Necrotizing fasciitis after high-dose rate brachytherapy and external beam radiation for prostate cancer: a case report

Abstract Background In recent years, the delayed side effects associated with radiotherapy for prostate cancer have drawn the interest of urologists. Although urosymphyseal fistula is one of these delayed side effects, this serious complication is rarely described in literature and is poorly recognized. Case presentation We report our experience in treating a 77-year-old male patient with necrotizing fasciitis after high-dose rate brachytherapy plus external beam radiation for prostate cancer. The patient was referred to our hospital with complaints of inguinal swelling and fever. He had a past history of radiotherapy for prostate cancer and subsequent transurethral operation for a stricture of the urethra. Computed tomography showed extensive gas within the femoral and retroperitoneal tissues and pubic bone fracture. Surgical exploration suggested that necrotizing fasciitis was caused by urosymphyseal fistula. Conclusion To the best of our knowledge, this is the first case report of necrotizing fasciitis caused by urosymphyseal fistula after radiotherapy for prostate cancer. There is a strong association between urosymphyseal fistula and prostate radiotherapy with subsequent surgical intervention for bladder neck contracture or urethral stricture. Therefore, surgical treatment for bladder neck contracture or urethral stricture after radiotherapy for prostate cancer should be performed with care. The present case emphasizes the importance of early diagnosis of urosymphyseal fistula. Immediate removal of necrotic tissues and subsequent urinary diversion in the present case may have led to good patient outcome

Additional file 1: Table S1. of Upper urinary tract stone disease in patients with poor performance status: active stone removal or conservative management?

The demographics and stone characteristics of patients, de-identified raw data. (XLSX 23 kb

Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis

International audienceTwo acute respiratory distress syndrome (ARDS) subphenotypes (hyperinflammatory and hypoinflammatory) with distinct clinical and biological features and differential treatment responses have been identified using latent class analysis (LCA) in seven individual cohorts. To facilitate bedside identification of subphenotypes, clinical classifier models using readily available clinical variables have been described in four randomised controlled trials. We aimed to assess the performance of these models in observational cohorts of ARDS. Methods: In this observational, multicohort, retrospective study, we validated two machine-learning clinical classifier models for assigning ARDS subphenotypes in two observational cohorts of patients with ARDS: Early Assessment of Renal and Lung Injury (EARLI; n=335) and Validating Acute Lung Injury Markers for Diagnosis (VALID; n=452), with LCA-derived subphenotypes as the gold standard. The primary model comprised only vital signs and laboratory variables, and the secondary model comprised all predictors in the primary model, with the addition of ventilatory variables and demographics. Model performance was assessed by calculating the area under the receiver operating characteristic curve (AUC) and calibration plots, and assigning subphenotypes using a probability cutoff value of 0·5 to determine sensitivity, specificity, and accuracy of the assignments. We also assessed the performance of the primary model in EARLI using data automatically extracted from an electronic health record (EHR; EHR-derived EARLI cohort). In Large Observational Study to Understand the Global Impact of Severe Acute Respiratory Failure (LUNG SAFE; n=2813), a multinational, observational ARDS cohort, we applied a custom classifier model (with fewer variables than the primary model) to determine the prognostic value of the subphenotypes and tested their interaction with the positive end-expiratory pressure (PEEP) strategy, with 90-day mortality as the dependent variable. Findings: The primary clinical classifier model had an area under receiver operating characteristic curve (AUC) of 0·92 (95% CI 0·90–0·95) in EARLI and 0·88 (0·84–0·91) in VALID. Performance of the primary model was similar when using exclusively EHR-derived predictors compared with manually curated predictors (AUC=0·88 [95% CI 0·81–0·94] vs 0·92 [0·88–0·97]). In LUNG SAFE, 90-day mortality was higher in patients assigned the hyperinflammatory subphenotype than in those with the hypoinflammatory phenotype (414 [57%] of 725 vs 694 [33%] of 2088; p<0·0001). There was a significant treatment interaction with PEEP strategy and ARDS subphenotype (p=0·041), with lower 90-day mortality in the high PEEP group of patients with the hyperinflammatory subphenotype (hyperinflammatory subphenotype: 169 [54%] of 313 patients in the high PEEP group vs 127 [62%] of 205 patients in the low PEEP group; hypoinflammatory subphenotype: 231 [34%] of 675 patients in the high PEEP group vs 233 [32%] of 734 patients in the low PEEP group). Interpretation: Classifier models using clinical variables alone can accurately assign ARDS subphenotypes in observational cohorts. Application of these models can provide valuable prognostic information and could inform management strategies for personalised treatment, including application of PEEP, once prospectively validated. Funding: US National Institutes of Health and European Society of Intensive Care Medicine