Machine Learning-Based Prediction of Post-Induction Hypotension: Identifying Risk Factors and Enhancing Anesthesia Management

Abstract Background: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool to analyze large amounts of data and identify perioperative complication factors. This study aims to identify risk factors for PIH and develop predictive models to support anesthesia management. Methods: A dataset of 5406 patients was analyzed using machine learning methods. Logistic regression, random forest, XGBoost, and neural network models were compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis (DCA). Results: The logistic regression model achieved the highest AUROC of 0.74 (95% CI, 0.71 - 0.77), indicating good discrimination. Calibration curves demonstrated satisfactory calibration for the logistic regression and random forest models. DCA revealed that the logistic regression model had the highest clinical benefit. The logistic regression model showed the best performance in predicting PIH and was selected as the final predictive model. Baseline blood pressure, age, sex, type of surgery, platelet count, and certain anesthesia-inducing drugs were identified as important features. Conclusions: This study provides a valuable tool for personalized preoperative risk assessment and customized anesthesia management, allowing for early intervention and improved patient outcomes. Integration of machine learning models into electronic medical record systems can facilitate real-time risk assessment and prediction.