Abstract：
OBJECTIVE: 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.
CONCLUSIONS: The nomogram can differentiate between epididymal TB and 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.
CONCLUSIONS: The nomogram can differentiate between epididymal TB and bacterial epididymitis.
摘要：
目的：我们开发并验证了用于区分附睾结核（TB）和细菌性附睾炎的诊断列线图。
方法：在这项回顾性研究中，我们建立了基于人口统计学和临床特征的预测模型.符合条件的患者被随机分为推导和验证队列(比率7:3)。使用单变量和多元回归分析来过滤变量并选择预测因子。采用多因素logistic回归构建列线图。协调指数（C指数），校准图,和决策曲线分析(DCA)用于评估歧视，校准,和列线图的临床实用性。
结果：我们包括147例患者（附睾结核，93；细菌性附睾炎，54).衍生队列包括66例附睾结核患者和38例细菌性附睾炎患者；验证队列包括27例附睾结核患者和16例细菌性附睾炎患者。从三个差异变量建立了一个回归模型：体重指数，纯化的蛋白质衍生物，和慢性感染。因此，开发了一个列线图。该模型具有良好的判别和校正效果。推导和验证队列的C指数分别为0.89和0.98(95%置信区间，0.83-0.95和0.94-1.01)，分别。DCA显示所提出的列线图对于区分是有用的。
结论：列线图可以区分附睾结核和细菌性附睾炎。
方法：在这项回顾性研究中，我们建立了基于人口统计学和临床特征的预测模型.符合条件的患者被随机分为推导和验证队列(比率7:3)。使用单变量和多元回归分析来过滤变量并选择预测因子。采用多因素logistic回归构建列线图。协调指数（C指数），校准图,和决策曲线分析(DCA)用于评估歧视，校准,和列线图的临床实用性。
结果：我们包括147例患者（附睾结核，93；细菌性附睾炎，54).衍生队列包括66例附睾结核患者和38例细菌性附睾炎患者；验证队列包括27例附睾结核患者和16例细菌性附睾炎患者。从三个差异变量建立了一个回归模型：体重指数，纯化的蛋白质衍生物，和慢性感染。因此，开发了一个列线图。该模型具有良好的判别和校正效果。推导和验证队列的C指数分别为0.89和0.98(95%置信区间，0.83-0.95和0.94-1.01)，分别。DCA显示所提出的列线图对于区分是有用的。
结论：列线图可以区分附睾结核和细菌性附睾炎。
