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Posts Tagged ‘Invasive FFR for PCI’


What is the Role of Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT vs Invasive FFR for PCI?

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 7/17/2018

WATCH VIDEO – Interview with Patrick Serruys, MD, PhD, Prof. of Interventional Cardiology, Imperial College, London

VIDEO: Will CT-FFR Replace Diagnostic Angiograms?

VIDEOS | COMPUTED TOMOGRAPHY (CT) | JULY 17, 2018

An interview with Patrick Serruys, M.D., Ph.D., Imperial College London, principal investigator of the SYNTAX III Trial presented earlier this year as a late-breaker at EuroPCR. He presented the trial again at the Society of Cardiovascular Computed Tomography (SCCT) 2018 meeting.

Read the article “SYNTAX III Revolution Trial Shows CT-FFR Could Replace Cine-angiography in Coming Years.”

SOURCE

https://www.dicardiology.com/videos/video-will-ct-ffr-replace-diagnostic-angiograms-0

What is the Role of Noninvasive Diagnostic Fractional Flow Reserve (FFR) CT vs Invasive FFR for PCI?

02/27/2018

We know that FFRCT, the method of obtaining FFR from computed tomography angiographic (CTA) images, has been approved by Medicare and other third-party payers. It is used before patients come to the cath lab. The use of FFRCT in the PLATFORM study1reduced the number of unnecessary cardiac caths that had normal coronary angiography, while maintaining the same number of patients needing PCI.  Before discussing the role of angio-derived FFR, let’s review how FFRCT is obtained (Figure 1). Starting with any good quality CTA, the images are sent, offline, to HeartFlow Inc.2 To derive the FFR, the CTA images are reconstructed into a 3-dimensional coronary tree, segmenting it into individual points with each point undergoing processing by specialized equations (i.e., Navier-Stokes equations) to compute pressure loss along the course of the artery at rest and again during an assumed hyperemic state. These computational fluid dynamic equations require several assumptions from a population model regarding the myocardial blood flow rates as a function of the myocardial arterial branches and the resistance of the myocardium. These values are put into the computational flow dynamics (CFD) model, and using high-power computers, the FFR is generated along the entire course of each vessel. FFRCT has been validated against invasive FFR and found to be about 80% correlative in several studies.3,4 FFRCT has better correlation with FFR than most stress tests, and based on clinical outcome data, will likely replace traditional stress testing, with a reduction in procedures in patients without significant coronary disease. However, there are some operators who may be confused, thinking that FFRCT will replace invasive FFR. FFRCT screens for important coronary artery disease (CAD) before the patient comes to the cath lab, and then once in the lab, the operators can confirm lesion significance with FFR.

Noninvasive FFR Derived From Angiography: Wireless FFR in the Lab?

Wouldn’t it be great to get the FFR from the angiogram without having to put in a guidewire? This is in our near future. The generation of a “virtual” FFR derived from angiography or other modalities (Table 1A-B, Figures 2-4) has been proposed using computational flow dynamics (CFD) or rapid flow analysis to obtain wireless image-based FFR, incorporated into the diagnostic angiography workflow. As one might expect, online implementation of angio-derived FFR requires novel concepts and systems to reduce computation time and make the analysis process acceptable to in-lab functions. Early data shows that angio-derived FFR can be obtained within several minutes during a regular coronary angiogram.5

Angio-FFR Validation StudiesTwo contenders for introduction to the cath labs in the near future are QFR and FFRangio. QFR (Quantitative Flow Ratio, Medis Medical Imaging Systems) validation was reported in the FAVOR II China study, which reported the vessel-level diagnostic accuracy of QFR in identifying hemodynamically-significant coronary stenosis was 97.7% and patient-level diagnostic accuracy was 92.4% (P<0.001 for both).6 In addition, the FAVOR II Europe-Japan trial demonstrated that QFR had superior sensitivity and specificity in comparison to 2-D QCA with FFR as the gold standard: 88% vs 46% and 88% vs 77% (P<0.001 for both). The overall diagnostic accuracy of QFR was 88%.7 For FFRangio (CathWorks), the sensitivity, specificity, and diagnostic accuracy of FFRangio were 88%, 95%, and 93%, respectively.5 The strong concordance with invasive, wire-based FFR will likely make these methods widely available, but of course, early favorable results require confirmation. Once confirmed in larger studies and for a wider spectrum of coronary lesions, angio-derived FFR should become a routine part of diagnostic angiography.

Accuracy in computing noninvasive FFR is based on the implementation of complex computational methods that can differ among the various competing methods. In contrast to FFRCT, which creates a complete and detailed 3D model of the coronary tree from CTA scans, Tu et al8 constructed vessel geometry from routine angiography, applying a simpler model for flow, derived from the division of coronary branches (as opposed to using an estimate of flow from myocardial mass)2, and an approximate algebraic computational method from experimental studies of flow through single arterial stenosis models8 (as opposed to CFD equations) to solve for pressure drop and FFR (Figure 5). Because Tu et al8 do not employ the complicated Navier-Stokes equations, the computational time is almost instantaneous once the geometry is segmented into “sub segments” from the 3D rendering. Pellicano et al5 constructed 3D artery geometry from routing angiography alone, applying rapid flow analysis where all stenoses are converted into resistances in a lumped model, while scaling laws (of branches) are used to estimate the microcirculatory bed resistance.

Competition for a winning method of angiographically-derived FFR is underway, with different companies using different models and different assumptions regarding flow and resistance inputs (Table 1A-B). An example is QFR that uses several assumptions related to flow variables. fQFR is specified hyperemic inflow, assuming a fixed inflow velocity of 0.35 m/s. cQFR is “virtual” hyperemic flow, determined from a model based on TIMI [Thrombolysis In Myocardial Infarction] frame count, relating measured flow under baseline conditions to hyperemic flow. Lastly, aQFR is the variable of directly measured hyperemic flow. From these assumptions, QFR gives highly comparable results to invasive FFR.

Advantages of Angio-Derived FFR

The in-lab computations of angio-derived FFR are fast and have the potential to provide wireless FFR lesion assessment to every angiographic procedure. Other advantages of angio-derived FFR are obvious. There is no need to insert a pressure guidewire. Pharmacologic hyperemia is not necessary. It is nearly operator independent. The angio-derived FFR is also co-registered on the angiogram with accurate and reproducible results. In addition, 3D reconstruction of the coronary tree can enhance the identification of reference vessel diameters for selection of stent sizing, and ultimately predict anatomic and physiological outcomes.5

Limitations of Angio-Derived FFR 

The image acquisition requirements and the user interface of an image-based FFR system should be seamlessly incorporated into the standard work of the catheterization laboratory. Data acquisition should minimally disrupt routine angiography. Angio-derived FFR should only require the acquisition of 2 to 3 conventional radiographic projections in which the lesions can be clearly seen. It is important to visualize the entire coronary tree on the screen and to optimize vessel opacification. Poor images or overlapped segments will limit the accuracy of angio-derived FFR. The image acquisition angles needed for angio-derived FFR are the same as those used for routine procedures. High resolution imaging at >10 frames/sec are needed.5

On the technical side, coronary microvascular resistance (CMV) is a fundamental assumption to compute pressure from flow. CMV in one study was derived from invasive measurements, something which will limit future acceptance.9 As the data sets are accumulated, it is hoped that invasive CMV will not be needed. One angio-derived FFR method, vFFR9,10, requires rotational angiography, which is not yet widely available, and may produce asymmetric coronary segmentations — a concern for accurate analysis.

Finally, the amount of time required to acquire and process the data to produce angio-derived FFR is likely to be longer than the 3-minute computation time. Acquisition time should realistically include the time to overcome the difficulties of imaging complex anatomy, eliminate artifacts, upload the study for CFD analysis, and create the volumetric mesh. Furthermore, there will probably be patient-specific errors related to abnormal coronary physiology which may account for outliers in the correlations between angiography-derived and invasive FFR measurements.11

Angio-derived FFR is currently reported for off-line results, but, recently, online applications have also been presented. Minimal operator interaction is necessary in the flow calculation process, which results in low inter-operator variability.

The Bottom Line

When FFRCT and angio-derived FFR technology ultimately become more widely available, they will radically change the way diagnostic angiography is performed in the same way that invasive FFR changed the way we approach patients needing PCI

References

  1. Douglas PS, De Bruyne B, Pontone G, et al. 1-Year Outcomes of FFRCT-Guided Care in Patients With Suspected Coronary Disease: The PLATFORM Study.  J Am Coll Cardiol. 2016 Aug 2; 68(5): 435-445. doi: 10.1016/j.jacc.2016.05.057.
  2. Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis. J Am Coll Cardiol. 2013; 61(22): 2233-2241.
  3. Norgaard BL, Leipsic J, Gaur S, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease. J Am Coll Cardiol. 2014; 63: 1145-1155.
  4. Min JK, Leipsic J, Pencina MJ, et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA. 2012; 308: 1237-1234.
  5. Pellicano M, Lavi I, Bruyne B, et al. Validation study of image-based fractional flow reserve during coronary angiography. Circ Cardiovasc Interv. 2017; 10: e005259. doi: 10.1161/CIRCINTERVENTIONS.116.005259.
  6. Xu B, Tu S, Qiao S, et al. Diagnostic accuracy of angiography-based quantitative flow ratio measurements for online assessment of coronary stenosis. J Am Coll Cardiol. 2017 Dec 26; 70(25): 3077-3087. doi: 10.1016/j.jacc.2017.10.035.
  7. Westra J. Late-Breaking Clinical Trials 2. Presented at: TCT Scientific Symposium; Oct. 29-Nov. 2, 2017; Denver, Colorado.
  8. Tu S, Westra J, Yang J, et al. Diagnostic accuracy of fast computational approaches to derive fractional flow reserve from diagnostic coronary angiography: the international multicenter FAVOR pilot study. J Am Coll Cardiol Intv. 2016; 9: 2024-2035.
  9. Morris PD, van de Vosse FN, Lawford PV, et al. “Virtual” (computed) fractional flow reserve: current challenges and limitations. JACC Cardiovasc Interv. 2015; 8: 1009-1017. doi: 10.1016/j.jcin.2015.04.006.
  10. Morris PD, Ryan D, Morton AC, et al. Virtual fractional flow reserve from coronary angiography: modeling the significance of coronary lesions: results from the VIRTU-1 (VIRTUal Fractional Flow Reserve From Coronary Angiography) study. JACC Cardiovasc Interv. 2013; 6: 149-157. doi: 10.1016/j.jcin.2012.08.024.
  11. Papafaklis MI, Muramatsu T, Ishibashi Y, et al. Fast virtual functional assessment of intermediate coronary lesions using routine angiographic data and blood flow simulation in humans: comparison with pressure wire – fractional flow reserve. EuroIntervention. 2014; 10: 574-583. doi: 10.4244/EIJY14M07_01
  12. Tu S, Barbato E, Köszegi Z, et al. Fractional flow reserve calculation from 3-dimensional quantitative coronary angiography and TIMI frame count: a fast computer model to quantify the functional significance of moderately obstructed coronary arteries. JACC Cardiovasc Interv. 2014 Jul; 7(7): 768-777. doi: 10.1016/j.jcin.2014.03.004.

Disclosure: Dr. Kern is a consultant for Abiomed, Merit Medical, Abbott Vascular, Philips Volcano, ACIST Medical, Opsens Inc., and Heartflow Inc. 

SOURCE

https://www.cathlabdigest.com/article/Noninvasive-Angiographic-Derived-FFR-Wireless-Physiology-Coming-Your-Cath-Lab-Soon

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