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

计算模型驱动的设计可减轻冠状动脉搭桥术后静脉移植失败的风险

基本信息

批准号：
10683327
负责人：
Jay D. Humphrey
金额：
$ 70.08万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
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/10683327
关键词：
3-Dimensional 3D Print Acceleration 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 Elastomers Estimation Techniques Failure Funding Agency Geometry Goals Growth Histology Human Image In Vitro Inflammation Joints Liquid substance Mechanics Mediating Medical Medical Imaging Methodology Modeling Morbidity - disease rate Operative Surgical Procedures Oryctolagus cuniculus Parameter Estimation Patients Performance Postoperative Period Preclinical Testing Prevention Process Property Publications Saphenous Vein Sheep Solid Stenosis Stress Structure Structure of jugular vein 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 manufacture mechanical stimulus mortality multidisciplinary novel open source predictive modeling prevent response simulation standard care transcriptome sequencing translational approach

项目摘要

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.

冠状动脉旁路移植术 (CABG) 手术是治疗弥漫性多支血管疾病患者的金标准冠状动脉疾病，美国每年进行超过 350,000 例手术。由于数量有限动脉移植物中，>95% 的患者使用大隐静脉移植物 (SVG)。尽管外科手术取得了进步由于技术和术后管理的原因，SVG 狭窄和闭塞的发生率高得惊人：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，导致显着的发病率和死亡率。目前，临床上没有可用的方法来预防 SVG 故障 CABG 后超出最佳药物治疗。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导致 SVG 失效的变化，然而，精确的机械生物学机制仍然知之甚少。在初步研究，我们量化了 CABG 模拟中的机械刺激，识别血流动力学标志物与 SVG 狭窄有关。重要的是，我们引入了第一个计算增长和重构（G&R）可以描述静脉移植物的适应性与适应不良反应的框架，将优化纳入加速参数估计。通过这个模型，我们预测外部生物可吸收护套，在术后短时间内出现，可以减轻中期移植失败。我们的科学前提得到了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个问题通过紧密集成的计算模型驱动的设计、实验和揭示动脉化机制并评估新型生物可吸收护套的临床方法 SVG 故障预防装置。在目标 1 中，我们将开发第一个 SVG 动脉化的 G&R 模型合并炎症。我们将使用来自纵向兔子手术的数据来告知和验证模型在这项研究中，我们将进行手术，将颈静脉移植物插入颈动脉。在目标 2 中，我们将将这些数据和模型综合到同类首个 3D 流固生长 (FSG) 模拟器中以预测 SVG 疾病进展，根据独立的动物数据子集进行验证。为了进一步了解我们的模型，我们将通过双轴组织测试来表征人类 SVG 组织。我们将通过纳入不确定性来提高严谨性量化。在目标 3 中，我们将设计、优化和评估一种新型外鞘装置，用于预防 SVG 失败的原因，整合到计算机和大型动物体内研究中。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，具有可调的降解率。该提案带来拥有心血管模拟、血管力学生物学专业知识的多学科团队，优化、成像、生物材料、增材制造和临床心血管护理以及轨道联合出版物、资助和开源软件的记录。我们的最终目标是改善结果 CABG 患者通过预测和预防 SVG 失败而治疗选择有限。