Haemodynamic numerical simulation of hybrid surgical repairs for thoracoabdominal aortic aneurysms

The hybrid surgical repair is a feasible alternative to conventional open surgical or total endovascular repairs for thoracoabdominal aneurysms. However, a small number of patients are treated every year with this procedure, and for this reason, a negligible amount of numerical or measured data is available in the literature. Moreover, the complex and highly variable stent graft design in hybrid surgical repairs means that a priori prediction of haemodynamic flow parameters and clinical or surgical outcomes remain challenging. The goal of this work is to appraise the clinical relevance of computational fluid dynamics and numerical results in the setting of hybrid surgical repairs.

Numerical simulations are carried out on three patients with a large range of elements of the meshes to assess the spatial convergence of the result. Flow rates and geometries are calculated and reconstructed in the post-operative conditions with phase-contrast magnetic resonance imaging.

Numerical results demonstrate higher accuracy with respect to measurements. In fact, the measured outflow rates are not able to match the measured inflow rate in any patient. From the point of view of the spatial convergence of the results, the acceptable mesh depends on the quantity of interest, e.g., (i) in terms of the time-averaged outflow rates, the mesh with 4 × 10⁶ elements is acceptable, (ii) in terms of the maximum values in the distribution of the wall shear stresses, the mesh with 16 × 10⁶ elements is acceptable, while (iii) in terms of the numerical dissipation, only the mesh with 64 × 10⁶ elements is acceptable for all patients.

Numerical results demonstrate that computational fluid dynamics can be used, especially in hybrid surgical repairs, to generate potentially actionable predictive insights with implications for surveillance and enhanced postoperative management. Moreover, numerical results can also be used in preoperative surgical planning coupled with geometry optimization algorithms to identify the best designs for the patient.