ARTIFICIAL INTELLIGENCE IN PREDICTING ANESTHESIA-RELATED COMPLICATIONS: A CROSS-SPECIALTY APPROACH INTEGRATING PULMONARY, RENAL, AND CARDIOVASCULAR RISK FACTORS

Abstract

The integration of artificial intelligence (AI) into anesthesiology offers a transformative approach to predicting anesthesia-related complications by leveraging multidimensional data from pulmonary, renal, and cardiovascular systems. This retrospective cohort study analyzed electronic health records from 4,200 adult surgical patients to develop and validate machine learning models capable of forecasting perioperative complications. Key anesthesia-related adverse events—such as hypoxemia (11.2%), acute kidney injury (8.9%), arrhythmias (7.5%), bronchospasm (6.4%), and intraoperative hypotension (15.8%)—were predicted using a comprehensive feature set including laboratory results, imaging data, comorbidities, and intraoperative parameters. XGBoost achieved the highest results (AUC-ROC: 0.92, F1-score: 0.87) and Gradient Boosting and Random Forest came in as close seconds.  Of all the things affecting all the problems, SHAP analysis pointed to intraoperative hypotension, a low eGFR, COPD, higher preop troponin, being over 70 and being obese.  Besides, the results of external testing showed that the XGBoost model generalised well and its Brier score (0.082) was low, with minimal overfitting.  It was revealed that many problems cause multi-organ failure during surgery which insists on creating a unified system for assessing risks among specialists.  Calibration assessements, levels of feature importance, evaluation metrics of the model and complication curves are shown visually in figures and tables.  All in all, making use of individualised risk classes, beginning early treatment and boosting safety can prove AI-driven predictive models useful in anaesthesiology.  It uses data to lay out a plan for accurate anesthesia and sets a standard for adding explainable AI to operations within the field.