Diagnostic performance of intracoronary optical coherence tomography-based versus angiography-based fractional flow reserve for the evaluation of coronary lesions

Huang, Jiayue; Emori, Hiroki; Ding, Daixin; Kubo, Takashi; Yu, Wenyan; Huang, Peng; Zhang, Su; Gutiérrez‐Chico, Juan Luis; Akasaka, Takashi; Wijns, William; Tu, Shengxian

doi:10.4244/eij-d-19-01034

Cited by 59 publications

(47 citation statements)

References 14 publications

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“…To the best of our knowledge, this is the first study to prove the feasibility of OFR prospectively in a real-world scenario, confirming the excellent diagnostic performance of previous retrospective reports [15,16]. Actually, the diagnostic accuracy in this prospective series was even better than in previous post-hoc studies that had reported a diagnostic accuracy of 90-92% [15,16], increasing to 93% in the current study. This observation can be likely explained by a more careful and dedicated OCT acquisition, specifically aiming to optimise the quality of OFR computation [20].…”

Section: Discussionsupporting

confidence: 89%

“…OFR showed optimal agreement with FFR in post-hoc retrospective analysis [15,16]. Furthermore, OFR might improve the accuracy of angiography-based morphofunctional computational methods [16][17][18] by overcoming the intrinsic limitations of angiography, namely overlapping vessels, foreshortening and calcium interfering with an accurate edge detection of the vessel lumen, because OCT renders the most accurate lumen reconstruction currently available.…”

Section: Introductionmentioning

confidence: 91%

“…The reference lumen area at the distal part of the bifurcation was then derived according to the principles of fractal geometry [24]. Finally, the reference lumen area was multiplied by a fixed flow velocity of 0.35 m/s to obtain the hypothetic volumetric flow used as boundary condition for the OFR algorithm [15,16], thus enabling the computation of OFR (i.e., the estimated FFR value) along the reconstructed vessel [14,15]. For sequential stenosis requiring two different pullbacks, both pullbacks were combined using a fiduciary landmark.…”

Section: Ofr and Qfr Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A novel OCT-based morphofunctional computational method dubbed optical flow ratio (OFR) has been recently developed, combining an estimation of physiology with high resolution imaging in the same OCT pullback [14][15][16]. OFR showed optimal agreement with FFR in post-hoc retrospective analysis [15,16]. Furthermore, OFR might improve the accuracy of angiography-based morphofunctional computational methods [16][17][18] by overcoming the intrinsic limitations of angiography, namely overlapping vessels, foreshortening and calcium interfering with an accurate edge detection of the vessel lumen, because OCT renders the most accurate lumen reconstruction currently available.…”

Section: Introductionmentioning

confidence: 99%

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Diagnostic accuracy and reproducibility of optical flow ratio for functional evaluation of coronary stenosis in a prospective series

Gutiérrez‐Chico¹,

Chen

et al. 2020

Cardiol J.

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Background: Evaluating prospectively the feasibility, accuracy and reproducibility of optical flow ratio (OFR), a novel method of computational physiology based on optical coherence tomography (OCT). Methods and results: Sixty consecutive patients (76 vessels) underwent prospectively OCT, angio graphybased quantitative flow ratio (QFR) and fractional flow ratio (FFR). OFR was computed offline in a central corelab by analysts blinded to FFR. OFR was feasible in 98.7% of the lesions and showed excellent agreement with FFR (ICCa = 0.83, r = 0.83, slope = 0.80, intercept = 0.17, kappa = 0.84). The area under curve to predict an FFR ≤ 0.80 was 0.95, higher than for QFR (0.91, p = 0.115) and for minimal lumen area (0.64, p < 0.001). Diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio and negative likelihood ratio were 93%, 92%,

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 91%

Section: Ofr and Qfr Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diagnostic accuracy and reproducibility of optical flow ratio for functional evaluation of coronary stenosis in a prospective series

Gutiérrez‐Chico¹,

Chen

et al. 2020

Cardiol J.

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Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law

Ding

Chang³

et al. 2021

Cathet Cardio Intervent

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102

138

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Objectives We aimed to evaluate the diagnostic accuracy of computation of fractional flow reserve (FFR) from a single angiographic view in patients with intermediate coronary stenosis. Background Computation of quantitative flow ratio (QFR) from a single angiographic view might increase the feasibility of routine use of computational FFR. In addition, current QFR solutions assume a linear tapering of the reference vessel size, which might decrease the diagnostic accuracy in the presence of the physiologically significant bifurcation lesions. Methods An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of major epicardial coronary arteries including their side branches. A step‐down reference diameter function was reconstructed based on the Murray bifurcation fractal law and used for QFR computation. Validation of this Murray law‐based QFR (μQFR) was performed on the FAVOR II China study population. The μQFR was computed separately in two angiographic projections, starting with the one with optimal angiographic image quality. Hemodynamically significant coronary stenosis was defined by pressure wire‐derived FFR ≤0.80. Results The μQFR was successfully computed in all 330 vessels of 306 patients. There was excellent correlation (r = 0.90, p < .001) and agreement (mean difference = 0.00 ± 0.05, p = .378) between μQFR and FFR. The vessel‐level diagnostic accuracy for μQFR to identify hemodynamically significant stenosis was 93.0% (95% CI: 90.3 to 95.8%), with sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio of 87.5% (95% CI: 80.2 to 92.8%), 96.2% (95% CI: 92.6 to 98.3%), 92.9% (95% CI: 86.5 to 96.9%), 93.1% (95% CI: 88.9 to 96.1%), 23.0 (95% CI: 11.6 to 45.5), 0.13 (95% CI: 0.08 to 0.20), respectively. Use of suboptimal angiographic image view slightly decreased the diagnostic accuracy of μQFR (AUC = 0.97 versus 0.92, difference = 0.05, p < .001). Intra‐ and inter‐observer variability for μQFR computation was 0.00 ± 0.03, and 0.00 ± 0.03, respectively. Average analysis time for μQFR was 67 ± 22 s. Conclusions Computation of μQFR from a single angiographic view has high feasibility and excellent diagnostic accuracy in identifying hemodynamically significant coronary stenosis. The short analysis time and good reproducibility of μQFR bear potential of wider adoption of physiological assessment in the catheterization laboratory.

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Diagnostic performance of intravascular ultrasound‐based fractional flow reserve versus angiography‐based quantitative flow ratio measurements for evaluating left main coronary artery stenosis

Sui

Yang

et al. 2022

Cathet Cardio Intervent

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Objectives We compared the diagnostic performance of the ultrasonic flow ratio (UFR) and quantitative flow ratio (QFR) for left main coronary artery (LMCA) stenosis. Background Evaluation of LMCA stenosis remains challenging because of its complex pathogenesis and severity. Computing QFR allows rapid determination of fractional flow reserve (FFR) from coronary angiograms. A novel intravascular ultrasound (IVUS)‐based FFR (UFR) allows rapid FFR computation from IVUS images. Neither of the computational approaches required a pressure wire or hyperemia induction. Previous studies have validated the good diagnostic accuracy of QFR and UFR in identifying hemodynamically significant coronary stenosis using FFR as the reference standard. Methods This retrospective observational study enrolled consecutive patients with intermediate‐grade LMCA stenosis who underwent IVUS evaluation. UFR and QFR of all LMCA stenosis patients were assessed, their correlation and agreement were analyzed, and diagnostic performance of UFR in LMCA stenosis was evaluated. Results Eighty‐three paired comparisons between UFR and QFR were obtained. UFR excellently correlated with QFR (r = 0.74, p < 0.01). The Bland‒Altman plot showed good agreement between UFR and QFR (mean differences: 0.01 ± 0.05, p = 0.34). The area under the curve of UFR for identifying physiological LMCA stenosis was 0.97 (95% confidence interval [CI]: 0.93–1.00, p < 0.01). The overall UFR diagnostic accuracy was 0.95 (95% CI: 0.88–0.99). Conclusions UFR showed excellent correlation and good agreement with QFR in LMCA stenosis assessment, indicating that it is highly feasible to use UFR for functional evaluation of LMCA stenosis.

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Diagnostic performance of intracoronary optical coherence tomography-based versus angiography-based fractional flow reserve for the evaluation of coronary lesions

Cited by 59 publications

References 14 publications

Diagnostic accuracy and reproducibility of optical flow ratio for functional evaluation of coronary stenosis in a prospective series

Diagnostic accuracy and reproducibility of optical flow ratio for functional evaluation of coronary stenosis in a prospective series

Diagnostic accuracy of quantitative flow ratio for assessment of coronary stenosis significance from a single angiographic view: A novel method based on bifurcation fractal law

Diagnostic performance of intravascular ultrasound‐based fractional flow reserve versus angiography‐based quantitative flow ratio measurements for evaluating left main coronary artery stenosis

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