Predicting adverse outcomes after cardiac surgery using multi-task deep neural networks, clinical features, and electrocardiograms

Abstract

Abstract 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. Condensed abstract In this study, deep learning (DL) is applied to electrocardiograms and clinical features used in the standard STS risk prediction tools to generate new high-performing risk calculators for cardiac surgical procedures. Preoperative voltage waveforms contain information about cardiovascular risk and cardiac function and are passed as inputs to the deep learning model. These risk models apply to all cardiac procedures including those procedures that do not have standard STS risk calculators and provide improved performance. DL models enable the incorporation of additional modalities of data to improve risk prediction in cardiac surgery.

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