# A resolved two-way coupled CFD/6-DOF approach for predicting embolus transport and the embolus-trapping efficiency of IVC filters

- 599 Downloads
- 5 Citations

## Abstract

Inferior vena cava (IVC) filters are medical devices designed to provide a mechanical barrier to the passage of emboli from the deep veins of the legs to the heart and lungs. Despite decades of development and clinical use, IVC filters still fail to prevent the passage of all hazardous emboli. The objective of this study is to (1) develop a resolved two-way computational model of embolus transport, (2) provide verification and validation evidence for the model, and (3) demonstrate the ability of the model to predict the embolus-trapping efficiency of an IVC filter. Our model couples computational fluid dynamics simulations of blood flow to six-degree-of-freedom simulations of embolus transport and resolves the interactions between rigid, spherical emboli and the blood flow using an immersed boundary method. Following model development and numerical verification and validation of the computational approach against benchmark data from the literature, embolus transport simulations are performed in an idealized IVC geometry. Centered and tilted filter orientations are considered using a nonlinear finite element-based virtual filter placement procedure. A total of 2048 coupled CFD/6-DOF simulations are performed to predict the embolus-trapping statistics of the filter. The simulations predict that the embolus-trapping efficiency of the IVC filter increases with increasing embolus diameter and increasing embolus-to-blood density ratio. Tilted filter placement is found to decrease the embolus-trapping efficiency compared with centered filter placement. Multiple embolus-trapping locations are predicted for the IVC filter, and the trapping locations are predicted to shift upstream and toward the vessel wall with increasing embolus diameter. Simulations of the injection of successive emboli into the IVC are also performed and reveal that the embolus-trapping efficiency decreases with increasing thrombus load in the IVC filter. In future work, the computational tool could be used to investigate IVC filter design improvements, the effect of patient anatomy on embolus transport and IVC filter embolus-trapping efficiency, and, with further development and validation, optimal filter selection and placement on a patient-specific basis.

## Keywords

Pulmonary embolism Immersed boundary method Coupled CFD/6-DOF Filter efficiency IVC filter Embolus transport## Notes

### Funding

This research was supported by the Walker Assistantship program at The Pennsylvania State Applied Research Laboratory.

### Compliance with ethical standards

### Conflict of interest

The authors declare that they have no conflict of interest.

## Supplementary material

Supplementary material 1 (mp4 393 KB)

Supplementary material 4 (mp4 3691 KB)

Supplementary material 5 (mp4 3678 KB)

## References

- Abolfazli E, Fatouraee N, Vahidi B (2014) Dynamics of motion of a clot through an arterial bifurcation: a finite element analysis. Fluid Dyn Res 46:055505MathSciNetCrossRefGoogle Scholar
- Aleman MM, Walton BL, Byrnes JR, Wolberg AS (2014) Fibrinogen and red blood cells in venous thrombosis. Thromb Res 133(01):S38–S40CrossRefGoogle Scholar
- Alkhouli M, Morad M, Narins CR, Raza F, Bashir R (2016) Inferior vena cava thrombosis. JACC Cardiovasc Interv 9(7):629–643CrossRefGoogle Scholar
- Argueta-Morales IR, Tran R, Ceballos A, Clark WD, Osorio R, Divo EA, Kassab AJ, DeCampli WM (2014) Mathematical modeling of patient-specific ventricular assist device implantation to reduce particulate embolization rate to cerebral vessels. J Biomech Eng 136(7):071008CrossRefGoogle Scholar
- Aycock KI, Campbell RL, Lynch FC, Manning KB, Craven BA (2016) The importance of hemorheology and patient anatomy on the hemodynamics in the inferior vena cava. Ann Biomed Eng 44(12):3568–3582Google Scholar
- Aycock KI, Campbell RL, Manning KB, Sastry SP, Shontz SM, Lynch FC, Craven BA (2014) A computational method for predicting inferior vena cava filter performance on a patient-specific basis. J Biomech Eng 136(8):1–13 (
**Erratum, 137:1–2, 2015)**Google Scholar - Campbell RL (2010) Fluid–structure interaction and inverse design simulations for flexible turbomachinery. PhD thesis, The Pennsylvania State UniversityGoogle Scholar
- Carr IA, Nemoto N, Schwartz RS, Shadden SC (2013) Size-dependent predilections of cardiogenic embolic transport. Am J Physiol Heart Circul Physiol 305:H732–H739CrossRefGoogle Scholar
- Cheng CP, Herfkens RJ, Taylor CA (2003) Inferior vena caval hemodynamics quantified in vivo at rest and during cycling exercise using magnetic resonance imaging. Am J Physiol Heart Circul Physiol 284(4):H1161-7CrossRefGoogle Scholar
- Choi HW, Navia JA, Kassab GS (2013) Stroke propensity is increased under atrial fibrillation hemodynamics: a simulation study. PLoS ONE 8(9):e73485CrossRefGoogle Scholar
- Cipolla J, Weger NS, Sharma R, Schrag SP, Sarani B, Truitt M, Lorenzo M, Sims CA, Kim PK, Torigian D, Temple-Lykens B, Sicoutris CP, Stawicki SP (2008) Complications of vena cava filters: a comprehensive clinical review. OPUS 12 Sci 2(2):11–24Google Scholar
- Clark WD, Eslahpazir BA, Argueta-Morales IR, Kassab AJ, Divo EA, DeCampli WM (2015) Comparison between bench-top and computational modelling of cerebral thromboembolism in ventricular assist device circulation. Cardiovasc Eng Technol 6(3):242–255CrossRefGoogle Scholar
- DCS Computing GmbH. LIGGGHTS public documentation, version 3.1.0 (2015)Google Scholar
- Di Renzo A, Di Maio FP (2004) Comparison of contact-force models for the simulation of collisions in DEM-based granular flow codes. Chem Eng Sci 59(3):525–541CrossRefGoogle Scholar
- Dunbar AJ, Craven BA, Paterson EG (2015) Development and validation of a tightly coupled CFD/6-DOF solver for simulating floating offshore wind turbine platforms. Ocean Eng 110:98–105CrossRefGoogle Scholar
- Fabbri D, Long Q, Das S, Pinelli M (2014) Computational modelling of emboli travel trajectories in cerebral arteries: influence of microembolic particle size and density. Biomech Model Mechanobiol 13:289–302CrossRefGoogle Scholar
- Ferreira JM, Chhabra RP (1998) Accelerating motion of a vertically falling sphere in incompressible Newtonian media: an analytical solution. Powder Technol 97(1):6–15Google Scholar
- Ferziger JH, Peric M (2012) Computational methods for fluid dynamics. Springer, New YorkzbMATHGoogle Scholar
- Glowinski R, Pan TW, Hesla TI, Joseph DD, Périaux J (2001) A fictitious domain approach to the direct numerical simulation of incompressible viscous flow past moving rigid bodies: application to particulate flow. J Comput Phys 169(2):363–426MathSciNetCrossRefzbMATHGoogle Scholar
- Guo J (2011) Motion of spheres falling through fluids. J Hydraul Res 49(1):32–41CrossRefGoogle Scholar
- Hager A (2014) CFD-DEM on multiple scales: An extensive investigation of particle–fluid interactions. PhD thesis, Technisch-Naturwissenschaftliche FakultätGoogle Scholar
- Hager A, Kloss C, Pirker S, Goniva C (2014) Parallel resolved open source CFD-DEM: method, validation and application. J Comput Multiph Flows 6(1):13–27Google Scholar
- Hammer FD, Rousseau HP, Joffre FG, Sentenac BP, Tran-Van T, Barthelemy RP (1994) In vitro evaluation of vena cava filters. J Vasc Interv Radiol 5(6):869–876CrossRefGoogle Scholar
- Hindmarsh AC (1983) ODEPACK, a systematized collection of ODE solvers. IMACS Trans Sci Comput 1:55–64MathSciNetGoogle Scholar
- Ingham DB, Tang T (1990) A numerical investigation into the steady flow past a rotating cylinder at low and intermediate Reynolds numbers. J Comput Phys 87:91–107CrossRefzbMATHGoogle Scholar
- Jaeger HJ, Kolb S, Mair T, Geller M, Christmann A, Kinne RKH, Mathias KD (1998) In vitro model for the evaluation of inferior vena cava filters: effect of experimental parameters on thrombus-capturing efficacy of the Vena Tech-LGM filter. J Vasc Interv Radiol 9(2):295–304CrossRefGoogle Scholar
- Jayaweera KOLF, Mason BJ (1965) The behaviour of freely falling cylinders and cones in a viscous fluid. J Fluid Mech 22(04):709CrossRefzbMATHGoogle Scholar
- Katsamouris AA, Waltman AC, Delichatsios MA, Athanasoulis CA (1988) Inferior vena cava filters: in vitro comparison of clot trapping and flow dynamics. Radiology 166(2):361–366CrossRefGoogle Scholar
- Khodaee F, Vahidi B, Fatouraee N (2016) Analysis of mechanical parameters on the thromboembolism using a patient-specific computational model. Biomech Model Mechanobiol 15(5):1295–1305Google Scholar
- Kloss C, Goniva C, Hager A, Amberger S, Pirker S (2012) Models, algorithms and validation for opensource DEM and CFD-DEM. PCFD 12:140–152CrossRefGoogle Scholar
- Kuzo RS, Pooley RA, Crook JE, Heckman MG, Gerber TC (2007) Measurement of caval blood flow with MRI during respiratory maneuvers: implications for vascular contrast opacification on pulmonary CT angiographic studies. Am J Roentgenol 188(3):839–842CrossRefGoogle Scholar
- Laborda A, Kuo WT, Ioakeim I, De Blas I, Malvè M, Lahuerta C, De Gregorio MA (2015) Respiratory-induced haemodynamic changes: a contributing factor to IVC filter penetration. Cardiovasc Interv Radiol 38(5):1192–1197CrossRefGoogle Scholar
- Leiderman K, Fogelson AL (2011) Grow with the flow: a spatial-temporal model of platelet deposition and blood coagulation under flow. Math Med Biol 28(1):47–84MathSciNetCrossRefzbMATHGoogle Scholar
- Mismetti P, Laporte S, Pellerin O, Ennezat P-V, Couturaud F, Elias A, Falvo N, Meneveau N, Quere I, Roy P-M, Sanchez O, Schmidt J, Seinturier C, Sevestre M-A, Beregi J-P, Tardy B, Lacroix P, Presles E, Leizorovicz A, Decousus H, Barral F-G, Meyer G (2015) Effect of a retrievable inferior vena cava filter plus anticoagulation vs anticoagulation alone on risk of recurrent pulmonary embolism. JAMA 313(16):1627CrossRefGoogle Scholar
- Mittal R, Iaccarino G (2005) Immersed boundary methods. Annu Rev Fluid Mech 37(1):239–261MathSciNetCrossRefzbMATHGoogle Scholar
- Mukherjee D, Padilla J, Shadden SC (2016) Numerical investigation of fluid–particle interactions for embolic stroke. Theor Comput Fluid Dyn 30(1):23–29CrossRefGoogle Scholar
- Nahirnyak V, Yoon SW, Holland CK (2006) Acousto-mechanical and thermal properties of clotted blood. J Acoust Soc Am 119(6):3766–3772CrossRefGoogle Scholar
- Newcombe RG (1998) Two-sided confidence intervals for the single proportion: comparison of seven methods. Stat Med 17(8):857–872CrossRefGoogle Scholar
- Nicolás M, Malvè M, Peña E, Martínez MA, Leask R (2015) In vitro comparison of Günther Tulip and Celect filters: testing filtering efficiency and pressure drop. J Biomech 48:504–511CrossRefGoogle Scholar
- OpenCFD Ltd. User guide, OpenFOAM version 2.3.x (2014)Google Scholar
- Osorio AF, Osorio R, Ceballos A, Tran R, Clark WD, Divo EA, Argueta-Morales IR, Kassab AJ, DeCampli WM (2011) Computational fluid dynamics analysis of surgical adjustment of left ventricular assist device implantation to minimise stroke risk. Comput Methods Biomech Biomed Eng 16(6):622–638CrossRefGoogle Scholar
- Pan RW, Glowinski R (2005) Direct simulation of the motion of neutrally buoyant balls in a three-dimensional Poiseuille flow. C R Mec 333(12):884–895CrossRefzbMATHGoogle Scholar
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2012) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830zbMATHGoogle Scholar
- Peskin CS (1972) Flow patterns around heart valves: a numerical method. J Comput Phys 10(2):252–271MathSciNetCrossRefzbMATHGoogle Scholar
- Peskin CS, McQueen DM (1980) Modeling prosthetic heart valves for numerical analysis of blood flow in the heart. J Comput Phys 37(1):113–132MathSciNetCrossRefzbMATHGoogle Scholar
- Plimpton S (1995) Fast parallel algorithms for short-range molecular dynamics. J Comput Phys 117(1):1–19CrossRefzbMATHGoogle Scholar
- PREPIC Study Group (2005) Eight-year follow-up of patients with permanent vena cava filters in the prevention of pulmonary embolism: the PREPIC (prevention du risque d’embolie pulmonaire par interruption cave) randomized study. Circulation 112(3):416–422Google Scholar
- Rahbar E, Mori D, Moore JE (2011) Three-dimensional analysis of flow disturbances caused by clots in inferior vena cava filters. J Vasc Interv Radiol 22(6):835–842CrossRefGoogle Scholar
- Ren Z, Wang SL, Singer MA (2012) Modeling hemodynamics in an unoccluded and partially occluded inferior vena cava under rest and exercise conditions. Med Biol Eng Comput 50(3):277–287CrossRefGoogle Scholar
- Robinson RA, Herbertson LH, Sarkar Das S, Malinauskas RA, Pritchard WF, Grossman LW (2013) Limitations of using synthetic blood clots for measuring in vitro clot capture efficiency of inferior vena cava filters. Med Devices (Auckl.) 6:49–57Google Scholar
- Segré G, Silberberg A (1961) Radial particle displacements in Poiseuille flow of suspensions. Nature 189(4760):209–210CrossRefGoogle Scholar
- Shirgaonkar AA, MacIver MA, Patankar NA (2009) A new mathematical formulation and fast algorithm for fully resolved simulation of self-propulsion. J Comput Phys 228(7):2366–2390MathSciNetCrossRefzbMATHGoogle Scholar
- Silverstein MD, Heit JA, Mohr DN, Petterson TM, O’Fallon WM, Melton J (1998) Trends in the incidence of deep vein thrombosis and pulmonary embolism. Arch Intern Med 158:585–593CrossRefGoogle Scholar
- Singer MA, Henshaw WD, Wang SL (2009) Computational modeling of blood flow in the TrapEase inferior vena cava filter. J Vasc Interv Radiol 20(6):799–805CrossRefGoogle Scholar
- Singer MA, Wang SL, Diachin DP (2010) Design optimization of vena cava filters: an application to dual filtration devices. J Biomech Eng 132(10):101006 1–10CrossRefGoogle Scholar
- Singer MA, Wang SL (2011) Modeling blood flow in a tilted inferior vena cava filter: does tilt adversely affect hemodynamics? J Vasc Interv Radiol 22(2):229–235CrossRefGoogle Scholar
- Stewart SFC, Robinson RA, Nelson RA, Malinauskas RA (2008) Effects of thrombosed vena cava filters on blood flow: flow visualization and numerical modeling. Ann Biomed Eng 36(11):1764–1781CrossRefGoogle Scholar
- Stoneham GW, Burbridge BE, Millward SF (1995) Temporary inferior vena cava filters: in vitro comparison with permanent IVC filters. J Vasc Interv Radiol 6(5):731–736CrossRefGoogle Scholar
- Swaminathan TN, Hu HH, Patel AA (2006) Numerical analysis of the hemodynamics and embolus capture of a Greenfield vena cava filter. J Biomech Eng 128(3):360–370CrossRefGoogle Scholar
- Taylor JO, Meyer RS, Deutsch S, Manning KB (2016) Development of a computational model for macroscopic predictions of device-induced thrombosis. Biomech Model Mechanobiol 15(6):1713–1731Google Scholar
- Teo TKB, Angle JF, Shipp JI, Bluett MK, Gilliland CA, Turba UC, Matsumoto AH (2011) Incidence and management of inferior vena cava filter thrombus detected at time of filter retrieval. J Vasc Interv Radiol 22(11):1514–1520CrossRefGoogle Scholar
- Tritton DJ (1959) Experiments on the flow past a circular cylinder at low Reynolds numbers. J Fluid Mech 6(04):547–567CrossRefzbMATHGoogle Scholar
- Vahidi B, Fatouraee N (2012) Large deforming buoyant embolus passing through a stenotic common carotid artery: a computational simulation. J Biomech 45(7):1312–1322CrossRefGoogle Scholar
- Wang SL, Singer MA (2010) Toward an optimal position for inferior vena cava filters: computational modeling of the impact of renal vein inflow with Celect and TrapEase filters. J Vasc Interv Radiol 21(3):367–374CrossRefGoogle Scholar
- Xian ZY, Roy S, Hosaka J, Kvernebo K, Laerum F (1995) Multiple emboli and filter function: an in vitro comparison of three vena cava filters. J Vasc Interv Radiol 6(6):887–893CrossRefGoogle Scholar
- Zhang Y (2013) Flow past a sphere and a prolate spheroid at low Reynolds numbers. Masters, Texas A&M UniversityGoogle Scholar
- Zhang P, Gao C, Zhang N, Slepian MJ, Deng Y, Bluestein D (2014) Multiscale particle-based modeling of flowing platelets in blood plasma using dissipative particle dynamics and coarse grained molecular dynamics. Cell Mol Bioeng 7(4):552–574CrossRefGoogle Scholar
- Zhang P, Zhang N, Deng Y, Bluestein D (2015) A multiple time stepping algorithm for efficient multiscale modeling of platelets flowing in blood plasma. J Comput Phys 1(284):668–686MathSciNetCrossRefzbMATHGoogle Scholar