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

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Author(s)
Seokhun YangBon-Kwon KooMasahiro HoshinoJoo Myung LeeTadashi MuraiJiesuck ParkJinlong ZhangDoyeon HwangEun-Seok ShinJoon-Hyung DohChang-Wook NamJianan WangShaoliang ChenNobuhiro TanakaHitoshi MatsuoTakashi AkasakaGilwoo ChoiKersten PetersenHyuk-Jae ChangTsunekazu KakutaJagat Narula
Keimyung Author(s)
Nam, Chang Wook
Department
Dept. of Internal Medicine (내과학)
Journal Title
JACC Cardiovascular Imaging
Issued Date
2021
Volume
14
Issue
3
Keyword
atherosclerosiscoronary artery diseasecoronary computed tomography angiographycoronary plaquefractional flow reserveischemia
Abstract
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 revascularization, 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 cluster 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, including 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.
Keimyung Author(s)(Kor)
남창욱
Publisher
School of Medicine (의과대학)
Citation
Seokhun Yang et al. (2021). CT Angiographic and Plaque Predictors of Functionally Significant Coronary Disease and Outcome Using Machine Learning. JACC Cardiovascular Imaging, 14(3), 629–641. doi: 10.1016/j.jcmg.2020.08.025
Type
Article
ISSN
1936-878X
Source
https://www.sciencedirect.com/science/article/pii/S1936878X20308111
DOI
10.1016/j.jcmg.2020.08.025
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43466
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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