Author + information
- Ran Kornowski, MD∗ (, )
- Ifat Lavi, PhD,
- Mariano Pellicano, MD,
- Panagiotis Xaplanteris, MD, PhD,
- Hana Vaknin-Assa, MD,
- Abid Assali, MD,
- Orna Valtzer, DMD,
- Yonit Lotringer, MSc and
- Bernard De Bruyne, MD, PhD
- ↵∗Department of Interventional Cardiology, Rabin Medical Center, Jabotinsky Road, Petach Tikva, 49100, Israel
Pressure wire-based fractional flow reserve (FFR) has become the standard of reference for decision making regarding coronary revascularization. Deriving FFR from routine angiograms could facilitate the uptake of FFR-based clinical decisions. Several angiography-derived FFR methods have recently been introduced (1,2). These methods are based on computational fluid dynamic simulations. FFRangio is a novel technology providing a functional angiography mapping of the coronaries in 3D based on a rapid flow analysis of a dynamically derived lumped model that can assess FFR using routine angiograms and hemodynamic data.
The primary element of the angiogram-based FFR measurement is the 3-dimensional (3D) rebuilding of the coronary tree, after which the system scans the entire reconstructed tree in 3D and analyzes each branch as well as each bifurcation (or trifurcation), looking for narrowed regions. A hemodynamic evaluation follows, where the contribution of each narrowing to the total resistance to flow is taken into account and a subsequent lumped model is built. This allows pressure drops and flow rates to be estimated. The accumulated volume of the coronary tree and the total coronary length, calculated from a reconstruction of its geometry, enable an estimation of normal supply through an assessment of the microcirculatory bed resistance. The solution of the lumped model based on the inlet and outlet boundary conditions allows us to evaluate ratios of flow rate for stenosed versus “healthy” coronary trees.
Eighty-eight patients with stable angina were included in this study. FFR measurements were performed for clinical reasons in ≥1 coronary artery. The stenosis was clearly delineated on the angiograms. Patients with left main stenoses, ostial stenosis, in-stent restenosis at the target vessel, and previous bypass surgery were excluded (7.7%).
At least 2 angiographic projections of the vessel to be measured were acquired at 15 frames/s. The exact inclination of the radiographic tube was left to the operator’s discretion. Care was taken to fill the artery as completely as possible with contrast medium and to image the entire coronary tree at each view. The FFRangio computations were performed offline from Digital Imaging and Communications in Medicine (DICOM) format files by operators not present in the catheterization laboratory and blinded to the invasive FFR results. Each series of DICOM cine sequences was loaded and processed along with the patient’s mean aortic pressure obtained at the time the angiogram was acquired. User interaction was required to guide automatic processing and included verification of cardiac phase synchronization and proper extraction of vessel centerlines and radii. The automatic processing consisted of the 3D tree reconstruction and the flow simulation.
Invasive FFR measurements were performed in duplicate with 6-F guide catheters, a pressure monitoring wire, and intracoronary adenosine (100–200 μg). Care was taken to document the exact anatomical position of the sensor. To test interobserver variability and the possible influence of human factors on the results of FFRangio, 2 independent operators analyzed all angiograms.
A total of 101 lesions were analyzed, 30% of which had FFR values between 0.70 and 0.90. FFRangio was calculated and compared to invasive FFR measurements at the exact location of the sensor (Figure 1A). A high degree of concordance was found between 2 measurements of FFRangio performed by 2 different operators (interclass correlation coefficient of 0.97; p < 0.001). Figure 1B shows linear regression analyses for the 2 independent observers. We found that invasive FFR was a robust predictor of FFRangio, with 86.8% of the variability of FFRangio explained by the invasive FFR values; the β coefficient of invasive FFR was 0.855 (95% confidence interval [CI]: 0.789 to 0.922) and the intercept was 0.124 (95% CI: 0.068 to 0.179). The estimated bias was 0.004 ± 0.042 and did not differ significantly from zero. The 95% limits of agreement were −0.1 to 0.1. We used 0.8 as the cutoff value for FFRangio, and found that the sensitivity, specificity, and diagnostic accuracy were 88%, 98%, and 94%, respectively.
This first-in-human study indicated high reproducibility and diagnostic accuracy of FFRangio compared with invasive FFR. The data were obtained in patients with characteristics encountered in most percutaneous coronary intervention trials and included relatively well-delineated lesions associated with a large range of FFR values. This high diagnostic accuracy is not only related to extreme values as almost one third of lesions were within a 5% range adjacent to the cutoff value. If the short turnaround time needed to obtain the FFRangio value and the diagnostic accuracy are confirmed in larger studies, FFRangio may foster a wider adoption of FFR-based decision making for revascularization in patients with coronary artery disease.
Please note: Dr. Kornowski is co-founder of and owns equity in CathWorks Ltd. Dr. Lavi is co-founder of and owns equity in CathWorks. Dr. Valtzer and Dr. Lotringer are employees of and hold equity in CathWorks. Dr. De Bruyne is an equity shareholder in Siemens, GE Healthcare, Bayer, Philips, HeartFlow, Edwards Lifesciences, Sanofi, Omega Pharma; has received grant support to his institution from Abbott, Boston Scientific, Biotronik, and St. Jude Medical; and is a consultant for St. Jude Medical, Opsens, and Boston Scientific. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- American College of Cardiology Foundation
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