Development and validation of non-invasive endothelial shear stress (ESS) estimations of coronary artery haemodynamics using computational modelling

Abstract

Abstract Background Vascular remodelling is influenced by various biomechanical forces such as endothelial shear stress (ESS) and is associated with development and progression of coronary artery disease (CAD). ESS is the parallel frictional force exerted by blood flow on the endothelial luminal surface of the arterial wall. While ESS may not be the only cause of atherosclerosis, it is postulated that low ESS creates a pro-atherogenic environment and high ESS is athero-protective. Computational fluid dynamics (CFD) is an engineering method used to analyse fluid flow and has been increasingly applied to simulate ESS in cardiovascular research. However, current CFD-derived ESS estimates have never been validated and can display significant variability between research groups. Purpose This study aims to provide a comparative analysis of coronary flow using in-silico modelling of coronary flow and particle image velocimetry (PIV). Methods A proximal left anterior descending (pLAD) coronary phantom model was constructed from a patient who had received same day cardiac computed tomography angiography (CTCA) and invasive coronary haemodynamic (Combowire XT) assessment. Patient-specific coronary flow data was applied to CFD boundary conditions and a mock circulatory loop. CFD was validated using PIV under steady flow in a 4-times-scaled model. Same plane velocity field and flow patterns (at mid-luminal of pLAD) from both in-silico and in-vivo data were compared and analysed. Results Mean velocity contours and magnitude were analysed from CFD and PIV. Patient specific average peak velocity obtained from invasive assessment was 0.21m/s. Patient-specific velocity average from CFD was 0.22m/s. The approximated magnitude difference is 4.7%. Both same-axial average cross-sectional velocity estimated by CFD-pLAD and PIV-pLAD are 0.087m/s. Velocity contours and flow patterns simulated in the CFD were also comparable with the PIV results. Conclusion Simulation data from CFD correlate well with experimental PIV results. This early data allows for a non-invasive approach to determine patient-specific coronary haemodynamics (such as velocity profiling and ESS) and form the basis of personalising cardiovascular risk. Insights from this work may aid future development of a novel tool that incorporates CFD-derived ESS estimates with patient-specific risk factors to better predict rapid CAD progression. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Cardiac Society of Australia and New Zealand Monash Institute of Medical Engineering