Diagnostic Performance of a Novel AI–Guided Coronary Computed Tomography Algorithm for Predicting Myocardial Ischemia (AI-QCTISCHEMIA) Across Sex and Age Subgroups

dc.contributor.authorKamila, Putri Annisa
dc.contributor.authorHojjati, Tara
dc.contributor.authorNurmohamed, Nick S.
dc.contributor.authorDanad, Ibrahim
dc.contributor.authorDing, Yipu
dc.contributor.authorJukema, Ruurt A.
dc.contributor.authorRaijmakers, Pieter G.
dc.contributor.authorDriessen, Roel S.
dc.contributor.authorBom, Michiel J.
dc.contributor.authorvan Diemen, Pepijn
dc.contributor.authorPontone, Gianluca
dc.contributor.authorAndreini, Daniele
dc.contributor.authorChang, Hyuk-Jae
dc.contributor.authorKatz, Richard J.
dc.contributor.authorChoi, Andrew D.
dc.contributor.authorKnaapen, Paul
dc.contributor.authorBax, Jeroen J.
dc.contributor.authorvan Rosendael, Alexander
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.authorde Waard, Guus A.
dc.contributor.organizationfi=PET-keskus|en=Turku PET Centre|
dc.contributor.organizationfi=tyks, vsshp|en=tyks, varha|
dc.contributor.organization-code1.2.246.10.2458963.20.14646305228
dc.converis.publication-id508397318
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/508397318
dc.date.accessioned2026-01-22T10:33:16Z
dc.date.available2026-01-22T10:33:16Z
dc.description.abstract<p>Background: <br>AI-QCT<sub>ISCHEMIA</sub> is a novel artificial intelligence algorithm that predicts myocardial ischemia using quantitative features from coronary computed tomography angiography, providing a noninvasive alternative to functional imaging. However, its diagnostic performance across key demographic subgroups, particularly by sex and age, remains underexplored. We aimed to evaluate the diagnostic performance of AI-QCT<sub>ISCHEMIA</sub> for predicting myocardial ischemia across these subgroups.<br></p><p>Methods: <br>This post-hoc analysis included symptomatic patients with suspected coronary artery disease from the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) (n = 305; 868 vessels) 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) (n = 208; 612 vessels) studies. All patients underwent coronary computed tomography angiography, myocardial perfusion imaging (SPECT and/or PET), and invasive coronary angiography with 3-vessel fractional flow reserve as the reference standard. Diagnostic performance was evaluated at the vessel level using receiver operating characteristic analysis and under the curve (AUC), stratified by sex and age groups.<br><br>Results: <br>In computed tomographic evaluation of atherosclerotic determinants of myocardial ischemia, AI-QCT<sub>ISCHEMIA</sub> demonstrated higher diagnostic performance than myocardial perfusion imaging, with AUCs of 0.87 vs 0.63 in men and 0.85 vs 0.71 in women (P < .001 for both). Similarly, in older (≥65 years) and younger (<65 years) patients, AUCs were 0.85 vs 0.67 and 0.87 vs 0.63 (P < .001 for both). In PACIFIC-1, AI-QCTISCHEMIA outperformed SPECT in men (AUC = 0.86 vs 0.67; P < .001) and women (0.81 vs 0.65; P < .001) while performing comparably with PET (0.86 vs 0.82; P = .140; 0.81 vs 0.72; P = .214). In older patients, AI-QCT<sub>ISCHEMIA</sub> showed higher performance than SPECT (0.85 vs 0.73; P < .001) and was similar to PET (0.85 vs 0.86; P = .816). In younger patients, it also outperformed SPECT (0.87 vs 0.66; P < .001) with comparable performance with PET (0.87 vs 0.84; P = .338).<br><br>​​​​​​​Conclusions: <br>AI-QCT<sub>ISCHEMIA</sub> demonstrated consistently high diagnostic performance to detect myocardial ischemia across sex and age groups, significantly outperforming SPECT and showing comparable performance with PET, supporting its role as a noninvasive alternative for ischemia assessment.<br></p>
dc.identifier.jour-issn2772-9303
dc.identifier.olddbid214202
dc.identifier.oldhandle10024/197220
dc.identifier.urihttps://www.utupub.fi/handle/11111/31711
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S2772930325015108?via%3Dihub
dc.identifier.urnURN:NBN:fi-fe202601216980
dc.language.isoen
dc.okm.affiliatedauthorBax, Jeroen
dc.okm.affiliatedauthorDataimport, tyks, vsshp
dc.okm.discipline3121 Internal medicineen_GB
dc.okm.discipline3126 Surgery, anesthesiology, intensive care, radiologyen_GB
dc.okm.discipline3121 Sisätauditfi_FI
dc.okm.discipline3126 Kirurgia, anestesiologia, tehohoito, radiologiafi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherElsevier BV
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.articlenumber104064
dc.relation.doi10.1016/j.jscai.2025.104064
dc.relation.ispartofjournalJournal of the Society for Cardiovascular Angiography & Interventions
dc.source.identifierhttps://www.utupub.fi/handle/10024/197220
dc.titleDiagnostic Performance of a Novel AI–Guided Coronary Computed Tomography Algorithm for Predicting Myocardial Ischemia (AI-QCTISCHEMIA) Across Sex and Age Subgroups
dc.year.issued2025

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