Development of a machine learning algorithm model to predict intraoperative hypotension in elderly patients undergoing thoracic and abdominal surgeries.

To develop and validate machine learning (ML) models for identifying key predictors and estimating the risk of intraoperative hypotension (IOH) in elderly patients undergoing general anesthesia. This secondary analysis included 1,720 elderly surgical patients from a randomized controlled trial. Data were split chronologically into training sets. Feature selection was performed using univariate analysis and the Boruta algorithm. Eight ML models - logistic regression, Bayesian model, K-nearest neighbor, support vector machine, neural network, classification and regression tree, extreme gradient boosting, and random forest - were developed with cross-validation, hyperparameter tuning, and random oversampling. Model performance was evaluated using ROC, PRC, calibration, and decision curve analyses, and interpretability was enhanced using SHapley Additive exPlanations (SHAP). Key predictors included anesthesia protocol, Charlson comorbidity index, preoperative sodium, creatinine, BUN/creatinine ratio, intraoperative drug use (e.g., sevoflurane, lidocaine, morphine), preoperative MAP and MHR, surgical and anesthesia duration, and surgical site. The random forest model achieved the best performance (accuracy=0.9917; MCC=0.9832; AUC-ROC=0.9998; AUC-PRC=0.9998). A robust ML-based model was established to accurately predict IOH in elderly patients. These findings may support individualized anesthesia management and targeted preventive strategies to reduce IOH incidence.