Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence–powered analysis of 12-lead intake electrocardiogram

Figures

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  Figure 1. Flowchart showing inclusion of patients into databases for both classification problems to be addressed via artificial intelligence–based predictive modeling. The left branch of the tree concerns the first convolutional neural network with long short-term memory (CNN-LSTM1), concerned with differentiating between electrocardiograms (ECGs) from patients who survived vs died. The right branch describes the database used for CNN-LSTM2, which independently predicts the likelihood that each ECG belongs to a patient from 4 groups (no event vs major adverse cardiovascular events [MACE], as shown in legend). AE = arrhythmic event; HF = heart failure; TE = thromboembolic event.

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  Figure 2. Schematics illustrating machine learning network architectures for convolutional neural network long short-term memories 1 and 2 (LSTM1/2). Each network consists of 3 sections: (I) convolution layers, shown here as “feature maps” and “pooling” subsections; (II) recurrent neural network layers (labeled “Long-Short Term Memory”); and (III) fully connected layers that produce outputs (ie, predicted probabilities for each class). AE = arrhythmic event; HF = heart failure; ReLu = rectified linear unit; TE = thromboembolic event.

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  Figure 3. Data summarizing predictive power of artificial intelligence–based and conventional models. A: Receiver operator characteristic (ROC) curves for convolutional neural network long short-term memory 1 (CNN-LSTM1) and the corresponding conventional model, which attempted to differentiate between electrocardiograms of patients who survived vs died. B: False-positive and false-negative rates (fpr and fnr, respectively) for CNN-LSTM1 as a function of model threshold; at a nominal threshold (dashed yellow line) associated with a 20% fnr, the corresponding fpr (∼70%) is shown by a dashed black arrow. C: ROC curves for CNN-LSTM2 (multilabel model independent prediction of different major adverse cardiovascular event types; single curve derived via macro-averaging) and the corresponding conventional model (binary prediction: any event vs no event). See Figure 4 and Supplemental Figure 2 for additional plots. AUC = area under the curve; MACE = major adverse cardiovascular event.

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  Figure 4. Individual receiver operator characteristic (ROC) curves for the 4 independent classification tasks performed by convolutional neural network long short-term memory 2 (CNN-LSTM2). A: Prediction of no event during hospitalization from intake electrocardiogram. B: Prediction of arrhythmic event. C: Prediction of thromboembolic event. D: Prediction of heart failure event. AUC = area under the curve; MACE = major adverse cardiovascular event.

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  Figure 5. Violin plots with box-and-whisker annotations showing raw network outputs. A: Results for classification task (survival vs death) in convolutional neural network long short-term memory 1 (CNN-LSTM1). B, C: Results for 2 classification tasks in CNN-LSTM2 (B: arrhythmic events; C: heart failure events). X-axis labels show ground truth labels; dashed lines in each panel show optimal classification thresholds, as explained in Methods. See Supplemental Figure 3 for additional plots. AE = arrhythmic event; HF = heart failure; TE = thromboembolic event.

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  Figure 6. Summary data for holdout testing of both artificial intelligence–based models trained with electrocardiograms (ECGs) from before a cutoff date (June 9, 2020) and tested with ECGs from after that date. A: Receiver operator characteristic (ROC) curve for convolutional neural network long short-term memory 1 (CNN-LSTM1) trained and tested using the holdout protocol defined above. B: Same as panel A but for CNN-LSTM2. Both macro-averaged and individual event type ROCs are shown superimposed on this plot. AUC = area under the curve; MACE = major adverse cardiovascular event.