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

项目摘要 这个项目将设计实验和临床验证的计算机模型，以阐明因果机制， 经导管或外科主动脉瓣置换术后人工生物心脏瓣膜（BHV）瓣叶血栓形成的原因 更换，从而改善风险分层和设备选择。每年有近30万例主动脉瓣 全世界都在进行置换术以治疗严重的主动脉瓣狭窄， 预计到2050年将超过85万/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术（TAVR） 作为SAVR的替代方案出现，已被证明可提供与SAVR相当的结局， 老年患者。直到最近，接受主动脉BHV的患者被认为需要有限的抗凝治疗，但 在过去的几年中，临床研究意外地揭示了亚临床瓣叶血栓形成的高发生率 (SLT)在SAVR和TAVR之后的BHV中。脑梗死与短暂性脑缺血发作的增加有关， 中风，已被证明会引发急性心肌梗死，并被怀疑会加速结构性瓣膜病， 恶化严重的是，心内膜炎可能发展为临床瓣膜血栓形成，这是一种毁灭性的并发症。我... 令人惊讶的是，最近一项关于PARTNER 3试验两年数据的研究发现， 与SAVR相比，TAVR后瓣膜血栓形成（TAVR后2.6% vs. SAVR后0.7%，p=0.02）。 假设TAVR中早期出血发生率增加的两种机制：1）异常血液 经导管主动脉瓣（TAV）附近的流动模式（例如，血流停滞，湍流，瓣周 泄漏）和2）支架压握诱导的TAV瓣叶损伤，这会激活凝血和血小板沉积。 虽然临床影像学可以检测主动脉瓣置换术后的二尖瓣狭窄，但目前还没有方法可以检测到主动脉瓣置换术后的二尖瓣狭窄。 预测哪些患者在SAVR或TAVR后会发生血管痉挛。这个项目的目标是设计 BHV的患者特异性计算流体-结构相互作用（FSI）模型， 生物病理学详细的血栓形成模型，以表征导致瓣叶血栓形成的机制， 最终，使用接受TAVR和SAVR的患者的临床数据预测瓣叶血栓形成风险。这 该项目有望改变基于计算的AVR设备选择和故障风险评估方法。 该项目的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研究主动脉瓣置换术后导致瓣叶血栓形成的机制; 目标3侧重于临床确认和器械选择。通过这些研究，一个多学科的团队， 一个建立的合作记录将整合数学，计算，实验和临床应用， 通过建立新颖的、经过严格验证的流动、FSI和 AVR后血栓形成，最终将使患者特异性血栓风险评估成为可能。此外，由于throm- bosis是许多类型的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.