目的：约8%-40%盆腔器官脱垂（POP）患者在盆底修复手术后出现压力性尿失禁（SUI），但尚无公认预测方法。既往模型不适用于术前存在主观性尿失禁，或接受阴道全/半封闭术、经阴道植入网片盆底重建手术（TVM）患者。本研究拟开发并验证用于预测POP术后1年内SUI发生的机器学习模型。

方法：回顾性纳入自2015.1.1至2019.12.31因盆腔前部或顶端Ⅱ-Ⅳ度脱垂在本中心行盆底修复手术女性患者。术式包含阴道全/半封闭手术、阴道骶骨固定术、自体组织盆底重建术及TVM。基于整个数据集对既往模型行外部验证，然后以4:1将数据集随机分组，前者用于构建Logistic回归、随机森林和XGBoost模型及内部验证，后者用于外部验证。使用曲线下面积（AUC）评估模型区分度，通过Spiegelhalter z检验、均方误差和校准曲线评估校准度。

结果：共纳入555例患者，其中116例在术后1年出现SUI。既往模型在本人群中表现较差（AUC 0.544-0.586, z检验P<0.001）。本研究构建了Logistic回归、随机森林和XGBoost模型，AUC分别为0.595、0.842和0.714。仅XGBoost模型在内部及外部验证中区分度和校准度良好，体质指数、C点、年龄、Aa点和TVM是五个最重要预测因素。

结论：既往模型在中国人群中表现不佳，本研究开发并验证了一个XGBoost模型。无论患者术前是否存在主观性尿失禁症状以及采用何种术式，该模型均表现良好。

Background: About 8% to 40% of patients with pelvic organ prolapse may have stress urinary incontinence after prolapse surgery. However, no unified standard exists for predicting the occurrence of postoperative stress urinary incontinence. Previous prediction models could not be applied to patients with preoperative subjective urinary incontinence or those receiving colpocleisis or transvaginal mesh surgery. This study aimed to develop and validate a new machine-learning model to predict stress urinary incontinence 1 year after pelvic organ prolapse surgery, and compare it to previous models.

Methods: Female patients who underwent pelvic floor reconstruction for stage Ⅱ-Ⅳ anterior or apical prolapse between January 1, 2015 and December 31, 2019 at Peking Union Medical College Hospital were retrospectively enrolled. Prolapse surgery included LeFort/colpocleisis, sacrocolpopexy, native tissue repair, and transvaginal mesh surgery. The existing models were externally validated. Subsequently, the dataset was randomly divided into two sets at a 4:1 ratio. The larger group was used to construct and internally validate models of logistic regression, random forest, and XGBoost, which were then externally validated in the smaller group. The discrimination of prediction models was evaluated using the area under the receiver operating characteristic curve, while the calibration of the models was measured via the Spiegelhalter z test, mean absolute error, and calibration curves.

Results: Overall, 555 patients with pelvic organ prolapse were enrolled in this study, and 116 (20.9%) experienced stress urinary incontinence in 1 year postoperatively. In the external validation, previous models revealed poor performance (areas under the curve 0.544 and 0.586, respectively; P values for the Spiegelhalter z test < 0.001). In this study, three models were constructed using logistic regression, random forest and XGBoost methods. The areas under the curve of them were 0.595, 0.842 and 0.714, respectively. However, only the XGBoost model exhibited good discrimination and calibration in both internal and external validations. Body mass index, C point of pelvic organ prolapse quantification stage, age, Aa point of pelvic organ prolapse quantification stage, and transvaginal mesh surgery were the five most important predictors in the XGBoost model.