A computational method for predicting inferior vena cava filter performance on a patient-specific basis

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Medical Device Simulation

Background:

• Inferior vena cava (IVC) filters are crucial for preventing pulmonary embolism.
• Patient-specific anatomical variations can influence IVC filter placement and efficacy.
• Current simulation methods may not fully capture complex IVC geometries and hemodynamics.

Purpose of the Study:

• To develop and demonstrate a computational methodology for simulating virtual IVC filter placement and hemodynamics.
• To analyze the impact of patient-specific IVC anatomies on filter placement forces and blood flow.
• To compare hemodynamic outcomes between a standard IVC and anatomically challenging IVCs.

Main Methods:

• Developed a computational methodology integrating nonlinear finite element analysis (FEA) and computational fluid dynamics (CFD).
• Utilized inverse analysis for in vivo stress approximation and contact modeling for filter placement simulation.
• Simulated hemodynamics under resting and exercise conditions with and without a filter and model embolus in two patient-specific IVC geometries.

Main Results:

• Contact forces and displacements during filter placement were significantly higher in the retroaortic IVC compared to the left-sided IVC.
• Hemodynamic simulations showed distinct differences between the two IVCs, including right-sided jets and altered flow recirculation.
• Left-sided IVC exhibited lower maximum flow velocities, indicating significant hemodynamic alterations due to filter placement.

Conclusions:

• The developed computational methodology effectively simulates patient-specific IVC filter placement and hemodynamics.
• Anatomical variations in the IVC significantly influence filter placement mechanics and hemodynamic outcomes.
• Patient-specific simulations are essential for optimizing IVC filter placement and predicting clinical efficacy.