Improved detection of coronary ischemia in patients with diabetes mellitus using a novel artificial intelligence-based ischemia algorithm derived from coronary ct angiography (AI-QCTISCHEMIA)

Abstract Background Diabetic patients have a higher risk of coronary artery disease (CAD), greater atherosclerotic burden, faster disease progression, and increased mortality compared to those without DM, making accurate evaluation crucial yet challenging. AI-enhanced coronary computed tomography angiography (CCTA) provides detailed quantitative assessment of coronary plaque and vascular features, offering a reliable estimate of coronary ischemia (AI-QCTISCHEMIA). Purpose To evaluate the diagnostic performance of AI-QCTISCHEMIA in detecting coronary ischemia in patients with diabetes. Methods This post-hoc analysis of the CREDENCE validation cohort included 305 patients (868 vessels) with suspected CAD who underwent CCTA, myocardial perfusion imaging (MPI; SPECT/PET), FFR-CT, and invasive coronary angiography (ICA) with FFR as reference. The diagnostic performance of AI-QCTISCHEMIA in predicting myocardial ischemia was evaluated using vessel-level area under the receiver operating characteristic curve (AUC) analysis, stratified by diabetic status. Missing values were imputed as positive in accordance with an intention-to-diagnose approach. Results Diabetic patients were older (66.3 ± 9.4 vs. 63.6 ± 9.9 years, p = 0.025) and had a higher prevalence of hypertension (75.8%, p = 0.007), with no differences in sex, BMI, or smoking. On a per-vessel basis, DM patients showed significantly greater total plaque burden (PAV: 20.1% vs. 16.6%, p