Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms.

Autor: Sun W; Department of Computing Science, University of Alberta, Edmonton, AB, Canada., Kalmady SV; Department of Computing Science, University of Alberta, Edmonton, AB, Canada.; Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.; Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada., Sepehrvand N; Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.; Department of Medicine, University of Alberta, Edmonton, AB, Canada., Salimi A; Department of Computing Science, University of Alberta, Edmonton, AB, Canada., Nademi Y; Department of Computing Science, University of Alberta, Edmonton, AB, Canada., Bainey K; Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.; Department of Medicine, University of Alberta, Edmonton, AB, Canada., Ezekowitz JA; Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.; Department of Medicine, University of Alberta, Edmonton, AB, Canada., Greiner R; Department of Computing Science, University of Alberta, Edmonton, AB, Canada.; Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada., Hindle A; Department of Computing Science, University of Alberta, Edmonton, AB, Canada., McAlister FA; Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.; Department of Medicine, University of Alberta, Edmonton, AB, Canada., Sandhu RK; Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada.; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, CA, USA., Kaul P; Canadian VIGOUR Centre, Department of Medicine, University of Alberta, Edmonton, AB, Canada. pkaul@ualberta.ca.; Department of Medicine, University of Alberta, Edmonton, AB, Canada. pkaul@ualberta.ca.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2023 Feb 06; Vol. 6 (1), pp. 21. Date of Electronic Publication: 2023 Feb 06.
DOI: 10.1038/s41746-023-00765-3
Abstrakt: 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.
(© 2023. The Author(s).)
Databáze: MEDLINE
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