• Adeel Abbasi, Cindy Li, Max Dekle, Christian A Bermudez, Daniel Brodie, Frank W Sellke, Neel R Sodha, Corey E Ventetuolo, and Carsten Eickhoff.
• Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Warren Alpert School of Medicine at Brown University, Providence, RI. Electronic address: adeel_abbasi@brown.edu.
• J. Thorac. Cardiovasc. Surg. 2025 Jan 1; 169 (1): 114123.e28114-123.e28.

BackgroundThe clinical applicability of machine learning predictions of patient outcomes following cardiac surgery remains unclear. We applied machine learning to predict patient outcomes associated with high morbidity and mortality after cardiac surgery and identified the importance of variables to the derived model's performance.MethodsWe applied machine learning to the Society of Thoracic Surgeons Adult Cardiac Surgery Database to predict postoperative hemorrhage requiring reoperation, venous thromboembolism (VTE), and stroke. We used permutation feature importance to identify variables important to model performance and a misclassification analysis to study the limitations of the model.ResultsThe study dataset included 662,772 subjects who underwent cardiac surgery between 2015 and 2017 and 240 variables. Hemorrhage requiring reoperation, VTE, and stroke occurred in 2.9%, 1.2%, and 2.0% of subjects, respectively. The model performed remarkably well at predicting all 3 complications (area under the receiver operating characteristic curve, 0.92-0.97). Preoperative and intraoperative variables were not important to model performance; instead, performance for the prediction of all 3 outcomes was driven primarily by several postoperative variables, including known risk factors for the complications, such as mechanical ventilation and new onset of postoperative arrhythmias. Many of the postoperative variables important to model performance also increased the risk of subject misclassification, indicating internal validity.ConclusionsA machine learning model accurately and reliably predicts patient outcomes following cardiac surgery. Postoperative, as opposed to preoperative or intraoperative variables, are important to model performance. Interventions targeting this period, including minimizing the duration of mechanical ventilation and early treatment of new-onset postoperative arrhythmias, may help lower the risk of these complications.Copyright © 2023 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

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