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

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dc.contributor.authorDahdal, Jorge
dc.contributor.authorJukema, Ruurt
dc.contributor.authorMaaniitty, Teemu
dc.contributor.authorNurmohamed, Nick
dc.contributor.authorRaijmakers, Pieter
dc.contributor.authorHoek, Roel
dc.contributor.authorDriessen, Roel
dc.contributor.authorTwisk, Jos
dc.contributor.authorBär, Sarah
dc.contributor.authorPlanken, Nils
dc.contributor.authorVan Royen, Niels
dc.contributor.authorNijveldt, Robin
dc.contributor.authorBax, Jeroen
dc.contributor.authorSaraste, Antti
dc.contributor.authorVan Rosendael, Alexander
dc.contributor.authorKnaapen, Paul
dc.contributor.authorKnuuti, Juhani
dc.contributor.authorDanad, Ibrahim
dc.date.accessioned2026-09-24T15:17:12Z
dc.date.available2026-09-24T15:17:12Z
dc.date.issued2025
dc.description.abstractAims: 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.
dc.description.versionVersión Publicada
dc.identifier.citationDahdal, J., Jukema, R. A., Maaniitty, T., Nurmohamed, N. S., Raijmakers, P. G., Hoek, R., Driessen, R. S., Twisk, J. W. R., Bär, S., Planken, R. N., Van Royen, N., Nijveldt, R., Bax, J. J., Saraste, A., Van Rosendael, A. R., Knaapen, P., Knuuti, J., & Danad, I. (2025). CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients. European heart journal. Cardiovascular Imaging, 26(6), 945–954. https://doi.org/10.1093/ehjci/jeaf093
dc.identifier.doihttps://doi.org/10.1093/ehjci/jeaf093
dc.identifier.urihttps://hdl.handle.net/11447/11178
dc.language.isoen
dc.subjectArtificial intelligence
dc.subjectCoronary artery calcium score
dc.subjectCoronary artery disease
dc.subjectCoronary computed tomography angiography
dc.subjectPrognosis
dc.titleCCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients
dc.typeArticle
dcterms.accessRightsAcceso Abierto
dcterms.sourceEuropean heart journal. Cardiovascular Imaging
dspace.entity.typePublication

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