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Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Authors
 Rebecca Jonas  ;  James Earls  ;  Hugo Marques  ;  Hyuk-Jae Chang  ;  Jung Hyun Choi  ;  Joon-Hyung Doh  ;  Ae-Young Her  ;  Bon Kwon Koo  ;  Chang-Wook Nam  ;  Hyung-Bok Park  ;  Sanghoon Shin  ;  Jason Cole  ;  Alessia Gimelli  ;  Muhammad Akram Khan  ;  Bin Lu  ;  Yang Gao  ;  Faisal Nabi  ;  Ryo Nakazato  ;  U Joseph Schoepf  ;  Roel S Driessen  ;  Michiel J Bom  ;  Randall C Thompson  ;  James J Jang  ;  Michael Ridner  ;  Chris Rowan  ;  Erick Avelar  ;  Philippe Généreux  ;  Paul Knaapen  ;  Guus A de Waard  ;  Gianluca Pontone  ;  Daniele Andreini  ;  Mouaz H Al-Mallah  ;  Robert Jennings  ;  Tami R Crabtree  ;  Todd C Villines  ;  James K Min  ;  Andrew D Choi 
Citation
 OPEN HEART, Vol.8(2) : e001832, 2021-11 
Journal Title
OPEN HEART
Issue Date
2021-11
MeSH
Aged ; Artificial Intelligence* ; Computed Tomography Angiography / methods* ; Coronary Angiography / methods* ; Coronary Stenosis / diagnosis* ; Coronary Stenosis / epidemiology ; Coronary Stenosis / etiology ; Coronary Vessels / diagnostic imaging* ; Female ; Follow-Up Studies ; Humans ; Incidence ; Male ; Middle Aged ; Plaque, Atherosclerotic / complications ; Plaque, Atherosclerotic / diagnosis* ; Plaque, Atherosclerotic / epidemiology ; Predictive Value of Tests ; Prospective Studies ; Severity of Illness Index ; United States / epidemiology
Keywords
atherosclerosis ; carotid artery diseases ; computed tomography angiography ; coronary angiography ; diagnostic imaging
Abstract
Objective: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT).

Methods: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years.

Results: The cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p<0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients.

Conclusion: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.
Files in This Item:
T202125693.pdf Download
DOI
10.1136/openhrt-2021-001832
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/188166
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