Computational and Experimental Modeling of Subclinical Leaflet Thrombosis in Bioprosthetic Aortic Valves

项目总结 该项目将设计出实验和临床验证的计算机模型，以阐明致病机制。 生物人工心脏瓣膜(BHV)经导管或外科手术后叶血栓形成的机制 替换，从而改进风险分层和设备选择。每年，近300,000个主动脉瓣 世界各地都在进行瓣膜置换手术，以治疗严重的主动脉瓣狭窄，以及瓣膜置换率 预计到2050年将超过850,000/年。传统上，外科主动脉瓣置换术(SAVR)是 治疗主动脉瓣狭窄的金标准；然而，经导管主动脉瓣置换(TAVR)有 作为SAVR的替代方案，已被证明在以下方面提供了与SAVR相当的结果 老年病人。直到最近，接受主动脉BHV的患者还被认为需要有限的抗凝治疗，但 在过去的几年里，临床研究出人意料地揭示了亚临床叶血栓的高发生率。 (SLT)在SAVR和TAVR后的BHV。系统性红斑狼疮与短暂性脑缺血发作增加相关， 中风，已被证明可引发急性心肌梗死，并被怀疑加速结构瓣膜 恶化。严重的是，SLT可进展为临床瓣膜血栓，这是一种毁灭性的并发症。工作-- 令人欣慰的是，最近一项关于Partner 3试验两年数据的研究发现，在统计上显著增加了 与SAVR相比，TAVR后的瓣膜血栓发生率为2.6%，SAVR后为0.7%，p=0.02。 TAVR中SLT早期发病率的增加有两个机制被假设：1)异常血液 经导管主动脉瓣(TAV)附近的血流模式(例如，流动停滞、湍流、瓣膜旁 泄漏)和2)支架卷曲引起的TAV叶损伤，激活凝血和血小板沉积。 虽然临床影像可以发现主动脉瓣置换术后的SLT，但目前还没有治疗方法。 预测哪些患者会在SAVR或TAVR后发生SLT。这个项目的目标是设计出 BHV的患者特有的计算流体-结构相互作用(FSI)模型 生物物理学详细的血栓形成模型，以表征导致叶血栓形成的机制， 最终，使用TAVR和SAVR患者的临床数据来预测叶血栓形成的风险。这 该项目承诺改变AVR设备选择和SLT风险评估的基于计算的方法。 项目目标将通过三个具体目标来实现。目标1侧重于实验验证 目的2研究导致主动脉瓣置换术后叶血栓形成的机制；以及 目标3侧重于临床验证和设备选择。通过这些研究，一个多学科团队 已建立的协作记录将整合数学、计算、实验和临床应用 通过建立新的、经过严格验证的流、FSI和 AVR后血栓形成，最终将能够进行针对患者的SLT风险评估。更进一步，因为节拍- BOSI是许多类型植入设备的主要挑战，该项目有望产生广泛的影响。

PROJECT SUMMARY This project will devise experimentally and clinically validated computer models to elucidate the causal mecha- nisms of leaflet thrombosis in bioprosthetic heart valves (BHVs) following transcatheter or surgical aortic valve replacement, and thereby improve risk stratification and device selection. Each year, nearly 300,000 aortic valve replacements are performed worldwide to treat severe aortic valve stenosis, and the rate of valve replacement is projected to exceed 850,000/year by 2050. Traditionally, surgical aortic valve replacement (SAVR) was the gold standard for treating aortic valve stenosis; however, transcatheter aortic valve replacement (TAVR) has emerged as an alternative to SAVR that has been demonstrated to provide outcomes comparable to SAVR for elderly patients. Until recently, patients receiving aortic BHVs were thought to require limited anticoagulation, but in the past few years, clinical studies have unexpectedly revealed high rates of subclinical leaflet thrombosis (SLT) in BHVs after both SAVR and TAVR. SLT is associated with increased transient ischemic attacks and strokes, has been shown to trigger acute myocardial infarction, and is suspected to accelerate structural valve deterioration. Critically, SLT can progress to clinical valve thrombosis, which is a devastating complication. Wor- ryingly, a very recent study on two-year data for the PARTNER 3 trial found a statistically significant increase in valve thrombosis following TAVR compared to SAVR (2.6% post-TAVR vs. 0.7% post-SAVR, p=0.02). Two mechanisms have been hypothesized for the increased early incidence of SLT in TAVR: 1) abnormal blood flow patterns in the vicinity of the transcatheter aortic valve (TAV) (e.g., flow stasis, turbulence, paravalvular leak) and 2) stent-crimp induced injury of the TAV leaflets, which activates coagulation and platelet deposition. Although clinical imaging can detect SLT following aortic valve replacement, there is currently no approach to predict which patients will develop SLT following either SAVR or TAVR. The goal of this project is to devise patient-specific computational fluid-structure interaction (FSI) models of BHVs coupled to biochemically and biophysically detailed thrombosis models to characterize the mechanisms that lead to leaflet thrombosis and, ultimately, to predict leaflet thrombosis risk using clinical data in patients undergoing TAVR and SAVR. This project promises to transform computation-based methods for AVR device selection and SLT risk assessment. The project goals will be accomplished through three Specific Aims. Aim 1 focuses on experimental validation of FSI models; Aim 2 studies mechanisms that lead to leaflet thrombosis after aortic valve replacement; and Aim 3 focuses on clinical validation and device selection. Through these studies, a multidisciplinary team with an established record of collaboration will integrate mathematical, computational, experimental, and clinical ap- proaches to yield substantial innovation by establishing novel, rigorously validated models of flow, FSI, and thrombosis post-AVR that will ultimately enable patient-specific SLT risk assessment. Further, because throm- bosis are major challenges for many types of implanted devices, the project promises to have a broad impact.