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Development and validation of a quantitative coronary CT Angiography model for diagnosis of vessel-specific coronary ischemia

2024Journal article Open access
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dc.contributor.authorCREDENCE and PACIFIC-1 Investigators
dc.contributor.authorNurmohamed, Nick S.
dc.contributor.authorDanad, Ibrahim
dc.contributor.authorJukema, Ruurt A.
dc.contributor.authorWinter, Ruben W. de
dc.contributor.authorGroot, Robin J. de
dc.contributor.authorDriessen, Roel S.
dc.contributor.authorBom, Michiel J.
dc.contributor.authorDiemen, Pepijn van
dc.contributor.authorPontone, Gianluca
dc.contributor.authorAndreini, Daniele
dc.contributor.authorChang, Hyuk Jae
dc.contributor.authorKatz, Richard J.
dc.contributor.authorStroes, Erik S. G.
dc.contributor.authorWang, Hao
dc.contributor.authorChan, Chung
dc.contributor.authorCrabtree, Tami
dc.contributor.authorAquino, Melissa
dc.contributor.authorMin, James K.
dc.contributor.authorEarls, James P.
dc.contributor.authorBax, Jeroen J.
dc.contributor.authorChoi, Andrew D.
dc.contributor.authorKnaapen, Paul
dc.contributor.authorRosendael, Alexander R. van
dc.contributor.authorHeo, Ran
dc.contributor.authorPark, Hyung Bok
dc.contributor.authorMarques, Hugo
dc.contributor.authorStuijfzand, Wijnand J.
dc.contributor.authorChoi, Jung Hyun
dc.contributor.authorDoh, Joon Hyung
dc.contributor.authorHer, Ae Young
dc.contributor.authorKoo, Bon Kwon
dc.contributor.authorNam, Chang Wook
dc.contributor.authorShin, Sang Hoon
dc.contributor.authorCole, Jason
dc.contributor.authorGimelli, Alessia
dc.contributor.authorKhan, Muhammad Akram
dc.contributor.authorLu, Bin
dc.contributor.authorGao, Yang
dc.contributor.authorNabi, Faisal
dc.contributor.authorAl-Mallah, Mouaz H.
dc.contributor.authorNakazato, Ryo
dc.contributor.authorSchoepf, U. Joseph
dc.contributor.authorThompson, Randall C.
dc.contributor.authorJang, James J.
dc.contributor.authorRidner, Michael
dc.contributor.authorRowan, Chris
dc.contributor.authorAvelar, Erick
dc.contributor.authorGénéreux, Philippe
dc.contributor.authorWaard, Guus A. de
dc.contributor.authorSprengers, Ralf W.
dc.date.accessioned2024-04-09T08:31:46Z
dc.date.available2024-04-09T08:31:46Z
dc.date.issued2024
dc.description.abstractBackground: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. Objectives: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). Methods: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. Results: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with an HR of 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. Conclusions: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.doi10.1016/j.jcmg.2024.01.007pt_PT
dc.identifier.eid85189093032
dc.identifier.issn1936-878X
dc.identifier.pmid38483420
dc.identifier.urihttp://hdl.handle.net/10400.14/44475
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectAtherosclerosispt_PT
dc.subjectCoronary computed tomography angiographypt_PT
dc.subjectCoronary ischemiapt_PT
dc.subjectStress testingpt_PT
dc.titleDevelopment and validation of a quantitative coronary CT Angiography model for diagnosis of vessel-specific coronary ischemiapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleJACC: Cardiovascular Imagingpt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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