Assessment with clinical data of a coupled bio-hemodynamics numerical model to predict leukocyte adhesion in coronary arteries

• Authors: Umberto Ciri, University of Texas Dallas; Ruth L Bennett, University of Texas Dallas; Rita Bhui, University of Texas Dallas; David S Molony, Emory University; Habib Samady, Emory University; Clark A Meyer, University of Texas Dallas; Heather N Hayenga, University of Texas Dallas; Stefano Leonardi, University of Texas Dallas
• Journal Title: SCIENTIFIC REPORTS
• Volume: Volume 11, Number 1
• Publisher: NATURE RESEARCH | 2021-06-16, Pages 12680-12680
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1038/s41598-021-92084-4
• Subjects: Health Sciences, Medicine and Surgery; Engineering, Mechanical
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; WALL SHEAR-STRESS; PULSATILE FLOW; ATHEROSCLEROTIC PLAQUE; SIMULATION; PROGRESSION; BIFURCATION; DYNAMICS; DISEASE

Abstract

Numerical simulations of coupled hemodynamics and leukocyte transport and adhesion inside coronary arteries have been performed. Realistic artery geometries have been obtained for a set of four patients from intravascular ultrasound and angiography images. The numerical model computes unsteady three-dimensional blood hemodynamics and leukocyte concentration in the blood. Wall-shear stress dependent leukocyte adhesion is also computed through agent-based modeling rules, fully coupled to the hemodynamics and leukocyte transport. Numerical results have a good correlation with clinical data. Regions where high adhesion is predicted by the simulations coincide to a good approximation with artery segments presenting plaque increase, as documented by clinical data from baseline and six-month follow-up exam of the same artery. In addition, it is observed that the artery geometry and, in particular, the tortuosity of the centerline are a primary factor in determining the spatial distribution of wall-shear stress, and of the resulting leukocyte adhesion patterns. Although further work is required to overcome the limitations of the present model and ultimately quantify plaque growth in the simulations, these results are encouraging towards establishing a predictive methodology for atherosclerosis progress.