This study aims to build up an innovative ECG DL model to predict four categories of first-ever major adverse cardiovascular events (MACE) within a one-year timeframe, encompassing non-fatal myocardial infarction (MI), non-fatal ischemic stroke (IS), hospitalization-requiring HF, and all-cause mortality.
We present a novel multi-task deep learning model, the ECG-MACE, which predicts the one-year first-ever MACE using 2,821,889 standard 12-lead ECGs, including training (n = 984,895), validation (n = 422,061), and test (n = 1,414,933) sets, from Chang Gung Memorial Hospital database in Taiwan. Data from TSGH (n = 113,224) was retrieved for external validation.
Results showed that ECG-MACE predicted 1-year events of HF, MI, IS, and all-cause mortality with the area under the receiver operating characteristic (AUROC) values of 0.90, 0.85, 0.76, and 0.89, respectively.
While predicting IS was particularly challenging under the single-task learning model, the multi-task learning approach improved IS prediction, resulting in a 0.1 increase in the AUROC score (15% improvement).
Furthermore, external validation has consistently demonstrated robust predictive capability for mortality, achieving an AUROC of 0.83.
In conclusion, our ECG-MACE model provides accurate prediction for 1-year multiple cardiovascular events, encompassing MI, HF, and all-cause mortality. The trend of the model's predictive capability extends up to 10 years, suggesting potential value in facilitating early intervention. Multitask learning approach strengthens the capacity of DL-based ECG in preventive medicine.
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