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Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study.

Authors :
Lee MS
Shin TG
Lee Y
Kim DH
Choi SH
Cho H
Lee MJ
Jeong KY
Kim WY
Min YG
Han C
Yoon JC
Jung E
Kim WJ
Ahn C
Seo JY
Lim TH
Kim JS
Choi J
Kwon JM
Kim K
Source :
European heart journal [Eur Heart J] 2025 Feb 24. Date of Electronic Publication: 2025 Feb 24.
Publication Year :
2025
Publisher :
Ahead of Print

Abstract

Background and Aims: Emerging evidence supports artificial intelligence-enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of AI-ECG in detecting AMI in the emergency department (ED).<br />Methods: The Rule-Out acute Myocardial Infarction using Artificial intelligence Electrocardiogram analysis (ROMIAE) study is a prospective cohort study conducted in the Republic of Korea from March 2022 to October 2023, involving 18 university-level teaching hospitals. Adult patients presenting to the ED within 24 h of symptom onset concerning for AMI were assessed. Exposure included AI-ECG score, HEART score, GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. The primary outcome was diagnosis of AMI during index admission, and the secondary outcome was 30 day major adverse cardiovascular event (MACE).<br />Results: The study population comprised 8493 adults, of whom 1586 (18.6%) were diagnosed with AMI. The area under the receiver operating characteristic curve for AI-ECG was 0.878 (95% CI, 0.868-0.888), comparable with the HEART score (0.877; 95% CI, 0.869-0.886) and superior to the GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. For predicting 30 day MACE, AI-ECG (area under the receiver operating characteristic, 0.866; 95% CI, 0.856-0.877) performed comparably with the HEART score (0.858; 95% CI, 0.848-0.868). The integration of the AI-ECG improved risk stratification and AMI discrimination, with a net reclassification improvement of 19.6% (95% CI, 17.38-21.89) and a C-index of 0.926 (95% CI, 0.919-0.933), compared with the HEART score alone.<br />Conclusions: In this multicentre prospective study, the AI-ECG demonstrated diagnostic accuracy and predictive power for AMI and 30 day MACE, which was similar to or better than that of traditional risk stratification methods and ED physicians.<br /> (© The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Details

Language :
English
ISSN :
1522-9645
Database :
MEDLINE
Journal :
European heart journal
Publication Type :
Academic Journal
Accession number :
39992309
Full Text :
https://doi.org/10.1093/eurheartj/ehaf004