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

  • Lee, Min Sung; 
  • Shin, Tae Gun; 
  • Lee, Youngjoo; 
  • Kim, Dong Hoon; 
  • Choi, Sung Hyuk; 
  • ... Ahn, Chiwon; 
  • 외 15명
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초록

OBJECTIVE: 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). 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). 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. 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. © The Author(s) 2025. Published by Oxford University Press on behalf of the European Society of Cardiology.

키워드

AI/ML-enabled SaMD; Acute coronary syndrome; Acute myocardial infarction; Artificial intelligence; Electrocardiogram; Emergency department; CHEST-PAIN PATIENTS; EMERGENCY-DEPARTMENT; SCORE; PREDICTORS; RISK; ESC
제목
Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study
저자
Lee, Min Sung; Shin, Tae Gun; Lee, Youngjoo; Kim, Dong Hoon; Choi, Sung Hyuk; Cho, Hanjin; Lee, Mi Jin; Jeong, Ki Young; Kim, Won Young; Min, Young Gi; Han, Chul; Yoon, Jae Chol; Jung, Eujene; Kim, Woo Jeong; Ahn, Chiwon; Seo, Jeong Yeol; Lim, Tae Ho; Kim, Jae Seong; Choi, Jeff; Kwon, Joon-Myoung; Kim, Kyuseok
DOI
10.1093/eurheartj/ehaf004
발행일
2025-05
유형
Article; Early Access
저널명
European Heart Journal
권
46
호
20
페이지
1917 ~ 1929

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