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

摘要 摘要升胸主动脉瘤(ATAA)是一种主要的心血管疾病，其特点是 一种扩张的大动脉，最终可能剥离或破裂。阿塔提出了一个严峻的挑战，因为手术是 困难和危险，因此动脉瘤修复标准必须平衡夹层和/或破裂的风险与 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%的 患有Ataa的患者在达到手术标准之前会经历一次夹层，因此显然有必要这样做。 寻找更多动脉瘤失败的生物标志物。可能的生物标记物分为广泛的类别，包括遗传， 微结构、几何和生物流体，但要获得足够的人体数据来计算和 将这些生物标记物与失败等关键结果相关联。单个生物标记物很可能不是 足够的，但在直觉上不明显的复合生物标志物可能是有意义的预测所必需的 关于病人的结果。在本提案中，我们将使用以下几种模型的组合：1)阿塔的小鼠模型 与马凡综合征有关，2)阿塔生长和重塑的多尺度、多物理模型，3) 从真实患者成像数据派生的虚拟患者模型，以确定可能 预测阿塔亚的成长、进步和失败。我们的第一个具体目标是使用Ataa的遗传小鼠模型 与马凡综合征相关的先前未实现的动脉瘤进展和失败的特征 详细，量化主动脉形状、组织成分、组织机械特性和血流动力学 时间到了。这种程度的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 我们的多尺度、多物理模型在特定目标2中的生长和重塑规则，并提供了一个初步的 为我们的虚拟患者评估一套特定目标的生物标记物3。我们的第二个特定目标是开发一种 阿塔亚生长和重塑的新的多尺度、多物理计算模型，以产生将 与特定目标1中的小鼠数据进行比较，并用于预测真实和 特定目标中的虚拟人类患者3.在我们的第三个特定目标中，我们将使用可用的人类Ataa扫描 从马凡综合征患者中生成一个统计形状模型的基础为Ataa几何，我们将 使用该基础来生成虚拟患者，其在整个进展和失败过程中的TAA过程将是 由特定目标2中的模型创建，参数由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，将使用真实和虚拟患者数据来训练机器学习工具 将复合生物标记物与重塑结果相关联，并预测失败风险。这项计划综合了 多项最新进展，并用新的想法补充它们，以生产一种能够 为阿塔做出了有用的失败预测。

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.