Individualized Rupture Risk Assessment and Analysis of Mechanobiological Interactions in Abdominal Aortic Aneurysms

• Applicants: Professor Dr. Hans-Henning Eckstein (†); Professor Dr.-Ing. Michael W. Gee
• Subject Area: Applied Mechanics, Statics and Dynamics ; General and Visceral Surgery
• Term: from 2016 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 318323882

A diameter increase of the abdominal aorta of >30mm is called an abdominal aortic aneurysm (AAA), where risk of a life threateningrupture increases with diameter. Established guidelines therefore recommend interventional therapy for AAA with 45-55mm. In thiscontext, it has to be considered that only 1/3 of AAA >50mm actually rupture, while rupture might also appear in much smaller AAA.Therefore, a refined, reliable and patient specific rupture risk prediction is of great interest. Clinically rupture risk stratification ismainly based on a AAA diameter criterion, which is not very specific. Consideration of potentially highly relevant parameters as e.g.morphology, wall thickness, thrombus, calcifications and aortic wall strength usually is not included. In the first period of this project, we developed a model for individualized rupture risk prediction. It is based on a computational model that considers patient specificgeometries and material properties from multilinear regression models based on our patient cohort. A data base of 80 patients and 221 tissue samples was created. Tissue samples were characterized mechanically, histologically and immunohistochemically. Amechanobiological surrogate model has been developed that predicts AAA properties that can not be assessed non-invasively. Theinformation enters the numerical models to compute wall stresses, which, compared to wall strength derived from tissue sample testing, yields a rupture risk quantification. It has been shown that the novel risk stratification can be superior to the clinically established. In this project period, we will enhance the rupture risk model by important additional aspects, namely the AAA progression in time and a rigorous quantification of prediction quality of the model. Additionally, we increase the data base in size and by additional serum and sample based, histological and immunohistological findings. In vitro experiments will be performed in primary aortal smooth muscle cells to identify functional relationships of mechanotransduction in human AAA wall. We develop a novel mechanobiological growth & remodeling (G&R) material law that not only considers mechanical state but also considers biological aspects of AAA wall degeneration. The G&R model parameters will be calibrated through an inverse analysis procedure based on longitudinal medical image data sets. Gaussian process regression will be applied to the biological and mechanical parameters to identify statistically significant surrogate model parameters to incorporate in the G&R material formulation andin wall strength prediction. Simultaneously, Gaussian process regression will also serve for the assessment of the predictivecapabilities by means of confidence intervals for the predicted G&R and material properties values. The final rupture risk stratification model will be evaluated retrospectively and prospectively on a small patient cohort.

• DFG Programme: Research Grants