TCT-332 Relation Between Fractional Flow Reserve And Coronary Bifurcation Anatomy: A Novel Quantitative Model To Assess The Stenotic Severity Of Bifurcation Lesions

Tu, Shengxian; Echavarría‐Pinto, Mauro; Birgelen, Clemens von; Holm, Niels Ramsing; Pyxaras, Stylianos A.; Kumsārs, Indulis; Lam, Ming Kai; Valkenburg, Ilona; Tóth, Gábor G.; Li, Yingguang; Escaned, Javier; Wijns, William; Reiber, Johan H. C.

doi:10.1016/j.jacc.2014.07.378

Cited by 18 publications

(29 citation statements)

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“…The lack of hemodynamic information explains why the sole use of the DS% or LL is far from sufficient to reflect true coronary physiology. This is in line with previous studies that reported a limited usefulness of anatomical parameters to predict FFR (22)(23)(24).…”

Section: Discussionsupporting

confidence: 93%

Quantification of disturbed coronary flow by disturbed vorticity index and relation with fractional flow reserve

Chu

Birgelen

et al. 2018

Atherosclerosis

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Section: Discussionsupporting

confidence: 93%

Quantification of disturbed coronary flow by disturbed vorticity index and relation with fractional flow reserve

Chu

Birgelen

et al. 2018

Atherosclerosis

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“…Finally, 893 patients with TFG 0/1 in the culprit artery and 73 patients with culprit lesion with <50% diameter stenosis in initial angiography were excluded. Among the remaining 185 STEMI patients, 29 cases were excluded because of unqualified coronary angiographic images with problems such as ostial lesion, severe vessel tortuosity and diffuse long lesions; 21 cases were excluded because of absence of two angiographic projections at least 25° apart in both index and staged procedures for computation of QFR . Five patients were excluded because of insufficient CMR imaging quality.…”

Section: Methodsmentioning

confidence: 99%

“…In the first step, two diagnostic angiographic projections before PCI, at least 25° apart, were selected and 3D reconstruction of the interrogated vessel without its side branches was performed. The software computed the following two QFR pullbacks: (a) fQFR pullback, a fixed empiric HFV of 0.35 m/s that was derived from previous FFR studies was used for computation; and (b) cQFR pullback, frame count analysis was performed without pharmacologically induced hyperemia and the modeled HFV, which was derived according to the frame count used for computation …”

Section: Methodsmentioning

confidence: 99%

“…[14][15][16] Recently, a novel approach has been developed, enabling rapid computation of FFR pullbacks from three-dimensional (3D) quantitative coronary angiography. [17][18][19][20] The computational FFR, also termed quantitative flow ratio (QFR), can be obtained using two different flow models: fixed-flow QFR (fQFR) using empiric hyperemic flow velocity (HFV) and contrast-flow QFR (cQFR) using HFV derived from coronary angiography. 21,22 Because fQFR is based only on anatomic information obtained from 3D-QCA, it does not reflect the influence of microcirculation function.…”

mentioning

confidence: 99%

“…(b) cQFR pullback, frame count analysis was performed without pharmacologically induced hyperemia and the modeled HFV, which was derived according to the frame count used for computation. 17,21,22 The following quadratic function was applied to quantify the relation between baseline flow velocity with injection of contrast medium (contrast-flow velocity, CFV) and the modeled HFV:…”

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confidence: 99%

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Novel application of quantitative flow ratio for predicting microvascular dysfunction after ST‐segment‐elevation myocardial infarction

Sheng

Qiao

et al. 2020

Cathet Cardio Intervent

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Objectives This study evaluated quantitative flow ratio (QFR) to predict microvascular dysfunction (MVD) in patients with ST‐segment elevation myocardial infarction (STEMI). Background QFR is a novel approach for the rapid computation of fractional flow reserve based on three‐dimensional quantitative coronary angiography. We hypothesized that QFR computation could be used to predict MVD after STEMI. Methods Indexes such as contrast‐flow QFR (cQFR), fixed‐flow QFR (fQFR), and hyperemic flow velocity (HFV) were calculated in 130 STEMI patients with culprit lesion with ≥50% diameter stenosis and TIMI flow grade 2/3 in the spontaneously recanalized culprit artery on initial angiography. MVD was defined as microvascular obstruction determined by contrast‐enhanced cardiac magnetic resonance at a median of 5 days after percutaneous coronary intervention. Results Patients were divided into the MVD group (76/130, 58.5%) and non‐MVD group (54/130, 41.5%). Patients with MVD had higher cQFR‐fQFR value (0.080 ± 0.058 vs. 0.038 ± 0.039, p < .001) and lower modeled HFV (0.096 ± 0.044 vs. 0.144 ± 0.041 m/s, p < .001). Receiver operator characteristic curve analysis revealed that both the cQFR‐fQFR value (area under the curve, AUC = 0.716, p < .001) and modeled HFV (AUC = 0.805, p < .001) had high specificity and positive predictive value to predict MVD. In multivariable logistic analysis, cQFR‐fQFR was identified as an independent predictor of MVD (odds ratio = 9.800, p < .001). Conclusions This proof‐of‐concept study suggested that QFR computation may be a useful tool to predict MVD after STEMI (Trial Registration:NCT03780335).

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Validation of novel 3‐dimensional quantitative coronary angiography based software to calculate fractional flow reserve post stenting

Masdjedi¹,

Balbi²,

Nuis³

et al. 2020

Catheter Cardiovasc Interv

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Objectives To validate novel dedicated 3D‐QCA based on the software to calculate post PCI vessel‐FFR (vFFR) in a consecutive series of patients, to assess the diagnostic accuracy, and to assess inter‐observer variability. Background Low post percutaneous coronary intervention (PCI) fractional flow reserve (FFR) predicts future adverse cardiac events. However, FFR assessment requires the insertion of a pressure wire in combination with the use of a hyperemic agent. Methods FAST POST study is an observational, retrospective, single‐center cohort study. One hundred patients presenting with stable angina or non ST‐elevation myocardial infarction, who underwent post PCI FFR assessment using a dedicated microcatheter were included. Two orthogonal angiographic projections were acquired to create a 3D reconstruction of the coronary artery using the CAAS workstation 8.0. vFFR was subsequently calculated using the aortic root pressure. Results Mean age was 65±12 years and 70% were male. Mean microcatheter based FFR and vFFR were 0.91±0.07 and 0.91±0.06, respectively. A good linear correlation was found between FFR and vFFR (r = 0.88; p <.001). vFFR had a higher accuracy in the identification of patients with FFR values <0.90, AUC 0.98 (95% CI: 0.96‐1.00) as compared with 3D‐QCA AUC 0.62 (95% CI: 0.94‐0.74). Assessment of vFFR had a low inter‐observer variability (r = 0.95; p <.001). Conclusion 3D‐QCA derived post PCI vFFR correlates well with invasively measured microcatheter based FFR and has a high diagnostic accuracy to detect FFR <0.90 with low inter‐observer variability.

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TCT-332 Relation Between Fractional Flow Reserve And Coronary Bifurcation Anatomy: A Novel Quantitative Model To Assess The Stenotic Severity Of Bifurcation Lesions

Cited by 18 publications

References 0 publications

Quantification of disturbed coronary flow by disturbed vorticity index and relation with fractional flow reserve

Quantification of disturbed coronary flow by disturbed vorticity index and relation with fractional flow reserve

Novel application of quantitative flow ratio for predicting microvascular dysfunction after ST‐segment‐elevation myocardial infarction

Validation of novel 3‐dimensional quantitative coronary angiography based software to calculate fractional flow reserve post stenting

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