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. Materials and Methods: This retrospective single-center cohort study included 522 adult inpatients from Cathay General Hospital who underwent surgery under general anesthesia with endotracheal intubation and were subsequently admitted to the post-anesthesia care unit (PACU). Early POUR was defined as postoperative urinary retention occurring during the PACU stay, generally within approximately one hour after extubation. A total of 11 routinely available non-invasive clinical features were used to develop and evaluate multiple machine learning models for predicting early POUR. These machine learning models included conventional classifiers, neural network-based models, and ensemble learning algorithms, including LightGBM, a gradient boosting-based ensemble learning algorithm. SHapley Additive exPlanations (SHAP) analysis was performed to interpret model predictions and identify key clinical features associated with early POUR. Results: Early POUR occurred in 58 of 522 patients (11.1%). Among the evaluated machine learning models, LightGBM demonstrated favorable overall classification performance in predicting early POUR, with an accuracy of 81.03% and an F1 score of 81.50%. Logistic Regression achieved the highest area under the receiver operating characteristic curve (AUC) among the evaluated models (AUC = 0.7099). SHAP analysis identified several key predictors, including glycopyrrolate use, age, ASA class, anesthesia duration, and postoperative pain. Conclusion: This study suggests that machine learning may be a feasible approach for early POUR risk prediction. Interpretable approaches may facilitate individualized anesthetic planning and support clinical decision-making in the PACU, particularly in guiding early bladder monitoring and catheterization decisions for patients at risk of POUR.