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

Abstract

Background: for predicting myocardial ischemia across these subgroups. Methods: 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. Results: = .338). Conclusions: 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.

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