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Computational predictions of the embolus-trapping performance of an IVC filter in patient-specific and idealized IVC geometries

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Abstract

Embolus transport simulations are performed to investigate the dependence of inferior vena cava (IVC) filter embolus-trapping performance on IVC anatomy. Simulations are performed using a resolved two-way coupled computational fluid dynamics/six-degree-of-freedom approach. Three IVC geometries are studied: a straight-tube IVC, a patient-averaged IVC, and a patient-specific IVC reconstructed from medical imaging data. Additionally, two sizes of spherical emboli (3 and 5 mm in diameter) and two IVC orientations (supine and upright) are considered. The embolus-trapping efficiency of the IVC filter is quantified for each combination of IVC geometry, embolus size, and IVC orientation by performing 2560 individual simulations. The predicted embolus-trapping efficiencies of the IVC filter range from 10 to 100%, and IVC anatomy is found to have a significant influence on the efficiency results (\(P < 0.0001\)). In the upright IVC orientation, greater secondary flow in the patient-specific IVC geometry decreases the filter embolus-trapping efficiency by 22–30 percentage points compared with the efficiencies predicted in the idealized straight-tube or patient-averaged IVCs. In a supine orientation, the embolus-trapping efficiency of the filter in the idealized IVCs decreases by 21–90 percentage points compared with the upright orientation. In contrast, the embolus-trapping efficiency is insensitive to IVC orientation in the patient-specific IVC. In summary, simulations predict that anatomical features of the IVC that are often neglected in the idealized models used for benchtop testing, such as iliac vein compression and anteroposterior curvature, generate secondary flow and mixing in the IVC and influence the embolus-trapping efficiency of IVC filters. Accordingly, inter-subject variability studies and additional embolus transport investigations that consider patient-specific IVC anatomy are recommended for future work.

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Acknowledgements

We thank Yao Huang and Nathaniel D. Porter for helpful discussions on the statistical analysis. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant No. ACI-1053575.

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

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Authors and Affiliations

  1. Applied Research Laboratory, The Pennsylvania State University, University Park, PA, USA

    Kenneth I. Aycock & Robert L. Campbell

  2. Department of Biomedical Engineering, The Pennsylvania State University, University Park, PA, USA

    Kenneth I. Aycock & Keefe B. Manning

  3. Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA, USA

    Robert L. Campbell

  4. Department of Surgery, Penn State Milton S. Hershey Medical Center, Hershey, PA, USA

    Frank C. Lynch & Keefe B. Manning

  5. Division of Applied Mechanics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD, USA

    Brent A. Craven

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  1. Kenneth I. Aycock
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Correspondence to Keefe B. Manning or Brent A. Craven.

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Aycock, K.I., Campbell, R.L., Lynch, F.C. et al. Computational predictions of the embolus-trapping performance of an IVC filter in patient-specific and idealized IVC geometries. Biomech Model Mechanobiol 16, 1957–1969 (2017). https://doi.org/10.1007/s10237-017-0931-5

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