KEYNOTE: Multiscale modeling and uncertainty quantification in coronary bypass graft surgery hemodynamics

TIME: 11:50 - 12:20 Coronary bypass graft surgery (CABG) is performed on approximately 500,000 patients every year in the United States. Because most patients require multi-vessel revascularization, roughly 70% of CABG... [ view full abstract ]

TIME: 11:50 - 12:20

Coronary bypass graft surgery (CABG) is performed on approximately 500,000 patients every year in the United States. Because most patients require multi-vessel revascularization, roughly 70% of CABG surgeries employ saphenous vein grafts, despite the superior performance of arterial grafts. Vein graft failure continues to be a major clinical problem, with as many as 50% of grafts failing within 5 years of surgery. When a vein graft is implanted in the arterial system it adapts to the high flow and pressure of the arterial environment by changing composition and geometry. Though hemodynamics is known to play an active role in growth and remodeling of blood vessels, the underlying mechanisms of vein graft failure remain poorly understood. We will describe our two-pronged approach to investigating the biomechanical underpinnings of vein graft failure following CABG. First, we perform patient-specific simulations of coronary and bypass graft hemodynamics to compare the biomechanical forces acting on venous and arterial grafts. We will present recent advances in computational methodology in which we employ multiscale modeling methods to couple closed loop lumped parameter models of the coronary physiology to 3D hemodynamics simulations with fluid structure interaction and variable material properties. Second, we adapt a constrained mixture theory of growth and remodeling for use in vein grafts, and explore potential causes and amelioration of vein graft failure. Parameter estimation in these models is accelerated via optimization. Finally, we present a Bayesian framework for uncertainty quantification combining automated parameter estimation for clinical data assimilation and efficient multi-resolution expansion for uncertainty propagation to simulation predictions.