Papers › A Closer Look at Mortality Risk Prediction from Electrocardiograms
A Closer Look at Mortality Risk Prediction from Electrocardiograms
Platon Lukyanenko, Joshua Mayourian, Mingxuan Liu, John K. Triedman, Sunil J. Ghelani, William G. La Cava
Several recent studies combine large private ECG databases with AI to predict patient mortality. These studies typically use a few, highly variable, modeling approaches. While benchmarking these approaches has historically been limited by a lack of public ECG datasets, this changed with the 2023 release of MIMIC-IV, containing 795,546 ECGs from a U.S. hospital system, and the 2020 release of Code-15, containing 345,779 ECGs collected during routine care in Brazil. We benchmark over 500 AI-ECG survival models predicting all-cause mortality on Code-15 and MIMIC-IV with 2 neural architectures, 4 Deep-Survival-Analysis approaches, and classifiers predicting mortality at 4 time horizons. We extend the highest-performing approach to a dataset from Boston Children's Hospital (BCH, 225,379 ECGs). Models train with and without demographics (age/sex) and evaluate across datasets. The best performing Deep-Survival-Analysis models trained with ECG and demographics yield good median Concordance Indices (Code-15: 0.82, MIMIC-IV: 0.78, BCH: 0.76) and AUPRC scores (median 1-yr/5-yr, Code-15: 0.07/0.15; MIMIC-IV: 0.45/0.55; BCH: 0.04/0.13) considering the percentage of ECGs linked to mortality (1-yr/5-yr, Code-15: 1.2%/3.4%; MIMIC-IV: 14.8%/24.5%; BCH: 0.9%/4.8%). Contrasting with Deep-Survival-Analysis models, classifier-based AI-ECG models exhibit significant, site-dependent sensitivity to the choice of time horizon (median Pearson's R, Code-15: 0.69, p<1E-5; MIMIC-IV: -0.80 p<1E-5). Demographic-only models perform surprisingly well on Code-15. Concordance drops 0.03-0.24 on external validation. We recommend Deep-Survival-Analysis over Classifier-Cox approaches and the inclusion of demographic covariates in ECG survival modeling. Comparisons to demographic-only and baseline models is crucial. External evaluations support fine-tuning models on site-specific data.
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