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

生物主动脉瓣亚临床小叶血栓形成的计算和实验模型

基本信息

批准号：
10544015
负责人：
AARON L FOGELSON
金额：
$ 67.19万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
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：
2022-01-01 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10544015
关键词：
Acceleration Acute myocardial infarction Address Age Anatomy Anticoagulation Aorta Aortic Valve Stenosis Biochemical Biophysics Bioprosthesis device Blood Cell Count Blood Coagulation Factor Blood Platelets Blood Vessels Blood flow Clinical Clinical Data Clinical Research Coagulation Process Collaborations Competence Complication Computer Models Coupled Data Deposition Deterioration Device Designs Devices Experimental Models Goals Guidelines Heart Valves Image Impact evaluation Impairment In Vitro Incidence Injury Lead Liquid substance Mathematics Measurement Methods Modeling Operative Surgical Procedures Oral Outcome Patient Selection Patient imaging Patients Pattern Physiologic pulse Prosthesis Quantitative Evaluations Randomized Recommendation Risk Risk Assessment Role Severities Software Framework Stents Stroke Structure Thrombosis Transient Ischemic Attack Validation Velocimetries Work aging population aortic valve aortic valve replacement clinical imaging clinically actionable computational platform effective therapy follow-up four-dimensional computed tomography implantable device implantation improved improved outcome innovation multidisciplinary novel older patient open source particle physical model platelet function predictive modeling preference prevent risk stratification simulation treatment planning valve replacement

项目摘要

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.

项目概要该项目将设计经过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后生物人工心脏瓣膜 (BHV) 中小叶血栓形成的现象更换，从而改善风险分层和设备选择。每年有近30万个主动脉瓣全世界都进行了置换手术来治疗严重的主动脉瓣狭窄，以及瓣膜置换率预计到 2050 年，每年将超过 850,000 例。传统上，手术主动脉瓣置换术 (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，已被证明可以提供与 SAVR 相当的结果老年患者。直到最近，接受主动脉 BHV 治疗的患者仍被认为需要有限的抗凝治疗，但在过去的几年里，临床研究意外地揭示了亚临床小叶血栓形成的高发生率 (SLT) 在 BHV 中，在 SAVR 和 TAVR 之后。 SLT 与短暂性脑缺血发作增加有关中风，已被证明会引发急性心肌梗死，并被怀疑会加速结构瓣膜的形成恶化。重要的是，SLT 可发展为临床瓣膜血栓形成，这是一种毁灭性的并发症。沃- 令人沮丧的是，最近一项针对 PARTNER 3 试验的两年数据的研究发现，在统计学上显着增加与 SAVR 相比，TAVR 后瓣膜血栓发生率更高（TAVR 后 2.6%，SAVR 后 0.7%，p=0.02）。 TAVR 中 SLT 早期发生率增加的两种机制被假设：1）血液异常经导管主动脉瓣 (TAV) 附近的血流模式（例如，血流停滞、湍流、瓣周泄漏）和 2）支架卷曲引起的 TAV 小叶损伤，从而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 SLT，但目前尚无方法预测哪些患者在 SAVR 或 TAVR 后会发生 SLT。该项目的目标是设计患者特定的 BHV 计算流固耦合 (FSI) 模型与生化和生物物理详细的血栓形成模型，用于表征导致小叶血栓形成的机制，最终，利用接受 TAVR 和 SAVR 患者的临床数据预测小叶血栓形成风险。这该项目有望改变基于计算的 AVR 设备选择和 SLT 风险评估方法。该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 FSI 模型；目标2研究主动脉瓣置换术后导致小叶血栓形成的机制；和目标 3 侧重于临床验证和设备选择。通过这些研究，一个多学科团队既定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、经过严格验证的流量、FSI 和模型来产生实质性创新 AVR 后血栓形成最终将实现患者特异性 SLT 风险评估。此外，因为血栓 bosis 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。