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      Angiography‐Derived Fractional Flow Reserve: More or Less Physiology?

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          Abstract

          Evidence robustly demonstrates that ischemia, rather than anatomy, is the optimal target for coronary revascularization. In the cardiac catheter laboratory, fractional flow reserve (FFR) and corresponding diastolic indices are regarded as the gold standard for physiological lesion assessment and ischemia detection (Table 1). Yet, despite a wealth of supporting data and indications in international guidelines, the use of FFR remains surprisingly low in the diagnostic assessment of coronary artery disease across the world.1, 2 To address this, multiple groups have developed methods for computing FFR from invasive angiography, without the need for passing a pressure wire or inducing hyperemia, thus removing the main barriers to uptake. Angiography‐derived FFR therefore has the potential to extend the benefits of physiological coronary lesion assessment to considerably more patients. Given the size of the interventional cardiology market, clinical and commercial motivation to deliver these tools as quickly as possible could hardly be greater. Several models are now approved as medical devices. Imminently, physicians and healthcare providers will have to decide whether to use these tools. But do they truly deliver physiology, and are they accurate enough? There are 3 particular areas of that deserve close scrutiny. Table 1 Angiography‐Based Coronary Physiological Assessment Techniques Index Abbreviation Calculated Equipment Potential Benefits Pitfalls/Limitations Fractional flow reserve FFR Whole cardiac cycle Pd/Pa at hyperemia Pressure wire Predicts percentage improvement in flow with PCI. Good clinical outcomes data Does not measure absolute flow and microvascular resistance Instantaneous wave‐free ratio/resting full‐cycle ratio iFR/RFR Pd/Pa during diastolic phase Pressure wire Good clinical outcome data, relative to FFR Does not measure absolute flow and microvascular resistance Index of myocardial resistance IMR (Pd) · (thermodilution derived mean transit time) Thermo‐ and pressure‐sensitive wire Microvascular resistance becoming of increasing interest (eg, PCI nonresponders, ANOCA, AMI, HFpEF) Thermodilution not widely used Hyperemic microvascular resistance HMR Pd/Doppler flow velocity Doppler and pressure wire Microvascular resistance becoming of increasing interest (eg, PCI nonresponders, ANOCA, AMI, HFpEF) Doppler flow velocity challenging to measure. Doppler wires not widely used Hyperemic stenosis resistance HSR (Pa‐Pd)/Doppler flow velocity Doppler and pressure wire Objective, direct measure of the resistance of proximal disease Doppler flow velocity challenging to measure. Doppler wires not widely used. Surrogate index Angiography‐derived FFR vFFR/FFRangio/QFR Fluid dynamics equations informed by anatomy Computational fluid dynamics software Delivering clinical benefits of FFR without factors that limit the invasive technique Relatively wide Bland–Altman limits of agreement compared with FFR. Requires excellent angiography. Less accurate in those with nonaverage microvascular resistance CT‐derived FFR CTFFR Fluid dynamics equations informed by anatomy Computational fluid dynamics software (offline) Reduce the number of unnecessary invasive catheterizations Relatively wide Bland–Altman limits of agreement compared with FFR Coronary flow reserve CFR (Hyperemic flow surrogate)/(baseline flow surrogate) Flow derived from Doppler velocity or thermodilution mean transit time Doppler or thermosensitive wire A surrogate for flow and vasodilatory reserve. Flow more important than pressure, but hard to measure Prone to same limitations as those for Doppler wire or thermodilution. Variability in baseline measurement can impair interpretation Absolute coronary flow Qb Infusion flow · (infusion temp/sensor temp) · 1.08 During continuous saline infusion Thermosensitive wire, pressure wire, monorail infusion catheter Predicts absolute (not percentage) coronary flow changes and microvascular resistance Additional time, expertise, and hardware All physiological indices are surrogate markers of physiology derived from other measures. AMI indicates acute myocardial infarction; ANOCA, angina and no obstructive coronary artery disease; FFR, fractional flow reserve; HFpEF, heart failure with preserved ejection fraction; MVR, microvascular resistance; Pa, proximal pressure; PCI, percutaneous coronary intervention; Pd, distal coronary pressure; and QFR, quantitative flow ratio. John Wiley & Sons, Ltd Simplification Methods for computing angiography‐derived FFR are software based. Three‐dimensional arterial anatomy is reconstructed from paired 2‐dimensional angiogram images. Mathematical equations that define hemodynamic laws are then applied to the reconstructed artery to predict the pressure dynamics along the artery, which are displayed as a color‐mapped 3‐dimensional artery. In an effort to rationalize these models to make them practical and expedient for clinical use, many groups have abandoned complex, numerical, computational fluid dynamics simulation in favor of analytical solutions based broadly upon the laws of Bernoulli and/or Poiseuille. These simpler physical laws characterize pressure losses attributable to convective acceleration and viscous friction, respectively. They are quick and simple to execute and perform well under steady (nonpulsatile), laminar flow conditions, in straight conduits. Coronary arteries, however, are not straight, and flow is pulsatile. Furthermore, these laws are unable to accurately characterize complex translesional pressure dynamics, particularly poststenosis pressure recovery, which is the basis of FFR. Some stenosis models make empiric assumptions or corrections for pressure loss and recovery. On average, these may perform adequately, but cannot represent the potentially complex flow patterns in a specific case. Moreover, they may be particularly vulnerable to inaccuracy in the context of serial lesions and diffuse disease in which 3‐dimensional computational fluid dynamics computations more reliably characterize interstenosis hemodynamic interaction. The impact this has on accuracy, in all disease patterns, is yet to be fully determined. Assumptions The discordance between angiographic severity and physiological (FFR) significance is well described and affects ≥30% of lesions. Discrepancies occur because, unlike angiography, FFR elegantly and automatically incorporates the combined and inter‐related effects of coronary flow and microvascular resistance. It is therefore imperative that computational models of angiography‐derived FFR include adequate physiological inputs or “tuning” to represent the maximum blood flow or minimum microvascular resistance; the latter dictates the former, which, in turn, dictates the pressure gradient and FFR. Hemodynamic equations are capable of accurately deriving a variety of physiological parameters, but only if other appropriate physiological inputs, such as flow or microvascular resistance, are included. A sensitivity analysis demonstrated that microvascular resistance was the dominant influence on angiography‐derived FFR, above and beyond the severity or anatomy of epicardial disease.3 Hyperemic flow and minimal microvascular resistance are variable in health and disease and are hard to measure, even with invasive instrumentation. Noninvasive models of angiography‐derived FFR therefore rely upon assumptions about these parameters, or predict them from surrogate markers such as arterial diameter. Again, empiric assumptions may be sufficient overall, for many cases, but will be inaccurate in nonaverage cases with discordant anatomy and physiology, that is, the very cases where FFR is superior to angiography. Therefore, unless models have an accurate method for achieving this, on a patient‐specific basis, the “physiological” prediction becomes simply a function of stenosis geometry and they cannot be a genuine model of FFR at all (Figure). As an example, 1 study of angiographically derived FFR observed a significant reduction in diagnostic accuracy in patients with elevated microvascular resistance.4 Paradoxically, physiologically weak models will appear more feasible relative to angiographic appearance, and a potential danger is that user confidence may therefore be increased with poorer methods. FFR has enabled a great stride forward in terms of physiologically guided revascularization. It would be unfortunate if, in an attempt to increase physiological assessment, we were to take half a step back toward assessment based on epicardial arterial anatomy. Table 2 summarizes major trials of angiography‐derived FFR.4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 Figure 1 Error in angiography‐derived FFR. (A) An anatomically severe circumflex case. In this case, the method applied an assumed value for microvascular resistance based on a population average, which resulted in considerable disagreement between angiography‐derived and invasive FFR (0.55 vs 0.82). (B) Bland–Altman plot from a meta‐analysis of 13 studies (1842 vessels). There is minimal bias (gray line), but the ±95% limits of agreement were FFR ±0.14. FFR indicates fractional flow reserve. Reprinted from Collet et al20 with permission. Copyright ©2018, Oxford University Press. Table 2 Major Trials/Studies of Angiographically Derived FFR Author Study Year N=Arteries Surrogate/Software/Company Mathematical Solution Diagnostic Accuracy 95% Limits of Agreement Morris et al5 VIRTU‐1 2013 35 vFFR from VIRTUheart (University of Sheffield) Transient 3D CFD 97% FFR ±0.16 Tu et al6 FAVOUR Pilot 2016 84 QFR from QAngio XA (Medis Medical Imaging Systems, NL) Empiric flow velocity (fQFR), TIMI frame counting‐derived contrast velocity at baseline (cQFR) and under hyperemia (aQFR). Analytical equations based on laws of Bernoulli and Poiseuille fQFR 80% cQFR 86% aQFR 87% FFR ±0.14 FFR ±0.12 FFR ±0.13 Kornowski et al7 FFRangio FIM 2016 101 FFRangio (CathWorks, Israel) Simple analytical equation, based on law of Poiseuille 94% FFR ±0.10 Trobs et al8 FFRangio 2016 100 FFRangio from Syngo IZ3D and prototype software (Siemens Healthcare GmbH, Germany) CFD based on BP, anatomy, and literature estimates of microvascular resistance 90% FFR ±0.13 Pellicano et al9 FFRangio validation 2017 203 FFRangio (CathWorks, Israel) Simple analytical equation, based on law of Poiseuille 93% FFR ±0.10 Xu et al10 FAVOUR II China 2017 328 QFR from QAngio XA (Medis Medical Imaging Systems, NL) TIMI frame counting‐derived contrast velocity at baseline (cQFR). Analytical equations based on laws of Bernoulli and Poiseuille 93% FFR ±0.13 Yazaki et al11 QFR in intermediate lesions 2017 151 QFR from QAngio XA (Medis Medical Imaging Systems, NL) TIMI frame counting‐derived contrast velocity at baseline (cQFR). Analytical equations based on laws of Bernoulli and Poiseuille 88% FFR ±0.10 Westra et al12 WIFI II 2018 240 QFR from QAngio XA (Medis Medical Imaging Systems, NL) TIMI frame counting‐derived contrast velocity at baseline (cQFR). Analytical equations based on laws of Bernoulli and Poiseuille 83% FFR ±0.16 Mejía‐Rentería et al4 QFR IMR study 2018 300 QFR from QAngio XA (Medis Medical Imaging Systems, NL) TIMI frame counting‐derived contrast velocity at baseline (cQFR). Analytical equations based on laws of Bernoulli and Poiseuille IMR <23 =88% IMR ≥23 =76% FFR ±0.12 FFR ±0.15 Westra et al13 FAVOUR II EJ 2018 317 QFR from QAngio XA (Medis Medical Imaging Systems, NL) TIMI frame counting‐derived contrast velocity at baseline (cQFR). Analytical equations based on laws of Bernoulli and Poiseuille 87% FFR ±0.12 Fearon et al14 FAST‐FFR 2019 319 FFRangio (CathWorks, Israel) Simple analytical equation, based on law of Poiseuille 92% FFR ±0.13 Omori et al15 FFRangio in multivessel disease 2019 118 FFRangio (CathWorks, Israel) Simple analytical equation, based on law of Poiseuille 92% FFR ±0.14 Stahli et al16 All comer QFR 2019 516 QFR from QAngio XA (Medis Medical Imaging Systems, NL) TIMI frame counting‐derived contrast velocity at baseline (cQFR). Analytical equations based on laws of Bernoulli and Poiseuille 93% FFR ±0.07 Masdjedi et al17 FAST‐study 2019 100 vFFR from 3D QCA software, CAAS workstation (PIE Medical Imaging, NL) Simple analytical equation, based on laws of Bernoulli and Poiseuille AUC=0.93 FFR ±0.07 Li et al18 FLASH‐FFR 2019 328 caFFR from FlashAngio (Rainmed Ltd, China) CFD based on postangiography TIMI frame counting of flow velocity 96% FFR ±0.10 Listed in chronological order. Invasive FFR (threshold ≤0.80) was comparator in each study. 3D indicates 3‐dimensional; aQFR, adenosine QFR; AUC, area under the curve; BP, blood pressure; caFFR, coronary angiography–derived fractional flow reserve; CFD, computational fluid dynamics; cQFR, contrast QFR; EJ, Europe and Japan; FFR, fractional flow reserve; FFRangio, FFR derived from coronary angiography; FIM, first in man; fQFR, fixed QFR; IMR, index of microcirculatory resistance; QFR, quantitative flow ratio; TIMI, thrombolysis in myocardial infarction; and vFFR, virtual fractional flow reserve. John Wiley & Sons, Ltd Accuracy and Error Range Headline validation results report “diagnostic” accuracy. This quantifies how well a method predicts physiological significance or nonsignificance (FFR ≤0.80), relative to invasive FFR, expressed as sensitivity, specificity, positive, and negative predictive values, area under a receiver operating curve, and overall diagnostic accuracy. Diagnostic accuracy is a function of (1) the method's accuracy and (2) the cases included in a particular study. The fewer cases close to the 0.80 threshold, the better the diagnostic accuracy will appear and vice versa. This is nicely illustrated in a study of FFR computed from computed tomography coronary angiography in which the diagnostic accuracy was 82% overall, but only 46% in cases in FFR were 0.70 to 0.80, which is precisely the range where most accuracy is required.19 The best test of how accurately angiography‐derived FFR agrees with invasive FFR is to plot the differences between predicted and observed FFR values against the mean (ie, a Bland–Altman plot). From this, the mean difference (delta), which quantifies any bias in the angiography‐derived method, and the 95% limits of agreement, are calculated. The limits of agreement (±1.96 SDs) comprise 95% of observed differences and are akin to the 95% CI of a computed, angiography‐derived FFR result or an error range (Figure). The wider the limits of agreement, the larger the method's error and vice versa. Unlike diagnostic accuracy, the limits of agreement are only a function of how accurate a method is. A recent meta‐analysis of 13 studies of angiography‐derived FFR demonstrated impressive diagnostic accuracy (sensitivity, 89%; specificity, 90%), but more‐sobering agreement, with limits of agreement of FFR ±0.14.20 This is remarkably similar to FFR computed from computed tomography in the NXT trial (limits of agreement FFR ±0.15).21 FFR computed from computed tomography, however, is a noninvasive screening tool, best used to reduce unnecessary invasive catheterization. Arguably, the accuracy “bar” should be set far higher for a test in the catheter laboratory, where results directly influence decisions regarding proceeding to percutaneous or surgical intervention. Is FFR ±0.14 accurate enough for interventional decision making? It is likely that noninferiority trials will be used to assess these methods. These should avoid the usual pitfalls and be appropriate in terms of power, significance, analysis protocol, sample size, patient population, and prespecified noninferiority margins. Moreover, it remains to be seen how accurate and reproducible these methods are, beyond academic core laboratories, in the hands of those who will be expected to use these tools (ie, the interventional cardiologist operating in the catheter laboratory). Conclusions Angiography‐derived FFR has the potential to change clinical practice for the considerable benefit of patients by providing routine physiological data, together with coronary anatomy, to provide personalized management and improved clinical outcomes. However, deriving physiology from anatomy is challenging and requires assumptions. Model simplification and physiological assumptions, based on extrapolated or averaged data, are likely to work in the majority of patients. However, much of FFR's success lies in its ability to identify those cases where nonstandard microvascular resistance and/or flow result in discordant physiology and anatomy. It is therefore important that models of angiography‐derived FFR retain the same patient‐specific physiology that separates traditional FFR from angiography, or at least that they highlight which cases require more‐reliable assessment. Operators must understand how accuracy and error are defined in all patient groups. Stringent validation is required to prove that models are accurate and physiologically sound, in the hands of those who will be using them. If this can be achieved, clinicians have the potential to achieve what could be a new level of patient‐specific medicine. Sources of Funding Dr Morris is funded by a Wellcome Trust Clinical Research Career Development Fellowship (214567/Z/18/Z). Disclosures Dr Morris has previously received honoraria (speaking fees) from Abbott UK. Professor Curzen has received unrestricted research grants from HeartFlow and Boston Scientific for the FORECAST and RIPCORD2 trials, respectively. The remaining authors have no disclosures to report.

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          Diagnostic Accuracy of Fast Computational Approaches to Derive Fractional Flow Reserve From Diagnostic Coronary Angiography: The International Multicenter FAVOR Pilot Study.

          The aim of this prospective multicenter study was to identify the optimal approach for simple and fast fractional flow reserve (FFR) computation from radiographic coronary angiography, called quantitative flow ratio (QFR).
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            Diagnostic Accuracy of Angiography-Based Quantitative Flow Ratio Measurements for Online Assessment of Coronary Stenosis.

            Quantitative flow ratio (QFR) is a novel angiography-based method for deriving fractional flow reserve (FFR) without pressure wire or induction of hyperemia. The accuracy of QFR when assessed online in the catheterization laboratory has not been adequately examined to date.
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              Diagnostic Performance of In‐Procedure Angiography‐Derived Quantitative Flow Reserve Compared to Pressure‐Derived Fractional Flow Reserve: The FAVOR II Europe‐Japan Study

              Background Quantitative flow ratio (QFR) is a novel modality for physiological lesion assessment based on 3‐dimensional vessel reconstructions and contrast flow velocity estimates. We evaluated the value of online QFR during routine invasive coronary angiography for procedural feasibility, diagnostic performance, and agreement with pressure‐wire–derived fractional flow reserve (FFR) as a gold standard in an international multicenter study. Methods and Results FAVOR II E‐J (Functional Assessment by Various Flow Reconstructions II Europe‐Japan) was a prospective, observational, investigator‐initiated study. Patients with stable angina pectoris were enrolled in 11 international centers. FFR and online QFR computation were performed in all eligible lesions. An independent core lab performed 2‐dimensional quantitative coronary angiography (2D‐QCA) analysis of all lesions assessed with QFR and FFR. The primary comparison was sensitivity and specificity of QFR compared with 2D‐QCA using FFR as a reference standard. A total of 329 patients were enrolled. Paired assessment of FFR, QFR, and 2D‐QCA was available for 317 lesions. Mean FFR, QFR, and percent diameter stenosis were 0.83±0.09, 0.82±10, and 45±10%, respectively. FFR was ≤0.80 in 104 (33%) lesions. Sensitivity and specificity by QFR was significantly higher than by 2D‐QCA (sensitivity, 86.5% (78.4–92.4) versus 44.2% (34.5–54.3); P<0.001; specificity, 86.9% (81.6–91.1) versus 76.5% (70.3–82.0); P=0.002). Area under the receiver curve was significantly higher for QFR compared with 2D‐QCA (area under the receiver curve, 0.92 [0.89–0.96] versus 0.64 [0.57–0.70]; P<0.001). Median time to QFR was significantly lower than median time to FFR (time to QFR, 5.0 minutes [interquartile range, –6.1] versus time to FFR, 7.0 minutes [interquartile range, 5.0–10.0]; P<0.001). Conclusions Online computation of QFR in the catheterization laboratory is clinically feasible and is superior to angiographic assessment for evaluation of intermediary coronary artery stenosis using FFR as a reference standard. Clinical Trial Registration URL: https://www.clinicaltrials.gov. Unique identifier: NCT02959814.
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                Author and article information

                Contributors
                paul.morris@sheffield.ac.uk
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                11 March 2020
                17 March 2020
                : 9
                : 6 ( doiID: 10.1002/jah3.v9.6 )
                : e015586
                Affiliations
                [ 1 ] Department of Infection, Immunity and Cardiovascular Disease University of Sheffield United Kingdom
                [ 2 ] Department of Cardiology Sheffield Teaching Hospitals NHS Foundation Trust Sheffield United Kingdom
                [ 3 ] Insigneo Institute for In Silico Medicine University of Sheffield United Kingdom
                [ 4 ] Coronary Research Group University Hospital Southampton NHS Foundation Trust Southampton United Kingdom
                [ 5 ] Faculty of Medicine University of Southampton United Kingdom
                Author notes
                [*] [* ]Correspondence to: Paul D. Morris, PhD, MRCP, Mathematical Modelling in Medicine Group, Department of Infection, Immunity and Cardiovascular Science, University of Sheffield, The Medical School, Beech Hill Road, Sheffield, S102RX, United Kingdom. E‐mail: paul.morris@ 123456sheffield.ac.uk
                Author information
                https://orcid.org/0000-0002-3965-121X
                Article
                JAH34949
                10.1161/JAHA.119.015586
                7335504
                32157954
                43d58ed7-e1ea-4020-bb49-9e0aba4d9512
                © 2020 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 1, Tables: 2, Pages: 6, Words: 3883
                Funding
                Funded by: Wellcome Trust Clinical Research Career Development Fellowship
                Award ID: 214567/Z/18/Z
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                2.0
                17 March 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.8 mode:remove_FC converted:17.03.2020

                Cardiovascular Medicine
                computational flow dynamics,computer‐based model,coronary microvascular resistance,fractional flow reserve,imaging,coronary circulation,translational studies,angiography,revascularization

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