Wang Z Q, Li Z N, Ding Y D, Zhang Y, Lin L, Xu L, Zeng Y
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing100029, China.
Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing100029, China.
Zhonghua Xin Xue Guan Bing Za Zhi. 2024 Nov 24;52(11):1277-1282. doi: 10.3760/cma.j.cn112148-20240626-00357.
To investigate the impact of the deep-learning-based CT fractional flow reserve (CT-FFR) on clinical decision-making and long-term prognosis in patients with obstructive coronary heart disease. In this single-center retrospective cohort study, consecutive patients with obstructive coronary heart disease (with at least one stenosis≥50%) on their first coronary computed tomography angiography (CCTA) in Beijing Anzhen Hospital from February 2017 to July 2018 were included. Baseline clinical and CT characteristics were collected. Deep-learning-based CT-FFR and Leiden CCTA risk score were calculated. All patients enrolled were followed up for at least 5 years. The study endpoint was major adverse cardiovascular events (MACE), defined as the composite of cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, and unplanned revascularization. Receiver operating characteristic (ROC) curves were drawn to define the optimal cut-off point of the Leiden score in predicting the 5-year MACE, and survival analysis and Cox regression were performed to explore the related factors of MACE. A total of 622 patients, aged 61 (54, 66) years, with 407 (65.4%) males were included. Diagnostic coronary angiography was performed in 78 patients after their baseline CCTA, with 34 (43.6%) patients had CT-FFR>0.80. During a follow-up time of 2 181 (2 093, 2 355) days, 155 patients (24.9%) suffered from MACE. ROC derived optimal cut-off point of Leiden score for predicting MACE was 15.48. Survival analysis found that male patients, Leiden risk score>15 and CT-FFR≤0.80 had worse prognosis. Multivariate Cox regression analysis identified CT-FFR≤0.80 as an robust and independent predictor of MACE (=4.98, 95% 3.15-7.86, <0.001). Deep-learning-based CT-FFR aids in clinical decision-making and the evaluation of long-term prognosis in patients with obstructive coronary heart disease.
研究基于深度学习的CT血流储备分数（CT-FFR）对阻塞性冠心病患者临床决策和长期预后的影响。在这项单中心回顾性队列研究中，纳入了2017年2月至2018年7月在北京安贞医院首次进行冠状动脉计算机断层扫描血管造影（CCTA）的连续阻塞性冠心病患者（至少有一处狭窄≥50%）。收集基线临床和CT特征。计算基于深度学习的CT-FFR和莱顿CCTA风险评分。所有纳入患者至少随访5年。研究终点为主要不良心血管事件（MACE），定义为心源性死亡、非致命性心肌梗死、需要住院治疗的不稳定型心绞痛和非计划性血运重建的复合事件。绘制受试者工作特征（ROC）曲线以确定莱顿评分预测5年MACE的最佳切点，并进行生存分析和Cox回归以探讨MACE的相关因素。共纳入622例患者，年龄61（54，66）岁，男性407例（65.4%）。78例患者在基线CCTA后进行了诊断性冠状动脉造影，其中34例（43.6%）患者的CT-FFR>0.80。在2181（2093，2355）天的随访期内，155例患者（24.9%）发生了MACE。ROC得出的预测MACE的莱顿评分最佳切点为15.48。生存分析发现男性患者、莱顿风险评分>15以及CT-FFR≤0.80的患者预后较差。多变量Cox回归分析确定CT-FFR≤0.80是MACE的一个稳健且独立的预测因子（=4.98，95% 3.15 - 7.86，<0.001）。基于深度学习的CT-FFR有助于阻塞性冠心病患者的临床决策和长期预后评估。
