Patient-specific static and dynamic 3D-printed models for planning endovascular repair of complex aortic disease with physician-modified stent grafts

Abstract

To develop a patient-specific static and dynamic 3D-printing workflow for preoperative planning of endovascular repair in complex aortic disease with physician-modified stent grafts, to assess the respective roles of rigid anatomical and dynamic models, and to explore its association with intraoperative efficiency compared with conventional image-based planning. This retrospective study included 46 consecutive patients with complex aortic disease encompassing various segments between the aortic arch and the abdominal aorta, who underwent endovascular repair with physician-modified stent grafts. Patients were assigned to a 3D printing-guided group (n = 22) or a conventional image-guided group (n = 24) based on the preoperative planning strategy. In the 3D printing group, patient-specific 1:1 rigid anatomical models and compliant dynamic models were generated from computed tomography angiography data. Rigid models were used for anatomical visualization, device sizing, and fenestration planning. Dynamic models, fabricated using silicone material (Shore hardness 10A) and integrated into a pulsatile flow system, were used to simulate device passage, deployment, and device–vessel interaction under physiological flow conditions. Model accuracy was evaluated by surface deviation analysis, and dynamic model performance was evaluated by Doppler-based flow comparison with in vivo measurements. The two groups were compared regarding operative time, radiation dose, contrast volume, perioperative complications, and short-term outcomes. Rigid anatomical models demonstrated high geometric fidelity, with over 97% of surface deviations within 0.2 mm compared with CTA data. Dynamic models showed close agreement with in vivo hemodynamics, with comparable peak systolic velocities at the inlet (1.48 ± 0.20 m/s vs. 1.52 ± 0.18 m/s, p = 0.274), aneurysmal segment (1.20 ± 0.18 m/s vs. 1.25 ± 0.15 m/s, p = 0.312), and outlet (1.35 ± 0.17 m/s vs. 1.38 ± 0.16 m/s, p = 0.401). In the 3D printing-guided group, simulation findings were concordant with intraoperative findings in 21 of 22 cases. Compared with rigid static models, dynamic models provided additional planning information in anatomically challenging cases, including severe angulation of arch or neck, severe luminal stenosis or true lumen collapse, target vessels arise from the aneurysm sac, complex target vessel proximal segment and severe access tortuosity. In the exploratory clinical comparison, the 3D-printing-guided group had significantly shorter operative time (133.6 ± 43.3 min vs. 171.4 ± 72.1 min, p = 0.039) and lower contrast volume (181.7 ± 68.6 mL vs. 234.9 ± 101.0 mL, p = 0.044) than the conventional image-guided group. The need for intraoperative device adjustment did not differ significantly between groups. No major adverse events occurred within 30 days in either group. A patient-specific dual-model 3D printing workflow combining rigid anatomical models and dynamic models is feasible for planning endovascular repair of complex aortic disease with physician-modified stent grafts. The dynamic model provides complementary information beyond static anatomical assessment, particularly in anatomically complex cases requiring simulation of device–vessel interaction. The observed reductions in operative time and contrast use suggest potential intraoperative benefits.