Overview of Quantitative Flow Ratio and Optical Flow Ratio in the Assessment of Intermediate Coronary Lesions

Westra, Jelmer; Tu, Shengxian

doi:10.15420/usc.2020.09

Cited by 7 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Coronary angiography‐based computational FFR methods have been developed as alternatives, based on combined geometrical data derived from coronary angiography and hemodynamic boundary conditions 4‐7 . Among them, quantitative flow ratio (QFR) is a novel approach for fast computation of FFR based on two angiographic views 4,8 . QFR has been validated extensively showing superior diagnostic metrics 9,10 and prognostic value 11,12 …”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6][7] Among them, quantitative flow ratio (QFR) is a novel approach for fast computation of FFR based on two angiographic views. 4,8 QFR has been validated extensively showing superior diagnostic metrics 9,10 and prognostic value. 11,12 The computation of QFR requires two angiographic views acquired at least 25 in separation with minimal vessel foreshortening and overlap, which is not always possible due to the curved course of coronary arteries.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Self Cite

101

106

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Self Cite

101

106

View full text Add to dashboard Cite

show abstract

“…QFR demonstrated an excellent discriminatory power to predict functionally significant FFR (AUC =0.92; 95% CI: 0.84 to 1.00; P<0.001) (Figure 3A) with good diagnostic Family history of IHD 9 [26] Previous MI, n (%) 4 [11] Previous CVA or TIA, n (%) 4 [11] Peripheral vascular disease, n (%) 1 [3] Atrial fibrillation, n (%) 4 [11] Chronic kidney disease, n (%) 3 [9] Echocardiographic parameters Diagonal artery 5 [9] Ramus intermedius artery 1 [2] Left circumflex artery 5 [9] Obtuse marginal artery 9 [16] Right coronary artery 3 [5] Posterior descending artery 3 [5] performance [sensitivity, 73%, specificity 91%, positive predictive value (PPV) 84%, negative predictive value (NPV) 84%, accuracy 84%] (Tables 3,4). QFR demonstrated similar diagnostic performance to iFR (difference in AUC =0.04; 95% CI: −0.04 to 0.12; P=0.…”

Section: Diagnostic Performance Of Qfrmentioning

confidence: 99%

“…One emerging technology for the physiological assessment of coronary stenoses is quantitative flow ratio (QFR), which is derived using complex mathematical methods built upon the principles of computational fluid dynamics (CFD) (5). QFR is computed using a modelled hyperaemic flow velocity, derived from thrombolysis in myocardial infarction frame count analysis, without pharmacologically-induced hyperaemia.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic performance of quantitative flow ratio, non-hyperaemic pressure indices and fractional flow reserve for the assessment of coronary lesions in severe aortic stenosis

Dowling¹,

Michail²,

Zhang³

et al. 2022

Cardiovasc Diagn Ther

View full text Add to dashboard Cite

Background: Quantitative flow ratio (QFR) may be used to assess the functional significance of coronary lesions. Only limited validation exists for this technology in the setting of severe aortic stenosis.Methods: A prospective study was performed on patients who were being considered for transcatheter aortic valve implantation. QFR analysis was performed (Medis Medical Imaging System, Leiden, The Netherlands) and compared to invasive measurements of haemodynamic assessment [fractional flow reserve (FFR), instantaneous wave-free ratio (iFR), diastolic pressure ratio during the wave-free period (dPR) and distal arterial pressure/arterial pressure (Pd/Pa)].Results: A total of 35 patients were included in the study. Mean age was 75.5±6.5 and mean aortic valve gradient was 44.3±11.8 mmHg. There were 57 vessels analysed. The mean FFR was 0.83±0.10 and 22 vessels (39%) had a functionally significant FFR ≤0.80. QFR demonstrated a discriminatory power to predict functionally significant FFR [area under the receiver operating characteristic curve (AUC), 0.92; 95% confidence interval (CI): 0.84 to 1.00], representing a sensitivity of 73%, specificity of 91%, positive predictive value of 84%, negative predictive value of 84% and an accuracy of 84%. QFR also demonstrated a discriminatory power to predict functionally significant iFR ≤0.89 (AUC =0.92; 95% CI: 0.85 to 0.99), dPR ≤0.89 (AUC =0.90; 95% CI: 0.83 to 0.98) and Pd/Pa ≤0.92 (AUC =0.89; 95% CI: 0.80 to 0.97). Conclusions: QFR demonstrates acceptable diagnostic performance in patients with severe aortic stenosis when both FFR and non-hyperaemic pressure indices are used as reference standards.

show abstract

“…During recent years, the image-based FFR was developed to expand the use of physiologyguided PCI. Both angiography and intracoronary imagingbased computational methods have a good agreement with FFR (5)(6)(7). Nevertheless, current methods do not account for plaque composition in the computation of FFR.…”

Section: Introductionmentioning

confidence: 99%

Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging

Zeng¹,

Holck²,

Westra³

et al. 2022

Cardiovasc Diagn Ther

Self Cite

View full text Add to dashboard Cite

Background: Computational fractional flow reserve (FFR) was recently developed to expand the use of physiology-guided percutaneous coronary intervention (PCI). Nevertheless, current methods do not account for plaque composition. It remains unknown whether the numerical precision of computational FFR is impacted by the plaque composition in the interrogated vessels.Methods: This study is an observational, retrospective, cross-sectional study. Patients who underwent both optical coherence tomography (OCT) and FFR prior to intervention between August 2011 and October 2018 at Wakayama Medical University Hospital were included. All frames from OCT pullbacks were analyzed using a deep learning algorithm to obtain coronary plaque morphology including thin-cap fibroatheroma (TCFA), lipidic plaque volume (LPV), fibrous plaque volume (FPV), and calcific plaque volume (CPV). The interrogated vessels were stratified into three subgroups: the overestimation group with the numerical difference between the optical flow ratio (OFR) and FFR >0.05, the reference group with the difference ≥−0.05 and ≤0.05, and the underestimation group with the difference <−0.05. Results:In total 230 vessels with intermediate coronary artery stenosis from 193 patients were analyzed.The mean FFR was 0.82±0.10. Among them, 21, 179, and 30 vessels were in the overestimation, the reference, and the underestimation group, respectively. TCFA was higher in the underestimation group (60%) compared with reference (36.3%) and overestimation group (19%). Besides, it was not associated with numerical difference between OFR and FFR (NDOF) after multilevel linear regression. LPV was associated with NDOF as OFR underestimated FFR with −0.028 [95% confidence interval (CI): −0.047, −0.009] for every 100 mm 3 increase in LPV.Conclusions: High lipid burden underestimates FFR when OFR is used to assess the hemodynamic importance of intermediate coronary artery stenosis. TCFA, FPV, and CPV were not independent predictors of NDOF.

show abstract

Overview of Quantitative Flow Ratio and Optical Flow Ratio in the Assessment of Intermediate Coronary Lesions

Cited by 7 publications

References 41 publications

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 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 quantitative flow ratio, non-hyperaemic pressure indices and fractional flow reserve for the assessment of coronary lesions in severe aortic stenosis

Impact of coronary plaque morphology on the precision of computational fractional flow reserve derived from optical coherence tomography imaging

Contact Info

Product

Resources

About