Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Vascular Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Division of Cardiology, The George Washington University School of Medicine, Washington, DC, USA. Electronic address: https://twitter.com/NickNurmohamed.
Department of Cardiology, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
JACC Cardiovasc Imaging. 2024 Mar;17(3):269-280. doi: 10.1016/j.jcmg.2023.05.020. Epub 2023 Jul 19.
BACKGROUND: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES: 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 coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS: 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. RESULTS: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (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). CONCLUSIONS: 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.
背景:人工智能引导的定量冠状动脉计算机断层扫描血管造影分析(AI-QCT)的最新发展使得对动脉粥样硬化斑块负担和特征的快速分析成为可能。
目的:本研究旨在探讨 AI-QCT 得出的动脉粥样硬化负担的 10 年预后价值,并比较斑块的分布与手动评估的冠状动脉计算机断层扫描血管造影(CCTA)、冠状动脉钙评分(CACS)和临床风险特征。
方法:这是一项对 536 名疑似冠心病患者的长期随访研究。使用 AI-QCT 分析 CCTA 扫描,并使用斑块分期系统(0 期:0%动脉粥样硬化体积[PAV];1 期:>0%-5% PAV;2 期:>5%-15% PAV;3 期:>15% PAV)对斑块负担进行分类。主要不良心脏事件(MACE)的主要终点是非致死性心肌梗死、非致死性卒中、冠状动脉血运重建和全因死亡率的复合终点。
结果:基线时的平均年龄为 58.6 岁,297 名患者(55%)为男性。在中位随访 10.3 年(IQR:8.6-11.5 年)期间,114 名患者(21%)发生了主要结局。与 0 期和 1 期相比,3 期 PAV 患者和非钙化斑块体积>7.5%的患者发生 MACE 的风险增加了 3 倍以上(调整后的 HR:3.57;95%CI:2.12-6.00;P<0.001)和 4 倍(调整后的 HR:4.37;95%CI:2.51-7.62;P<0.001)。在不同的随访时间点,AI-QCT 与临床危险因素和 CACS 的联合使用改善了不同模型的预后(10 年 AUC:0.82[95%CI:0.78-0.87] vs 0.73[95%CI:0.68-0.79];P<0.001;净重新分类改善:0.21[95%CI:0.09-0.38])。此外,与冠状动脉疾病报告和数据系统 2.0(10 年 AUC:0.78;95%CI:0.73-0.83;P=0.023)和手动 QCT(10 年 AUC:0.78;95%CI:0.73-0.83;P=0.040)相比,AI-QCT 获得了更高的曲线下面积,尽管净重新分类改善幅度较小(0.09[95%CI:-0.02 至 0.29]和 0.04[95%CI:-0.05 至 0.27])。
结论:通过 10 年随访,AI-QCT 斑块分期对 MACE 具有重要的预后价值,并显示出比临床危险因素、CACS 和指南推荐的手动 CCTA 评估更具判别能力。
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