Artificial Intelligence–Enabled Quantitative Coronary Plaque and Hemodynamic Analysis for Predicting Acute Coronary Syndrome

急性冠脉综合征 心脏病学 内科学 血流动力学 医学 心肌梗塞
作者
Bon‐Kwon Koo,Seokhun Yang,Jae Wook Jung,Jinlong Zhang,Keehwan Lee,Doyeon Hwang,Kyu‐Sun Lee,Joon‐Hyung Doh,Chang‐Wook Nam,Tae Hyun Kim,Eun‐Seok Shin,Eun Ju Chun,Suyeon Choi,Hyun Kuk Kim,Young Joon Hong,Hun‐Jun Park,Song‐Yi Kim,Mirza Husic,Jess Lambrechtsen,Jesper Møller Jensen
出处
期刊:Jacc-cardiovascular Imaging [Elsevier BV]
卷期号:17 (9): 1062-1076 被引量:12
标识
DOI:10.1016/j.jcmg.2024.03.015
摘要

A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. This study sought to investigate the additive value of artificial intelligence–enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328)
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
思源应助赞zan采纳,获得10
1秒前
young完成签到,获得积分10
4秒前
7秒前
stuuuuuuuuuuudy完成签到 ,获得积分10
10秒前
黄饱饱发布了新的文献求助10
10秒前
vc完成签到,获得积分10
15秒前
黄饱饱完成签到,获得积分10
16秒前
18秒前
Zack发布了新的文献求助10
24秒前
liancheng完成签到,获得积分10
29秒前
zhaoxuelian完成签到,获得积分10
31秒前
洪亮完成签到,获得积分0
31秒前
32秒前
科研通AI5应助科研通管家采纳,获得10
32秒前
32秒前
皮肤科应助科研通管家采纳,获得10
32秒前
Rita应助科研通管家采纳,获得30
32秒前
可乐应助科研通管家采纳,获得10
32秒前
科研通AI5应助科研通管家采纳,获得10
32秒前
皮肤科应助科研通管家采纳,获得10
32秒前
科研通AI5应助石刘气泡shui采纳,获得10
34秒前
赘婿应助Galaxee采纳,获得10
38秒前
hyfan发布了新的文献求助10
38秒前
38秒前
40秒前
41秒前
端庄幻桃完成签到 ,获得积分10
41秒前
赞zan发布了新的文献求助10
43秒前
杨gj发布了新的文献求助10
44秒前
45秒前
Galaxee发布了新的文献求助10
49秒前
小丸子完成签到 ,获得积分10
50秒前
55秒前
赞zan完成签到,获得积分10
56秒前
Levent完成签到 ,获得积分10
58秒前
1分钟前
WHG发布了新的文献求助10
1分钟前
彭于晏应助阿索采纳,获得10
1分钟前
1分钟前
Petrichor完成签到 ,获得积分10
1分钟前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
ISCN 2024 – An International System for Human Cytogenomic Nomenclature (2024) 3000
Continuum Thermodynamics and Material Modelling 2000
Encyclopedia of Geology (2nd Edition) 2000
105th Edition CRC Handbook of Chemistry and Physics 1600
Maneuvering of a Damaged Navy Combatant 650
基于CZT探测器的128通道能量时间前端读出ASIC设计 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3777104
求助须知:如何正确求助?哪些是违规求助? 3322512
关于积分的说明 10210474
捐赠科研通 3037840
什么是DOI,文献DOI怎么找? 1666936
邀请新用户注册赠送积分活动 797849
科研通“疑难数据库(出版商)”最低求助积分说明 758044