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 ...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 CTFFR 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 longterm outcomes. The combined model exhibits improved prediction abilities, which is beneficial for risk stratification.展开更多
文摘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 CTFFR 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 longterm outcomes. The combined model exhibits improved prediction abilities, which is beneficial for risk stratification.
