Early Prediction of Postoperative Urinary Retention After General Anesthesia Using Explainable Machine Learning

Purpose

Postoperative urinary retention (POUR) is a common and clinically important perioperative complication associated with pharmacological effects and autonomic dysfunction, which may lead to increased patient discomfort and delayed recovery. Its multifactorial etiology makes early clinical identification challenging. This study investigated the feasibility of using machine learning to predict early POUR after general anesthesia using readily available, non-invasive perioperative data. Model interpretation techniques were further applied to improve transparency and identify key clinical features contributing to risk stratification.