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

This is the authors' abstract. We don't add key points for this paper.

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.