Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Am J Cardiol. 2019 Feb 15;123(4):537-543. doi: 10.1016/j.amjcard.2018.11.024. Epub 2018 Nov 24.
Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is a noninvasive application to evaluate the hemodynamic impact of coronary artery disease by simulating invasively measured FFR based on CT data. CT-FFR is based on the assumption of a normal coronary microvascular response. We assessed the diagnostic performance of a machine-learning based application for on-site computation of CT-FFR in patients with and without diabetes mellitus with suspected coronary artery disease. The study population included 75 diabetic and 276 nondiabetic patients who were enrolled in the MACHINE consortium. The overall diagnostic performance of coronary CT angiography alone and in combination with CT-FFR were analyzed with direct invasive FFR comparison in 110 coronary vessels of the diabetic group and in 415 coronary vessels of the nondiabetic group. Per-vessel discrimination of lesion-specific ischemia by CT-FFR was assessed by the area under the receiver operating characteristic curves. The overall diagnostic accuracy of CT-FFR in diabetic patients was 83% and in nondiabetic patients 75% (p = 0.088), showing improvement over the diagnostic accuracy of coronary CT angiography, which was 58% and 65% (p = 0.223), respectively. In addition, the diagnostic accuracy of CT-FFR was similar between diabetic and nondiabetic patients per stratified CT-FFR group (CT-FFR < 0.6, 0.6 to 0.69, 0.7 to 0.79, 0.8 to 0.89, ≥0.9). The area under the curves for diabetic and nondiabetic patients were also comparable, 0.88 and 0.82 (p = 0.113), respectively. In conclusion, on-site machine-learning CT-FFR analysis improved the diagnostic performance of coronary CT angiography and accurately discriminated lesion-specific ischemia in both diabetic and nondiabetic patients suspected of coronary artery disease.
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）是一种非侵入性应用方法，通过基于 CT 数据模拟侵入性测量的 FFR 来评估冠状动脉疾病的血流动力学影响。CT-FFR 基于正常冠状动脉微血管反应的假设。我们评估了一种基于机器学习的应用程序在有和没有糖尿病的疑似冠状动脉疾病患者中进行现场计算 CT-FFR 的诊断性能。研究人群包括 75 例糖尿病患者和 276 例非糖尿病患者，他们被纳入 MACHINE 联盟。在糖尿病组的 110 个冠状动脉和非糖尿病组的 415 个冠状动脉中，通过直接侵入性 FFR 比较分析了单独进行冠状动脉 CT 血管造影和结合 CT-FFR 的整体诊断性能。通过接受者操作特征曲线下面积评估 CT-FFR 对特定病变缺血的血管内鉴别能力。在糖尿病患者中，CT-FFR 的整体诊断准确性为 83%，在非糖尿病患者中为 75%（p=0.088），优于冠状动脉 CT 血管造影的诊断准确性，分别为 58%和 65%（p=0.223）。此外，根据分层 CT-FFR 组（CT-FFR<0.6、0.6 至 0.69、0.7 至 0.79、0.8 至 0.89、≥0.9），CT-FFR 在糖尿病患者和非糖尿病患者之间的诊断准确性也相似。糖尿病患者和非糖尿病患者的曲线下面积也相当，分别为 0.88 和 0.82（p=0.113）。总之，现场机器学习 CT-FFR 分析提高了冠状动脉 CT 血管造影的诊断性能，并准确区分了疑似冠状动脉疾病的糖尿病和非糖尿病患者的特定病变缺血。
