CT-derived fractional flow reserve combined with atherosclerotic extent to determine long-term outcomes in diabetic patients with coronary artery disease.

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; 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.