Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke
JoonNyung Heo1, Joonsang Yoo1, Hyungwoo Lee1
1From the Department of Neurology (J.H., H.L., I.H.L., Y.D.K., H.S.N.) and Department of Internal Medicine (J.-S.K.), Division of Cardiology, Yonsei University College of Medicine, Seoul; Department of Neurology (J.Y.), Yonsei University College of Medicine, Yongin Severance Hospital; and Integrative Research Center for Cerebrovascular and Cardiovascular Diseases (E.P.), Seoul, Korea.
Insights
Machine learning models can identify hidden coronary artery disease (CAD) in acute ischemic stroke patients. These predictions correlate with major adverse cardiovascular events, aiding long-term outcome assessment.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Coronary artery disease (CAD) is a significant concern in patients with acute ischemic stroke.
- Identifying hidden CAD in these patients is crucial for risk stratification and management.
- Machine learning (ML) offers a potential tool for detecting undiagnosed CAD.
Purpose of the Study:
- To develop and validate ML models for predicting hidden CAD in patients with acute ischemic stroke.
- To assess the association between ML-based CAD prediction and long-term cardiovascular outcomes.
Main Methods:
- Multidetector coronary CT angiography was used to identify CAD.
- ML models were trained and validated using demographic, clinical, and laboratory data.
- Primary outcomes included any CAD and obstructive CAD (≥50% stenosis).
- Long-term outcomes were evaluated by major adverse cardiovascular events (MACEs).
Main Results:
- An extreme gradient boosting model predicted any CAD with an AUC of 0.763.
- A logistic regression model predicted obstructive CAD with an AUC of 0.714.
- MACEs occurred more frequently in patients predicted to have CAD, with increased hazard ratios for any CAD (1.5) and obstructive CAD (1.9).
Conclusions:
- ML models can effectively identify hidden CAD in acute ischemic stroke patients.
- CAD predictions derived from ML models are associated with long-term cardiovascular event risk.
Background And Objectives:
A machine learning technique for identifying hidden coronary artery disease (CAD) might be useful. We developed and validated machine learning models to predict patients with hidden CAD and to assess long-term outcomes in patients with acute ischemic stroke.
Methods:
Multidetector coronary CT was performed for patients without a known history of CAD. Primary outcomes were defined as having any degree of CAD and having obstructive CAD (≥50% stenosis). Demographic variables, risk factors, laboratory results, Trial of ORG 10172 in Acute Stroke Treatment classification, NIH Stroke Scale score, blood pressure, and carotid artery stenosis were used to develop and validate machine learning models to predict CAD. Area under the receiver operating characteristic curves (AUC) was calculated for performance analysis, and Kaplan-Meier and Cox survival analyses of long-term outcomes were performed. Major adverse cardiovascular events (MACEs) were defined as ischemic stroke, myocardial infarction, unstable angina, urgent coronary revascularization, and cardiovascular mortality.
Results:
Overall, 1,710 patients were included for the training dataset and 348 patients for the validation dataset. An extreme gradient boosting model was developed to predict any degree of CAD, which showed an AUC of 0.763 (95% CI 0.711-0.814) on validation. A logistic regression model was used to predict obstructive CAD and had an AUC of 0.714 (95% CI 0.692-0.799). During the first 5 years of follow-up, MACEs occurred more frequently with predictions of any CAD (p = 0.022) or obstructive CAD (p < 0.001). Cox proportional analysis showed that the hazard ratio of MACE was 1.5 (95% CI 1.1-2.2; p = 0.016) with prediction of any CAD, whereas it was 1.9 (95% CI 1.3-2.6; p < 0.001) for obstructive CAD.
Discussion:
We demonstrated that machine learning may help identify hidden CAD in patients with acute ischemic stroke. Long-term outcomes were also associated with prediction results.
Classification Of Evidence:
This study provides Class II evidence that in patients with acute ischemic stroke with CAD risk factors but no known history of CAD, a machine learning model predicts CAD on multidetector coronary CT with an AUC of 0.763 (95% CI 0.711-0.814).
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