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Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke

 JoonNyung Heo  ;  Joonsang Yoo  ;  Hyungwoo Lee  ;  Il Hyung Lee  ;  Jung-Sun Kim  ;  Eunjeong Park  ;  Young Dae Kim  ;  Hyo Suk Nam 
 NEUROLOGY, Vol.99(1) : e55-e65, 2022-07 
Journal Title
Issue Date
Coronary Angiography / methods ; Coronary Artery Disease* / diagnosis ; Coronary Artery Disease* / diagnostic imaging ; Humans ; Ischemic Stroke* / diagnostic imaging ; Machine Learning ; Multidetector Computed Tomography ; Predictive Value of Tests ; Prognosis ; Risk Assessment ; Risk Factors
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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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Young Dae(김영대) ORCID logo https://orcid.org/0000-0001-5750-2616
Kim, Jung Sun(김중선) ORCID logo https://orcid.org/0000-0003-2263-3274
Nam, Hyo Suk(남효석) ORCID logo https://orcid.org/0000-0002-4415-3995
Yoo, Joon Sang(유준상) ORCID logo https://orcid.org/0000-0003-1169-6798
Lee, Ilhyung(이일형)
Lee, Hyung Woo(이형우)
Heo, JoonNyung(허준녕)
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