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Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery Disease
Shinwan Kany1, Samuel F Friedman2, Mostafa Al-Alusi3
1Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA; Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany.
Insights
A new deep learning model, ECG2CAD, can detect coronary artery disease (CAD) using electrocardiograms (ECGs) and identify high-risk individuals. This AI tool shows promise for early detection and preventive care in primary settings.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Diagnostics
Background:
- Coronary artery disease (CAD) is a major cause of illness and death.
- Early detection of CAD is crucial for effective management and prevention of adverse outcomes.
Purpose of the Study:
- To develop a deep learning model (ECG2CAD) for detecting CAD using electrocardiograms (ECGs).
- To identify individuals at risk for adverse events in a primary care setting using ECG data.
Main Methods:
- Trained ECG2CAD on over 760,000 ECGs from Massachusetts General Hospital (MGH).
- Evaluated model performance using AUROC and AUPRC in MGH, Brigham and Women's Hospital (BWH), and UK Biobank datasets.
- Assessed risk for incident CAD-related events in a BWH primary care cohort.
Main Results:
- ECG2CAD demonstrated consistent performance in discriminating prevalent CAD across all test sets.
- The model showed incremental value over traditional risk models (age, sex, Pooled Cohort Equations).
- High ECG2CAD risk scores correlated with significantly increased risk of myocardial infarction, heart failure, and all-cause mortality.
Conclusions:
- AI-powered ECG analysis can aid in identifying individuals with potential undiagnosed CAD.
- This technology can inform further diagnostic testing and preventive strategies for CAD.
- ECG2CAD offers a promising tool for risk stratification in primary care settings.
Background:
Coronary artery disease (CAD) results in substantial morbidity and mortality.
Objectives:
The purpose of this study was to develop a deep learning model to detect CAD defined using diagnostic codes ("ECG2CAD") and identify people at risk for adverse events using electrocardiograms (ECGs) in a primary care setting.
Methods:
ECG2CAD was trained on 764,670 ECGs representing 137,199 individuals at Massachusetts General Hospital (MGH). Model performance for discrimination of prevalent CAD was measured using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), and compared against model of age and sex, and Pooled Cohort Equations, in 3 test sets: MGH, Brigham and Women's Hospital (BWH), and UK Biobank. Subgroups were assessed for incident CAD-related events in a BWH primary care cohort.
Results:
ECG2CAD was evaluated in MGH (N = 18,706 [6,051 cases], age 57 ± 16 years), BWH (N = 88,270 [27,898 cases], age 57 ± 16 years), and UK Biobank (N = 42,147 [1,509 cases], age 65 ± 8 years). ECG2CAD consistently discriminated prevalent CAD (MGH AUROC: 0.782; AUPRC: 0.639; BWH: AUROC: 0.747; AUPRC: 0.588; UK Biobank AUROC: 0.760; AUPRC: 0.155) and incrementally vs models based on age and sex or Pooled Cohort Equations (P < 0.01) in MGH and BWH. In the BWH primary care subset, model performance was consistent across subgroups. Being in the highest quintile of ECG2CAD risk was associated with higher risk for adverse events compared with low-risk group (myocardial infarction HR: 5.59; 95% CI: 4.76-6.56, heart failure 10.49; 95% CI: 7.96-13.84, all-cause mortality 2.68; 95% CI: 2.32-3.10).
Conclusions:
Artificial intelligence-enabled analysis of the ECG may facilitate identification of individuals with possible undiagnosed CAD and inform downstream testing and preventive measures.
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