Analýza vývoje rizika ruptury Aneurysmatu abdominální aorty v čase

Abstract

This bachelor’s thesis examines the evolution of the risk of abdominal aortic aneurysm (AAA) rupture over time using biomechanical and probabilistic methods. Current clinical assessment, based primarily on the maximum diameter of the aneurysm, is not sufficiently accurate, as rupture can occur even in smaller aneurysms. The thesis therefore explores more advanced approaches utilizing the finite element method and biomechanical analysis of the aortic wall.

The theoretical section reviews current methods for assessing the risk of AAA rupture, including statistical models, biomechanical indicators, and probabilistic approaches. Particular attention is paid to peak wall stress (PWS), the rupture risk index (PWRI), and the probabilistic rupture risk index (PRRI), which combines numerical stress calculations with the distribution of vascular wall strength. Furthermore, the effects of aneurysm geometry, vascular wall thickness, intraluminal thrombus, extracellular matrix degradation, and tissue remodeling on aortic mechanical failure are discussed.

The practical part of the work utilizes the geometries of actual patients obtained from CT scans at various time points. These geometries were reconstructed, discretized using the finite element method, and subsequently subjected to strength simulations of blood pressure loading. The simulations resulted in the determination of peak stresses in the aneurysm wall and the calculation of a probabilistic rupture risk index. The thesis also included a mesh convergence analysis and an evaluation of the limitations of the models used.

The results showed that biomechanical models based on the finite element method are better able to capture the individual progression of the disease than clinical assessment based solely on the maximum aneurysm diameter. Numerical simulations corresponded to the clinical course of the patients studied and confirmed the potential of probabilistic methods in predicting the risk of AAA rupture. At the same time, however, the study highlights the significance of uncertainties in input parameters, simplifications in material models, and the need for further validation on larger datasets.