Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May 30;26(6):945-954.
doi: 10.1093/ehjci/jeaf093.

CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients

Jorge Dahdal  1   2 , Ruurt A Jukema  1   3 , Teemu Maaniitty  4   5 , Nick S Nurmohamed  1   6   7 , Pieter G Raijmakers  8 , Roel Hoek  1 , Roel S Driessen  1 , Jos W R Twisk  9 , Sarah Bär  4   10 , R Nils Planken  8 , Niels van Royen  11 , Robin Nijveldt  11 , Jeroen J Bax  12 , Antti Saraste  4   13 , Alexander R van Rosendael  12 , Paul Knaapen  1 , Juhani Knuuti  4   5 , Ibrahim Danad  1   11
Affiliations

CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients

Jorge Dahdal et al. Eur Heart J Cardiovasc Imaging. .

Abstract

Aims: To assess the prognostic utility of coronary artery calcium (CAC) scoring and coronary computed tomography angiography (CCTA)-derived quantitative plaque metrics for predicting adverse cardiovascular outcomes.

Methods and results: The study enrolled 2404 patients with suspected coronary artery disease (CAD) but without a prior history of CAD. All participants underwent CAC scoring and CCTA, with plaque metrics quantified using an artificial intelligence (AI)-based tool (Cleerly, Inc). Percent atheroma volume (PAV) and non-calcified plaque volume percentage (NCPV%), reflecting total plaque burden and the proportion of non-calcified plaque volume normalized to vessel volume, were evaluated. The primary endpoint was a composite of all-cause mortality and non-fatal myocardial infarction (MI). Cox proportional hazard models, adjusted for clinical risk factors and early revascularization, were employed for analysis. During a median follow-up of 7.0 years, 208 patients (8.7%) experienced the primary endpoint, including 73 cases of MI (3%). The model incorporating PAV demonstrated superior discriminatory power for the composite endpoint (AUC = 0.729) compared to CAC scoring (AUC = 0.706, P = 0.016). In MI prediction, PAV (AUC = 0.791) significantly outperformed CAC (AUC = 0.699, P < 0.001), with NCPV% showing the highest prognostic accuracy (AUC = 0.814, P < 0.001).

Conclusion: AI-driven assessment of coronary plaque burden enhances prognostic accuracy for future adverse cardiovascular events, highlighting the critical role of comprehensive plaque characterization in refining risk stratification strategies.

Keywords: artificial intelligence; coronary artery calcium score; coronary artery disease; coronary computed tomography angiography; prognosis.

PubMed Disclaimer

Conflict of interest statement

Conflict of interest: N.S.N. reports grants from the Dutch Heart and the European Atherosclerosis Society, along with research funding and speaker fees from Cleerly, Daiichi Sankyo, and Novartis; he is also the co-founder of Lipid Tools. S.B. was supported by the Swiss National Science Foundation and the University of Turku, Finland, and received research grants to her institution from Abbott, Medis, and the Bangerter-Rhyner Stiftung. R.N. received a research grant from Philips Volcano and Biotronik, as well as speaker fees from BMS, Sanofi Genzyme, and Pfizer. J. J. B. received speaker fees from Abbott. A. S. received consultancy and lecture fees from Abbott, AstraZeneca, BMS, Janssen, Novo Nordisk, and Pfizer. A. R. R. serves on the scientific advisory board of Cleerly. P. K. received research grants from HeartFlow and Cleerly Inc. J. K. received consultancy fees from GE Healthcare and AstraZeneca, as well as speaker fees from GE Healthcare, Bayer, Lundbeck, Boehringer Ingelheim, Pfizer, and Merck, outside of the submitted work. I. D. received a research grant from Cleerly Inc. All other authors have disclosed no relevant relationships pertaining to the contents of this paper.

Comment in

MeSH terms