Finite element analysis of abdominal aortic aneurysms: predicted rupture risk correlates with aortic wall histology in individual patients - Erhart, Philipp; Grond-Ginsbach, Caspar; Hakimi, Maani; Lasitschka, Felix; Dihlmann, Susanne; Böckler, Dittmar; Hyhlik-Dürr, Alexander (Journal of endovascular therapy, 2014)
Purpose:To evaluate a finite element analysis (FEA) model as a predictive tool for abdominal aortic aneurysm (AAA) rupture risk assessment.Methods:FEA of asymptomatic infrarenal AAAs in 15 men (mean age 72 years) was performed preoperatively using semiautomatic finite element analysis software (A4clinics) to calculate peak wall stress (PWS) and regions of highest and lowest rupture risk index (RRI). The areas of high and low RRI identified on the preoperative FEA were sampled during open surgery; aortic wall specimens were prepared for histological analysis. A semiquantitative score compared the histological findings from high and low rupture risk samples.Results:Significant correlation was found between histological AAA wall integrity and RRI in individual patients. AAA wall regions with highest RRI showed advanced histological disintegrity compared to regions with lower RRI within the same AAA: mean smooth muscle cells: 0.43 vs. 1.21, respectively (p=0.031); elastic fibers: 0.57 vs. 1.29, respectively (p=0.008); cholesterol plaque: 2.60 vs. 2.20, respectively (p=0.034); and calcified plaque: 2.27 vs. 1.40, respectively (p=0.017). The amount of calcified plaque was significantly correlated with PWS (r=0.528, p=0.043) by univariate regression analysis. However, there was no correlation between PWS or RRI and the histological findings between patients.Conclusion:These preliminary results show that high rupture risk regions estimated by FEA contain increased histopathological degeneration compared to low rupture risk samples within the same AAA. Until now, the role of FEA in predicting individual AAA rupture risk has not been established as a validated diagnostic tool. However, these data provide promising results for FEA model verification.