Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke. (5th July 2022)

• Record Type: Journal Article
• Authors: Heo, JoonNyung; Yoo, Joonsang; Lee, Hyungwoo; Lee, Il Hyung; Kim, Jung-Sun; Park, Eunjeong; Kim, Young Dae; Nam, Hyo Suk
• Is Part Of: Neurology. Volume 99:Number 1(2022)
• Page Start: e55
• Page End: e65
• Publication Date: 2022-07-05
• Subjects: Neurology -- Periodicals; Neurology -- Periodicals; Neurologie -- Périodiques; 616.8
• DOI: 10.1212/WNL.0000000000200576
• Languages: English
• ISSNs: 0028-3878

Abstract

Abstract : 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).