Prediction models for urinary incontinence after robotic-assisted laparoscopic radical prostatectomy: a systematic review.

ABSTRACT

Even though robotic-assisted laparoscopic radical prostatectomy (RARP) is superior to open surgery in reducing postoperative complications, 6-20% of patients still experience urinary incontinence (UI) after surgery. Therefore, many researchers have established predictive models for UI occurrence after RARP, but the predictive performance of these models is inconsistent. This study aims to systematically review and critically evaluate the published prediction models of UI risk for patients after RARP. We conducted a comprehensive literature search in the databases of PubMed, Cochrane Library, Web of Science, and Embase. Literature published from inception to March 20, 2024, which reported the development and/or validation of clinical prediction models for the occurrence of UI after RARP. We identified seven studies with eight models that met our inclusion criteria. Most of the studies used logistic regression models to predict the occurrence of UI after RARP. The most common predictors included age, body mass index, and nerve sparing procedure. The model performance ranged from poor to good, with the area under the receiver operating characteristic curves ranging from 0.64 to 0.98 in studies. All the studies have a high risk of bias. Despite their potential for predicting UI after RARP, clinical prediction models are restricted by their limited accuracy and high risk of bias. In the future, the study design should be improved, the potential predictors should be considered from larger and representative samples comprehensively, and high-quality risk prediction models should be established. And externally validating models performance to enhance their clinical accuracy and applicability.