TY - JOUR
T1 - Fractional flow reserve estimated at coronary CT angiography in intermediate lesions
T2 - Comparison of diagnostic accuracy of different methods to determine coronary flow distribution
AU - Kishi, Satoru
AU - Giannopoulos, Andreas A.
AU - Tang, Anji
AU - Kato, Nahoko
AU - Chatzizisis, Yiannis S.
AU - Dennie, Carole
AU - Horiuchi, Yu
AU - Tanabe, Kengo
AU - Lima, João A.C.
AU - Rybicki, Frank J.
AU - Mitsouras, Dimitris
N1 - Publisher Copyright:
© 2017 RSNA.
PY - 2018/4
Y1 - 2018/4
N2 - 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 r = 0.80), followed by the Huo-Kassab rule (r = 0.68) and Murray law (r = 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.
AB - 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 r = 0.80), followed by the Huo-Kassab rule (r = 0.68) and Murray law (r = 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.
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U2 - 10.1148/radiol.2017162620
DO - 10.1148/radiol.2017162620
M3 - Article
C2 - 29156145
AN - SCOPUS:85044275688
SN - 0033-8419
VL - 287
SP - 76
EP - 84
JO - Radiology
JF - Radiology
IS - 1
ER -