{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/benchmarking-mortality-risk-prediction-from","title":"A Closer Look at Mortality Risk Prediction from Electrocardiograms","arxiv_id":"2406.17002","date":"2024-06-24","proceeding":null,"authors":["Platon Lukyanenko","Joshua Mayourian","Mingxuan Liu","John K. Triedman","Sunil J. Ghelani","William G. La Cava"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2406.17002v3","url_pdf":"https://arxiv.org/pdf/2406.17002v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"benchmarking-mortality-risk-prediction-from","repo_url":"https://github.com/cavalab/ecg-survival-benchmark","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}