NSF/FDA SIR: Patient-Specific Computational Assessment of Inferior Vena Cava Filter Performance

下腔静脉(IVC)是将低氧血从下肢(中段和腿部)输送回心脏的主要静脉。IVC过滤器是一种植入式医疗设备，旨在捕获血液凝块，在它们到达肺部并导致潜在致命的肺栓塞(由血块导致的流向肺的血流阻塞)之前，由于深静脉血栓形成(在静脉或动脉中形成的凝块)，血液凝块可以从下肢逸出。尽管每年植入100,000个下腔静脉滤器，但下腔静脉滤器并发症(包括滤器断裂、装置移位、静脉壁穿孔、滤器移位和装置向下游输送到心脏)仍然是未解决的问题，尽管装置开发了50年。主要原因可能是人体下腔静脉出现的复杂负荷和血流状况，在临床前测试中没有考虑到这一点。北卡罗来纳大学教堂山分校和美国食品和药物管理局的这一合作项目旨在建立、验证和验证一个开源的计算平台，用于预测IVC过滤器针对患者的性能。该平台将应用于从临床CT数据重建的多个特定于患者的模型，以解决有关下腔静脉过滤器的安全性和有效性的重要问题。项目完成后，该计算平台将作为开源非临床评估模型提交给FDA医疗器械开发工具(MDDT)计划，以便业界可以使用它来预测临床前下腔静脉过滤器的性能(例如，抗疲劳、凝块捕获)。由于经过验证和验证的计算平台将作为开源软件发布，其他人可能会使用它来设计下一代IVC过滤器。通过临床验证和适当的监管批准或许可，患者特定建模平台还有可能用于医院环境中，以优化患者特定设备的选择和放置。本项目的研究目标是建立、验证、验证和应用一个开源的计算平台，用于使用FDA收集的数据对患者特定的下腔静脉滤器性能进行预测。研究计划是按照三个目标组织的。第一个目标是建立一个用于预测患者特定下腔静脉过滤器性能的开源计算平台，其特点是使用由PI和他的研究小组开发和维护的开源沉浸边界(IB)软件IBAMR，用现实的柔性血栓的流固耦合(FSI)模型取代高度理想化的刚性球形血栓的CFD/6自由度模型。为模拟IVC过滤器捕获的血栓而开发的建模和模拟基础设施将是对循环系统中任何位置(例如，在用于预测缺血性中风的脑血管中)的真实血栓进行建模的当前技术水平的显著进步，由于FSI的计算费用，目前仅限于对多个坚硬的球形血栓或相对较少数量的可变形血栓进行建模。与对流体和结构使用单独的贴体网格的方法相比，采用具有自适应网格优化的浸没边界方法将是一个重大进步，因此需要网格修复以适应大的结构变形。该项目的方法允许在极其复杂的患者特定几何形状中模拟大量可变形的凝块。第二个目标是使用FDA正在获得的实验数据来验证和验证开源计算平台，从而利用由FDA合作者及其同事设计并由领先的Nitinol医疗设备制造商Confluent Medical Technologies制造的现有通用IVC过滤器。因此，该项目还将推动验证和确认(V&amp；V)方法在计算生物力学中的使用，这将成为医疗器械行业正确使用V&amp；V技术的榜样。第三个目标是应用开源计算平台来预测多个患者特定模型中的下腔静脉过滤器性能，即经过验证和验证的计算平台将用于评估根据患者CT数据重建的多个解剖模型中的下腔静脉过滤器的力学、血流动力学和凝块捕获性能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The inferior vena cava (IVC) is the primary vein that brings deoxygenated blood from the lower extremities (midsection and legs) back to the heart. IVC filters are implanted medical devices designed to capture blood clots, which can escape from the lower extremities due to deep vein thrombosis (a clot formed in a vein or an artery), before they reach the lungs and cause a potentially-fatal pulmonary embolism (occlusion of blood flow to the lung caused by a clot). Although 100,000 IVC filters are implanted each year, IVC filter complications (including filter fracture, device migration, perforation of the vein wall, and dislodgment of the filter and transport of the device downstream to the heart) remain unresolved problems despite 50 years of device development. A primary reason for this may be the complex loading and blood flow conditions that occur in the human IVC, which are unaccounted for in pre-clinical testing. This collaborative project between The University of North Carolina at Chapel Hill and the U.S. Food and Drug Administration seeks to establish, verify, and validate an open-source, computational platform for predicting patient-specific performance of IVC filters. This platform will be applied in multiple patient-specific models reconstructed from clinical CT data to address important questions about IVC filter safety and effectiveness. At the completion of the project, the computational platform will be submitted as an open-source non-clinical assessment model to the FDA Medical Device Development Tools (MDDT) program, so that it may be used by industry to predict pre-clinical IVC filter performance (e.g. fatigue resistance, clot trapping). Because the verified and validated computational platform will be released as open-source software, others may use it to design next-generation IVC filters. With clinical validation and appropriate regulatory approval or clearance, the patient-specific modeling platform also has the potential to be used in a hospital setting for optimized patient specific device selection and placement. The research objective of this project is to establish, verify, validate and apply an open-source computational platform for the patient-specific prediction of IVC filter performance using data collected by the FDA. The Research Plan is organized under three objectives. The first objective is to establish an open-source computational platform for predicting patient-specific IVC filter performance that features the replacement of the highly idealized CFD/6-DOF model of rigid, spherical blood clots with fluid-structure interaction (FSI) models of realistic, flexible clots using the open-source immersed boundary (IB) software IBAMR, which is developed and maintained by the PI and his research group. The modeling and simulation infrastructure developed to simulate clot capture by IVC filters will be a significant advance over the current state of the art for modeling realistic blood clots anywhere in the circulatory system (e.g., in the cerebral vasculature for predicting ischemic stroke), which is currently restricted to either modeling multiple rigid spherical clots or a relatively small number of deformable clots due to the computational expense of FSI. The use of the immersed boundary method with adaptive mesh refinement will be a significant advance over approaches that use separate body-fitted meshes for the fluid and structure and, consequently, require mesh repair to accommodate large structural deformations. The project's approach permits the simulation of a large number of deformable clots in extremely complex patient-specific geometries. The second objective is to verify and validate the open-source computational platform using experimental data being acquired at the FDA, thereby leveraging the existing generic IVC filter designed by the FDA collaborator and his colleagues and fabricated by Confluent Medical Technologies, a leading manufacturer of Nitinol medical devices. Thus, the project will also advance the use of verification and validation (V&V) methods in computational biomechanics that will serve as an example to the medical device industry on the proper use of V&V techniques. The third objective is to apply the open-source computational platform to predict IVC filter performance in multiple patient-specific models, i.e., the verified and validated computational platform will be used to evaluate IVC filter mechanics, hemodynamics, and clot trapping performance in multiple anatomical models reconstructed from patient CT data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.