A ResNet deep learning model based on ECG traces alone predicted 1-year mortality with an AUROC of 0.812, significantly outperforming an XGBoost model based on standard ECG measurements.
Cohort (n=244,077)
Yes
Does a ResNet-based Deep Learning model using 12-lead ECG traces improve mortality prediction compared to standard ECG measurements in patients presenting to the emergency department or hospital?
Deep learning models using raw 12-lead ECG traces significantly outperform standard ECG measurement-based models for predicting short- and long-term mortality at the population level.
Effect estimate: AUROC 0.812 (95% CI 0.808-0.816)
Absolute Event Rate: 0.812% vs 0.784%
p-value: p=<0.001
The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason. Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (2007-2020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality. The models for 30-day, 1-year, and 5-year mortality were trained on 146,173, 141,072, and 111,020 patients and evaluated on 97,144, 89,379, and 55,650 patients, respectively. In the evaluation cohort, 7.6%, 17.3%, and 32.9% patients died by 30-days, 1-year, and 5-years, respectively. ResNet models based on ECG traces alone had good-to-excellent performance with area under receiver operating characteristic curve (AUROC) of 0.843 (95% CI: 0.838-0.848), 0.812 (0.808-0.816), and 0.798 (0.792-0.803) for 30-day, 1-year and 5-year prediction, respectively; and were superior to XGB models based on ECG measurements with AUROC of 0.782 (0.776-0.789), 0.784 (0.780-0.788), and 0.746 (0.740-0.751). This study demonstrates the validity of ECG-based DL mortality prediction models at the population-level that can be leveraged for prognostication at point of care.
Sun et al. (Mon,) conducted a cohort in All-cause emergency department or hospital presentations (n=244,077). ResNet-based Deep Learning (DL) model using 12-lead ECG traces vs. XGBoost (XGB) model using standard ECG measurements was evaluated on 1-year mortality prediction (AUROC) (AUROC 0.812, 95% CI 0.808-0.816, p=<0.001). A ResNet deep learning model based on ECG traces alone predicted 1-year mortality with an AUROC of 0.812, significantly outperforming an XGBoost model based on standard ECG measurements.
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