Impact of Vein Wall Hyperelasticity and Blood Flow Turbulence on Hemodynamic Parameters in the Inferior Vena Cava with a Filter

Inferior vena cava (IVC) filters are vital in preventing pulmonary embolism (PE) by trapping large blood clots, especially in patients unsuitable for anticoagulation. In this study, the accuracy of two common simplifying assumptions in numerical studies of IVC filters-the rigid wall assumption and the laminar flow model-is examined, contrasting them with more realistic hyperelastic wall and turbulent flow models. Using fluid-structure interaction (FSI) and computational fluid dynamics (CFD) techniques, the investigation focuses on three hemodynamic parameters: time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). Simulations are conducted with varying sizes of clots captured in the filter. The findings show that, in regions of high wall shear stress, the rigid wall model predicted higher TAWSS values, suggesting an increased disease risk compared to the hyperelastic model. However, the laminar and turbulent flow models did not show significant differences in TAWSS predictions. Conversely, in areas of low wall shear stress, the rigid wall model indicated lower OSI and RRT, hinting at a reduced risk compared to the hyperelastic model, with this discrepancy being more evident with larger clots. While the predictions for OSI and TAWSS were closely aligned for both laminar and turbulent flows, divergences in RRT predictions became apparent, especially in scenarios with very large clots.

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Publication Details

Journal
Micromachines
Published
2024-12-31
DOI
https://doi.org/10.3390/mi16010051
Primary Topic
Venous Thromboembolism Diagnosis and Management
Type
article
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article

Impact of Vein Wall Hyperelasticity and Blood Flow Turbulence on Hemodynamic Parameters in the Inferior Vena Cava with a Filter

Jafar Moradicheghamahi, Debkalpa Goswami
Micromachines
Venous Thromboembolism Diagnosis and Management
article

Impact of Vein Wall Hyperelasticity and Blood Flow Turbulence on Hemodynamic Parameters in the Inferior Vena Cava with a Filter

Jafar Moradicheghamahi, Debkalpa Goswami
article en

Abstract

Inferior vena cava (IVC) filters are vital in preventing pulmonary embolism (PE) by trapping large blood clots, especially in patients unsuitable for anticoagulation. In this study, the accuracy of two common simplifying assumptions in numerical studies of IVC filters-the rigid wall assumption and the laminar flow model-is examined, contrasting them with more realistic hyperelastic wall and turbulent flow models. Using fluid-structure interaction (FSI) and computational fluid dynamics (CFD) techniques, the investigation focuses on three hemodynamic parameters: time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). Simulations are conducted with varying sizes of clots captured in the filter. The findings show that, in regions of high wall shear stress, the rigid wall model predicted higher TAWSS values, suggesting an increased disease risk compared to the hyperelastic model. However, the laminar and turbulent flow models did not show significant differences in TAWSS predictions. Conversely, in areas of low wall shear stress, the rigid wall model indicated lower OSI and RRT, hinting at a reduced risk compared to the hyperelastic model, with this discrepancy being more evident with larger clots. While the predictions for OSI and TAWSS were closely aligned for both laminar and turbulent flows, divergences in RRT predictions became apparent, especially in scenarios with very large clots.

MicromachinesVol. 16(1)
Cleveland Clinic (US), Université de Bordeaux (FR), Electrophysiology and Heart Modeling Institute (FR), Institut Polytechnique de Bordeaux (FR), Case Western Reserve University (US)
Openalex Percentile: Top 51%
Venous Thromboembolism Diagnosis and Management
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Impact of Vein Wall Hyperelasticity and Blood Flow Turbulence on Hemodynamic Parameters in the Inferior Vena Cava with a Filter — Jafar Moradicheghamahi, Debkalpa Goswami · Micromachines (2024) | TGRS Research Map | TGRS