From the Departments of Radiology (A.C., M.M.L., A. Kurata, A.Kono, A.D., R.G.C., M.L.D., M.O., R.J.M.v.G, K.N.) and Cardiology (A.C., M.M.L., A.D., R.G.C., F.J.G., R.J.M.v.G., K.N.), Erasmus University Medical Center, 's-Gravendijkwal 230, Rotterdam 3015 CE, the Netherlands.
Radiology. 2015 Mar;274(3):674-83. doi: 10.1148/radiol.14140992. Epub 2014 Oct 13.
To validate an on-site algorithm for computation of fractional flow reserve (FFR) from coronary computed tomographic (CT) angiography data against invasively measured FFR and to test its diagnostic performance as compared with that of coronary CT angiography.
The institutional review board provided a waiver for this retrospective study. From coronary CT angiography data in 106 patients, FFR was computed at a local workstation by using a computational fluid dynamics algorithm. Invasive FFR measurement was performed in 189 vessels (80 of which had an FFR ≤ 0.80); these measurements were regarded as the reference standard. The diagnostic characteristics of coronary CT angiography-derived computational FFR, coronary CT angiography, and quantitative coronary angiography were evaluated against those of invasively measured FFR by using C statistics. Sensitivity and specificity were compared by using a two-sided McNemar test.
For computational FFR, sensitivity was 87.5% (95% confidence interval [CI]: 78.2%, 93.8%), specificity was 65.1% (95% CI: 55.4%, 74.0%), and accuracy was 74.6% (95% CI: 68.4%, 80.8%), as compared with the finding of lumen stenosis of 50% or greater at coronary CT angiography, for which sensitivity was 81.3% (95% CI: 71.0%, 89.1%), specificity was 37.6% (95% CI: 28.5%, 47.4%), and accuracy was 56.1% (95% CI: 49.0%, 63.2%). C statistics revealed a larger area under the receiver operating characteristic curve (AUC) for computational FFR (AUC, 0.83) than for coronary CT angiography (AUC, 0.64). For vessels with intermediate (25%-69%) stenosis, the sensitivity of computational FFR was 87.3% (95% CI: 76.5%, 94.3%) and the specificity was 59.3% (95% CI: 47.8%, 70.1%).
With use of a reduced-order algorithm, computation of the FFR from coronary CT angiography data can be performed locally, at a regular workstation. The diagnostic accuracy of coronary CT angiography-derived computational FFR for the detection of functionally important coronary artery disease (CAD) was good and was incremental to that of coronary CT angiography within a population with a high prevalence of CAD.
验证一种基于冠状动脉 CT 血管造影（CTA）数据计算分比流量储备（FFR）的现场算法与有创测量 FFR 的一致性，并检验其与冠状动脉 CTA 相比的诊断性能。
本回顾性研究获得机构审查委员会豁免。通过使用计算流体动力学算法，在 106 例患者的冠状动脉 CTA 数据上，在本地工作站计算 FFR。对 189 支血管（其中 80 支 FFR≤0.80）进行有创 FFR 测量；这些测量被视为参考标准。通过 C 统计量评估冠状动脉 CTA 衍生计算 FFR、冠状动脉 CTA 和定量冠状动脉造影的诊断特征，与有创测量 FFR 进行比较。采用双侧 McNemar 检验比较敏感性和特异性。
对于计算 FFR，敏感性为 87.5%（95%置信区间：78.2%，93.8%），特异性为 65.1%（95%置信区间：55.4%，74.0%），准确性为 74.6%（95%置信区间：68.4%，80.8%），与冠状动脉 CTA 显示管腔狭窄 50%或以上的结果相比，敏感性为 81.3%（95%置信区间：71.0%，89.1%），特异性为 37.6%（95%置信区间：28.5%，47.4%），准确性为 56.1%（95%置信区间：49.0%，63.2%）。受试者工作特征曲线下面积（AUC）显示，计算 FFR 的 AUC 大于冠状动脉 CTA（AUC 分别为 0.83 和 0.64）。对于中间狭窄（25%-69%）的血管，计算 FFR 的敏感性为 87.3%（95%置信区间：76.5%，94.3%），特异性为 59.3%（95%置信区间：47.8%，70.1%）。
通过使用降阶算法，可以在常规工作站上在冠状动脉 CTA 数据本地进行 FFR 计算。在冠状动脉 CAD 患病率较高的人群中，基于冠状动脉 CTA 计算的 FFR 对检测功能性重要 CAD 的诊断准确性较好，并且优于冠状动脉 CTA。
