CT Angiographic and Plaque Predictors of Functionally Significant Coronary Disease and Outcome Using Machine Learning

被引：59

作者：

Yang, Seokhun ^{[1
,2
]}

Koo, Bon-Kwon ^{[1
,2
,3
]}

Hoshino, Masahiro ^{[4
]}

Lee, Joo Myung ^{[5
]}

Murai, Tadashi ^{[4
]}

Park, Jiesuck ^{[1
,2
]}

Zhang, Jinlong ^{[1
,2
]}

Hwang, Doyeon ^{[1
,2
]}

Shin, Eun-Seok ^{[6
]}

Doh, Joon-Hyung ^{[7
]}

Nam, Chang-Wook ^{[8
]}

Wang, Jianan ^{[9
]}

Chen, Shaoliang ^{[10
]}

Tanaka, Nobuhiro ^{[11
]}

Matsuo, Hitoshi ^{[12
]}

Akasaka, Takashi ^{[13
]}

Choi, Gilwoo ^{[14
]}

Petersen, Kersten ^{[14
]}

Chang, Hyuk-Jae ^{[15
]}

Kakuta, Tsunekazu ^{[4
]}

Narula, Jagat ^{[16
]}

机构：

[1] Seoul Natl Univ Hosp, Dept Internal Med, 101 Daehang Ro, Seoul 110744, South Korea

[2] Seoul Natl Univ Hosp, Cardiovasc Ctr, 101 Daehang Ro, Seoul 110744, South Korea

[3] Seoul Natl Univ, Inst Aging, Seoul, South Korea

[4] Tsuchiura Kyodo Gen Hosp, Div Cardiovasc Med, Ibaraki, Japan

[5] Sungkyunkwan Univ, Samsung Med Ctr, Heart Vasc Stroke Inst, Div Cardiol,Dept Internal Med,Sch Med, Seoul, South Korea

[6] Ulsan Hosp, Ulsan Med Ctr, Dept Cardiol, Ulsan, South Korea

[7] Inje Univ, Dept Med, Ilsan Paik Hosp, Goyang, South Korea

[8] Keimyung Univ, Dept Med, Dongsan Med Ctr, Daegu, South Korea

[9] Zhejiang Univ, Affiliated Hosp 2, Sch Med, Dept Cardiol, Hangzhou, Peoples R China

[10] Nanjing Med Univ, Nanjing Hosp 1, Dept Cardiol, Nanjing, Peoples R China

[11] Tokyo Med Univ, Dept Cardiol, Tokyo, Japan

[12] Gifu Heart Ctr, Dept Cardiol, Gifu, Japan

[13] Wakayama Med Univ, Wakayama, Japan

[14] HeartFlow Inc, Redwood City, CA USA

[15] Yonsei Univ, Yonsei Cedars Sinai Integrat Cardiovasc Imaging R, Severance Cardiovasc Hosp, Div Cardiol,Coll Med, Seoul, South Korea

[16] Icahn Sch Med Mt Sinai, New York, NY 10029 USA

来源：

JACC-CARDIOVASCULAR IMAGING | 2021年 / 14卷 / 03期

关键词：

atherosclerosis; coronary artery disease; coronary computed tomography angiography; coronary plaque; fractional flow reserve; ischemia; FRACTIONAL FLOW RESERVE; ARTERY-DISEASE; SEVERITY; LESIONS;

D O I：

10.1016/j.jcmg.2020.08.025

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

OBJECTIVES The goal of this study was to investigate the association of stenosis and plaque features with myocardial ischemia and their prognostic implications. BACKGROUND Various anatomic, functional, and morphological attributes of coronary artery disease (CAD) have been independently explored to define ischemia and prognosis. METHODS A total of 1,013 vessels with fractional flow reserve (FFR) measurement and available coronary computed tomography angiography were analyzed. Stenosis and plaque features of the target lesion and vessel were evaluated by an independent core laboratory. Relevant features associated with low FFR (<= 0.80) were identified by using machine learning, and their predictability of 5-year risk of vessel-oriented composite outcome, including cardiac death, target vessel myocardial infarction, or target vessel revascutarization, were evaluated. RESULTS The mean percent diameter stenosis and invasive FFR were 48.5 +/- 17.4% and 0.81 +/- 0.14, respectively. Machine learning interrogation identified 6 clusters for low FFR, and the most relevant feature from each duster was minimum lumen area, percent atheroma volume, fibrofatty and necrotic core volume, plaque volume, proximal left anterior descending coronary artery lesion, and remodeling index (in order of importance). These 6 features showed predictability for low FFR (area under the receiver-operating characteristic curve: 0.797). The risk of 5-year vessel-oriented composite outcome increased with every increment of the number of 6 relevant features, and it had incremental prognostic value over percent diameter stenosis and FFR (area under the receiver-operating characteristic curve: 0.706 vs. 0.611; p = 0.031). CONCLUSIONS Six functionally relevant features, induding minimum lumen area, percent atheroma volume, fibrofatty and necrotic core volume, plaque volume, proximal left anterior descending coronary artery lesion, and remodeling index, help define the presence of myocardial ischemia and provide better prognostication in patients with CAD. (CCTA-FFR Registry for Risk Prediction; (C) 2021 by the American College of Cardiology Foundation.

引用

页码：629 / 641

页数：13

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