Validation of a three-dimensional quantitative coronary angiography-based software to calculate fractional flow reserve: the FAST study

Masdjedi, Kaneshka; Zandvoort, Laurens J.C. van; Balbi, Matthew Mercieca; Gijsen, Frank; Ligthart, Jürgen; Rutten, Marcel; Lemmert, Miguel E.; Wilschut, Jeroen; Diletti, Roberto; Jaegere, Peter de; Zijlstra, Felix; Mieghem, Nicolas Van; Daemen, Joost

doi:10.4244/eij-d-19-00466

Cited by 99 publications

(150 citation statements)

References 33 publications

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“…The use of three-dimensional QCA could be a potential alternative to lessen interand intra-core lab variability in LL, because accurate and robust reconstruction of the vessel centerline is achieved and the reproducibility of its applications, for example, the assessments of obstruction length and optimal viewing angle, is guaranteed. 2,19 Difference in projection number and its effects on QCA outcome We assumed that the number of projection(s) might have affected the MLD measurements because a single view could show larger %DS than mean of two orthogonal views in eccentric lesions. However, this was not the case in our study.…”

Section: Variability In Initial Data Between the Two Core Labsmentioning

confidence: 99%

“…The use of three-dimensional QCA could be a potential alternative to lessen inter- and intra-core lab variability in LL, because accurate and robust reconstruction of the vessel centerline is achieved and the reproducibility of its applications, for example, the assessments of obstruction length and optimal viewing angle, is guaranteed. 2,19…”

Section: Variability In Initial Data Between the Two Core Labsmentioning

confidence: 99%

“…1 Beyond angiographic metrics, recently developed angiography-derived fractional flow reserve (FFR) may be a useful tool for diagnosing ischemia-producing lesions in patients with non-complex coronary artery disease. 1,2 Meanwhile, although QCA metrics such as acute gain and late lumen loss proved to be instrumental in assessing new technologies, it is true that there are still multiple factors that affect QCA results, including guide catheter size, 3 contrast injection into the vessel, cine projection selection, number of cine projections, cine frame selection, 4 and decision of reference vessel segment. During the QCA process, manual processes cannot be completely eliminated.…”

Section: Introductionmentioning

confidence: 99%

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Inter- and intra-core laboratory variability in the quantitative coronary angiography analysis for drug-eluting stent treatment and follow up

Ito

Kinoshita²,

Endo³

et al. 2020

Therapeutic Advances in Cardiovascular Disease

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Aim: To evaluate inter-core laboratory variability of quantitative coronary angiography (QCA) parameters in comparison with intra-core laboratory variability in a randomized controlled trial evaluating drug-eluting stents. Methods: A total of 50 patients with 62 coronary lesions were analyzed by four analysis experts belonging to an Angiographic Core Laboratory (ACL: 1 expert) and a Cardiovascular Imaging Core Laboratory (CICL: 3 experts). QCA was based on the same standard operating procedure, but selections of projection and cine frames were at the discretion of each analyst. Inter- and intra-core laboratory variabilities were evaluated by accuracy, precision, Bland Altman analysis, and coefficient of variation. Results: Pre-MLD (minimal lumen diameter) was significantly smaller in results from ACL than those from all CICL experts. Number of analyzed projections did not affect pre-MLD results. Acute gain was larger in ACL than in CICL2. No significant difference was observed in late loss and loss index between inter-core laboratories. Agreement between core labs in the Bland-Altman analysis for each QCA parameter was as follows (mean difference, 95% limits of agreement): pre-MLD (–0.32, –0.74 to 0.10), stent MLD (0.08, –0.28 to 0.44), acute gain (0.22, –0.44 to 0.88), and late loss (–0.07, –0.69 to 0.55). Agreement between analysts in CICL (mean difference, 95% limits of agreement) was: pre MLD (–0.03, –0.37 to 0.31), stent MLD (0.15, –0.15 to 0.45), acute gain (0.05, –0.45 to 0.55), and late loss (0.04, –0.52 to 0.60). The widest limits of agreement among three analyses were shown in both analyses. Width of limited agreement in the intra-core laboratory analysis tended to be smaller than the inter-core laboratory analysis with these parameters. Coefficient of variation tended to be larger in lesion length (LL), acute gain, late loss, and loss index in inter- and in intra- core laboratory comparisons. Conclusion: Inter-core laboratory QCA variability in late loss and loss index analysis could be similar to intra-core laboratory variability, but more strict alignment between core laboratories would be necessary for initial procedural data analysis.

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Section: Variability In Initial Data Between the Two Core Labsmentioning

confidence: 99%

Section: Variability In Initial Data Between the Two Core Labsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Inter- and intra-core laboratory variability in the quantitative coronary angiography analysis for drug-eluting stent treatment and follow up

Ito

Kinoshita²,

Endo³

et al. 2020

Therapeutic Advances in Cardiovascular Disease

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“…CT-based FFR analysis for clinical diagnosis is gaining mainstream acceptance with various health governing bodies such as UK National Institute of Care and Excellence (NICE) recommending it as an diagnostic option, and the performance has been demonstrated in various clinical studies. [11][12][13] Effort has been made to further this technology, mostly to make it simplified for faster computation such as QFR [14], vFFR [15] and for the use with wider variety of imaging modality.…”

Section: Introductionmentioning

confidence: 99%

On outflow boundary conditions for CT-based computation of FFR: Examination using PET images

Menezes

Torii

2020

Medical Engineering & Physics

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CT-based computations of fractional flow reserve (FFR) have been widely utilized for evaluating functional severity of a coronary artery stenosis. Whilst this approach has been successful clinically, assumptions involved in the analysis still need to be investigated for further improvement of predictive accuracy. To better understand the sensitivity of computational FFRs on outflow boundary condition-typically reflecting patient's own physiology only through anatomical features-FFR computations for 10 patients with different degree of stenosis was conducted. The computations were based on 3D anatomical model reconstructed from CT images and patient-specific in/outflow boundary conditions (BC). Two outflow BCs were considered: (1) conventional morphology-based and (2) PET perfusion-based conditions. The results showed that the FFRs derived from the two boundary conditions agree in general. It was also found that the FFRs computed with the morphology-based BC tend to estimate higher functional severity, especially in patients with reduced vasodilatory response under hyperaemia-an essential physiological condition in FFR measurement. Further investigation was made by varying hyperaemic resistances (30%-90% of the baseline) in the morphology-based BC. The variation of FFR for the varied resistances was narrow for patients with mild stenosis and wider for those who have severe stenosis. This latter approach confirmed that variability of FFR due to outflow condition tends to come from overestimation of vasodilatory response, especially those who have abnormal myocardial perfusion. The results suggest that outflow conditions that are more representative of each patient could be an effective way to improve CT-based FFR computation.

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“…Noninvasive FFR (NiFFR) or quantitative flow ratio (QFR) draws upon computational flow dynamics along with information extracted from CAG to virtually simulate FFR 9 . The diagnostic performance of the modality has been assayed by several investigators, who have reported acceptable accuracy, sensitivity, and specificity 10‐18 . In all of these investigations, due to the supposed limitations of 2D images, a 3D reconstruction of the coronary artery segments has been used for computational modeling 19 …”

Section: Introductionmentioning

confidence: 99%

Diagnostic accuracy of two‐dimensional coronary angiographic‐derived fractional flow reserve—Preliminary results

Babakhani

Sadeghipour

Beheshti

et al. 2020

Cathet Cardio Intervent

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Aim Noninvasive fractional flow reserve (NiFFR) is an emerging method for evaluating the functional significance of a coronary lesion during diagnostic coronary angiography (CAG). The method relies on the computational flow dynamics and the three‐dimensional (3D) reconstruction of the vessel extracted from CAG. In the present study, we sought to evaluate the diagnostic performance and applicability of 2D‐based NiFFR. Methods In this prospective observational study, we evaluated 2D‐based NiFFR in 279 candidates for invasive CAG and invasive fractional flow reserve (FFR). NiFFR was calculated via two methods: variable NiFFR, in which the contrast transport time was extracted from the angiographic view, and fixed NiFFR, in which a prespecified frame count was applied. Results The final analysis was performed on 245 patients (250 lesions). Variable NiFFR had an area under the receiver operating characteristic curve of 81.5%, an accuracy of 80.0%, a sensitivity of 82.2%, a specificity of 82.2%, a negative predictive value of 91.4%, and a positive predictive value of 63.6%. The mean difference between FFR and NiFFR was −0.0244 ±.0616 (p ≤.0001). A pressure wire‐free hybrid strategy was possible in 68.8% of our population with variable NiFFR. Conclusions Our 2D‐based NiFFR yielded results comparable to those derived from 3D‐based software. Our findings should; however, be confirmed in larger trials.

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Validation of a three-dimensional quantitative coronary angiography-based software to calculate fractional flow reserve: the FAST study

Cited by 99 publications

References 33 publications

Inter- and intra-core laboratory variability in the quantitative coronary angiography analysis for drug-eluting stent treatment and follow up

Inter- and intra-core laboratory variability in the quantitative coronary angiography analysis for drug-eluting stent treatment and follow up

On outflow boundary conditions for CT-based computation of FFR: Examination using PET images

Diagnostic accuracy of two‐dimensional coronary angiographic‐derived fractional flow reserve—Preliminary results

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