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

基本信息

批准号：
10503513
负责人：
VICTOR H BAROCAS
金额：
$ 62.38万
依托单位：
WASHINGTON UNIVERSITY
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：
2022-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10503513
关键词：
Age Aneurysm Animal Model Aorta Autopsy Biological Markers Biomechanics Biophysics Blood flow Cadaver Caliber Categories Cessation of life Clinical Computer Models Computer Systems Coupled Dangerousness Data 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 Principal Component Analysis Publishing Reproducibility Risk Rupture Scanning Shapes Solid Stress System Testing Thoracic Aortic Aneurysm Time Tissue Model Tissues Training Work base biomarker identification cardiovascular health cost experience experimental study falls hemodynamics human data human tissue mechanical properties mouse model novel outcome prediction public health relevance repaired simulation surgical risk tool virtual virtual human virtual patient

项目摘要

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.

抽象的胸主动脉瘤（ATAA）是一种主要的心血管健康问题，其特征是扩张的主动脉最终可能会解剖或破裂。 ATAA 提出了严峻的挑战，因为手术困难且危险，因此动脉瘤修复标准必须平衡夹层和/或破裂的风险手术的风险。目前的手术指南是基于 ATAA 直径或生长率，但高达 60% ATAA 患者在达到手术标准之前经历了解剖，因此显然需要寻找动脉瘤失败的其他生物标志物。可能的生物标志物分为广泛的类别，包括遗传、微观结构、几何和生物流体，但获取足够的人类数据来计算和将这些生物标志物与失败等关键结果相关联。单个生物标志物很可能不是足够但直观上不明显的复合生物标志物可能是重要预测所必需的患者的治疗结果。在本提案中，我们将使用模型组合：1）ATAA 小鼠模型与马凡氏综合症相关，2) ATAA 生长和重塑的多尺度、多物理模型，以及 3) 从真实患者成像数据导出的虚拟患者模型，以确定可能的复合生物标志物预测 ATAA 的生长、进展和失败。我们的第一个具体目标是使用 ATAA 基因小鼠模型与马凡氏综合症相关，以表征以前未实现的动脉瘤进展和失败细节，量化主动脉形状、组织成分、组织机械特性和血流动力学时间。这种详细程度在人类患者中是不可能的，并且对于验证和测试假设是必要的具体目标 2 中我们的多尺度、多物理场模型中的增长和重塑规则，并提供初始一组生物标志物，用于在特定目标 3 中评估我们的虚拟患者。我们的第二个特定目标是开发 ATAA 生长和重塑的新颖多尺度、多物理计算模型，以产生将产生的结果与特定目标 1 中的小鼠数据进行比较，并用于预测真实和 Specific Aim 3 中的虚拟人类患者。在我们的第三个 Specific Aim 中，我们将使用可用的人体 ATAA 扫描来自马凡氏综合症患者的数据，为 ATAA 几何形状生成统计形状模型基础，我们将使用该基础生成虚拟患者，其 TAA 整个进展和失败的过程将是由特定目标 2 中的模型创建，参数根据已发表的文献和我们的鼠标确定具体目标 1 中的数据。然后，真实和虚拟患者数据将用于训练机器学习工具将复合生物标志物与重塑结果联系起来并预测失败风险。这个计划综合了多项最新进展，并用新想法对其进行补充，以产生能够为 ATAA 做出有用的故障预测。