Scripts and modules to train deep learning models for mortality prediction using ECGs

Companion code for Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms Weijie Sun, MSc1†; Sunil Vasu Kalmady, PhD1,2†; Nariman Sepehrvand, MD, PhD2,3; Amir Salimi, MSc1; Yousef Nademi, PhD1; Kevin Bainey, MD, MSc2,3; Justin A. Ezekowitz, MBBCh, MSc2,3; Russell Greiner, PhD1, 5; Abram Hindle, PhD1; Finlay A. McAlister, MD, MSc2,3; Roopinder K. Sandhu, MD, MPH2,4; Padma Kaul, PhD2,3* †Both contributed equally as first authors 1 - Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada 2 - Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada 3 - Department of Medicine, University of Alberta, Edmonton, Alberta, Canada 4 – Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA 5 – Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Alberta, Canada Abstract 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.

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