Table 1_Development and validation of a machine learning model for preoperative prediction of intraoperative hypothermia in gynecological laparoscopic surgery: a two-center cohort study.docx

Background<p>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.</p>Methods<p>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 (n = 322) and a test set (n = 138) at a 7:3 ratio. For external validation, we used an independent cohort of 183 cases from a second medical institution during the same period. Model performance was evaluated using metrics including the area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), with SHapley Additive exPlanations (SHAP) method applied for feature importance analysis.</p>Results<p>We initially analyzed 20 preoperative variables.