Pengju Liu1, Guofeng Cai1, Hai Gu1, Yong Qin2. 1. Department of Urology, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, 208 East Huancheng Road, Hangzhou, China. 2. Department of Urology, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, 208 East Huancheng Road, Hangzhou, China. hhyyqinyong@163.com.
Abstract
PURPOSE: We developed and validated a diagnostic nomogram for differentiating epididymal tuberculosis (TB) from bacterial epididymitis. METHODS: In this retrospective study, we developed a prediction model based on demographics and clinical characteristics. Eligible patients were randomly divided into derivation and validation cohorts (ratio 7:3). Univariate and multivariate regression analyses were used to filter variables and select predictors. Multivariate logistic regression was used to construct the nomogram. Concordance index (C-index), calibration plots, and decision curves analysis (DCA) were used to assess the discrimination, calibration, and clinical usefulness of the nomogram. RESULTS: We included 147 patients (epididymal TB, 93; bacterial epididymitis, 54). The derivation cohort included 66 patients with epididymal TB and 38 with bacterial epididymitis; the validation cohort included 27 patients with epididymal TB and 16 with bacterial epididymitis. One regression model was built from three differential variables: body mass index, purified protein derivative, and chronic infection. Accordingly, one nomogram was developed. The model had good discrimination and calibration. C-indexes of the derivation and validation cohorts were 0.89 and 0.98 (95% confidence intervals, 0.83-0.95 and 0.94-1.01), respectively. DCA showed that the proposed nomogram was useful for differentiation. CONCLUSION: The nomogram can differentiate between epididymal TB and bacterial epididymitis.
PURPOSE: We developed and validated a diagnostic nomogram for differentiating epididymal tuberculosis (TB) from bacterial epididymitis. METHODS: In this retrospective study, we developed a prediction model based on demographics and clinical characteristics. Eligible patients were randomly divided into derivation and validation cohorts (ratio 7:3). Univariate and multivariate regression analyses were used to filter variables and select predictors. Multivariate logistic regression was used to construct the nomogram. Concordance index (C-index), calibration plots, and decision curves analysis (DCA) were used to assess the discrimination, calibration, and clinical usefulness of the nomogram. RESULTS: We included 147 patients (epididymal TB, 93; bacterial epididymitis, 54). The derivation cohort included 66 patients with epididymal TB and 38 with bacterial epididymitis; the validation cohort included 27 patients with epididymal TB and 16 with bacterial epididymitis. One regression model was built from three differential variables: body mass index, purified protein derivative, and chronic infection. Accordingly, one nomogram was developed. The model had good discrimination and calibration. C-indexes of the derivation and validation cohorts were 0.89 and 0.98 (95% confidence intervals, 0.83-0.95 and 0.94-1.01), respectively. DCA showed that the proposed nomogram was useful for differentiation. CONCLUSION: The nomogram can differentiate between epididymal TB and bacterial epididymitis.