Machine Learning Model for Atherosclerosis Evaluation and Cardiovascular Risk Prediction Based on Coronary CT Angiography-Analysis From the CREATION Registry

作者：Ying Song, Na Xu, Jianan Zheng, Sida Jia, Cheng Cui, Yin Zhang, Lijian Gao, Zhan Gao, Jue Chen, Lei Song, Jinqing Yuan, Bin Lü, Zhi-hui Hou · 发表于：Circulation Cardiovascular Imaging · 年份：2025 · DOI：10.1161/circimaging.125.018443 · 被引用次数：5 · 研究领域：Cardiac Imaging and Diagnostics、Cardiovascular Disease and Adiposity、Cerebrovascular and Carotid Artery Diseases

BACKGROUND: Current atherosclerotic cardiovascular disease risk prediction tools based on traditional risk factors and the coronary artery calcium score have limitations. METHODS: The CREATION study includes patients with suspected coronary artery disease who underwent coronary computed tomography angiography (CCTA) at Fuwai Hospital between 2016 and 2019. The primary outcome was major adverse cardiac events defined as a composite end point of all-cause death, acute myocardial infarction, coronary revascularization, or stroke. Six machine learning survival models were used to create an atherosclerotic cardiovascular disease prediction model. RESULTS: Overall, 8431 participants with analyzable CCTA data were included with a median follow-up of 3.68 years, and 319 major adverse cardiac events (3.8%) occurred (mean age: 54.73±10.21 years, 48.2% were male, 50.9% with symptomatic chest pain). Among 6 machine learning models trained with 48 CCTA parameters, XGBoost showed the best performance and was selected for model development. In the training cohort (n=5901, 70%), the XGBoost model significantly outperformed the clinical risk factors and coronary artery calcium score model (area under the curve, 0.903 versus 0.830; P <0.001). Testing cohort showed similar performance (area under the curve, 0.899 versus 0.753; P <0.001). The CCTA model demonstrates consistent predictive performance across sex (female or male), onset-age (early onset or late-onset), and symptom (asymptomat...