Objectives: This study sought to assess the ability of a novel virtual coronary intervention (VCI) tool based on invasive angiography to predict the patient's physiological response to stenting. Background: Fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) is associated with improved clinical and economic outcomes compared with angiographic guidance alone. Virtual (v)FFR can be calculated based upon a 3-dimensional (3D) reconstruction of the coronary anatomy from the angiogram, using computational fluid dynamics (CFD) modeling. This technology can be used to perform virtual stenting, with a predicted post-PCI FFR, and the prospect of optimized treatment planning. Methods: Patients undergoing elective PCI had pressure-wire-based FFR measurements pre- and post-PCI. A 3D reconstruction of the diseased artery was generated from the angiogram and imported into the VIRTUheart workflow, without the need for any invasive physiological measurements. VCI was performed using a radius correction tool replicating the dimensions of the stent deployed during PCI. Virtual FFR (vFFR) was calculated pre- and post-VCI, using CFD analysis. vFFR pre- and post-VCI were compared with measured (m)FFR pre- and post-PCI, respectively. Results: Fifty-four patients and 59 vessels underwent PCI. The mFFR and vFFR pre-PCI were 0.66 ± 0.14 and 0.68 ± 0.13, respectively. Pre-PCI vFFR deviated from mFFR by ±0.05 (mean Δ = -0.02; SD = 0.07). The mean mFFR and vFFR post-PCI/VCI were 0.90 ± 0.05 and 0.92 ± 0.05, respectively. Post-VCI vFFR deviated from post-PCI mFFR by ±0.02 (mean Δ = -0.01; SD = 0.03). Mean CFD processing time was 95 s per case. Conclusions: The authors have developed a novel VCI tool, based upon the angiogram, that predicts the physiological response to stenting with a high degree of accuracy.
Gosling, R. C., Morris, P. D., Silva Soto, D. A., Lawford, P. V., Hose, D. R., & Gunn, J. P. (2018, May 1). Virtual Coronary Intervention. A Treatment Planning Tool Based Upon the Angiogram. JACC: Cardiovascular Imaging. Elsevier Inc. https://doi.org/10.1016/j.jcmg.2018.01.019