Predicting the occurrence of stress urinary incontinence after prolapse surgery: a machine learning-based model.

ABSTRACT:

Background

Previous prediction models for postoperative stress urinary incontinence (SUI) cannot be applied to patients receiving transvaginal mesh (TVM) surgery and colpocleisis or those with preoperative subject urinary incontinence. This study aimed to develop and validate a new machine learning model and compare it to previous models.

Methods

Female patients who underwent prolapse surgeries for stage 2-4 anterior or apical prolapse between January 1, 2015, and December 31, 2019, at Peking Union Medical College Hospital were enrolled. Prolapse surgeries included native tissue repair, LeFort/colpocleisis, sacrocolpopexy, and TVM surgery. The existing models to predict postoperative SUI were externally validated. Subsequently, the dataset was randomly divided into 2 sets in

SUBMITTER: Fu L 

PROVIDER: S-EPMC10113075 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature