Predicting Postoperative Stress Urinary Incontinence After Prolapse Surgery via Machine Learning and Regression Models: Development and Validation Study

TL;DR

This study develops and validates machine learning models to predict stress urinary incontinence after prolapse surgery, aiming to guide clinical decisions on additional procedures.

Contribution

The novel contribution is the development of five machine learning models, with SVM showing the best performance, to predict postoperative SUI in prolapse surgery patients.

Findings

Six risk factors for postoperative SUI were identified, including preoperative SUI and urodynamic occult SUI.

The SVM model demonstrated the best performance with an AUC of 0.846 in validation.

A Shiny-based application was developed for model deployment to support clinical decision-making.

Abstract

Pelvic organ prolapse (POP) and stress urinary incontinence (SUI) often concurrently exist. The incontinence in some patients with POP resolves after POP surgery, but it persists in others. Some patients without SUI before surgery may develop de novo SUI. It is unclear whether a concomitant anti-incontinence procedure should be performed at the time of POP surgery to prevent postoperative incontinence. A prediction model is needed to guide clinical decision-making.

This study aimed to analyze the risk factors and develop prediction models for SUI after POP surgery based on machine learning to provide new tools for evaluating and predicting postoperative SUI.

Sample size calculation was performed using the Riley 4-step method. Data of patients undergoing prolapse surgery in Shanxi Bethune Hospital were prospectively collected from August 2022 to February 2025 and were retrospectively collected from January 2021 to August 2022. General clinical data, relevant laboratory test results, urodynamic examination findings, and pelvic floor ultrasound findings were collected. Lasso regression, univariate analysis, and logistic analysis were used to screen the predictors of SUI after prolapse surgery. Data were split randomly in a 7:3 ratio into training and validation sets. The training set was used to develop the prediction model involving Lasso regression, random forest, support vector machine (SVM), extreme gradient boosting (XGBoost), classification and regression tree (CART), and logistic regression, and the validation set was used for internal verification. The final implementation was achieved by developing a Shiny-based application for model deployment.

A total of 286 patients were enrolled in this study, and 91 patients had postoperative SUI. The following 6 risk factors were identified through univariate, logistic, and Lasso regression analyses: preoperative SUI, urge urinary incontinence, urodynamic occult SUI, anti-incontinence surgery, genital hiatus, and anterior colporrhaphy. Five prediction models were constructed by using logistic regression, random forest, XGBoost, SVM, and CART. Based on a comprehensive evaluation of model discrimination, calibration, and clinical utility, the SVM model demonstrated optimal overall performance, with an area under the curve of 0.821 in the training set and 0.846 in the validation set.

This study developed 5 prediction models for postoperative SUI following prolapse surgery, which demonstrated good performance in internal validation. Among them, the SVM prediction model appeared to be the most promising. However, further external validation data are required to assess its generalizability. This model has the potential to become a high-quality clinical risk prediction tool for postoperative SUI in patients with prolapse, guiding clinical decisions on whether concurrent prolapse and incontinence surgeries are necessary.

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