Computational model-driven design to mitigate vein graft failure after coronary artery bypass

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Coronary artery bypass graft (CABG) surgery is the gold standard treatment for patients with diffuse, multi-vessel coronary artery disease, with >350,000 surgeries performed each year in the USA. Due to the limited availability of arterial grafts, saphenous vein grafts (SVG) are used in >95% of patients. Despite advances in surgical technique and post-surgical management, SVG stenoses and occlusions occur at alarmingly high rates: 5-10% of SVGs fail within one month after surgery, 25% within 12-18 months, and 40-50% within 10 years, resulting in significant morbidity and mortality. Currently, there are no clinically available means to prevent SVG failure following CABG beyond optimal medical therapy. Mechanical stimuli, including hemodynamic loads and associated vessel wall deformations and stresses, are known to contribute to the cell-mediated structural changes leading to SVG failure, yet, the precise mechanobiological mechanisms remain poorly understood. In preliminary studies, we quantified mechanical stimuli in CABG simulations, identifying hemodynamic markers associated with SVG stenosis. Importantly, we introduced the first computational growth and remodeling (G&R) framework that can delineate adaptive vs. maladaptive responses of vein grafts, incorporating optimization to accelerate parameter estimation. With this model, we then predicted that an external bioabsorbable sheath, present over a short post-operative period, could mitigate intermediate-term graft failure. Our scientific premise is supported by a preliminary in vivo ovine study. Our collaborative multi-disciplinary team will address this critical unmet need through tightly integrated computational model-driven design, experimental, and clinical approaches to uncover arterialization mechanisms and evaluate a novel bioabsorbable sheath device for SVG failure prevention. In Aim 1, we will develop the first G&R model of SVG arterialization incorporating inflammation. We will inform and validate the model with data from a longitudinal rabbit surgical study, in which we will perform surgery to interpose a jugular graft in the carotid artery. In Aim 2, we will synthesize these data and models into a first-of-its-kind 3D fluid-solid-growth (FSG) simulator to predict SVG disease progression, validated against an independent subset of animal data. To further inform our models, we will characterize human SVG tissue with biaxial tissue testing. We will increase rigor by incorporating uncertainty quantification. In Aim 3, we will design, optimize and evaluate a novel external sheath device for the prevention of SVG failure, integrating in silico and large animal in vivo studies. We will rapidly 3D print sheath designs from a unique class of bioabsorbable elastomeric materials with tunable degradation rates. This proposal brings together a multidisciplinary team with expertise in cardiovascular simulation, vascular mechanobiology, optimization, imaging, biomaterials, additive manufacturing, and clinical cardiovascular care as well as a track record of joint publications, funding, and open-source software. Our ultimate goal is to improve outcomes of CABG patients via prediction and prevention of SVG failure, for whom there are limited treatment options.