文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

人工智能指导的定量斑块分期预测动脉粥样硬化性心血管疾病风险患者的长期心血管结局。

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

机构信息

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.

出版信息

JACC Cardiovasc Imaging. 2024 Mar;17(3):269-280. doi: 10.1016/j.jcmg.2023.05.020. Epub 2023 Jul 19.


DOI:10.1016/j.jcmg.2023.05.020
PMID:37480907
Abstract

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.

摘要

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。

相似文献

[1]
AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD.

JACC Cardiovasc Imaging. 2024-3

[2]
AI-Quantitative CT Coronary Plaque Features Associate With a Higher Relative Risk in Women: CONFIRM2 Registry.

Circ Cardiovasc Imaging. 2025-6

[3]
Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography.

Eur Heart J Cardiovasc Imaging. 2025-6-30

[4]
Interaction of AI-Enabled Quantitative Coronary Plaque Volumes on Coronary CT Angiography, FFR, and Clinical Outcomes: A Retrospective Analysis of the ADVANCE Registry.

Circ Cardiovasc Imaging. 2024-3

[5]
Polygenic Risk Is Associated With Long-Term Coronary Plaque Progression and High-Risk Plaque.

JACC Cardiovasc Imaging. 2024-12

[6]
Incremental predictive value of liver fat fraction based on spectral detector CT for major adverse cardiovascular events in T2DM patients with suspected coronary artery disease.

Cardiovasc Diabetol. 2025-4-2

[7]
First comparison between artificial intelligence-guided coronary computed tomography angiography versus single-photon emission computed tomography testing for ischemia in clinical practice.

Coron Artery Dis. 2025-8-1

[8]
AI-Guided Cardiac Computer Tomography in Type 1 Diabetes Patients with Low Coronary Artery Calcium Score.

Diabetes Technol Ther. 2025-8-6

[9]
Effects of Combining Coronary Calcium Score With Treatment on Plaque Progression in Familial Coronary Artery Disease: A Randomized Clinical Trial.

JAMA. 2025-4-22

[10]
Effects of Pitavastatin on Coronary Artery Disease and Inflammatory Biomarkers in HIV: Mechanistic Substudy of the REPRIEVE Randomized Clinical Trial.

JAMA Cardiol. 2024-4-1

引用本文的文献

[1]
Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.

BJR Open. 2025-6-6

[2]
Coronary CT angiography evaluation with artificial intelligence for individualized medical treatment of atherosclerosis: a Consensus Statement from the QCI Study Group.

Nat Rev Cardiol. 2025-8-1

[3]
Identifying High-Risk Obese Individuals Without Diabetes for GLP-1RA Therapy Using Coronary CTA.

JACC Adv. 2025-7-18

[4]
Artificial intelligence in coronary CT angiography: transforming the diagnosis and risk stratification of atherosclerosis.

Int J Cardiovasc Imaging. 2025-6-27

[5]
Coronary Plaque, Inflammation, Subclinical Myocardial Injury, and Major Adverse Cardiovascular Events in the REPRIEVE Substudy.

JACC Adv. 2025-5-14

[6]
The Role of Computed Tomography and Artificial Intelligence in Evaluating the Comorbidities of Chronic Obstructive Pulmonary Disease: A One-Stop CT Scanning for Lung Cancer Screening.

Int J Chron Obstruct Pulmon Dis. 2025-5-6

[7]
A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images.

BMC Med Imaging. 2025-5-1

[8]
Derivation and validation of an artificial intelligence-based plaque burden safety cut-off for long-term acute coronary syndrome from coronary computed tomography angiography.

Eur Heart J Cardiovasc Imaging. 2025-6-30

[9]
Detection of arterial remodeling using epicardial adipose tissue assessment from CT calcium scoring scan.

Front Cardiovasc Med. 2025-3-14

[10]
Impact of technical, patient-related and measurement variables on serial Hounsfield unit-based quantitative coronary plaque analysis in computed tomography: time for a new chapter.

Eur Heart J Imaging Methods Pract. 2025-1-29

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索