid capsules were discontinued. During the real‐time and multi‐angle scanning, the capsular infiltration and extrathyroidal invasion on CEUS were shown as low‐ or nonenhancing areas on the thyroid capsule invaded by malignant nodules.16\n\nCareful history‐taking and thorough physical examinations were conducted in all patients in our department. All patients who qualified for RFA were subject to thyroid US, CEUS, and determination of the levels of free thyroid hormones T3 (normal reference range: 2.76‐6.30 pmol/L) and T4 (10.42‐24.32 pmol/L), thyroid‐stimulating hormone (TSH) (0.35‐5.5 mU/L), TPOAb (＜60 IU/mL) and TgAb (＜60 IU/mL).\nPatients were supine with the neck extended during the procedure. An intravenous line was introduced into an elbow vein. US appearances were evaluated and recorded according to the multidisciplinary consensus statement for thyroid nodules.15 For each tumor, the size, volume, location, echogenicity, margin, shape (height/width), calcifications, and vascularity were evaluated by US. The volume of each tumor was calculated as V = πabc/6 (V: volume, a: transverse diameter of tumor, b: vertical diameter of tumor, and c: anteroposterior diameter of tumor). CEUS with a low mechanical index (0.19‐0.24) was used to describe the blood supply region of the tumor before and after RFA. The contrast agent used was 59 mg of dry powder SonoVue constituted in 5 mL of normal saline. CEUS was performed after a bolus injection of SonoVue (2.4 mL), followed by a normal saline flush (5 mL). Real‐time microbubble perfusions within the tumor and surrounding tissues were observed for a minimum of 2 minutes and recorded electronically.\nCapsular and extrathyroidal invasion of thyroid cancer were evaluated by both US and CEUS. Extracapsular extension on the US image was defined as cases in which the anterior and posterior hyperechoic thyroid capsules were discontinued. During the real‐time and multi‐angle scanning, the capsular infiltration and extrathyroidal invasion on CEUS were shown as low‐ or nonenhancing areas on the thyroid capsule invaded by malignant nodules.16\n\n Ablation procedure Conventional US was performed to evaluate the relationship between the tumor and critical structures in the neck, such as the trachea, esophagus, jugular vein, common carotid artery (CCA), and recurrent laryngeal nerves, in order to decide the best insertion way. A local anesthetic (1% lidocaine) was injected at the subcutaneous puncture site and the thyroid anterior capsule. If the distance between the tumor and critical neck structures was <5 mm, normal saline was injected using another needle (23 gauge) to form at least a 1cm distance between the tumor and critical structure for the prevention of thermal injuries. RFA was performed using the moving‐shot technique (P–Q); 3W was the initial radiofrequency power which was increased to 5W if a transient hyperechoic zone did not form at the electrode tip within 5‐10 s. To prevent residual tumor and recurrence, the RFA area exceeded the tumor margin. The ablation procedure ended when the tumor was completely covered with hyperechoic zone in three‐dimensional space on US. The damage range of ablation was evaluated by CEUS immediately after ablation, and the electrode needle was pulled out of the tissue after ensuring that no residual tumor (completely no enhancement in the ablation area). If the enhancement was showed in some areas of tumor, complementary ablation could be applied promptly to avoid residual cancer cells. During the procedure, special attention was given to the protection of critical neck structures to prevent the significant complications such as hematoma or nerve injury. All complications occurring during and after RFA were carefully assessed according to patients' clinical signs and symptoms. Each patient was observed for 1‐2 hours after RFA.\nConventional US was performed to evaluate the relationship between the tumor and critical structures in the neck, such as the trachea, esophagus, jugular vein, common carotid artery (CCA), and recurrent laryngeal nerves, in order to decide the best insertion way. A local anesthetic (1% lidocaine) was injected at the subcutaneous puncture site and the thyroid anterior capsule. If the distance between the tumor and critical neck structures was <5 mm, normal saline was injected using another needle (23 gauge) to form at least a 1cm distance between the tumor and critical structure for the prevention of thermal injuries. RFA was performed using the moving‐shot technique (P–Q); 3W was the initial radiofrequency power which was increased to 5W if a transient hyperechoic zone did not form at the electrode tip within 5‐10 s. To prevent residual tumor and recurrence, the RFA area exceeded the tumor margin. The ablation procedure ended when the tumor was completely covered with hyperechoic zone in three‐dimensional space on US. The damage range of ablation was evaluated by CEUS immediately after ablation, and the electrode needle was pulled out of the tissue after ensuring that no residual tumor (completely no enhancement in the ablation area). If the enhancement was showed in some areas of tumor, complementary ablation could be applied promptly to avoid residual cancer cells. During the procedure, special attention was given to the protection of critical neck structures to prevent the significant complications such as hematoma or nerve injury. All complications occurring during and after RFA were carefully assessed according to patients' clinical signs and symptoms. Each patient was observed for 1‐2 hours after RFA.\n Follow‐up Patients were followed‐up using conventional US and CEUS at months 1, 3, 6, and 12 after RFA, and every 6 months thereafter. The ablation area was evaluated by CEUS to detect volume of ablation and provide a baseline to screen for recurrence. The volume reduction ratio (VRR) was calculated as follows: VRR (%) = [(1 − final volume)/initial volume] × 100%. The involvement of cervical lymph nodes was evaluated by US; suspicious lymph nodes (a globular shape, loss of the normal echogenic hilum, presence of peripheral rather than hilar flow, and microcalcifications17) were biopsied. US‐guided CNB was performed at the center and the margin of the ablation area, and in the surrounding thyroid parenchyma 3 months after RFA. Complications during the follow‐up period were assessed according to the reporting standards of the Society of Interventional Radiology.18\n\nAll RFA procedures and preoperative examinations were performed by one experienced physician to exclude bias associated with different operators. Postoperative CNB was also performed by the same physician who performed RFA. Follow‐up conventional US and CEUS examinations were performed by two other experienced physicians who were blinded to the histological and imaging findings. Discrepancies were resolved by the judgment of a third experienced physician who had specialized in thyroid US and CEUS for over 15 years.\nPatients were followed‐up using conventional US and CEUS at months 1, 3, 6, and 12 after RFA, and every 6 months thereafter. The ablation area was evaluated by CEUS to detect volume of ablation and provide a baseline to screen for recurrence. The volume reduction ratio (VRR) was calculated as follows: VRR (%) = [(1 − final volume)/initial volume] × 100%. The involvement of cervical lymph nodes was evaluated by US; suspicious lymph nodes (a globular shape, loss of the normal echogenic hilum, presence of peripheral rather than hilar flow, and microcalcifications17) were biopsied. US‐guided CNB was performed at the center and the margin of the ablation area, and in the surrounding thyroid parenchyma 3 months after RFA. Complications during the follow‐up period were assessed according to the reporting standards of the Society of Interventional Radiology.18\n\nAll RFA procedures and preoperative examinations were performed by one experienced physician to exclude bias associated with different operators. Postoperative CNB was also performed by the same physician who performed RFA. Follow‐up conventional US and CEUS examinations were performed by two other experienced physicians who were blinded to the histological and imaging findings. Discrepancies were resolved by the judgment of a third experienced physician who had specialized in thyroid US and CEUS for over 15 years.\n Statistical analysis All statistical analyses were performed using SPSS software package, Version 13 for Windows (SPSS Inc, Chicago, IL). A chi‐squared test (χ2) was used to analyze the categorical variables. Continuous data were reported as mean ± standard deviation (range). Volume and VRR of the ablation area before RFA and at each follow‐up were analyzed by the t test. The Wilcoxon signed rank test was used to compare tumor calcification, color Doppler flow imaging (CDFI) blood flow grades, and changes in the number of patients with tumor disappearance at each follow‐up between CLT+PTMC and PTMC groups. The Wilcoxon rank sum test was used to compare Free T3, T4, and TSH values in patients with and without CLT P values <0.05 were considered statistically significant.\nAll statistical analyses were performed using SPSS software package, Version 13 for Windows (SPSS Inc, Chicago, IL). A chi‐squared test (χ2) was used to analyze the categorical variables. Continuous data were reported as mean ± standard deviation (range). Volume and VRR of the ablation area before RFA and at each follow‐up were analyzed by the t test. The Wilcoxon signed rank test was used to compare tumor calcification, color Doppler flow imaging (CDFI) blood flow grades, and changes in the number of patients with tumor disappearance at each follow‐up between CLT+PTMC and PTMC groups. The Wilcoxon rank sum test was used to compare Free T3, T4, and TSH values in patients with and without CLT P values <0.05 were considered statistically significant.", "Patients who fulfilled the following criteria were enrolled: (1) presence of PTC confirmed by US‐guided CNB; (2) maximum diameter less than 1 cm; (3) absence of capsular infiltration and extrathyroidal invasion, and lack of LNM detection; (4) absence of neck irradiation history; and (5) unable or refused to receive surgery. Exclusion criteria were: the presence of (1) multifocal cancer; (2) aggressive histological PTMC, such as tall cell, insular, or columnar cell carcinoma; (3) suspicious cervical LNM; (4) pregnancy and lactation; (5) severe coagulation disorders, respiratory failure, myocardial infarction, systemic infection, or uncontrolled diabetes; (6) neuropsychiatric disturbance or neck extension disorder leading to RFA nontolerance; (7) cardiac pacemaker implantation; (8) contralateral vocal cord paralysis; and (9) allergic to sulfur hexafluoride microbubbles (SonoVue, Bracco International, Milan, Italy).\nBetween February 2013 and March 2017, 60 tumors in 60 patients (12 men and 48 women) treated with US‐guided RFA were included, comprising 30 patients with only PTMC, and 30 with CLT+PTMC. CLT was confirmed by pathologic examination and serological test, and was defined as diffuse lymphocytic and plasma cell infiltrates, lymphoid follicles formation with germinal centers, varying degree of fibrosis, parenchymal atrophy, and the presence of large follicular cells with oxyphilic cell changes,7 as well as positive hyroperoxidase antibody (TPOAb) and thyroglobulin antibody (TgAb). The presence of peritumoral inflammatory reaction was not considered CLT. All patients had complete records and were followed for more than 18 months.", "US and contrast‐enhanced US (CEUS) examinations were performed using a Siemens Acuson Sequoia 512 Ultrasound System (Siemens, Mountain View, CA) with a 15L8W linear array transducer. US‐guided RFA and CNB were performed using a Siemens Acuson Sequoia 512 Ultrasound System with a 6L3 linear array transducer. CNB on each nodule was performed using an 18‐gauge biopsy needle after RFA (Biopty; Bard, Covington, GA).\nA bipolar RFA generator (CelonLabPOWER; Olympus Surgical Technologies Europe, Hamburg, Germany) and an 18‐gauge bipolar radiofrequency (RF) applicator with a 0.9‐cm active tip (CelonProSurge micro 100‐T09; Olympus Surgical Technologies Europe) were used for RFA treatment in this study. During the application of RF energy, the electric impedance of the tissue between the two electrodes at the tip of the RF applicator was measured continuously by the generator. The power is automatically reduced if the temperature at the electrodes reaches 100°C.", "Careful history‐taking and thorough physical examinations were conducted in all patients in our department. All patients who qualified for RFA were subject to thyroid US, CEUS, and determination of the levels of free thyroid hormones T3 (normal reference range: 2.76‐6.30 pmol/L) and T4 (10.42‐24.32 pmol/L), thyroid‐stimulating hormone (TSH) (0.35‐5.5 mU/L), TPOAb (＜60 IU/mL) and TgAb (＜60 IU/mL).\nPatients were supine with the neck extended during the procedure. An intravenous line was introduced into an elbow vein. US appearances were evaluated and recorded according to the multidisciplinary consensus statement for thyroid nodules.15 For each tumor, the size, volume, location, echogenicity, margin, shape (height/width), calcifications, and vascularity were evaluated by US. The volume of each tumor was calculated as V = πabc/6 (V: volume, a: transverse diameter of tumor, b: vertical diameter of tumor, and c: anteroposterior diameter of tumor). CEUS with a low mechanical index (0.19‐0.24) was used to describe the blood supply region of the tumor before and after RFA. The contrast agent used was 59 mg of dry powder SonoVue constituted in 5 mL of normal saline. CEUS was performed after a bolus injection of SonoVue (2.4 mL), followed by a normal saline flush (5 mL). Real‐time microbubble perfusions within the tumor and surrounding tissues were observed for a minimum of 2 minutes and recorded electronically.\nCapsular and extrathyroidal invasion of thyroid cancer were evaluated by both US and CEUS. Extracapsular extension on the US image was defined as cases in which the anterior and posterior hyperechoic thyroid capsules were discontinued. During the real‐time and multi‐angle scanning, the capsular infiltration and extrathyroidal invasion on CEUS were shown as low‐ or nonenhancing areas on the thyroid capsule invaded by malignant nodules.16\n", "Conventional US was performed to evaluate the relationship between the tumor and critical structures in the neck, such as the trachea, esophagus, jugular vein, common carotid artery (CCA), and recurrent laryngeal nerves, in order to decide the best insertion way. A local anesthetic (1% lidocaine) was injected at the subcutaneous puncture site and the thyroid anterior capsule. If the distance between the tumor and critical neck structures was <5 mm, normal saline was injected using another needle (23 gauge) to form at least a 1cm distance between the tumor and critical structure for the prevention of thermal injuries. RFA was performed using the moving‐shot technique (P–Q); 3W was the initial radiofrequency power which was increased to 5W if a transient hyperechoic zone did not form at the electrode tip within 5‐10 s. To prevent residual tumor and recurrence, the RFA area exceeded the tumor margin. The ablation procedure ended when the tumor was completely covered with hyperechoic zone in three‐dimensional space on US. The damage range of ablation was evaluated by CEUS immediately after ablation, and the electrode needle was pulled out of the tissue after ensuring that no residual tumor (completely no enhancement in the ablation area). If the enhancement was showed in some areas of tumor, complementary ablation could be applied promptly to avoid residual cancer cells. During the procedure, special attention was given to the protection of critical neck structures to prevent the significant complications such as hematoma or nerve injury. All complications occurring during and after RFA were carefully assessed according to patients' clinical signs and symptoms. Each patient was observed for 1‐2 hours after RFA.", "Patients were followed‐up using conventional US and CEUS at months 1, 3, 6, and 12 after RFA, and every 6 months thereafter. The ablation area was evaluated by CEUS to detect volume of ablation and provide a baseline to screen for recurrence. The volume reduction ratio (VRR) was calculated as follows: VRR (%) = [(1 − final volume)/initial volume] × 100%. The involvement of cervical lymph nodes was evaluated by US; suspicious lymph nodes (a globular shape, loss of the normal echogenic hilum, presence of peripheral rather than hilar flow, and microcalcifications17) were biopsied. US‐guided CNB was performed at the center and the margin of the ablation area, and in the surrounding thyroid parenchyma 3 months after RFA. Complications during the follow‐up period were assessed according to the reporting standards of the Society of Interventional Radiology.18\n\nAll RFA procedures and preoperative examinations were performed by one experienced physician to exclude bias associated with different operators. Postoperative CNB was also performed by the same physician who performed RFA. Follow‐up conventional US and CEUS examinations were performed by two other experienced physicians who were blinded to the histological and imaging findings. Discrepancies were resolved by the judgment of a third experienced physician who had specialized in thyroid US and CEUS for over 15 years.", "All statistical analyses were performed using SPSS software package, Version 13 for Windows (SPSS Inc, Chicago, IL). A chi‐squared test (χ2) was used to analyze the categorical variables. Continuous data were reported as mean ± standard deviation (range). Volume and VRR of the ablation area before RFA and at each follow‐up were analyzed by the t test. The Wilcoxon signed rank test was used to compare tumor calcification, color Doppler flow imaging (CDFI) blood flow grades, and changes in the number of patients with tumor disappearance at each follow‐up between CLT+PTMC and PTMC groups. The Wilcoxon rank sum test was used to compare Free T3, T4, and TSH values in patients with and without CLT P values <0.05 were considered statistically significant.", " Preoperative patient and tumor characteristics There were no significant differences in terms of age, sex, and tumor volume between the PTMC and PTMC+CLT groups (P > 0.05) (Table 1). All thyroid nodules were hypoechoic; other characteristics are shown in Table 2. There were no significant differences in nodule location, margin, shape, height/width, calcification, and CDFI type between the two groups (P > 0.05). The distance between the nodule and trachea or common carotid artery (CCA) was <2 mm in 13 nodules, including eight near the trachea (0.117 ± 0.024) cm, and five near the CCA (0.094 ± 0.013) cm. There were no significant differences in free T3, T4 and TSH values between the two groups (U = 0.434, P = 0.664; U = 0.452, P = 0.651; and U = 0.886, P = 0.376, respectively). All patients with CLT had positive TgAb and TPOAb. The thyroid function test results of those with PTMC were in the normal range.\nComparison of the preoperative data of the patients and tumor volume between the PTMC+CLT and PTMC groups\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.\nUltrasonic characteristics of the tumors before RFA in the PTMC+CLT and PTMC groups\nAbbreviations: CDFI, color Doppler flow imaging; CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.\nThere were no significant differences in terms of age, sex, and tumor volume between the PTMC and PTMC+CLT groups (P > 0.05) (Table 1). All thyroid nodules were hypoechoic; other characteristics are shown in Table 2. There were no significant differences in nodule location, margin, shape, height/width, calcification, and CDFI type between the two groups (P > 0.05). The distance between the nodule and trachea or common carotid artery (CCA) was <2 mm in 13 nodules, including eight near the trachea (0.117 ± 0.024) cm, and five near the CCA (0.094 ± 0.013) cm. There were no significant differences in free T3, T4 and TSH values between the two groups (U = 0.434, P = 0.664; U = 0.452, P = 0.651; and U = 0.886, P = 0.376, respectively). All patients with CLT had positive TgAb and TPOAb. The thyroid function test results of those with PTMC were in the normal range.\nComparison of the preoperative data of the patients and tumor volume between the PTMC+CLT and PTMC groups\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.\nUltrasonic characteristics of the tumors before RFA in the PTMC+CLT and PTMC groups\nAbbreviations: CDFI, color Doppler flow imaging; CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.\n Ablation power and time in the PTMC and PTMC+CLT groups The ablation powers in the PTMC and PTMC+CLT groups were 0.9 ± 0.5 KJ and 0.7 ± 0.5 KJ, respectively (t = 1.453, P = 0.1515); ablation times were 3.8 ± 2.1 min and 2.9 ± 2 0.1 min, respectively (t = 1.801, P = 0.0768).\nThe ablation powers in the PTMC and PTMC+CLT groups were 0.9 ± 0.5 KJ and 0.7 ± 0.5 KJ, respectively (t = 1.453, P = 0.1515); ablation times were 3.8 ± 2.1 min and 2.9 ± 2 0.1 min, respectively (t = 1.801, P = 0.0768).\n Tumor volume and VRR after RFA In terms of postoperative ablation volume, there was no significant difference between the two groups at 1, 3, 6, 12, and 18 months after RFA (P > 0.05) (Table 3, Figure 1). While there was a significant difference in VRR between the two groups 3 months after ablation (t = 1.28, P = 0.03), no difference was observed at any other time‐point (P > 0.05) (Table 4, Figure 2).\nChanges in the tumor volume between the PTMC + CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nRadiofrequency ablation (RFA) treatment and follow‐up of one case of papillary thyroid microcarcinoma with chronic lymphocytic thyroiditis. (A) A hypoechoic nodule sized 0.4 × 0.5 × 0.4 cm, with irregular margins and microcalcifications was displayed in the right thyroid lobe (arrow). (B) Uneven and irregular hypo‐enhancement in the nodule was observed by contrast‐enhanced ultrasound (CEUS) (arrow, left image). (C) During RFA, the nodule was covered by a hyperechoic area (arrow) on US. (D) Immediately after RFA, the ablation area was showed completely no enhancement by CEUS, and its size (0.7 × 1.1 × 1.0 cm) was larger than the initial nodule size. (E) One month after RFA, the ablation area decreased in size to 0.9 × 0.8 × 0.5 cm. (F) Three months after RFA, the ablation area decreased to 0.6 × 0.5 × 0.6 cm. (G) Six months after RFA, the ablation area decreased to 0.3 × 0.2 × 0.3 cm. (H) The ablation area could not be identified on both US and CEUS. (I) Before RFA, the pathologic examination of this nodule showed the presence of papillary thyroid carcinoma accompanied by chronic lymphocytic thyroiditis. (J) Three months after RFA, pathology showed degenerated and necrotic follicular epithelia, interstitial fibrous tissue hyperplasia, and hyaline degeneration in the ablation lesion, with lymphocyte infiltration and multinucleated giant cell reaction in the adjacent thyroid tissue. No residual cancer was found\nChanges in the tumor volume reduction ratio between the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nChanges in ablation zone volume in PTMC cases with and without CLT at each follow‐up. PTMC, papillary thyroid microcarcinoma; CLT, chronic lymphocytic thyroiditis\nAfter RFA, the times to tumor disappearance were 9.800 ± 5.041 months and 10.0 ± 4.8 months (t = 0.16, P = 0.88) in the PTMC and PTMC + CLT groups, respectively. A total of 43% of the nodules in the PTMC+CLT group resolved in 12 months, and 47% in the PTMC group resolved in 6 months. No significant differences were observed with respect to the number of ablation areas between the groups (u = 0.319, P > 0.05) (Table 5). Calcification was observed in 20 PTMC cases, with time to tumor disappearance of 10.2 ± 5.1 months; no calcification was found in 40 PTMC cases, with time to tumor disappearance of 9.8 ± 4.9 months; no significant difference in time was observed between the PTMC cases with and without calcification (t = 0.28, P = 0.78). No residual cancer cells were found by CNB 3 months after ablation (Figure 1). No recurrent tumors or suspicious metastatic lymph nodes were detected.\nNumber of patients with tumors disappearance in the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation.\nIn terms of postoperative ablation volume, there was no significant difference between the two groups at 1, 3, 6, 12, and 18 months after RFA (P > 0.05) (Table 3, Figure 1). While there was a significant difference in VRR between the two groups 3 months after ablation (t = 1.28, P = 0.03), no difference was observed at any other time‐point (P > 0.05) (Table 4, Figure 2).\nChanges in the tumor volume between the PTMC + CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nRadiofrequency ablation (RFA) treatment and follow‐up of one case of papillary thyroid microcarcinoma with chronic lymphocytic thyroiditis. (A) A hypoechoic nodule sized 0.4 × 0.5 × 0.4 cm, with irregular margins and microcalcifications was displayed in the right thyroid lobe (arrow). (B) Uneven and irregular hypo‐enhancement in the nodule was observed by contrast‐enhanced ultrasound (CEUS) (arrow, left image). (C) During RFA, the nodule was covered by a hyperechoic area (arrow) on US. (D) Immediately after RFA, the ablation area was showed completely no enhancement by CEUS, and its size (0.7 × 1.1 × 1.0 cm) was larger than the initial nodule size. (E) One month after RFA, the ablation area decreased in size to 0.9 × 0.8 × 0.5 cm. (F) Three months after RFA, the ablation area decreased to 0.6 × 0.5 × 0.6 cm. (G) Six months after RFA, the ablation area decreased to 0.3 × 0.2 × 0.3 cm. (H) The ablation area could not be identified on both US and CEUS. (I) Before RFA, the pathologic examination of this nodule showed the presence of papillary thyroid carcinoma accompanied by chronic lymphocytic thyroiditis. (J) Three months after RFA, pathology showed degenerated and necrotic follicular epithelia, interstitial fibrous tissue hyperplasia, and hyaline degeneration in the ablation lesion, with lymphocyte infiltration and multinucleated giant cell reaction in the adjacent thyroid tissue. No residual cancer was found\nChanges in the tumor volume reduction ratio between the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nChanges in ablation zone volume in PTMC cases with and without CLT at each follow‐up. PTMC, papillary thyroid microcarcinoma; CLT, chronic lymphocytic thyroiditis\nAfter RFA, the times to tumor disappearance were 9.800 ± 5.041 months and 10.0 ± 4.8 months (t = 0.16, P = 0.88) in the PTMC and PTMC + CLT groups, respectively. A total of 43% of the nodules in the PTMC+CLT group resolved in 12 months, and 47% in the PTMC group resolved in 6 months. No significant differences were observed with respect to the number of ablation areas between the groups (u = 0.319, P > 0.05) (Table 5). Calcification was observed in 20 PTMC cases, with time to tumor disappearance of 10.2 ± 5.1 months; no calcification was found in 40 PTMC cases, with time to tumor disappearance of 9.8 ± 4.9 months; no significant difference in time was observed between the PTMC cases with and without calcification (t = 0.28, P = 0.78). No residual cancer cells were found by CNB 3 months after ablation (Figure 1). No recurrent tumors or suspicious metastatic lymph nodes were detected.\nNumber of patients with tumors disappearance in the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation.\n Complications Slight voice hoarseness was observed in one patient (1.7%, 1/60) after RFA; voice recovery occurred without treatment in 1 week. The presence of moderate‐intensity pain was reported by two patients (3.3%, 2/60), and slight fever was noted in one patient (1.7%, 1/60); in all cases, spontaneous recovery was observed.\nSlight voice hoarseness was observed in one patient (1.7%, 1/60) after RFA; voice recovery occurred without treatment in 1 week. The presence of moderate‐intensity pain was reported by two patients (3.3%, 2/60), and slight fever was noted in one patient (1.7%, 1/60); in all cases, spontaneous recovery was observed.", "There were no significant differences in terms of age, sex, and tumor volume between the PTMC and PTMC+CLT groups (P > 0.05) (Table 1). All thyroid nodules were hypoechoic; other characteristics are shown in Table 2. There were no significant differences in nodule location, margin, shape, height/width, calcification, and CDFI type between the two groups (P > 0.05). The distance between the nodule and trachea or common carotid artery (CCA) was <2 mm in 13 nodules, including eight near the trachea (0.117 ± 0.024) cm, and five near the CCA (0.094 ± 0.013) cm. There were no significant differences in free T3, T4 and TSH values between the two groups (U = 0.434, P = 0.664; U = 0.452, P = 0.651; and U = 0.886, P = 0.376, respectively). All patients with CLT had positive TgAb and TPOAb. The thyroid function test results of those with PTMC were in the normal range.\nComparison of the preoperative data of the patients and tumor volume between the PTMC+CLT and PTMC groups\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.\nUltrasonic characteristics of the tumors before RFA in the PTMC+CLT and PTMC groups\nAbbreviations: CDFI, color Doppler flow imaging; CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma.", "The ablation powers in the PTMC and PTMC+CLT groups were 0.9 ± 0.5 KJ and 0.7 ± 0.5 KJ, respectively (t = 1.453, P = 0.1515); ablation times were 3.8 ± 2.1 min and 2.9 ± 2 0.1 min, respectively (t = 1.801, P = 0.0768).", "In terms of postoperative ablation volume, there was no significant difference between the two groups at 1, 3, 6, 12, and 18 months after RFA (P > 0.05) (Table 3, Figure 1). While there was a significant difference in VRR between the two groups 3 months after ablation (t = 1.28, P = 0.03), no difference was observed at any other time‐point (P > 0.05) (Table 4, Figure 2).\nChanges in the tumor volume between the PTMC + CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nRadiofrequency ablation (RFA) treatment and follow‐up of one case of papillary thyroid microcarcinoma with chronic lymphocytic thyroiditis. (A) A hypoechoic nodule sized 0.4 × 0.5 × 0.4 cm, with irregular margins and microcalcifications was displayed in the right thyroid lobe (arrow). (B) Uneven and irregular hypo‐enhancement in the nodule was observed by contrast‐enhanced ultrasound (CEUS) (arrow, left image). (C) During RFA, the nodule was covered by a hyperechoic area (arrow) on US. (D) Immediately after RFA, the ablation area was showed completely no enhancement by CEUS, and its size (0.7 × 1.1 × 1.0 cm) was larger than the initial nodule size. (E) One month after RFA, the ablation area decreased in size to 0.9 × 0.8 × 0.5 cm. (F) Three months after RFA, the ablation area decreased to 0.6 × 0.5 × 0.6 cm. (G) Six months after RFA, the ablation area decreased to 0.3 × 0.2 × 0.3 cm. (H) The ablation area could not be identified on both US and CEUS. (I) Before RFA, the pathologic examination of this nodule showed the presence of papillary thyroid carcinoma accompanied by chronic lymphocytic thyroiditis. (J) Three months after RFA, pathology showed degenerated and necrotic follicular epithelia, interstitial fibrous tissue hyperplasia, and hyaline degeneration in the ablation lesion, with lymphocyte infiltration and multinucleated giant cell reaction in the adjacent thyroid tissue. No residual cancer was found\nChanges in the tumor volume reduction ratio between the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; M, mean; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation; SD, standard deviation.\nChanges in ablation zone volume in PTMC cases with and without CLT at each follow‐up. PTMC, papillary thyroid microcarcinoma; CLT, chronic lymphocytic thyroiditis\nAfter RFA, the times to tumor disappearance were 9.800 ± 5.041 months and 10.0 ± 4.8 months (t = 0.16, P = 0.88) in the PTMC and PTMC + CLT groups, respectively. A total of 43% of the nodules in the PTMC+CLT group resolved in 12 months, and 47% in the PTMC group resolved in 6 months. No significant differences were observed with respect to the number of ablation areas between the groups (u = 0.319, P > 0.05) (Table 5). Calcification was observed in 20 PTMC cases, with time to tumor disappearance of 10.2 ± 5.1 months; no calcification was found in 40 PTMC cases, with time to tumor disappearance of 9.8 ± 4.9 months; no significant difference in time was observed between the PTMC cases with and without calcification (t = 0.28, P = 0.78). No residual cancer cells were found by CNB 3 months after ablation (Figure 1). No recurrent tumors or suspicious metastatic lymph nodes were detected.\nNumber of patients with tumors disappearance in the PTMC+CLT and PTMC groups after RFA and at each follow‐up\nAbbreviations: CLT, chronic lymphocytic thyroiditis; PTMC, papillary thyroid microcarcinoma; RFA, radiofrequency ablation.", "Slight voice hoarseness was observed in one patient (1.7%, 1/60) after RFA; voice recovery occurred without treatment in 1 week. The presence of moderate‐intensity pain was reported by two patients (3.3%, 2/60), and slight fever was noted in one patient (1.7%, 1/60); in all cases, spontaneous recovery was observed.", "The feasibility of RFA in thyroid cancer treatment is still controversial, as some surgeons believe that it is difficult to detect tiny lymph node metastases using US19; in addition, the use of prophylactic central compartment neck dissection (CCND) is more reassuring for patients with clinically node‐negative PTC. However, with the increasing incidence of thyroid cancer, more patients with low‐risk microcarcinomas are receiving unnecessary extended thyroidectomy and prophylactic CCND. Previous studies have shown that the proportion of CCND with negative findings ranges from 57.6% to 84.8%,20, 21 and that excessive surgery decreases patients' quality of life (postoperative complications included transient hypoparathyroidism, permanent hypoparathyroidism, vocal cord palsy, and bleeding,). Therefore, the treatment approach for this disease is changing. In recent years, prophylactic CCND has not been recommended for PTMC patients without LNM.8, 22, 23, 24, 25 The median risk of local‐regional lymph node recurrence varies markedly by clinical staging in patients with pathologically proven neck LNM, with recurrence rates of 2% (range 0%‐9%) in patients with an initial N0 stage vs 22% (range 10%‐42%) in those with initially positive lymph nodes. Furthermore, the median risk of recurrence in LNM patients varies markedly by the number of positive nodes, with values of 4% (range 3%‐8%) in cases with <5 nodes and 19% (range 7%‐21%) in those with >5 nodes.23 At the 5‐year follow‐up, no difference was observed in the outcomes of patients treated with total thyroidectomy and those treated with total thyroidectomy + prophylactic CCND.24 The results of these studies support the use of RFA for PTMC.\nThe presence of multifocality and capsular infiltration indicates a high risk of cancer invasion and metastasis.25, 26 Therefore, cases with a single tumor in the thyroid parenchyma and within the thyroid capsule were eligible for RFA in this study. Our preliminary study showed that RFA is efficient with a low complication rate in PTMC treatment.14 CLT is a type of chronic inflammation, and its effect on the ability to recover from the heat damage caused by RFA is still unknown.\nNo differences were observed in the preoperative volume and US characteristics of the tumors between the PTMC and PTMC+CLT groups (P > 0.05), and the patients' age and sex did not differ between the two groups (P > 0.05). Our results showed that recovery after ablation in patients with PTMC+CLT is similar to that in patients with only PTMC. The ablation volume in the PTMC group decreased rapidly in the first 3 months, and the VRR in the PTMC group was greater than that in the PTMC+CLT group (P = 0.03). Tissue damage due to trauma induces acute inflammation. The features of acute and subacute inflammation include the expansion of blood vessels (vasodilation), increase in blood flow (hyperemia), capillary permeability, and migration of neutrophils into the damaged tissue. However, the composition of white blood cells changes rapidly, with macrophages and lymphocytes replacing neutrophils. The hallmark of chronic inflammation is the infiltration of the primary inflammatory cells into the site, leading to the production of inflammatory cytokines, growth factors, and enzymes, thereby leading to progression of tissue damage and secondary repair processes, such as fibrosis and granuloma formation.27 The thyroid parenchyma in the case of CLT is already infiltrated by diffuse chronic inflammatory cells, so inflammation of the ablation zone was more marked in the acute and subacute periods after RFA. Immune cells were activated to phagocytize and remove the necrotic tissue, activating the autoimmune system so we saw no significant difference in ablation zone outcomes between the PTMC and PTMC+CLT groups. Three months after ablation, US‐guided CNB was performed at the center and margin of the ablation zone and in the adjacent thyroid parenchyma; pathological results showed degenerated and necrotic follicular epithelia, interstitial fibrous tissue hyperplasia, and hyaline degeneration in the central and peripheral areas of the ablation lesion, with lymphocyte infiltration and multinucleated giant cell reaction in the adjacent thyroid tissue. No residual cancer was found. Previous studies28, 29 with larger sample size showed that PTC with CLT has a good prognosis, and that the recurrence rate was lower than that associated with only PTC. This may be attributed to the fact that inflammation inhibits cancer‐cell proliferation. CLT may be involved in the destruction of cancer cells that express thyroid‐specific antigens in PTC as a result of its autoimmune response to the thyroid‐specific antigens. Few studies30 have shown no relationship between CLT and PTC. This study indicated that RFA is efficient and safe for PTMC cases with CLT; CLT should not be regarded as a factor that excludes a patient from receiving RFA.\nIn terms of safety, adhesions are found between the thyroid and adjacent connective tissue and muscles after ablation, distorting local anatomy, which results in difficulty performing surgery after ablation; therefore some surgeons have a negative attitude towards ablation. However, liquid isolation zone injection can be utilized to separate the thyroid from critical neck structures; this method is effective in preventing the occurrence of significant complications such as hematoma, and tracheal and nerve injury. Postoperative examination by both US and CEUS has shown clear boundaries of the thyroid gland and surrounding structures without signs of adhesion. In this study, 13 PTMC cases in which the distance to the trachea or CCA was <2 mm were successfully treated with RFA as a result of liquid isolation zone injection without serious complications.\nThis study had the limitation of a relatively short follow‐up period; longest follow‐up was 4 years and the shortest 20 months. The outcomes of all these patients should be confirmed in studies with larger sample size and extended follow‐up period.\nIn conclusion, this study found that RFA was effective and safe in PTMC+CLT patients. As evidenced by the low recurrence and high survival rates of these patients, CLT may be regarded as a protective factor for patients enrolled in PTMC treatment using RFA. The present study provides a basis for the study of immune regulation mechanisms induced by thyroid cancer necrosis.", "The authors made no disclosures.", "Yan Zhang contributed to the collection, analysis, and interpretation of data and writing initial draft. Mingbo Zhang performed the statistical analyses, had full access to all data, and takes responsibility for the accuracy of the data analysis in the study. Ying Zhang contributed to the collection, analysis and interpreted the results. Jie Li: contributed to the pathological figures analysis and interpretation. Jie Tang contributed to writing–review and revisions. Yukun Luo contributed to the study design, writing–initial draft, guarantor of the study and had full access to all data in the study, and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to critical revisions and approved the final version." ]

[ null, "materials-and-methods", null, null, null, null, null, null, "results", null, null, null, null, "discussion", "COI-statement", null ]

[ "ablation", "contrast media", "radiofrequency", "thyroid carcinoma; ultrasonography" ]

[ 447, 310, 174, 364, 312, 251, 147, 285, 75, 795, 68, 138 ]

[ "ptmc", "rfa", "ablation", "clt", "thyroid", "tumor", "patients", "months", "groups", "volume" ]

[ "thyroid carcinoma", "thyroid microcarcinoma clt", "thyroid microcarcinoma chronic", "thyroid microcarcinomas ptmcs", "papillary thyroid carcinoma" ]