Computational Modeling of Blood Flow in the TrapEase Inferior Vena Cava Filter

Singer, Michael; Henshaw, William D.; Wang, Stephen L.

doi:10.1016/j.jvir.2009.02.015

Cited by 39 publications

(41 citation statements)

References 24 publications

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“…The model assumes that all vessels are straight and rigid, all thrombus are circular or elliptical, and flow through the IVC is steady; the applicability of these approximations is discussed elsewhere (e.g., [13,16]). It can be noted, however, that the design optimization methodology used here is broadly applicable to different sizes and shapes of clots.…”

Section: Discussionmentioning

confidence: 99%

“…All vessels are taken to be straight and rigid (e.g., [12][13][14][15][16]), and the IVC has an inner diameter of 23mm, per the average cava diameter in [17]. The diameters and entry angles of the common iliac and renal veins into the IVC, as noted in Fig.…”

Section: Computational Modelmentioning

confidence: 99%

“…This flow scenario has little clinical relevance to dual filtration filters because thrombus trapped near the center of the vessel are, in some sense, trapped in the ideal location -far from the cava walls. In contrast, thrombus trapped along the wall are known to induce regions of stagnant/recirculating flow along the vessel wall that may be thrombogenic [13][14][15]25] Based on the design optimization criteria described above, it is reasonable to question the clinical value of dual filtration IVC filters that trap thrombus along the cava wall: If the optimal WSS of a dual filtration device is that of a single filtration device, why design dual filtration devices? But, the potential benefits of vena cava filters that trap thrombus along the wall and do not elevate the risk of thrombosis are numerous.…”

Section: Design Optimization Criteriamentioning

confidence: 99%

“…In all of the figures below, the flow is bottom-to-top, and all velocities are normalized by the average velocity, in the y-direction, of unoccluded flow into the vena cava (e.g., [13][14][15][16]). Similarly, the wall shear stresses are normalized by the value in the unoccluded IVC.…”

Section: Design Optimization Criteriamentioning

confidence: 99%

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Design Optimization of Vena Cava Filters: An Application to Dual Filtration Devices

Singer

Wang

Diachin³

2010

Journal of Biomechanical Engineering

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Background: Pulmonary embolism (PE) is a significant medical problem that results in over 300,000 fatalities per year. A common preventative treatment for PE is the insertion of a metallic filter into the inferior vena cava that traps thrombi before they reach the lungs. The goal of this work is to use methods of mathematical modeling and design optimization to determine the configuration of trapped thrombi that minimizes the hemodynamic disruption. The resulting configuration has implications for constructing an optimally designed vena cava filter.Approach: Computational fluid dynamics is coupled with a nonlinear optimization algorithm to determine the optimal configuration of trapped model thrombus in the inferior vena cava. The location and shape of the thrombus are parameterized, and an objective function, based on wall shear stresses, determines the worthiness of a given configuration. The methods are fully automated and demonstrate the capabilities of a design optimization framework that is broadly applicable.Results: Changes to thrombus location and shape alter the velocity contours and wall shear stress profiles significantly. For vena cava filters that trap two thrombi simultaneously, the undesirable flow dynamics past one thrombus can be mitigated by leveraging the flow past the other thrombus. Streamlining the shape of thrombus trapped along the cava wall reduces the disruption to the flow, but increases the area exposed to abnormal wall shear stress. Conclusions:Computer-based design optimization is a useful tool for developing vena cava filters. Characterizing and parameterizing the design requirements and constraints is essential for constructing devices that address clinical complications. In addition, formulating a well-defined objective function that quantifies clinical risks and benefits is needed for designing devices that are clinically viable.

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Section: Discussionmentioning

confidence: 99%

Section: Computational Modelmentioning

confidence: 99%

Section: Design Optimization Criteriamentioning

confidence: 99%

Section: Design Optimization Criteriamentioning

confidence: 99%

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Design Optimization of Vena Cava Filters: An Application to Dual Filtration Devices

Singer

Wang

Diachin³

2010

Journal of Biomechanical Engineering

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“…Unlike previous computational studies, which considered blood flow in idealized IVC geometries without IVC-IVC filter interactions [17,5,7,19,20], the present study addresses new aspects of IVC filter placement: 1) the in Keefe B. Manning BIO-13-1517…”

Section: Discussionmentioning

confidence: 99%

A Computational Method for Predicting Inferior Vena Cava Filter Performance on a Patient-Specific Basis

Aycock

Campbell

Manning

et al. 2014

Journal of Biomechanical Engineering

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A comparative CFD study of four inferior vena cava filters

López

Fortuny

Puigjaner

et al. 2018

Numer Methods Biomed Eng

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Computational fluid dynamics was used to simulate the flow of blood within an inferior vena cava (IVC) geometry model that was reconstructed from computed tomography images obtained from a real patient. The main novelty of the present work is that we simulated the implantation of 4 different filter models in this realistic IVC geometry. We considered different blood flow rates in the range between V =20 and V =80 cm /s, and all simulations were performed with both the Newtonian and a non-Newtonian model for the blood viscosity. We compared the hemodynamics performance of the different filter models, and we paid a special attention to the total drag force, F , exerted by the blood flow on the filter surface. This force is the sum of 2 contributions: the viscous skin friction force, which was found to be roughly proportional to the filter surface area, and the pressure force, which depended on the particular filter geometry design. The F force is relevant because it must be balanced by the total force exerted by the filter hooks/struts on the IVC wall at the attachment locations. For the highest V value investigated, the variation in F among filters was from 116 to 308 dyne. We also showed how the present results can be extrapolated to obtain good estimates of the drag forces if the blood viscosity levels change, ie, if the patient with a filter implanted is treated with anticoagulant therapy.

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Computational Modeling of Blood Flow in the TrapEase Inferior Vena Cava Filter

Cited by 39 publications

References 24 publications

Design Optimization of Vena Cava Filters: An Application to Dual Filtration Devices

Design Optimization of Vena Cava Filters: An Application to Dual Filtration Devices

A Computational Method for Predicting Inferior Vena Cava Filter Performance on a Patient-Specific Basis

A comparative CFD study of four inferior vena cava filters

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