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

冠状动脉旁路移植术（CABG）是治疗弥漫性、多支血管病变患者的金标准。 冠状动脉疾病，在美国每年进行超过350，000例手术。由于供应有限， 在动脉移植物中，隐静脉移植物（SVG）用于>95%的患者。尽管外科手术取得了进步， 技术和术后管理，SVG狭窄和闭塞发生率高得惊人：5-10% 的SVG在术后1个月内失效，25%在12-18个月内失效，40-50%在10年内失效，导致 严重的发病率和死亡率。目前，临床上还没有预防SVG故障的方法 冠状动脉旁路移植术后超过最佳药物治疗。机械刺激，包括血流动力学负荷和 相关的血管壁变形和应力，已知有助于细胞介导的结构损伤。 虽然这些变化导致SVG失效，但精确的机械生物学机制仍然知之甚少。在 初步研究中，我们量化了CABG模拟中的机械刺激， 与SVG狭窄相关。重要的是，我们引入了第一个计算增长和重塑（G&R） 该框架可以描述静脉移植物的适应性与适应不良反应， 加速参数估计。利用该模型，我们预测外部生物可吸收鞘管， 在术后短期内出现，可以减轻中期移植物失败。我们的科学前提 得到初步的绵羊体内研究的支持。我们的多学科协作团队将解决这一问题 通过紧密集成的计算模型驱动设计、实验和 揭示动脉化机制和评价新型生物可吸收鞘管的临床方法 SVG故障预防装置。在目标1中，我们将开发SVG动脉化的第一个G&R模型 并伴有炎症。我们将告知和验证模型的数据，从纵向兔手术 在这项研究中，我们将在颈动脉中进行颈静脉移植手术。在目标2中，我们将 将这些数据和模型合成为第一个3D流固生长（FSG）模拟器，以预测SVG 疾病进展，根据动物数据的独立子集进行验证。为了进一步了解我们的模型，我们 将通过双轴组织试验表征人体SVG组织。我们将通过引入不确定性来增加严谨性 量化在目标3中，我们将设计、优化和评价一种新型外鞘装置， SVG失败，整合在硅片和大型动物体内研究。我们将快速3D打印护套设计， 一类独特的生物可吸收弹性材料，具有可调的降解速率。这项提议带来了 在心血管模拟，血管机械生物学， 优化，成像，生物材料，增材制造和临床心血管护理以及跟踪 联合出版物、资金和开源软件的记录。我们的最终目标是改善 CABG患者通过预测和预防SVG失败，对他们来说，治疗选择有限。

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.