Background Risk stratification models estimate the probabilities of adverse outcomes after cardiac surgical procedures, which helps clinicians and patients make informed decisions. Objectives We used the 12-lead electrocardiogram (ECG) and/or Society for Thoracic Surgeons (STS) variables to predict postoperative outcomes using deep learning methods that can incorporate diverse data types. Methods We developed a deep convolutional neural network ("ECGNet") that predicts operative mortality and other adverse outcomes using preoperative 12-lead ECGs (n=30,877) from 12,933 patients who underwent 13,299 cardiac surgical procedures. We also developed a deep neural network applied to preoperative STS variables ("STSNet"). STSNet and ECGNet are multi-task neural networks that utilize secondary outcomes to augment prediction of mortality using the same neural network. Results ECGNet achieved a mean area under the receiver operating characteristic curve (AUC) of 0.85 for predicting operative mortality for all procedures and 0.93 for valve procedures. STSNet achieved a mean AUC of 0.85 for all procedures, with statistically similar performance as ECGNet for all procedures. Combining ECGNet and STSNet achieved a mean AUC of 0.90 for predicting operative mortality after all procedures, which is significantly higher than either ECGNet or STSNet alone. Conclusions A deep neural network trained on STS features has higher predictive performance than previously reported for existing conventional models and is not limited to certain types of cardiac surgical procedures. A model trained on ECG alone can predict operative mortality with similar performance as STS features and adding ECG to STS features in a neural network can improve performance. These findings demonstrate the potential in leveraging deep learning on multidimensional data sources to predict outcomes after cardiac surgery.

A.D.A. has received sponsored research support from Amgen Inc and from Philips Research North America but declares no non-financial competing interests. All other authors declare no financial or non-financial competing interests.

A.D.A. discloses support for the research of this work from Controlled Risk Insurance Company / Risk Management Foundation (CRICO) and the Massachusetts General Hospital Corrigan Minehan Heart Center Hassenfeld Cardiovascular Scholar Award. Images in the Central Illustration are used with license from Adobe Stock, Shutterstock and Flaticon.

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The datasets generated and/or analyzed during the current study are not publicly available due to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule protecting individually identifiable health information but are available from the corresponding authors on reasonable request with appropriate data sharing agreements in place. The underlying code for this study will be made available in GitHub after publication and can be accessed via this link https://github.com/aguirre-lab/ml4c3.

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