{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T16:03:40Z","timestamp":1785945820674,"version":"3.56.0"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wake Forest Center for Artificial Intelligence Research (WFCAIR)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCDD"],"abstract":"<jats:p>Background: Fatal coronary heart disease (FCHD) affects ~650,000 people yearly in the US. Electrocardiographic artificial intelligence (ECG-AI) models can predict adverse coronary events, yet their application to FCHD is understudied. Objectives: The study aimed to develop ECG-AI models predicting FCHD risk from ECGs. Methods (Retrospective): Data from 10 s 12-lead ECGs and demographic\/clinical data from University of Tennessee Health Science Center (UTHSC) were used for model development. Of this dataset, 80% was used for training and 20% as holdout. Data from Atrium Health Wake Forest Baptist (AHWFB) were used for external validation. We developed two separate convolutional neural network models using 12-lead and Lead I ECGs as inputs, and time-dependent Cox proportional hazard models using demographic\/clinical data with ECG-AI outputs. Correlation of the predictions from the 12- and 1-lead ECG-AI models was assessed. Results: The UTHSC cohort included data from 50,132 patients with a mean age (SD) of 62.50 (14.80) years, of whom 53.4% were males and 48.5% African American. The AHWFB cohort included data from 2305 patients with a mean age (SD) of 63.04 (16.89) years, of whom 51.0% were males and 18.8% African American. The 12-lead and Lead I ECG-AI models resulted in validation AUCs of 0.84 and 0.85, respectively. The best overall model was the Cox model using simple demographics with Lead I ECG-AI output (D1-ECG-AI-Cox), with the following results: AUC = 0.87 (0.85\u20130.89), accuracy = 83%, sensitivity = 69%, specificity = 89%, negative predicted value (NPV) = 92% and positive predicted value (PPV) = 55% on the AHWFB validation cohort. For this, the 2-year FCHD risk prediction accuracy was AUC = 0.91 (0.90\u20130.92). The 12-lead versus Lead I ECG FCHD risk prediction showed strong correlation (R = 0.74). Conclusions: The 2-year FCHD risk can be predicted with high accuracy from single-lead ECGs, further improving when combined with demographic information.<\/jats:p>","DOI":"10.3390\/jcdd11120395","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T08:17:49Z","timestamp":1733732269000},"page":"395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study"],"prefix":"10.3390","volume":"11","author":[{"given":"Liam","family":"Butler","sequence":"first","affiliation":[{"name":"Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Ivanov","sequence":"additional","affiliation":[{"name":"Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8418-0130","authenticated-orcid":false,"given":"Turgay","family":"Celik","sequence":"additional","affiliation":[{"name":"Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ibrahim","family":"Karabayir","sequence":"additional","affiliation":[{"name":"Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1258-8353","authenticated-orcid":false,"given":"Lokesh","family":"Chinthala","sequence":"additional","affiliation":[{"name":"Center for Biomedical Informatics, University of Tennessee Health Sciences Center, Memphis, TN 38163, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7235-4914","authenticated-orcid":false,"given":"Mohammad S.","family":"Tootooni","sequence":"additional","affiliation":[{"name":"Health Informatics and Data Science, Loyola University Chicago, Maywood, IL 60660, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byron C.","family":"Jaeger","sequence":"additional","affiliation":[{"name":"Division of Public Health Science, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke T.","family":"Patterson","sequence":"additional","affiliation":[{"name":"Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam J.","family":"Doerr","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David D.","family":"McManus","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert L.","family":"Davis","sequence":"additional","affiliation":[{"name":"Center for Biomedical Informatics, University of Tennessee Health Sciences Center, Memphis, TN 38163, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Herrington","sequence":"additional","affiliation":[{"name":"Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0313-9254","authenticated-orcid":false,"given":"Oguz","family":"Akbilgic","sequence":"additional","affiliation":[{"name":"Cardiovascular Section, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.15420\/aer.2018:15:2","article-title":"Sudden cardiac death and arrhythmias","volume":"7","author":"Srinivasan","year":"2018","journal-title":"Arrhythmia Electrophysiol. 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