International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction.
Acute coronary syndrome
Artificial intelligence
Electrocardiogram
Myocardial infarction
NSTEMI
Occlusion myocardial infarction
Journal
European heart journal. Digital health
ISSN: 2634-3916
Titre abrégé: Eur Heart J Digit Health
Pays: England
ID NLM: 101778323
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
received:
14
09
2023
revised:
13
10
2023
accepted:
02
11
2023
medline:
20
3
2024
pubmed:
20
3
2024
entrez:
20
3
2024
Statut:
epublish
Résumé
A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.
Identifiants
pubmed: 38505483
doi: 10.1093/ehjdh/ztad074
pii: ztad074
pmc: PMC10944682
doi:
Types de publication
Journal Article
Langues
eng
Pagination
123-133Informations de copyright
© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.
Déclaration de conflit d'intérêts
Conflict of interest: R.H. is the co-founder and Chief Medical Officer of Powerful Medical; M.M., J.B., A.I., B.V., V.B., V.K., and A.D. are employees and shareholders of Powerful Medical. S.W.S., H.P.M., and L.P. are shareholders in Powerful Medical.