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

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.