An aortic hemodynamic fingerprint reduced order modeling analysis reveals traits associated with vascular disease in a medical biobank

Abstract

PurposeTo determine the clinical relevance of reduced order model (ROM) aortic hemodynamic imaging-derived phenotypes (IDPs) for a range of flow conditions applied to computed tomography (CT) scan data in the Penn Medicine Biobank (PMBB). MethodsThe human thoracic aorta was automatically segmented in 3,204 chest CT scans from patients in the Penn Medicine Biobank (PMBB) patients using deep learning. Thoracic aorta anatomic IDPs such as aortic diameter and length were computed. Resistance, and flow boundary conditions, were varied, resulting in 125,000 ROM simulations, producing a fingerprint of aortic hemodynamics IDPs for a range of flow conditions. To determine the clinical relevance of the aortic hemodynamic fingerprint, untargeted phenome wide association studies (PheWAS) for disease conditions were performed using aortic geometries and pulse pressure as IDPs. ResultsBy utilizing patient metadata from the PMBB, the human aortic radius for different age groups over a normalized radius was visualized, showing how the vessel deforms with age, as well as other characteristic geometric information. The average radius of the ascending thoracic aortic data set was 26.6 {+/-} 3.1 mm, with an average length of 310 {+/-} 37 mm. A combination of pathology codes (phecodes) and hemodynamic simulations were utilized to develop a relationship between them, showing a strong relationship between the resulting pulse pressure and diseases relating to aortic aneurysms and heart valve disorders. The average pulse pressure calculated by the model was 22.5 {+/-} 8.5 mmHg, with the maximum pressure modeled by the system being 201 mmHg, with the minimum being 63.6 mmHg. The pulse pressures of the most significant phecodes were examined for patients with and without the condition, showing a slight separation between the two cases. The pulse pressure was also slightly negatively correlated with the calculated tapering angle of the ascending thoracic aorta. ConclusionsROM hemodynamic simulations can be applied to aortic imaging traits from thoracic imaging data in a medical biobank. The derived hemodynamic fingerprint, describing the response of the aorta to a range of flow conditions, shows clinically relevant associations with disease.