Development and validation of a machine learning model for preoperative prediction of intraoperative hypothermia in gynecological laparoscopic surgery: a two-center cohort study.

Background: Intraoperative hypothermia (IOH, temperature <36.0 °C) is a common complication in gynecological laparoscopic surgery that leads to significant adverse outcomes that would benefit from preoperative risk prediction. Methods: We developed and compared two predictive models-a conventional multivariable logistic regression (LR) model and a gradient boosting machine (XGBoost) model-to preoperatively predict IOH based on 20 objectively measurable preoperative clinical variables that could be directly extracted from electronic health records (EHRs). The derivation cohort consisted of 460 cases from a tertiary care center in 2024, which were randomly divided into a training set ( Results: We initially analyzed 20 preoperative variables. The final model incorporated eight key indicators: body mass index (BMI), ASA physical status, fasting period, basal body temperature, estimated surgical duration, hypertension status, serum albumin level, and alanine aminotransferase (ALT). The derivation cohort consisted of 460 patients, split into a training set ( Conclusions: We developed and validated an interpretable XGBoost model using eight readily available preoperative clinical indicators. The model demonstrated good performance for the preoperative prediction of IOH in gynecological laparoscopic surgery, which may facilitate early identification of high-risk patients and timely intervention.