Usefulness and limitations of computational models in aortic disease risk stratification

In risk stratification of aortic diseases such as aneurysm and aortic dissection, diameter is one parameter whose influence on the average aortic wall stress is directly described by the Laplace law. More advanced mechanical models can be used and may yield additional information, such as transmural stress distributions. The question then arises of how refined models need to be to provide clinicians with practical help.

Two sets of finite element models were used. The relative roles of diameter, material stiffness, longitudinal stretch, blood pressure, wall thickness, and vessel curvature were explored using simplified aortic models for comparison with the Laplace law. The influences of the material properties nonlinearity and residual stress on the transmural stress distribution were investigated using an advanced aortic model including recent experimental findings in older humans.

The Laplace law was confirmed as one effective, basic tool to assess the average wall stress in the aortic wall, both in the circumferential and longitudinal directions. However, the simplified models were sufficient to show that, as already reported in the literature, longitudinal stretch and vessel curvature have potentially equally strong or even stronger contributions to wall stress than the parameters included in the Laplace law. When the advanced model was used, and residual stress induced by large opening angles such as found in older subjects was introduced, the transmural stress gradient was found inverted compared with expectations, with the largest stresses now toward the adventitia. The results suggested that the intima may be increasingly shielded from higher stresses as one gets older, which might be protective against the initiation of dissection tears in the thoracic aorta.

Biomechanical analysis of the aorta may be refined by using increasingly detailed computational models. Simplified models can readily improve on the Laplace law in the assessment of aortic wall stress, and as such, may already contribute to better risk stratification of aortic disease. Advanced models may also enhance our understanding of the mechanistic aspects in the pathogenesis of aortic disease. However, their applicability in a patient-specific context may be limited by the large number of input data they require, some of which might stay out of the clinicians' reach.