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

计算模型驱动设计减轻冠状动脉搭桥术后静脉移植失败

• 批准号: 10539814
• 负责人: Jay D. Humphrey
• 金额: $ 75.24万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10539814
• 关键词: 3-Dimensional; 3D Print; Address; Animals; Biocompatible Materials; Biological; Biology; Blood Vessels; Cardiovascular system; Caring; Carotid Arteries; Cells; Clinical; Computer Models; Computer software; Computing Methodologies; Coronary Arteriosclerosis; Coronary Artery Bypass; Coronary Vessels; Data; Data Set; Device Designs; Devices; Diffuse; Disease Progression; Estimation Techniques; Failure; Funding Agency; Geometry; Goals; Gold; Growth; Histology; Human; Image; In Vitro; Inflammation; Joints; Lead; Liquid substance; Mechanics; Mediating; Medical; Medical Imaging; Methodology; Modeling; Morbidity - disease rate; Operative Surgical Procedures; Oryctolagus cuniculus; Patients; Performance; Postoperative Period; Preclinical Testing; Prevention; Process; Property; Publications; Saphenous Vein; Sheep; Solid; Stenosis; Stress; Structure; Structure of jugular vein; Surgical Management; Techniques; Testing; Tissue Grafts; Tissues; Uncertainty; Vein graft; Veins; Venous; animal data; design; elastomeric; experimental study; graft failure; hemodynamics; high risk population; human data; human study; improved outcome; in silico; in vivo; innovation; mechanical stimulus; mortality; multidisciplinary; novel; open source; predictive modeling; prevent; response; simulation; standard care; transcriptome sequencing; translational approach

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.

冠状动脉搭桥移植物（CABG）手术是弥漫性，多重血管患者的金标准治疗 冠状动脉疾病，每年在美国进行35万次手术。由于有限的可用性 在> 95％的患者中，使用了动脉移植物，隐静脉移植物（SVG）。尽管手术进展 技术和手术后的管理，SVG促炎和闭塞以令人震惊的高率发生：5-10％ SVG在手术后一个月内失败，12-18个月内25％，在10年内40-50％ 显着的发病率和死亡率。目前，尚无临床上可用的方法来防止SVG失败 遵循最佳药物疗法之外的CABG。机械刺激，包括血流动力学负荷和 相关血管壁的变形和应力已知有助于细胞介导的结构 导致SVG失败的变化，但是，确切的机械生物学机制仍然很少理解。在 初步研究，我们在CABG模拟中量化了机械刺激，鉴定了血液动力学标记 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） 可以描述静脉移植物的自适应与适应不良反应的框架，并将优化纳入 加速参数估计。然后，使用此模型，我们预测外部生物吸收鞘， 在术后短期内，可能会减轻中期移植物的失败。我们的科学前提 由初步体内卵子研究支持。我们的协作多学科团队将解决这个问题 通过紧密整合的计算模型驱动的设计，实验和 发现动脉化机制的临床方法并评估一种新型的生物吸附鞘 SVG预防故障的设备。在AIM 1中，我们将开发第一个SVG动脉化的G＆R模型 纳入炎症。我们将通过纵向兔手术的数据告知和验证模型 研究，我们将进行手术以插入颈动脉中的颈移植物。在AIM 2中，我们将 将这些数据和模型合成为首先的3D流体 - 固体生长（FSG）模拟器以预测SVG 疾病进展，以独立的动物数据子集进行了验证。为了进一步告知我们的模型，我们 将通过双轴组织测试来表征人类SVG组织。我们将通过纳入不确定性来增加严格 定量。在AIM 3中，我们将设计，优化和评估一种新型的外部护套装置以进行预防 SVG衰竭，在体内研究中整合在硅和大动物中。我们将从 具有可调降解速率的独特一类可生物吸收弹性材料。该提议带来了 一个多学科的团队，具有心血管模拟，血管机械生物学方面的专业知识， 优化，成像，生物材料，添加剂制造和临床心血管护理以及轨道 共同出版物，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衰竭，治疗方案有限。