International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary
Robert Herman1,2,3, Harvey Pendell Meyers4, Stephen W Smith5,6
1Department of Advanced Biomedical Sciences, University of Naples Federico II, C.so Umberto I, 40, 80138 Naples, Italy.
Insights
A new artificial intelligence (AI) model accurately detects occlusion myocardial infarction (OMI) using electrocardiograms (ECGs). This AI tool shows superior performance to STEMI criteria, aiding in timely OMI diagnosis and treatment.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Medical Diagnostics
Background:
- Most acute coronary syndromes (ACS) lack ST-elevation on ECGs.
- A significant subset of non-ST-elevation myocardial infarction (NSTEMI) patients have occlusion myocardial infarction (OMI), associated with adverse outcomes.
- Delayed identification of OMI leads to poorer patient prognosis.
Purpose of the Study:
- To develop a versatile artificial intelligence (AI) model for detecting acute OMI using standard 12-lead ECGs.
- To compare the AI model's diagnostic performance against established criteria and expert interpretation.
Main Methods:
- An AI model was trained on a large international dataset of 18,616 ECGs from 10,543 patients with suspected ACS.
- The model's performance was evaluated on an independent international cohort.
- The AI model was compared with ST-elevation myocardial infarction (STEMI) criteria and ECG expert diagnoses for OMI detection.
Main Results:
- The AI model achieved an AUC of 0.938 in identifying OMI.
- The AI model demonstrated superior accuracy (90.9%) compared to STEMI criteria (83.6%) in detecting OMI.
- AI model performance was comparable to ECG experts, with higher sensitivity than STEMI criteria.
Conclusions:
- The novel AI ECG model exhibits superior accuracy for acute OMI detection compared to STEMI criteria.
- This AI tool has the potential to enhance ACS triage and facilitate prompt revascularization.
- Timely identification and management of OMI can significantly improve patient outcomes.
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.
