Development and validation of a machine learning model for predicting 30-day major morbidity and mortality following radical cystectomy: An American College of Surgeons National Surgical Quality Improvement Program study.

Objective: To develop and validate machine learning models for predicting 30-day major morbidity and mortality in patients undergoing radical cystectomy (RC) using the American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) and to compare performance against a logistic regression model. Material and methods: We identified 11 241 patients from the ACS-NSQIP database 2020-2024 who underwent radical cystectomy. Demographics and comorbidities were extracted along with targeted variables from the NSQIP RC-targeted database. The cohort was split into a training and an independent validation dataset. Predictive models were developed for logistic regression, random forest (RF) and XGBoost. Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve, SHapley Additive exPlanations (SHAP) analysis, sensitivity, specificity and Brier scores. Results: Of 11 241 patients, 2691 (23.9%) experienced at least one major complication, and 185 (1.6%) died within 30 days. Overall complications were 6365 (56.62%). Non-survivors were significantly older (72.65 ± 10.14 vs. 68.50 ± 10.32 years, Conclusions: We developed and validated machine learning models for 30-day major morbidity and 30-day mortality following radical cystectomy. These findings support the integration of machine learning into clinical workflows to enhance preoperative counselling and personalized risk reduction.