INTERNATIONAL EVALUATION OF AN ARTIFICIAL INTELLIGENCE-POWERED ECG MODEL DETECTING OCCLUSION MYOCARDIAL INFARCTION
ABSTRACT <jats:sec id="s1a">Background One third of Non-ST-elevation myocardial infarction (NSTEMI) patients present with an acutely occluded culprit coronary artery (occlusion myocardial infarction [OMI]), which is associated with poor short and long-term outcomes due to delayed identification and consequent delayed invasive management. We sought to develop and validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art diagnostic criteria.<jats:sec id="s1b">Methods An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international online database and a tertiary care center. This AI model was tested on an international test set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, predictive values and time to OMI diagnosis. OMI was based on a combination of angiographic and biomarker outcomes.<jats:sec id="s1c">Results The AI model achieved an area under the curve (AUC) of 0.941 (95% CI: 0.926-0.954) in identifying the primary outcome of OMI, with superior performance (accuracy 90.7% [95% CI: 89.5-91.9], sensitivity 82.6% [95% CI: 78.9-86.1], specificity 92.8 [95% CI: 91.5-93.9]) compared to STEMI criteria (accuracy 84.9% [95% CI: 83.5-86.3], sensitivity 34.4% [95% CI: 30.0-38.8], specificity 97.6% [95% CI: 96.8-98.2]) and similar performance compared to ECG experts (accuracy 91.2% [95% CI: 90.0-92.4], sensitivity 75.9% [95% CI: 71.9-80.0], specificity 95.0 [95% CI: 94.0-96.0]). The average time from presentation to a correct diagnosis of OMI was significantly shorter when relying on the AI model compared to STEMI criteria (2.0 vs. 4.9 hours, p<0.001).<jats:sec id="s1d">Conclusions The present novel ECG AI model demonstrates superior accuracy and earlier diagnosis of AI to detect acute OMI when compared to the STEMI criteria. Its external and international validation suggests its potential to improve ACS patient triage with timely referral for immediate revascularization.<jats:sec id="s1e">CLINICAL PERSPECTIVE <jats:sec id="s1f">What is new? <jats:list list-type="bullet">A novel artificial intelligence (AI) model detecting acute occluded coronary artery (OMI) using standard 12-lead electrocardiograms (ECGs) was developed from an international cohort.The OMI AI model is the first of its kind to be validated in an external international cohort of patients using an objective angiographically confirmed endpoint of OMI.Our study demonstrated the OMI AI models superior accuracy in identifying OMI and shorter time to correct diagnosis compared to standard of care STEMI criteria.<jats:sec id="s1g">What are the clinical implications? <jats:list list-type="bullet">The OMI AI model has the potential to improve ACS triage and clinical decision-making by enabling timely and accurate detection of OMI in NSTEMI patients.The robustness and versatility of the OMI AI model indicate its potential for real-world clinical implementation in ECG devices from multiple vendors.Prospective studies are essential to evaluate the efficacy of the OMI AI model and its impact on patient outcomes in real-world settings..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 23. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Herman, Robert [VerfasserIn] |
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| doi: |
10.1101/2023.04.26.23289180 |
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| PPN (Katalog-ID): |
XBI039430367 |
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| 520 | |a ABSTRACT <jats:sec id="s1a">Background One third of Non-ST-elevation myocardial infarction (NSTEMI) patients present with an acutely occluded culprit coronary artery (occlusion myocardial infarction [OMI]), which is associated with poor short and long-term outcomes due to delayed identification and consequent delayed invasive management. We sought to develop and validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art diagnostic criteria.<jats:sec id="s1b">Methods An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international online database and a tertiary care center. This AI model was tested on an international test set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, predictive values and time to OMI diagnosis. OMI was based on a combination of angiographic and biomarker outcomes.<jats:sec id="s1c">Results The AI model achieved an area under the curve (AUC) of 0.941 (95% CI: 0.926-0.954) in identifying the primary outcome of OMI, with superior performance (accuracy 90.7% [95% CI: 89.5-91.9], sensitivity 82.6% [95% CI: 78.9-86.1], specificity 92.8 [95% CI: 91.5-93.9]) compared to STEMI criteria (accuracy 84.9% [95% CI: 83.5-86.3], sensitivity 34.4% [95% CI: 30.0-38.8], specificity 97.6% [95% CI: 96.8-98.2]) and similar performance compared to ECG experts (accuracy 91.2% [95% CI: 90.0-92.4], sensitivity 75.9% [95% CI: 71.9-80.0], specificity 95.0 [95% CI: 94.0-96.0]). The average time from presentation to a correct diagnosis of OMI was significantly shorter when relying on the AI model compared to STEMI criteria (2.0 vs. 4.9 hours, p<0.001).<jats:sec id="s1d">Conclusions The present novel ECG AI model demonstrates superior accuracy and earlier diagnosis of AI to detect acute OMI when compared to the STEMI criteria. Its external and international validation suggests its potential to improve ACS patient triage with timely referral for immediate revascularization.<jats:sec id="s1e">CLINICAL PERSPECTIVE <jats:sec id="s1f">What is new? <jats:list list-type="bullet">A novel artificial intelligence (AI) model detecting acute occluded coronary artery (OMI) using standard 12-lead electrocardiograms (ECGs) was developed from an international cohort.The OMI AI model is the first of its kind to be validated in an external international cohort of patients using an objective angiographically confirmed endpoint of OMI.Our study demonstrated the OMI AI models superior accuracy in identifying OMI and shorter time to correct diagnosis compared to standard of care STEMI criteria.<jats:sec id="s1g">What are the clinical implications? <jats:list list-type="bullet">The OMI AI model has the potential to improve ACS triage and clinical decision-making by enabling timely and accurate detection of OMI in NSTEMI patients.The robustness and versatility of the OMI AI model indicate its potential for real-world clinical implementation in ECG devices from multiple vendors.Prospective studies are essential to evaluate the efficacy of the OMI AI model and its impact on patient outcomes in real-world settings. | ||
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