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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.

项目总结