Rebecca Jonas, Thomas Jefferson UniversityFollow
James Earls, Cleerly Health
Hugo Marques, CHRC Campus Nova Medical School
Hyuk-Jae Chang, Yonsei University Health System
Jung Hyun Choi, Ontact Health, Inc.
Joon-Hyung Doh, Inje University Ilsan Paik Hospital
Ae-Young Her, Kangwon National University Hospital
Bon Kwon Koo, Seoul National University Hospital
Chang-Wook Nam, Keimyung University Dongsan Hospital
Hyung-Bok Park, Catholic Kwandong University International Saint Mary's Hospital
Sanghoon Shin, Ewha Women's University Mokdong Hospital
Jason Cole, Mobile Cardiology Associates
Alessia Gimelli, Fondazione Toscana Gabriele Monasterio
Muhammad Akram Khan, Cardiac Center of Texas
Bin Lu, Fuwai Hospital State Key Laboratory of Cardiovascular Disease
Yang Gao, Fuwai Hospital State Key Laboratory of Cardiovascular Disease
Faisal Nabi, Houston Methodist Hospital
Ryo Nakazato, Saint Luke's International Hospital
U Joseph Schoepf, Medical University of South Carolina
Roel S Driessen, VU University Medical Centre Amsterdam
Michiel J Bom, Vrije Universiteit Amsterdam
Randall C Thompson, Saint Luke's Mid America Heart Institute
James J Jang, Kaiser Permanente
Michael Ridner, Heart Center Research
Chris Rowan, Renown Health
Erick Avelar, Saint Marys Medical Group
Philippe Généreux, Hopital du Sacre-Coeur de Montreal
Paul Knaapen, VU University Medical Centre Amsterdam
Guus A de Waard, VU University Medical Centre Amsterdam
Gianluca Pontone, Centro Cardiologico Monzino Istituto di Ricovero e Cura a Carattere Scientifico
Daniele Andreini, Centro Cardiologico Monzino Istituto di Ricovero e Cura a Carattere Scientifico
Mouaz H Al-Mallah, Houston Methodist Hospital
Robert Jennings, Cleerly Health
Tami R Crabtree, Cleerly Health
Todd C Villines, University of Virginia Health System
James K Min, Cleerly Health
Andrew D Choi, The George Washington University School of Medicine and Health Sciences

Document Type


Publication Date



This article is the author’s final published version in Open Heart, Volume 8, Issue 216, November 2021, Article number e001832.

The published version is available at Copyright © Jonas et al.


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.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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