• Nicolas Allou, Jérôme Allyn, Sophie Provenchere, Benjamin Delmas, Eric Braunberger, Matthieu Oliver, Jean Louis De Brux, Cyril Ferdynus, and EpiCard investigators.
• Intensive Care Unit, Félix Guyon University Hospital, Saint Denis, France; Clinical Informatics Department, Félix Guyon University Hospital, Saint Denis, France. Electronic address: nicolas.allou@chu-reunion.fr.
• J. Thorac. Cardiovasc. Surg. 2023 Dec 1; 166 (6): e567e578e567-e578.

ObjectivesThe aim of this study using decision curve analysis (DCA) was to evaluate the clinical utility of a deep-learning mortality prediction model for cardiac surgery decision making compared with the European System for Cardiac Operative Risk Evaluation (EuroSCORE) II and to 2 machine-learning models.MethodsUsing data from a French prospective database, this retrospective study evaluated all patients who underwent cardiac surgery in 43 hospital centers between January 2012 and December 2020. A receiver operating characteristic analysis was performed to compare the accuracy of the EuroSCORE II, machine-learning models, and an adapted Tabular Bidirectional Encoder Representations from Transformers deep-learning model in predicting postoperative in-hospital mortality. The clinical utility of these models for cardiac surgery decision making was compared using DCA.ResultsOver the study period, 165,640 patients underwent cardiac surgery, with a mean EuroSCORE II of 3.99 ± 6.67%. In the receiver operating characteristic analysis, the area under the curve was significantly greater for the deep-learning model (0.834; 95% confidence interval, 0.831-0.838) than the EuroSCORE II (P < .001), the random forest model (P = .03), and the Extreme Gradient Boosting model (P = .03). In the DCA, the clinical utility of the 3 artificial intelligence models was superior to that of the EuroSCORE II, especially when the threshold probability of death was high (>45%). The deep-learning model showed the greatest advantage over the EuroSCORE II.ConclusionsThe deep-learning model had better predictive accuracy and greater clinical utility than the EuroSCORE II and the 2 machine-learning models. These findings suggest that deep learning with Tabular Bidirectional Encoder Representations from Transformers prediction model could be used in the future as the gold standard for cardiac surgery decision making.Copyright © 2023 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

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