Machine Learning to Predict Outcomes and Cost by Phase of Care after Coronary Artery Bypass Grafting.

Abstract Background Machine learning may enhance prediction of outcomes after coronary artery bypass grafting (CABG). We sought to develop and validate a dynamic machine learning model to predict CABG outcomes at clinically relevant pre- and postoperative timepoints. Methods The Society of Thoracic Surgeons (STS) registry data elements from 2,086 isolated CABG patients were divided into training and testing datasets and input into XGBoost decision-tree machine learning algorithms. Two prediction models were developed based on data from the pre- (80 parameters) and postoperative (125 parameters) phases of care. Outcomes included operative mortality, major morbidity or mortality, high-cost, and 30-day readmission. Machine learning and STS model performance was assessed using accuracy and the area under the precision-recall curve (AUC-PR). Results Preoperative machine learning models predicted mortality (Accuracy=98%; AUC-PR=0.16; F1=0.24), major morbidity or mortality (Accuracy =75%; AUC-PR=0.33; F1=0.42), high cost (Accuracy =83%; AUC-PR=0.51; F1=0.52), and 30-day readmission (Accuracy =70%; AUC-PR=0.47; F1=0.49) with high accuracy. Preoperative machine learning models performed similar to the STS for prediction of mortality (STS AUC-PR=0.11;p=0.409) and outperformed STS for prediction of mortality or major morbidity (STS AUC-PR=0.28;p Conclusions Machine learning can predict mortality, major morbidity, high cost, and readmission after isolated CABG. Prediction based on the phase of care allows for dynamic risk assessment through the hospital course, which may benefit quality assessment and clinical decision making.