Prognostic Significance of Computed Tomography‐Derived Fractional Flow Reserve for Long‐Term Outcomes in Individuals With Coronary Artery Disease - Zhennan Li; Tingfeng Xu; Zhiqiang Wang; Yaodong Ding; Yang Zhang; Li Lin; Minxian Wang; Lei Xu; Yong Zeng (Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease)
Background Data on the predictive value of coronary computed tomography angiography–derived fractional flow reserve (CT‐FFR) for long‐term outcomes are limited. Methods and Results A retrospective pooled analysis of individual patient data was performed. Deep‐learning‐based CT‐FFR was calculated. All patients enrolled were followed‐up for at least 5 years. The primary outcome was major adverse cardiovascular events. The secondary outcome was death or nonfatal myocardial infarction. Predictive abilities for outcomes were compared among 3 models (model 1, constructed using clinical variables; model 2, model 1+coronary computed tomography angiography–derived anatomical parameters; and model 3, model 2+CT‐FFR). A total of 2566 patients (median age, 60 [53–65] years; 56.0% men) with coronary artery disease were included. During a median follow‐up time of 2197 (2127–2386) days, 237 patients (9.2%) experienced major adverse cardiovascular events. In multivariable‐adjusted Cox models, CT‐FFR≤0.80 (hazard ratio [HR], 5.05 [95% CI, 3.64–7.01]; P<0.001) exhibited robust predictive value. The discriminant ability was higher in model 2 than in model 1 (Harrell's C‐statistics, 0.79 versus 0.64; P<0.001) and was further promoted by adding CT‐FFR to model 3 (Harrell's C‐statistics, 0.83 versus 0.79; P<0.001). Net reclassification improvement was 0.264 (P<0.001) for model 2 beyond model 1. Of note, compared with model 2, model 3 also exhibited improvement (net reclassification improvement=0.085; P=0.001). As for predicting death or nonfatal myocardial infarction, only incorporating CT‐FFR into model 3 showed improved reclassification (net reclassification improvement=0.131; P=0.021). Conclusions CT‐FFR provides strong and incremental prognostic information for predicting long‐term outcomes. The combined models incorporating CT‐FFR exhibit modest improvement of prediction abilities, which may aid in risk stratification and decision‐making.