International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction
© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology..
Aims: 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.
Methods and results: 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)].
Conclusion: 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
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| Enthalten in: |
European heart journal. Digital health - 5(2024), 2 vom: 20. März, Seite 123-133 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Herman, Robert [VerfasserIn] |
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| Links: |
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| Themen: |
Acute coronary syndrome |
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| Anmerkungen: |
Date Revised 26.04.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1093/ehjdh/ztad074 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369950917 |
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| 100 | 1 | |a Herman, Robert |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction |
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| 500 | |a Date Revised 26.04.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. | ||
| 520 | |a Aims: 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 | ||
| 520 | |a Methods and results: 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)] | ||
| 520 | |a Conclusion: 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Acute coronary syndrome | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Electrocardiogram | |
| 650 | 4 | |a Myocardial infarction | |
| 650 | 4 | |a NSTEMI | |
| 650 | 4 | |a Occlusion myocardial infarction | |
| 700 | 1 | |a Meyers, Harvey Pendell |e verfasserin |4 aut | |
| 700 | 1 | |a Smith, Stephen W |e verfasserin |4 aut | |
| 700 | 1 | |a Bertolone, Dario T |e verfasserin |4 aut | |
| 700 | 1 | |a Leone, Attilio |e verfasserin |4 aut | |
| 700 | 1 | |a Bermpeis, Konstantinos |e verfasserin |4 aut | |
| 700 | 1 | |a Viscusi, Michele M |e verfasserin |4 aut | |
| 700 | 1 | |a Belmonte, Marta |e verfasserin |4 aut | |
| 700 | 1 | |a Demolder, Anthony |e verfasserin |4 aut | |
| 700 | 1 | |a Boza, Vladimir |e verfasserin |4 aut | |
| 700 | 1 | |a Vavrik, Boris |e verfasserin |4 aut | |
| 700 | 1 | |a Kresnakova, Viera |e verfasserin |4 aut | |
| 700 | 1 | |a Iring, Andrej |e verfasserin |4 aut | |
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| 700 | 1 | |a Bahyl, Jakub |e verfasserin |4 aut | |
| 700 | 1 | |a Kisova, Timea |e verfasserin |4 aut | |
| 700 | 1 | |a Schelfaut, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Vanderheyden, Marc |e verfasserin |4 aut | |
| 700 | 1 | |a Perl, Leor |e verfasserin |4 aut | |
| 700 | 1 | |a Aslanger, Emre K |e verfasserin |4 aut | |
| 700 | 1 | |a Hatala, Robert |e verfasserin |4 aut | |
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| 700 | 1 | |a Bartunek, Jozef |e verfasserin |4 aut | |
| 700 | 1 | |a Barbato, Emanuele |e verfasserin |4 aut | |
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