Predicting stroke and myocardial infarction risk in Takayasu arteritis with automated machine learning models

Yi-Ting Lu; Zeng-Lei Zhang; Xing-Yu Zhou; Di Zhang; Tao Tian; Peng Fan; Ying Zhang; Xian-Liang Zhou

doi:10.1016/j.isci.2023.108421

Predicting stroke and myocardial infarction risk in Takayasu arteritis with automated machine learning models

iScience. 2023 Nov 9;26(12):108421. doi: 10.1016/j.isci.2023.108421. eCollection 2023 Dec 15.

Authors

Yi-Ting Lu¹, Zeng-Lei Zhang¹, Xing-Yu Zhou¹, Di Zhang¹, Tao Tian¹, Peng Fan¹, Ying Zhang¹, Xian-Liang Zhou¹

Affiliation

¹ Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.

Abstract

Few models exist for predicting severe ischemic complications (SIC) in patients with Takayasu arteritis (TA). We conducted a retrospective analysis of 703 patients with TA from January 2010 to December 2019 to establish an SIC prediction model for TA. SIC was defined as ischemic stroke and myocardial infarction. SIC was present in 97 of 703 (13.8%) patients with TA. Common iliac artery, coronary artery, internal carotid artery, subclavian artery, vertebral artery, renal artery involvement, chest pain, hyperlipidemia, absent pulse, higher BMI, vascular occlusion, asymmetric blood pressure in both upper limbs, visual disturbance, and older age were selected as predictive risk factors. Considering both discrimination and calibration performance, the Weighted Subspace Random Forest model was the most optimal model, boasting an area under the curve of 0.773 (95% confidence interval [0.652, 0.894]) in the validation cohort. Effective models for predicting SIC in TA may help clinicians identify high-risk patients and make targeted interventions.

Keywords: Health sciences; Health technology; cardiovascular medicine.