Interpretable artificial intelligence to predict preoperative risk factors for failure to rescue after coronary artery bypass grafting.

Abstract

Failure to rescue (FTR) is a significant quality indicator for postoperative cardiothoracic care. We developed an interpretable artificial intelligence model to identify, interpret, and integrate patient risk factors to predict FTR after coronary artery bypass grafting (CABG). Adults who underwent isolated CABG in an academic health system from 2011 to 2022 were analyzed. FTR was defined as 30-day postoperative mortality after a stroke, renal failure, reoperation, or prolonged ventilation. The study evaluated 35 patient-specific preoperative variables using recursive feature elimination with cross-validation algorithm and artificial intelligence methods to determine optimal set of risk factors to predict FTR. SHapley Additive exPlanations were performed to visualize and interpret models. A total of 9974 patients were identified and the overall FTR rate was 2.5% (n = 249). FTR rates were 12.9% for stroke, 24.8% for renal failure, 11.4% for reoperation, and 11.6% for prolonged ventilation. The model produced the top-12 risk factors: age, albumin level, bilirubin level, body mass index, creatinine level, ejection fraction, hematocrit, hemoglobin level, hemoglobin A1c, model for end stage liver disease score, platelets count, and white blood cell count. The random forest algorithm demonstrated good performance with an area under the precision-recall curve of 0.78. This study utilized artificial intelligence algorithms to evaluate and interpret an optimal set of preoperative risk factors for FTR after CABG. The random forest model demonstrated good discrimination in identifying at-risk patients. The proposed framework can serve as proof-of-concept that can translate, with further research, into a real-time clinical decision support tool for at-risk patients.