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Fractional Flow Reserve Estimated at Coronary CT Angiography in Intermediate Lesions: Comparison of Diagnostic Accuracy of Different Methods to Determine Coronary Flow Distribution.

TitleFractional Flow Reserve Estimated at Coronary CT Angiography in Intermediate Lesions: Comparison of Diagnostic Accuracy of Different Methods to Determine Coronary Flow Distribution.
Publication TypeJournal Article
Year of Publication2018
AuthorsKishi, S., Giannopoulos A. A., Tang A., Kato N., Chatzizisis Y. S., Dennie C., Horiuchi Y., Tanabe K., Lima J. A. C., Rybicki F. J., & Mitsouras D.
JournalRadiology
Volume287
Issue1
Pagination76-84
Date Published2018 04
ISSN1527-1315
KeywordsAged, Algorithms, Computed Tomography Angiography, Coronary Angiography, Coronary Stenosis, Cross-Sectional Studies, Female, Fractional Flow Reserve, Myocardial, Humans, Male, Reproducibility of Results, Retrospective Studies, Sensitivity and Specificity
Abstract

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.

DOI10.1148/radiol.2017162620
Alternate JournalRadiology
PubMed ID29156145
PubMed Central IDPMC5896162
Grant ListK01 EB015868 / EB / NIBIB NIH HHS / United States

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