Local wall thickness in finite element models improves prediction of abdominal aortic aneurysm growth.
OBJECTIVE: Growing evidence suggests that peak wall stress (PWS) derived from finite element analysis (FEA) of abdominal aortic aneurysms (AAAs) predicts clinical outcomes better than diameter alone. Prior models assume uniform wall thickness (UWT). We hypothesize that the inclusion of locally variable wall thickness (VWT) into FEA of AAAs will improve its ability to predict clinical outcomes.
METHODS: Patients with AAAs (n = 26) undergoing radiologic surveillance were identified. Custom MATLAB algorithms generated UWT and VWT aortic geometries from computed tomography angiography images, which were subsequently loaded with systolic blood pressure using FEA. PWS and aneurysm expansion (as a proxy for rupture risk and the need for repair) were examined.
RESULTS: The average radiologic follow-up time was 22.0 ± 13.6 months and the average aneurysm expansion rate was 2.8 ± 1.7 mm/y. PWS in VWT models significantly differed from PWS in UWT models (238 ± 68 vs 212 ± 73 kPa; P = .025). In our sample, initial aortic diameter was not found to be correlated with aneurysm expansion (r = 0.26; P = .19). A stronger correlation was found between aneurysm expansion and PWS derived from VWT models compared with PWS from UWT models (r = 0.86 vs r = 0.58; P = .032 by Fisher r to Z transformation).
CONCLUSIONS: The inclusion of locally VWT significantly improved the correlation between PWS and aneurysm expansion. Aortic wall thickness should be incorporated into future FEA models to accurately predict clinical outcomes.
Published In/Presented At
Shang, E. K., Nathan, D. P., Woo, E. Y., Fairman, R. M., Wang, G. J., Gorman, R. C., Gorman, J. H., 3rd, & Jackson, B. M. (2015). Local wall thickness in finite element models improves prediction of abdominal aortic aneurysm growth. Journal of vascular surgery, 61(1), 217–223. https://doi.org/10.1016/j.jvs.2013.08.032
Medicine and Health Sciences
Department of Medicine, Cardiology Division