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.
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