From the Division of Diabetes, Mitsui Memorial Hospital, Tokyo, Japan (S.K.); Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115 (A.A.G., A.T., D.M.); Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan (N.K., Y.H., K.T.); Department of Cardiology, University of Nebraska Medical Center, Omaha, Neb (Y.S.C.); Department of Radiology, the University of Ottawa Faculty of Medicine, and the Ottawa Hospital Research Institute, Ottawa, Canada (C.D., F.J.R., D.M.); and Department of Cardiovascular Imaging, Johns Hopkins Medicine, Baltimore, Md (J.A.C.L.).
Radiology. 2018 Apr;287(1):76-84. doi: 10.1148/radiol.2017162620. Epub 2017 Nov 20.
Purpose To compare the diagnostic accuracy of different computed tomographic (CT) fractional flow reserve (FFR) algorithms for vessels with intermediate stenosis. Materials and Methods This cross-sectional HIPAA-compliant and human research committee-approved study applied a four-step CT FFR algorithm in 61 patients (mean age, 69 years ± 10; age range, 29-89 years) with a lesion of intermediate-diameter stenosis (25%-69%) at CT angiography who underwent FFR measurement within 90 days. The per-lesion diagnostic performance of CT FFR was tested for three different approaches to estimate blood flow distribution for CT FFR calculation. The first two, the Murray law and the Huo-Kassab rule, used coronary anatomy; the third used contrast material opacification gradients. CT FFR algorithms and CT angiography percentage diameter stenosis (DS) measurements were compared by using the area under the receiver operating characteristic curve (AUC) to detect FFRs of 0.8 or lower. Results Twenty-five lesions (41%) had FFRs of 0.8 or lower. The AUC of CT FFR determination by using contrast material gradients (AUC = 0.953) was significantly higher than that of the Huo-Kassab (AUC = 0.882, P = .043) and Murray law models (AUC = 0.871, P = .033). All three AUCs were higher than that for 50% or greater DS at CT angiography (AUC = 0.596, P < .001). Correlation of CT FFR with FFR was highest for gradients (Spearman ρ = 0.80), followed by the Huo-Kassab rule (ρ = 0.68) and Murray law (ρ = 0.67) models. All CT FFR algorithms had small biases, ranging from -0.015 (Murray) to -0.049 (Huo-Kassab). Limits of agreement were narrowest for gradients (-0.182, 0.147), followed by the Huo-Kassab rule (-0.246, 0.149) and the Murray law (-0.285, 0.256) models. Conclusion Clinicians can perform CT FFR by using a four-step approach on site to accurately detect hemodynamically significant intermediate-stenosis lesions. Estimating blood flow distribution by using coronary contrast opacification variations may improve CT FFR accuracy. RSNA, 2017 Online supplemental material is available for this article.
目的 比较不同计算机断层扫描（CT）分数血流储备（FFR）算法在中度狭窄血管中的诊断准确性。
材料与方法 本研究为横断面向 HIPAA 兼容和人类研究委员会批准的研究，共纳入 61 例 CT 血管造影显示中度直径狭窄（25%-69%）病变的患者（平均年龄 69 岁±10 岁；年龄范围：29-89 岁），这些患者在 CT 血管造影后 90 天内行 FFR 测量。通过使用四种方法来估计 CT FFR 计算的血流分布，来测试 CT FFR 的每例病变的诊断性能。前两种方法，即 Murray 定律和 Huo-Kassab 法则，使用冠状动脉解剖学；第三种方法使用对比剂的对比度梯度。通过使用受试者工作特征曲线下面积（AUC）比较 CT FFR 算法和 CT 血管造影的百分比直径狭窄（DS）测量值，以检测 FFR 为 0.8 或更低的病变。
结果 25 个病变（41%）的 FFR 为 0.8 或更低。使用对比剂梯度确定的 CT FFR 的 AUC（AUC=0.953）显著高于 Huo-Kassab 法则（AUC=0.882，P=0.043）和 Murray 定律模型（AUC=0.871，P=0.033）。所有三种 AUC 均高于 CT 血管造影时 50%或更大的 DS（AUC=0.596，P<0.001）。CT FFR 与 FFR 的相关性以梯度最高（Spearman ρ=0.80），其次是 Huo-Kassab 法则（ρ=0.68）和 Murray 定律（ρ=0.67）模型。所有 CT FFR 算法的偏倚均较小，范围为-0.015（Murray）至-0.049（Huo-Kassab）。梯度的一致性限最窄（-0.182，0.147），其次是 Huo-Kassab 法则（-0.246，0.149）和 Murray 定律（-0.285，0.256）模型。
结论 临床医生可以在现场使用四步方法进行 CT FFR，以准确检测血流动力学显著的中度狭窄病变。通过冠状动脉对比剂显影变化来估计血流分布可能会提高 CT FFR 的准确性。
RSNA，2017 在线补充材料可从本文获得。
