Explainable machine learning to predict postoperative ileus after radical cystectomy: an 11-year real-world cohort

Background Post-operative ileus (POI) is a frequent complication after radical cystectomy (RC). Conventional scores capture only linear relations and have limited accuracy. Interpretable machine learning (ML) may improve early risk stratification. Methods In a single-centre real-world cohort ( n = 1,062, 2013–2023), POI was defined by ≥2 standard clinical–radiological criteria. We extracted pre-operative comorbidities/medications, operative factors (approach, urinary diversion, lymph-node dissection, fluids, blood loss, nasogastric-tube placement) and first-day laboratory indices. After LASSO selection, five ML models were trained/validated on a stratified split; discrimination (AUC), accuracy, precision, recall and Brier score were compared. SHAP delivered global and patient-level explanations. Results POI occurred in 28.9%. The back-propagation neural network performed best (AUC 0.828; accuracy 78.4%; Brier 0.143). Intra-operative nasogastric-tube placement and surgical approach dominated feature attribution, followed by medication history, lymph-node dissection, lymphocyte count and C-reactive protein. SHAP clarified feature effects and enabled interpretable, case-level risk summaries. Conclusion An interpretable ML model based on routinely captured peri-operative variables accurately stratifies RC patients at risk for POI as early as postoperative day 0, outperforming existing nomograms and highlighting modifiable factors. Embedding this tool into electronic-health-record workflows could enable real-time alerts and risk-adapted management. Prospective multicentre validation is warranted.