Multiscale modeling for vein graft failure risk stratification in CABG patients

冠状动脉搭桥术(CABG)是晚期冠心病患者的金标准治疗方法。 动脉疾病，在美国每年有超过40万例。而动脉移植物有更长的- 术语通畅性与静脉移植相比，它们的使用受到可用性的限制，而大隐静脉移植(SVG)是 在大多数患者中使用。冠状动脉旁路手术后，SVG失败率高得惊人， 10%的SVG在术后1个月内闭塞，40%-50%的SVG在10年内失败。这个 已知SVG病的风险和移植物失败的复杂机制生物学与 机械刺激，包括血流动力学和血管壁力学。但是，标准计算 断层扫描(CT)成像不提供直接的手段来表征这些刺激。的最新进展 多尺度建模现在允许使用真实的材料属性进行生理闭环模拟，从而避免 理想化的解剖结构、僵硬的壁和不完整的冠状动脉模型先前的局限性。我们提议写一部小说 可综合表征搭桥血管血流动力学和管壁的冠状动脉模拟框架 机械师只使用非侵入性的临床数据。我们建议，验证的模拟与现实 血流动力学和室壁运动，结合现代成像技术，将使冠状动脉搭桥术后 隐匿性移植物失败高危患者的风险分层和早期识别。至 为了实现这些目标，我们提出了三个具体的目标：1)设计并验证了一种新的闭环系统多尺度 CABG模拟框架，仅使用非侵入性方法即可预测局部血流动力学和壁力学 临床数据，2)量化和比较作用于患者动脉和静脉移植物的机械刺激- 具体的模型，以及3)为冠状动脉搭桥术后的患者开发试点风险分层评分系统 机械刺激对有无SVG疾病的血管的临床结果的影响。建议的工作是 重大和创新，因为它将(1)使用特定于患者的模拟来虚拟逆转SVG疾病 因此，将患者作为自己的对照(2)能够及早识别出SVG风险增加的患者 预后可通过更严格的治疗和监测得到改善的梗阻，(3)使未来 基于机械刺激数据的血管壁生长和重塑模拟，(4)结合高 分辨率成像，具有复杂的完整冠状动脉循环的多尺度建模，以及(5)直接 对照临床数据验证模型预测，并报告模拟结果的可信区间。这 项目组建了一支独特的团队，其中包括一名成年心脏病专家和影像专家，具有 以及一支在心血管生物力学方面拥有成熟专业知识的工程团队。我们将建造 基于我们在患者特定血流模拟方面的丰富经验，以及成功的记录 临床翻译和多学科协作。我们的翻译目标是为临床医生提供新的 改善有移植失败风险的冠状动脉旁路移植术患者的管理决策并改善预后的工具。

Coronary artery bypass graft (CABG) surgery is a gold standard treatment for patients with advanced coronary artery disease, with over 400,000 cases performed each year in the US. While arterial grafts have greater long- term patency compared to vein grafts, their use is limited by availability, and saphenous vein grafts (SVGs) are used in the majority of patients. Following CABG surgery, SVG failure occurs at alarmingly high rates, with 5- 10% of SVGs occluding within the first month after surgery, and 40-50% of SVGs failing within 10 years. The risk of SVG disease and the complex mechanobiology of graft failure are known to be associated with mechanical stimuli, including hemodynamics and vessel wall mechanics. However, standard computed tomography (CT) imaging provides no direct means to characterize these stimuli. Recent advances in multiscale modeling now permit physiologic closed-loop simulations with realistic material properties, avoiding prior limitations of idealized anatomy, rigid walls, and incomplete coronary models. We propose a novel coronary simulation framework that can comprehensively characterize bypass graft hemodynamics and wall mechanics using only non-invasive clinical data. We propose that validated simulations with realistic hemodynamics and wall motion, in concert with modern imaging techniques will enable post-CABG risk stratification and early identification of patients at high risk for saphenous graft failure. To accomplish these goals, we propose three specific aims: 1) design and validate a novel closed-loop multiscale CABG simulation framework that can predict local hemodynamics and wall mechanics using only non-invasive clinical data, 2) quantify and compare the mechanical stimuli acting on arterial and vein grafts in patient- specific models, and 3) develop a pilot risk stratification scoring system for post-CABG patients by correlating mechanical stimuli with clinical outcomes in vessels with and without SVG disease. The proposed work is significant and innovative because it will (1) use patient-specific simulations to virtually reverse SVG disease thus using patients as their own control (2) enable early identification of patients at increased risk of SVG obstruction whose outcomes may be improved by more intensive treatment and monitoring, (3) enable future vessel wall growth and remodeling simulations which rely on mechanical stimuli data, (4) combine high resolution imaging with sophisticated multiscale modeling of the complete coronary circulation, and (5) directly validate model predictions against clinical data and report confidence intervals on simulation results. This project assembles a unique team including an adult cardiologist and imaging specialist with a background in physics, and an engineering team with established expertise in cardiovascular biomechanics. We will build upon our extensive experience with patient-specific blood flow simulations, and a successful track record of clinical translation and multi-disciplinary collaboration. Our translational goal is to provide clinicians with new tools to improve management decisions for CABG patients at risk for graft failure and improve outcomes.