Complementary animal and computational models for biomarker identification in ascending thoracic aortic aneurysm

升主动脉瘤生物标志物识别的补充动物和计算模型

• 批准号: 10646286
• 负责人: VICTOR H BAROCAS
• 金额: $ 60.04万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10646286
• 关键词: Age; Aneurysm; Animal Model; Aorta; Autopsy; Biological Markers; Biomechanics; Biophysics; Blood flow; Cadaver; Categories; Cessation of life; Clinical; Computer Models; Computer Systems; Coupled; Dangerousness; Data; Diameter; Disease Outcome; Dissection; Equilibrium; Event; Experimental Models; Failure; Genetic; Geometry; Growth; Guidelines; Human; Image; Individual; Intervention; Liquid substance; Literature; Longitudinal Studies; Machine Learning; Marfan Syndrome; Mechanics; Modeling; Monitor; Mus; Operative Surgical Procedures; Outcome; Patient imaging; Patient-Focused Outcomes; Patients; Physics; Principal Component Analysis; Publishing; Reproducibility; Risk; Rupture; Scanning; Shapes; Solid; Stress; System; Testing; Thoracic Aortic Aneurysm; Time; Tissue Model; Tissues; Training; Work; biomarker identification; cardiovascular health; cost; experience; experimental study; falls; hemodynamics; human data; human tissue; mechanical properties; models and simulation; mouse model; novel; outcome prediction; public health relevance; repaired; simulation; surgical risk; tool; virtual; virtual human; virtual patient; virtual reality simulation

ABSTRACT Ascending thoracic aortic aneurysm (ATAA) is a major cardiovascular health problem characterized by a dilated aorta that may eventually dissect or rupture. ATAA presents a serious challenge in that the surgery is difficult and dangerous, so aneurysm repair criteria must balance the risk of a dissection and/or rupture with the risk of surgery. Current surgical guidelines are based on ATAA diameter or growth rate, but up to 60% of patients with an ATAA experience a dissection before surgical criteria are reached, hence there is a clear need for additional biomarkers of aneurysm failure. Possible biomarkers fall into broad categories including genetic, microstructural, geometrical, and biofluids, but it is challenging to obtain enough human data to calculate and correlate these biomarkers with critical outcomes such as failure. It is likely that a single biomarker is not sufficient, but composite biomarkers that are not intuitively obvious may be necessary for significant predictions of patient outcomes. In this proposal we will use a combination of models: 1) a mouse model of ATAA associated with Marfan Syndrome, 2) a multiscale, multiphysics model of ATAA growth and remodeling, and 3) virtual patient models derived from real patient imaging data, to determine composite biomarkers that may predict ATAA growth, progression, and failure. Our first Specific Aim is to use a genetic mouse model of ATAA associated with Marfan Syndrome to characterize aneurysm progression and failure in previously unachieved detail, quantifying aortic shape, tissue composition, tissue mechanical properties, and hemodynamics over time. This level of detail is not possible in human patients and is necessary to validate and test hypotheses on the growth and remodeling rules in our multiscale, multiphysics model in Specific Aim 2 and to provide an initial set of biomarkers to evaluate for our virtual patients in Specific Aim 3. Our second Specific Aim is to develop a novel multiscale, multiphysics computational model of ATAA growth and remodeling to produce results that will be compared to the mouse data in Specific Aim 1 and used to predict remodeling progression in real and virtual human patients in Specific Aim 3. In our third Specific Aim, we will use available human ATAA scans from Marfan Syndrome patients to generate a statistical shape model basis for the ATAA geometry, and we will use that basis to generate virtual patients, whose TAA course throughout progression and failure will be created by the model in Specific Aim 2, with parameters determined from published literature and our mouse data in Specific Aim 1. Both real and virtual patient data will then be used to train a machine learning tool to relate the composite biomarkers to the remodeling outcomes and predict failure risk. This plan synthesizes multiple recent advances and supplements them with new ideas to produce a computer system capable of making useful failure predictions for ATAA.

抽象的 上升胸动脉瘤（ATAA）是一个主要的心血管健康问题 最终可能剖析或破裂的扩张主动脉。 ATAA提出了一个严重的挑战，因为手术是 困难和危险，因此动脉瘤修复标准必须平衡解剖和/或破裂的风险 手术的风险。当前的手术指南基于ATAA直径或增长率，但最多可达60％ 具有ATAA经验的患者达到手术标准之前的解剖，因此有明显的需求 用于动脉瘤衰竭的其他生物标志物。可能的生物标志物属于广泛类别，包括遗传， 微观结构，几何和生物流体，但是获得足够的人类数据来计算和 将这些生物标志物与诸如失败之类的关键结果相关联。单个生物标志物可能不是 足够的，但没有直观地明显的复合生物标志物对于重要的预测可能是必需的 患者预后。在此提案中，我们将使用模型组合：1）ATAA的鼠标模型 与Marfan综合征相关，2）ATAA生长和重塑的多尺度，多物理模型，以及3） 来自真实患者成像数据的虚拟患者模型，以确定可能的复合生物标志物 预测ATAA的增长，进展和失败。我们的第一个具体目的是使用ATAA的遗传小鼠模型 与Marfan综合征相关，以表征先前未经观察的动脉瘤进展和失败 细节，量化主动脉形状，组织组成，组织机械特性和血液动力学 时间。在人类患者中不可能进行这种细节，这对于验证和检验假设是必要的 特定目标2中的多尺度多物理模型中的增长和重塑规则，并提供初始 在特定目标3中为我们的虚拟患者评估的一组生物标志物。我们的第二个特定目的是开发一个 新型的多尺度，多物理计算模型的ATAA增长和重塑，以产生将会产生的结果 与特定目标1中的鼠标数据进行比较，用于预测实际和 特定目标3的虚拟人类患者。在我们的第三个特定目的中，我们将使用可用的人类ATAA扫描 从Marfan综合征患者到ATAA几何形状生成统计形状模型基础，我们将 利用该基础来产生虚拟患者，在整个进展和失败过程中，他们的TAA课程将是 由模型在特定AIM 2中创建的，其参数由已发表的文献和我们的鼠标确定 特定目的1中的数据。然后将使用真实和虚拟患者数据来训练机器学习工具 将复合生物标志物与重塑结果相关联并预测失败风险。该计划合成 最近的多次进步，并用新想法为他们补充，以产生能够的计算机系统 对ATAA进行有用的故障预测。