Development and temporal validation of a machine learning–based model for predicting intraoperative hypothermia in patients undergoing robot-assisted urological tumor resection: A retrospective study

BackgroundIntraoperative hypothermia (IOH) is prevalent in robotic urological surgery, yet existing prediction models, largely based on linear methods, may have limited capacity to capture complex non-linear risk patterns.ObjectiveTo develop and validate a preoperative machine learning model for predicting IOH to guide individualized thermal management.MethodsWe analyzed 787 patients (2022-2024) undergoing robot-assisted urological tumor resection. A CatBoost model was developed using 2022-2023 data with 5-fold nested cross-validation for robust feature selection. Independent temporal validation was conducted using 2024 data.ResultsThe model, utilizing 17 preoperative predictors, demonstrated temporal generalization with a temporal validation AUC of 0.877 and precise calibration (slope = 0.964). SHAP analysis highlighted markers of physiological stress and metabolic reserve as key drivers. Decision curve analysis confirmed positive net benefit across a broad probability threshold (10%-90%).ConclusionsThis robust machine learning tool accurately identifies high-risk phenotypes using only baseline characteristics. It enables clinicians to implement proactive, personalized warming strategies before anesthesia induction.