Li Zhennan, Xu Tingfeng, Wang Zhiqiang, Ding Yaodong, Zhang Yang, Lin Li, Wang Minxian, Xu Lei, Zeng Yong
Department of Cardiology Beijing Anzhen Hospital, Capital Medical University Beijing China.
CAS Key Laboratory of Genome Sciences and Information Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation Beijing China.
J Am Heart Assoc. 2025 Jan 21;14(2):e037988. doi: 10.1161/JAHA.124.037988. Epub 2025 Jan 10.
Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited.
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]; <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; <0.001) and was further promoted by adding CT-FFR to model 3 (Harrell's C-statistics, 0.83 versus 0.79; <0.001). Net reclassification improvement was 0.264 (<0.001) for model 2 beyond model 1. Of note, compared with model 2, model 3 also exhibited improvement (net reclassification improvement=0.085; =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; =0.021).
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.
关于冠状动脉计算机断层扫描血管造影衍生的血流储备分数（CT-FFR）对长期预后的预测价值的数据有限。
进行了一项个体患者数据的回顾性汇总分析。计算基于深度学习的CT-FFR。所有纳入的患者均随访至少5年。主要结局是主要不良心血管事件。次要结局是死亡或非致命性心肌梗死。在3种模型（模型1，使用临床变量构建；模型2，模型1加上冠状动脉计算机断层扫描血管造影衍生的解剖学参数；模型3，模型2加上CT-FFR）之间比较结局的预测能力。共纳入2566例冠状动脉疾病患者（中位年龄60[53 - 65]岁；56.0%为男性）。在中位随访时间2197（2127 - 2386）天期间，237例患者（9.2%）发生了主要不良心血管事件。在多变量调整的Cox模型中，CT-FFR≤0.80（风险比[HR]，5.05[95%CI，3.64 - 7.01]；<0.001）显示出强大的预测价值。模型2的判别能力高于模型1（Harrell's C统计量，0.79对0.64；<0.001），并且通过在模型3中加入CT-FFR进一步提高（Harrell's C统计量，0.83对0.79；<0.001）。模型2相对于模型1的净重新分类改善为0.264（<0.001）。值得注意的是，与模型2相比，模型3也显示出改善（净重新分类改善 = 0.085； = 0.001）。至于预测死亡或非致命性心肌梗死，仅将CT-FFR纳入模型3显示出重新分类的改善（净重新分类改善 = 0.131； = 0.021）。
CT-FFR为预测长期预后提供了强大且递增的预后信息。纳入CT-FFR的联合模型显示出预测能力的适度改善，这可能有助于风险分层和决策制定。
