Author + information
- Received November 18, 2017
- Revision received December 19, 2017
- Accepted January 4, 2018
- Published online May 6, 2019.
- Rebecca C. Gosling, BSca,b,∗∗ (, )@RebeccaGosling3,
- Paul D. Morris, PhDa,b,c,∗,
- Daniel A. Silva Soto, MEng, PhDa,
- Patricia V. Lawford, BSc, PhDa,c,
- D. Rodney Hose, BSc, PhDa,c,d and
- Julian P. Gunn, MDa,b,c
- aDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
- bDepartment of Cardiology, Sheffield Teaching Hospitals, National Health Service Foundation Trust, Northern General Hospital, Sheffield, United Kingdom
- cInsigneo Institute for In Silico Medicine, Sheffield, United Kingdom
- dDepartment of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- ↵∗Address for correspondence:
Dr. Rebecca Gosling, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Room O137, Medical School, Beech Hill Road, Sheffield S10 2RX, United Kingdom.
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.
- computational fluid dynamics
- coronary artery disease
- coronary physiology
- fractional flow reserve
- percutaneous coronary intervention
↵∗ Drs. Gosling and Morris are joint first authors.
Supported by Wellcome Trust/Health Innovation Challenge Fund grant R/135171-11-1 and British Heart Foundation grants R/147462-11-1 and R/134747-11-1. All authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received November 18, 2017.
- Revision received December 19, 2017.
- Accepted January 4, 2018.
- 2019 The Authors