AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD

医学 狼牙棒 心肌梗塞 内科学 动脉粥样硬化 冠状动脉疾病 心脏病学 阶段(地层学) 易损斑块 血运重建 冲程(发动机) 放射科 经皮冠状动脉介入治疗 机械工程 古生物学 工程类 生物
作者
Nick S. Nurmohamed,Michiel J. Bom,Ruurt Jukema,Robin J. de Groot,Roel S. Driessen,Pepijn A. van Diemen,Ruben W. de Winter,Émilie Gaillard,Ralf W. Sprengers,Erik S.G. Stroes,James K. Min,James P. Earls,Rhanderson Cardoso,Ron Blankstein,Ibrahim Danad,Andrew Choi,Paul Knaapen
出处
期刊:Jacc-cardiovascular Imaging [Elsevier BV]
卷期号:17 (3): 269-280 被引量:45
标识
DOI:10.1016/j.jcmg.2023.05.020
摘要

The recent development of artificial intelligence–guided quantitative coronary computed tomography angiography (CCTA) analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed CCTA, coronary artery calcium scoring (CACS), and clinical risk characteristics. This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 (IQR: 8.6-11.5) years, 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: −0.02 to 0.29] and 0.04 [95% CI: −0.05 to 0.27], respectively). Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.
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