Optimization of a Transcatheter Heart Valve Frame Using Patient-Specific Computer Simulation

  • Giorgia Rocatello
  • Gianluca De Santis
  • Sander De Bock
  • Matthieu De Beule
  • Patrick Segers
  • Peter MortierEmail author



This study proposes a new framework to optimize the design of a transcatheter aortic valve through patient-specific finite element and fluid dynamics simulation.


Two geometrical parameters of the frame, the diameter at ventricular inflow and the height of the first row of cells, were examined using the central composite design. The effect of those parameters on postoperative complications was investigated by response surface methodology, and a Nonlinear Programming by Quadratic Lagrangian algorithm was used in the optimization. Optimal and initial devices were then compared in 12 patients. The comparison was made in terms of device performance [i.e., reduced contact pressure on the atrioventricular conduction system and paravalvular aortic regurgitation (AR)].


Results suggest that large diameters and high cells favor higher anchoring of the device within the aortic root reducing the contact pressure and favor a better apposition of the device to the aortic root preventing AR. Compared to the initial device, the optimal device resulted in almost threefold lower predicted contact pressure and limited AR in all patients.


In conclusion, patient-specific modelling and simulation could help to evaluate device performance prior to the actual first-in-human clinical study and, combined with device optimization, could help to develop better devices in a shorter period.


Computer simulation Design of experiment Optimization Patient-specific Transcatheter aortic valve 



G. Rocatello is supported by the European Commission within the Horizon 2020 Framework through the Marie Sklodowska-Curie Action-International Training Network (MSCA-ITN) European Training Networks (Project Number 642458). Computer modeling and simulation was performed using the TAVIguide framework developed at FEops NV.

Conflict of Interest

Matthieu De Beule and Peter Mortier are shareholders of FEops. Sander De Bock and Gianluca de Santis are employees of FEops.

Ethical Approval

For this retrospective study, formal consent is not required. This article does not contain any studies with animals performed by any of the authors.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

13239_2019_420_MOESM1_ESM.docx (249 kb)
Supplementary material 1 (DOCX 250 kb)


  1. 1.
    Alaimo, G., F. Auricchio, M. Conti, and M. Zingales. Multi-objective optimization of nitinol stent design. Med. Eng. Phys. 47:13–24, 2017. Scholar
  2. 2.
    Allen, D. M. The relationship between variable selection and data agumentation and a method for prediction. Technometrics 16(1):125–127, 1974. Scholar
  3. 3.
    Bailey, J., N. Curzen, and N. W. Bressloff. Assessing the impact of including leaflets in the simulation of TAVI deployment into a patient-specific aortic root. Comput. Methods Biomech. Biomed. Eng. 19(7):733–744, 2016. Scholar
  4. 4.
    Bianchi, M., G. Marom, R. P. Ghosh, O. M. Rotman, P. Parikh, L. Gruberg, et al. Patient-specific simulation of transcatheter aortic valve replacement: impact of deployment options on paravalvular leakage. Biomech. Model. Mechanobiol. 2018. Scholar
  5. 5.
    Bosi, G. M., C. Capelli, M. H. Cheang, N. Delahunty, M. Mullen, A. M. Taylor, et al. Population-specific material properties of the implantation site for transcatheter aortic valve replacement finite element simulations. J. Biomech. 71:236–244, 2018. Scholar
  6. 6.
    Bosmans, B., N. Famaey, E. Verhoelst, J. Bosmans, and J. Vander Sloten. A validated methodology for patient specific computational modeling of self-expandable transcatheter aortic valve implantation. J. Biomech. 49(13):2824–2830, 2016. Scholar
  7. 7.
    Buzzatti, N., A. Castiglioni, E. Agricola, M. Barletta, S. Stella, F. Giannini, et al. Five-year evolution of mild aortic regurgitation following transcatheter aortic valve implantation: early insights from a single-centre experience. Interact. Cardiovasc. Thorac. Surg. 25(1):75–82, 2017. Scholar
  8. 8.
    de Jaegere, P., G. De Santis, R. Rodriguez-Olivares, J. Bosmans, N. Bruining, T. Dezutter, et al. Patient-specific computer modeling to predict aortic regurgitation after transcatheter aortic valve replacement. JACC Cardiovasc. Interv. 9(5):508–512, 2016. Scholar
  9. 9.
    Deeb, G. M., M. J. Reardon, S. Chetcuti, H. J. Patel, P. M. Grossman, S. J. Yakubov, et al. 3-year outcomes in high-risk patients who underwent surgical or transcatheter aortic valve replacement. J. Am. Coll. Cardiol. 67(22):2565–2574, 2016. Scholar
  10. 10.
    Finotello, A., S. Morganti, and F. Auricchio. Finite element analysis of TAVI: impact of native aortic root computational modeling strategies on simulation outcomes. Med. Eng. Phys. 47:2–12, 2017. Scholar
  11. 11.
    Gessat, M., L. Altwegg, T. Frauenfelder, A. Plass, and V. Falk. Cubic Hermite Bezier spline based reconstruction of implanted aortic valve stents from CT images. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011:2667–2670, 2011. Scholar
  12. 12.
    Gundert, T. J., A. L. Marsden, W. Yang, and J. F. LaDisa, Jr. Optimization of cardiovascular stent design using computational fluid dynamics. J. Biomech. Eng. 134(1):011002, 2012. Scholar
  13. 13.
    Gunning, P. S., T. J. Vaughan, and L. M. McNamara. Simulation of self expanding transcatheter aortic valve in a realistic aortic root: implications of deployment geometry on leaflet deformation. Ann. Biomed. Eng. 42(9):1989–2001, 2014. Scholar
  14. 14.
    Kapadia, S. R., M. B. Leon, R. R. Makkar, E. M. Tuzcu, L. G. Svensson, S. Kodali, et al. 5-year outcomes of transcatheter aortic valve replacement compared with standard treatment for patients with inoperable aortic stenosis (PARTNER 1): a randomised controlled trial. Lancet 385(9986):2485–2491, 2015. Scholar
  15. 15.
    Kodali, S., P. Pibarot, P. S. Douglas, M. Williams, K. Xu, V. Thourani, et al. Paravalvular regurgitation after transcatheter aortic valve replacement with the Edwards sapien valve in the PARTNER trial: characterizing patients and impact on outcomes. Eur. Heart J. 36(7):449–456, 2015. Scholar
  16. 16.
    Leon, M. B., C. R. Smith, M. J. Mack, R. R. Makkar, L. G. Svensson, S. K. Kodali, et al. Transcatheter or surgical aortic-valve replacement in intermediate-risk patients. N. Engl. J. Med. 374(17):1609–1620, 2016. Scholar
  17. 17.
    Li, H., J. Gu, M. Wang, D. Zhao, Z. Li, A. Qiao, et al. Multi-objective optimization of coronary stent using kriging surrogate model. Biomed. Eng. Online 15(Suppl 2):148, 2016. Scholar
  18. 18.
    Li, H., T. Liu, M. Wang, D. Zhao, A. Qiao, X. Wang, et al. Design optimization of stent and its dilatation balloon using kriging surrogate model. BioMed. Eng. OnLine 16(1):13, 2017. Scholar
  19. 19.
    Li, H., T. Qiu, B. Zhu, J. Wu, and X. Wang. Design optimization of coronary stent based on finite element models. Sci. World J. 2013:630243, 2013. Scholar
  20. 20.
    Li, N., H. Zhang, and H. Ouyang. Shape optimization of coronary artery stent based on a parametric model. Finite Elem. Anal. Des. 45(6–7):468–475, 2009. Scholar
  21. 21.
    Mack, M. J., M. B. Leon, C. R. Smith, D. C. Miller, J. W. Moses, E. M. Tuzcu, et al. 5-year outcomes of transcatheter aortic valve replacement or surgical aortic valve replacement for high surgical risk patients with aortic stenosis (PARTNER 1): a randomised controlled trial. Lancet 385(9986):2477–2484, 2015. Scholar
  22. 22.
    Mao, W., Q. Wang, S. Kodali, and W. Sun. Numerical parametric study of paravalvular leak following a transcatheter aortic valve deployment into a patient-specific aortic root. J. Biomech. Eng. 2018. Scholar
  23. 23.
    Migliavacca, F., L. Petrini, V. Montanari, I. Quagliana, F. Auricchio, and G. Dubini. A predictive study of the mechanical behaviour of coronary stents by computer modelling. Med. Eng. Phys. 27(1):13–18, 2005. Scholar
  24. 24.
    Morganti, S., N. Brambilla, A. S. Petronio, A. Reali, F. Bedogni, and F. Auricchio. Prediction of patient-specific post-operative outcomes of TAVI procedure: the impact of the positioning strategy on valve performance. J. Biomech. 49(12):2513–2519, 2016. Scholar
  25. 25.
    Morrison, T. M., M. L. Dreher, S. Nagaraja, L. M. Angelone, and W. Kainz. The role of computational modeling and simulation in the total product life cycle of peripheral vascular devices. J. Med. Devices 11(2):024503, 2017. Scholar
  26. 26.
    Pant, S., N. W. Bressloff, and G. Limbert. Geometry parameterization and multidisciplinary constrained optimization of coronary stents. Biomech. Model. Mechanobiol. 11(1–2):61–82, 2012. Scholar
  27. 27.
    Pant, S., G. Limbert, N. P. Curzen, and N. W. Bressloff. Multiobjective design optimisation of coronary stents. Biomaterials 32(31):7755–7773, 2011. Scholar
  28. 28.
    Putra, N. K., P. S. Palar, H. Anzai, K. Shimoyama, and M. Ohta. Multiobjective design optimization of stent geometry with wall deformation for triangular and rectangular struts. Med. Biol. Eng. Comput. 57(1):15–26, 2018. Scholar
  29. 29.
    Rocatello, G., N. El Faquir, G. De Santis, F. Iannaccone, J. Bosmans, O. De Backer, et al. Patient-specific computer simulation to elucidate the role of contact pressure in the development of new conduction abnormalities after catheter-based implantation of a self-expanding aortic valve. Circ. Cardiovasc. Interv. 11(2):e005344, 2018. Scholar
  30. 30.
    Schittkowski, K. NLPQL: a fortran subroutine solving constrained nonlinear programming problems. Ann. Oper. Res. 5(2):485–500, 1986. Scholar
  31. 31.
    Schultz, C. J., R. Rodriguez-Olivares, J. Bosmans, T. Lefèvre, G. De Santis, N. Bruining, et al. Patient-specific image-based computer simulation for the prediction of valve morphology and calcium displacement after TAVI with the Medtronic CoreValve and the Edwards SAPIEN valve. EuroIntervention 11(9):1044–1052, 2016. Scholar
  32. 32.
    Sinning, J. M., A. Stundl, S. Pingel, M. Weber, A. Sedaghat, C. Hammerstingl, et al. Pre-procedural hemodynamic status improves the discriminatory value of the aortic regurgitation index in patients undergoing transcatheter aortic valve replacement. JACC Cardiovasc. Interv. 9(7):700–711, 2016. Scholar

Copyright information

© Biomedical Engineering Society 2019

Authors and Affiliations

  • Giorgia Rocatello
    • 1
  • Gianluca De Santis
    • 2
  • Sander De Bock
    • 2
  • Matthieu De Beule
    • 2
  • Patrick Segers
    • 1
  • Peter Mortier
    • 2
    Email author
  1. 1.IBiTech-bioMMedaGhent UniversityGhentBelgium
  2. 2.FEops NVGhentBelgium

Personalised recommendations