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Controlled Clinical Trial
. 2021 Nov;8(2):e001832.
doi: 10.1136/openhrt-2021-001832.

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Rebecca Jonas  1 , James Earls  2 , Hugo Marques  3 , Hyuk-Jae Chang  4 , Jung Hyun Choi  5 , Joon-Hyung Doh  6 , Ae-Young Her  7 , Bon Kwon Koo  8 , Chang-Wook Nam  9 , Hyung-Bok Park  10 , Sanghoon Shin  11 , Jason Cole  12 , Alessia Gimelli  13 , Muhammad Akram Khan  14 , Bin Lu  15 , Yang Gao  16 , Faisal Nabi  17 , Ryo Nakazato  18 , U Joseph Schoepf  19 , Roel S Driessen  20 , Michiel J Bom  21 , Randall C Thompson  22 , James J Jang  23 , Michael Ridner  24 , Chris Rowan  25 , Erick Avelar  26 , Philippe Généreux  27 , Paul Knaapen  28 , Guus A de Waard  28 , Gianluca Pontone  29 , Daniele Andreini  29 , Mouaz H Al-Mallah  30 , Robert Jennings  2 , Tami R Crabtree  2 , Todd C Villines  31 , James K Min  2 , Andrew D Choi  32
Affiliations
Controlled Clinical Trial

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Rebecca Jonas et al. Open Heart. 2021 Nov.

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.

Keywords: atherosclerosis; carotid artery diseases; computed tomography angiography; coronary angiography; diagnostic imaging.

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Conflict of interest statement

Competing interests: Equity Interest Cleerly, JE, HM, ADC, Employees of Cleerly – RJe, TRC, JKM, JE.

Figures

Figure 1
Figure 1
A 39-year-old with coronary CT angiography (CCTA) undergoing artificial intelligence (AI)-aided evaluation of stenosis and quantitative atherosclerosis burden. The patient demonstrates left anterior descending coronary artery obstructive stenosis (82%) with a burden of plaque (352.5 mm3) consisting predominantly of non-calcified (321.8 mm3) that includes low-density non-calcified plaque (LD-NCP 30.5 mm3). (A) shows a CCTA straight reformat with plaque identified, while (B) shows a straight reformat with a colour overlay of non-calcified plaque (yellow), LD-NCP (red) and calcified plaque (blue). (C) shows a curved multiplanar reformat. (D) shows a graphical output of the quantified plaque volume by AI-aided evaluation. dLAD, distal left anterior descending; LM, left main; mLAD, mid left anterior descending; pLAD, proximal left anterior descending.
Figure 2
Figure 2
A 55-year-old with coronary CT angiography (CCTA) undergoing artificial intelligence (AI)-aided evaluation of stenosis and quantitative atherosclerosis burden. The patient demonstrates left anterior descending coronary artery non-obstructive stenosis (25%) with a burden of plaque (160.2 mm3) consisting predominantly of non-calcified (159.4 mm3) that includes non-negligible low-density non-calcified plaque (8 mm3). (A) shows a CCTA straight reformat with plaque identified, while (B) shows a straight reformat with a colour overlay of non-calcified plaque (yellow). (C) shows a curved multiplanar reformat. (D) shows a graphical output of the quantified plaque volume by AI-aided evaluation. dLAD, distal left anterior descending; LM, left main; mLAD, mid left anterior descending; pLAD, proximal left anterior descending.
Figure 3
Figure 3
A 74-year-old with coronary CT angiography (CCTA) undergoing artificial intelligence (AI)-aided evaluation of stenosis and quantitative atherosclerosis burden. The patient demonstrates right coronary artery obstructive stenosis (61%) with a high burden of plaque (796.8 mm3) consisting predominantly of calcified plaque (550.8 mm3). (A) shows a CCTA straight reformat with plaque identified, while (B) shows a straight reformat with a colour overlay of non-calcified plaque (yellow), and calcified plaque (blue). (C) shows a curved multiplanar reformat. (D) shows a graphical output of the quantified plaque volume by AI-aided evaluation. dLAD, distal left anterior descending; LM, left main; mLAD, mid left anterior descending; pLAD, proximal left anterior descending.
See this image and copyright information in PMC

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