J Geriatr Cardiol. 2026 Jan 28;23(1):27-35. doi: 10.26599/1671-5411.2026.01.008.
ABSTRACT
BACKGROUND: There is still limited data on predictive value of coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) for long term outcomes. We examined the long-term prognostic value of CT-FFR combined with CCTA-defined atherosclerotic extent in diabetic patients with coronary artery disease (CAD).
METHODS: A retrospective pooled analysis of individual patient data was performed. Deep-learning-based vessel-specific CT-FFR was calculated. All patients enrolled were followed-up for at least 5 years. Predictive abilities for major adverse cardiac events (MACE) were compared among three models (model 1, constructed using clinical variables; model 2, model 1+CCTA-derived atherosclerotic extent (Leiden risk score); and model 3, model 2+CT-FFR.
RESULTS: A total of 480 diabetic patients [median age, 61 (55-66) years; 52.9% men] were included. During a median follow-up time of 2197 (2126-2355) days, 55 patients (11.5%) experienced MACE. In multivariate-adjusted Cox models, Leiden risk score (HR: 1.06; 95% CI: 1.01-1.11; P = 0.013) and CT-FFR ≤ 0.80 (HR: 6.54; 95% CI: 3.18-13.45; P < 0.001) were the independent predictors. The discriminant ability was higher in model 2 than in model 1 (C-index, 0.75 vs. 0.63; P < 0.001) and was further promoted by adding CT-FFR to model 3 (C-index, 0.81 vs. 0.75; P = 0.002). Net reclassification improvement (NRI) was 0.19 (P = 0.009) for model 2 beyond model 1. Of note, adding CT-FFR to model 3 also exhibited significantly improved reclassification compared with model 2 (NRI = 0.14; P = 0.011).
CONCLUSION: In diabetic patients with CAD, CT-FFR provides robust and incremental prognostic information for predicting long-term outcomes. The combined model exhibits improved prediction abilities, which is beneficial for risk stratification.
PMID:41777404 | PMC:PMC12951723 | DOI:10.26599/1671-5411.2026.01.008