Abstract：
BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.
摘要：
背景：根据冠状动脉CTA（FFR-CT）估算血流储备分数（FFR）是一种评估冠状动脉病变血流动力学意义的既定方法。然而,临床实施进展缓慢，部分与异地数据传输有关，在等待结果时需要较长的周转时间。目标：我们旨在使用基于深度学习的高速算法评估现场计算的FFR-CT的诊断性能，使用侵入性血液动力学指数作为参考标准。方法：这项回顾性研究包括59例患者(46例男性，13名女性；平均年龄66.5±10.2岁），自2014年12月至2021年10月，接受冠状动脉CTA（包括钙评分），随后在90天内通过有创血管造影术进行有创FFR和/或瞬时无波比（iwFR）测量。在存在侵入性FFR≤0.80和/或iwFR≤0.89的情况下，认为冠状动脉病变显示血流动力学显著狭窄。一位心脏病专家使用基于现场深度学习的半自动算法评估CTA图像，该算法采用3D计算流动力学模型来确定通过有创血管造影术检测到的冠状动脉病变的FFR-CT。记录FFR-CT分析的时间。FFR-CT分析由同一心脏病专家在26项随机选择的检查中重复进行，和不同的心脏病专家在45个随机选择的检查。评估诊断性能和一致性。结果：有创血管造影发现74个病灶。FFR-CT与侵入性FFR表现出强相关性(r=0.81),and,在Bland-Altman分析中,显示0.01和95％的偏差-0.13至+0.15的一致性极限。FFR-CT对血流动力学显著狭窄的AUC为0.975。截止值≤0.80时，FFR-CT的准确率为95.9%，灵敏度为93.5%，特异性为97.7%。在39个严重钙化的病变（≥400Agatston单位）中，FFR-CT的AUC为0.991，截止值≤0.80，灵敏度为94.7%，特异性为95.0%，准确率为94.9%。每位患者的平均分析时间为7分54秒。观察者间和观察者内的一致性从好到优(组内相关系数，0.944和0.854；偏差-0.01和-0.01；95%的一致性极限，-0.08至+0.07，分别为-0.12和+0.10）。结论:基于FFR-CT的高速现场深度学习算法对血流动力学显著狭窄表现出优异的诊断性能,具有高重现性。临床影响：该算法应促进FFR-CT技术在常规临床实践中的实施。
