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AI-driven quantitative coronary CT angiography in suspected coronary artery disease

2026-03Artigo de investigação Acesso aberto
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dc.contributor.authorRosendael, Alexander van
dc.contributor.authorNakanishi, Rine
dc.contributor.authorBax, Jeroen J.
dc.contributor.authorPontone, Gianluca
dc.contributor.authorMushtaq, Saima
dc.contributor.authorBuechel, Ronny R.
dc.contributor.authorGräni, Christoph
dc.contributor.authorFeuchtner, Gudrun
dc.contributor.authorLacaita, Pietro G.
dc.contributor.authorPatel, Amit R.
dc.contributor.authorSingulane, Cristiane C.
dc.contributor.authorChoi, Andrew D.
dc.contributor.authorAl-Mallah, Mouaz
dc.contributor.authorAndreini, Daniele
dc.contributor.authorKarlsberg, Ronald P.
dc.contributor.authorCho, Geoffrey W.
dc.contributor.authorRochitte, Carlos E.
dc.contributor.authorAlasnag, Mirvat
dc.contributor.authorHamdan, Ashraf
dc.contributor.authorCademartiri, Filippo
dc.contributor.authorMaffei, Erica
dc.contributor.authorMarques, Hugo
dc.contributor.authorGonçalves, Pedro de Araújo
dc.contributor.authorGupta, Himanshu
dc.contributor.authorHadamitzky, Martin
dc.contributor.authorKhalique, Omar
dc.contributor.authorKalra, Dinesh
dc.contributor.authorMills, James D.
dc.contributor.authorNurmohamed, Nick S.
dc.contributor.authorKnaapen, Paul
dc.contributor.authorBudoff, Matthew
dc.contributor.authorShaikh, Kashif
dc.contributor.authorMartin, Enrico
dc.contributor.authorGerman, David M.
dc.contributor.authorFerencik, Maros
dc.contributor.authorOehler, Andrew C.
dc.contributor.authorDeaño, Roderick
dc.contributor.authorNagpal, Prashant
dc.contributor.authorAssen, Marly van
dc.contributor.authorCecco, Carlo N. De
dc.contributor.authorKamperidis, Vasileios
dc.contributor.authorFoldyna, Borek
dc.contributor.authorBrendel, Jan M.
dc.contributor.authorCheng, Victor Y.
dc.contributor.authorBranch, Kelley R.
dc.contributor.authorBittencourt, Marcio
dc.contributor.authorBhatti, Sabha
dc.contributor.authorPolsani, Venkateshwar
dc.contributor.authorWesbey, George
dc.contributor.authorCardoso, Rhanderson
dc.contributor.authorBlankstein, Ron
dc.contributor.authorDelago, Augustin
dc.contributor.authorPursnani, Amit
dc.contributor.authorAlsaid, Amro
dc.contributor.authorBloom, Stephen
dc.contributor.authorAquino, Melissa
dc.contributor.authorDanad, Ibrahim
dc.date.accessioned2026-04-28T10:35:22Z
dc.date.available2026-04-28T10:35:22Z
dc.date.issued2026-03
dc.description.abstractBackground: Plaque assessment by quantitative coronary CT angiography has demonstrated to correlate highly with intravascular ultrasound and optical coherence tomography, and these modalities have shown strong prognostic value.ObjectivesThe purpose of this study was to identify the prognostic value of artificial intelligence–guided quantitative CCTA (AI-QCT) for major adverse cardiovascular events (MACE) against the risk factor–weighted clinical likelihood model. Methods: The CONFIRM2 (COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is a multicenter, international, observational cohort study that included patients with clinically indicated CCTA and follow-up for MACE. Patients without cardiac symptoms and prior coronary artery disease (CAD) were excluded. Across the entire coronary artery tree, the presence, extent, and composition of CAD were analyzed by an AI-QCT software, and 24 variables at a patient, vessel, and plaque level were derived, including percent luminal narrowing, remodeling index, plaque volumes (total, calcified, noncalcified, low attenuation), and plaque composition. The primary MACE endpoint was defined as a composite of all-cause death, myocardial infarction (MI), stroke, congestive heart failure, late revascularizations, and hospitalization for unstable angina. The secondary MACE endpoint was defined as all-cause death and MI. Results: A total of 3,551 patients (age 58.8 ± 12.5 years, 50.5% male) were followed for a median of 4.27 (IQR: 3.47-5.08) years during which 167 (4.7%) events occurred. After excluding collinear variables, diameter stenosis (HR: 1.25 [95% CI: 1.18-1.32]) per 10% increase and noncalcified plaque volume (HR: 1.07 [95% CI: 1.03-1.11]) per 50 mm3 increase were the only independent predictors for MACE. In multivariable modeling, the discriminatory value defined by area under the curve (AUC) improved from 0.63 (95% CI: 0.58-0.67) based on the risk factor–weighted clinical likelihood model to 0.76 (95% CI: 0.77-0.80), P < 0.001 when adding AI-QCT-based diameter stenosis and noncalcified plaque volume. A similar improvement in risk prediction was seen when adding AI-QCT (AUC 0.77; P < 0.001) to a model with traditional risk factors, age, and sex (AUC: 0.67). In addition, AI-QCT significantly improved discrimination compared to the atherosclerotic cardiovascular disease risk score (AUC: 0.63; 95% CI: 0.58-0.68) to 0.75 (95% CI: 0.69-0.80; P < 0.001). Similar results were seen for the secondary MACE endpoint of death/MI. Conclusions: This first multicenter global registry with AI-guided quantitative CT identified noncalcified plaque burden and increment in stenosis severity as the most powerful predictors of MACE, demonstrating the interplay between traditional and novel measures of the severity of CAD. Standardized and rapid quantitative assessment of CAD may improve clinical implementation of multidimensional assessment of CAD as a cornerstone for risk assessment.eng
dc.identifier.citationRosendael, A. V., Nakanishi, R., Bax, J. J., Pontone, G., Mushtaq, S., Buechel, R. R., Gräni, C., Feuchtner, G., Lacaita, P. G., Patel, A. R., Singulane, C. C., Choi, A. D., Al-Mallah, M., Andreini, D., Karlsberg, R. P., Cho, G. W., Rochitte, C. E., Alasnag, M., Hamdan, A., ... Danad, I. (2026). AI-driven quantitative coronary CT angiography in suspected coronary artery disease. JACC: Advances, 5(3), Article 102618. https://doi.org/10.1016/j.jacadv.2026.102618
dc.identifier.doi10.1016/j.jacadv.2026.102618
dc.identifier.eid105034224719
dc.identifier.issn2772-963X
dc.identifier.pmid41906604
dc.identifier.urihttp://hdl.handle.net/10400.14/57620
dc.identifier.wos001734903100001
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial intelligence
dc.subjectCoronary artery disease
dc.subjectCoronary CT
dc.subjectNoncalcified plaque volume
dc.subjectPlaque quantification
dc.subjectQuantitative coronary CT
dc.subjectRisk stratification
dc.subjectTotal plaque volume
dc.titleAI-driven quantitative coronary CT angiography in suspected coronary artery disease
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage17
oaire.citation.issue1
oaire.citation.startPage1
oaire.citation.titleJACC: Advances
oaire.citation.volume5
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85

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