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Effect of Sinotubular Junction Size on TAVR Leaflet Thrombosis: A Fluid–Structure Interaction Analysis

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Abstract

TAVR has emerged as a standard approach for treating severe aortic stenosis patients. However, it is associated with several clinical complications, including subclinical leaflet thrombosis characterized by Hypoattenuated Leaflet Thickening (HALT). A rigorous analysis of TAVR device thrombogenicity considering anatomical variations is essential for estimating this risk. Clinicians use the Sinotubular Junction (STJ) diameter for TAVR sizing, but there is a paucity of research on its influence on TAVR devices thrombogenicity. A Medtronic Evolut® TAVR device was deployed in three patient models with varying STJ diameters (26, 30, and 34 mm) to evaluate its impact on post-deployment hemodynamics and thrombogenicity, employing a novel computational framework combining prosthesis deployment and fluid-structure interaction analysis. The 30 mm STJ patient case exhibited the best hemodynamic performance: 5.94 mmHg mean transvalvular pressure gradient (TPG), 2.64 cm2 mean geometric orifice area (GOA), and the lowest mean residence time (TR)—indicating a reduced thrombogenic risk; 26 mm STJ exhibited a 10 % reduction in GOA and a 35% increase in mean TPG compared to the 30 mm STJ; 34 mm STJ depicted hemodynamics comparable to the 30 mm STJ, but with a 6% increase in TR and elevated platelet stress accumulation. A smaller STJ size impairs adequate expansion of the TAVR stent, which may lead to suboptimal hemodynamic performance. Conversely, a larger STJ size marginally enhances the hemodynamic performance but increases the risk of TAVR leaflet thrombosis. Such analysis can aid pre-procedural planning and minimize the risk of TAVR leaflet thrombosis.

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Data Availability

Data is contained within the article and Appendix.

Abbreviations

TR :

Residence time

AVR:

Aortic valve replacement

CFD:

Computational fluid dynamics

CT:

Computed tomography.

DTE:

Device thrombogenicity emulation.

FE:

Finite element.

FEA:

Finite element analysis.

FSI:

Fluid-structure interaction.

GOA:

Geometric Orifice Area.

HALT:

Hypoattenuated leaflet thickening.

LES:

Large eddy simulation.

LVOT:

Left ventricle outflow tract.

PDF:

Probability density function

RoI:

Region of interest.

SA:

Stress accumulation

SAVR:

Surgical aortic valve replacement

SoV:

Sinus of valsalva

STJ :

Sinotubular junction

TAVR:

Transcatheter aortic valve replacement.

TPG:

Transvalvular pressure gradient

WSS:

Wall shear stress

References

  1. iData, Cardiac Surgery Market Size, Share, and COVID-19 Impact Analysis | United States | 2020-2026 | MedSuite | Includes: Tissue Heart Valve Market, TAVI/TAVR Market, and 21 more. 2022.

  2. Yudi, M. B., et al. Coronary angiography and percutaneous coronary intervention after transcatheter aortic valve replacement. J. Am. Coll. Cardiol. 71(12):1360–1378, 2018.

    Article  PubMed  Google Scholar 

  3. Adams, D. H., et al. Transcatheter aortic-valve replacement with a self-expanding prosthesis. N. Engl. J. Med. 370(19):1790–1798, 2014.

    Article  PubMed  CAS  Google Scholar 

  4. Leon, M. B., et al. Transcatheter or surgical aortic-valve replacement in intermediate-risk patients. N. Engl. J. Med. 374(17):1609–1620, 2016.

    Article  PubMed  CAS  Google Scholar 

  5. Popma, J. J., et al. Transcatheter aortic valve replacement using a self-expanding bioprosthesis in patients with severe aortic stenosis at extreme risk for surgery. J. Am. Coll. Cardiol. 63(19):1972–1981, 2014.

    Article  PubMed  Google Scholar 

  6. Nishimura, R. A., et al. 2014 AHA/ACC Guideline for the Management of Patients With Valvular Heart Disease: Executive Summary. Circulation. 129(23):2440–2492, 2014.

    Article  PubMed  Google Scholar 

  7. Vahanian, A., et al. ESC Committee for Practice Guidelines (CPG); Joint Task Force on the Management of Valvular Heart Disease of the European Society of Cardiology (ESC); European Association for Cardio-Thoracic Surgery (EACTS). Eur J Cardiothorac Surg. 42(4):S1-44, 2012.

    PubMed  Google Scholar 

  8. Chakravarty, T., et al. Subclinical leaflet thrombosis in surgical and transcatheter bioprosthetic aortic valves: an observational study. Lancet. 389:2383–2392, 2017.

    Article  PubMed  Google Scholar 

  9. Jose, J., et al. Clinical bioprosthetic heart valve thrombosis after transcatheter aortic valve replacement: incidence, characteristics, and treatment outcomes. JACC. 10(7):686–697, 2017.

    PubMed  Google Scholar 

  10. Mangione, F. M., et al. Leaflet thrombosis in surgically explanted or post-mortem TAVR valves. JACC. 10(1):82–85, 2017.

    PubMed  Google Scholar 

  11. Makkar, R. R., et al. Subclinical leaflet thrombosis in transcatheter and surgical bioprosthetic valves. J. Am. Coll. Cardiol. 75(24):3003–3015, 2020.

    Article  PubMed  Google Scholar 

  12. Makkar RR et al. Possible subclinical leaflet thrombosis in bioprosthetic aortic valves. N. Engl. J. Med. 2015. 0(0): p. null.

  13. Nagpal, P., et al. Imaging of the aortic root on high-pitch non-gated and ECG-gated CT: awareness is the key! Insights Imaging. 11:1–14, 2020.

    Article  Google Scholar 

  14. Nappi, F., et al. Are the dynamic changes of the aortic root determinant for thrombosis or leaflet degeneration after transcatheter aortic valve replacement? J. Thorac. Dis. 12(5):2919, 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hsiung, I., et al. Left main protection during transcatheter aortic valve replacement with a balloon-expandable valve. J. Soc. Cardiovasc. Angiography Interv. 1(4):100339, 2022.

    Article  MathSciNet  Google Scholar 

  16. Pan, Y., A. Qiao, and N. Dong. Fluid-structure interaction simulation of aortic valve closure with various sinotubular junction and sinus diameters. Ann. Biomed. Eng. 43:1363–1369, 2014.

    Article  PubMed  Google Scholar 

  17. Marom, G., et al. Numerical model of the aortic root and valve: optimization of graft size and sinotubular junction to annulus ratio. J. Thorac. Cardiovasc. Surg. 146(5):1227–1231, 2013.

    Article  PubMed  Google Scholar 

  18. Raghav, V., P. Midha, R. Sharma, V. Babaliaros, and A. Yoganathan. Transcatheter aortic valve thrombosis: a review of potential mechanisms. J. R. Soc. Interface. 18(184):20210599, 2021.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Casa, L. D. C., D. H. Deaton, and D. N. Ku. Role of high shear rate in thrombosis. J. Vasc. Surg. 61(4):1068–1080, 2015.

    Article  PubMed  Google Scholar 

  20. Hellums, J. D. 1993 Whitaker lecture: Biorheology in thrombosis research. Ann. Biomed. Eng. 22:445–455, 2006.

    Article  Google Scholar 

  21. Casa, L. D. C., and D. N. Ku. Thrombus formation at high shear rates. Annu. Rev. Biomed. Eng. 19(1):415–433, 2017.

    Article  PubMed  CAS  Google Scholar 

  22. Ducci, A., et al. Transcatheter aortic valves produce unphysiological flows which may contribute to thromboembolic events: an in-vitro study. J. Biomech. 49:4080–4089, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Ghosh, R. P., et al. Numerical evaluation of transcatheter aortic valve performance during heart beating and its post-deployment fluid–structure interaction analysis. Biomech. Model. Mechanobiol. 19(5):1725–1740, 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Hatoum, H., et al. Aortic sinus flow stasis likely in valve-in-valve transcatheter aortic valve implantation. J. Thorac. Cardiovasc. Surg. 154:32–43, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Midha, P. A., et al. The fluid mechanics of transcatheter heart valve leaflet thrombosis in the neosinus. Circulation. 136(17):1598–1609, 2017.

    Article  PubMed  Google Scholar 

  26. Vahidkhah, K., and A. N. Azadani. Supra-annular Valve-in-Valve implantation reduces blood stasis on the transcatheter aortic valve leaflets. J. Biomech. 58:114–122, 2017.

    Article  PubMed  Google Scholar 

  27. Vahidkhah, K., et al. Valve thrombosis following transcatheter aortic valve replacement: significance of blood stasis on the leaflets. Eur. J. Cardiothorac. Surg. 51(5):927–935, 2017.

    PubMed  Google Scholar 

  28. Bluestein, D., S. Einav, and M. J. Slepian. Device thrombogenicity emulation: a novel methodology for optimizing the thromboresistance of cardiovascular devices. J. Biomech. 46(2):338–44, 2013.

    Article  PubMed  Google Scholar 

  29. Girdhar, G., et al. Device thrombogenicity emulation: a novel method for optimizing mechanical circulatory support device thromboresistance. PLoS ONE. 7(3):e34263, 2012.

    Article  Google Scholar 

  30. Alemu, Y., and D. Bluestein. Flow-induced platelet activation and damage accumulation in a mechanical heart valve: numerical studies. Artif. Organs. 31(9):677–688, 2007.

    Article  PubMed  Google Scholar 

  31. Xenos, M., et al. Device thrombogenicity emulator (DTE)—design optimization methodology for cardiovascular devices: a study in two bileaflet MHV designs. J. Biomech. 43(12):2400–2409, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yin, W., et al. Flow-induced platelet activation in bileaflet and monoleaflet mechanical heart valves. Ann. Biomed. Eng. 32:1058–1066, 2004.

    Article  PubMed  Google Scholar 

  33. Marom, G., et al. Numerical model of full-cardiac cycle hemodynamics in a total artificial heart and the effect of its size on platelet activation. J. Cardiovasc. Transl. Res. 7:788–796, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Anam, S. B., et al. Assessment of paravalvular leak severity and thrombogenic potential in transcatheter bicuspid aortic valve replacements using patient-specific computational modeling. J. Cardiovasc. Transl. Res. 2021. https://doi.org/10.1007/s12265-021-10191-z.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bianchi, M., et al. Patient-specific simulation of transcatheter aortic valve replacement: impact of deployment options on paravalvular leakage. Biomech. Model. Mechanobiol. 18(2):435–451, 2019.

    Article  PubMed  Google Scholar 

  36. Kovarovic, B. J., et al. Mild paravalvular leak may pose an increased thrombogenic risk in transcatheter aortic valve replacement (TAVR) patients-insights from patient specific in vitro and in silico studies. Bioengineering. 10(2):188, 2023.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Hatoum, H., et al. Predictive model for thrombus formation after transcatheter valve replacement. Cardiovasc. Eng. Technol. 12(6):576–588, 2021.

    Article  PubMed  Google Scholar 

  38. Singh-Gryzbon, S., et al. Influence of patient-specific characteristics on transcatheter heart valve neo-sinus flow: an in silico study. Ann. Biomed. Eng. 48:2400–2411, 2020.

    Article  PubMed  Google Scholar 

  39. International Organization for Standardization, Cardiovascular Implants—Cardiac Valve Prostheses—Part 3: Heart Valve Substitutes Implanted by Transcatheter Techniques—ISO 5840-3:2013(E), 2013. https://www.iso.org/standard/67606.html

  40. Reza, S., et al. A computational framework for post-TAVR cardiac conduction abnormality (CCA) risk assessment in patient-specific anatomy. Artif. Organs. 46(7):1305–1317, 2022.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Yushkevich P et al. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig GUser-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116-1128. NeuroImage, 2006. 31: p. 1116-28.

  42. Smith M. ABAQUS/Standard User's Manual, Version 6.9. 2009, United States: Dassault Systèmes Simulia Corp.

  43. Wang, Y., et al. Anatomic predictor of severe prosthesis malposition following transcatheter aortic valve replacement with self-expandable Venus-A Valve among pure aortic regurgitation: a multicenter retrospective study. Front. Cardiovasc. Med. 9:1002071, 2022.

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  44. Romero, P., et al. Clinically-driven virtual patient cohorts generation: an application to aorta. Front. Physiol. 2021. https://doi.org/10.3389/fphys.2021.713118.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Hamdan, A., et al. Sex differences in aortic root and vascular anatomy in patients undergoing transcatheter aortic valve implantation: a computed-tomographic study. J. Cardiovasc. Comput. Tomography. 11(2):87–96, 2017.

    Article  Google Scholar 

  46. Morganti, S., et al. 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.

    Article  PubMed  CAS  Google Scholar 

  47. Martin, C., T. Pham, and W. Sun. Significant differences in the material properties between aged human and porcine aortic tissues. Eur. J. Cardio-Thorac. Surg. 40(1):28–34, 2011.

    Article  Google Scholar 

  48. Martin, C., and W. Sun. Biomechanical characterization of aortic valve tissue in humans and common animal models. J. Biomed. Mater. Res. Part A. 100(6):1591–1599, 2012.

    Article  Google Scholar 

  49. Anam, S. B., et al. Validating in silico and in vitro patient-specific structural and flow models with transcatheter bicuspid aortic valve replacement procedure. Cardiovasc. Eng. Technol. 13(6):840–56, 2022.

    Article  PubMed  Google Scholar 

  50. Systems, B.C., ANSA pre-processor. 2021.

  51. Oks D et al. Fluid-structure interaction analysis of eccentricity and leaflet rigidity on thrombosis biomarkers in bioprosthetic aortic valve replacements. bioRxiv, 2022.

  52. Sochi T. Non-Newtonian rheology in blood circulation. arXiv: Fluid Dyn. 2013.

  53. Bavo, A. M., et al. Fluid-structure interaction simulation of prosthetic aortic valves: comparison between immersed boundary and arbitrary lagrangian-eulerian techniques for the mesh representation. PLoS ONE. 11(4):e0154517, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Kandail, H. S., et al. Impact of annular and supra-annular CoreValve deployment locations on aortic and coronary artery hemodynamics. J. Mech. Behav. Biomed. Mater. 86:131–142, 2018.

    Article  PubMed  Google Scholar 

  55. Lehmkuhl, O., et al. A low-dissipation finite element scheme for scale resolving simulations of turbulent flows. J. Comput. Phys. 390:51–65, 2019.

    Article  ADS  MathSciNet  CAS  Google Scholar 

  56. Calmet, H., et al. Large-scale CFD simulations of the transitional and turbulent regime for the large human airways during rapid inhalation. Comput. Biol. Med. 69:166–180, 2016.

    Article  PubMed  Google Scholar 

  57. Mira, D., et al. Heat transfer effects on a fully premixed methane impinging flame. Flow Turbul. Combust. 97:339–436, 2016.

    Article  CAS  Google Scholar 

  58. Vázquez, M., et al. Alya: multiphysics engineering simulation toward exascale. J. Comput. Sci. 14:15–27, 2016.

    Article  MathSciNet  Google Scholar 

  59. Gövert, S., et al. Heat loss prediction of a confined premixed jet flame using a conjugate heat transfer approach. Int. J. Heat Mass Trans. 107:882–894, 2017.

    Article  Google Scholar 

  60. Santiago, A., et al. Design and execution of a Verification, Validation, and Uncertainty Quantification plan for a numerical model of left ventricular flow after LVAD implantation. PLoS Comput. Biol. 18(6):e1010141, 2022.

    Article  MathSciNet  PubMed  PubMed Central  CAS  Google Scholar 

  61. Rodriguez, I., et al. Fluid dynamics and heat transfer in the wake of a sphere. Int. J. Heat Fluid Flow. 76:141–153, 2019.

    Article  ADS  Google Scholar 

  62. Oyarzun G, Mira D, Houzeaux G. Performance assessment of CUDA and OpenACC in large scale combustion simulations. arXiv [cs.DC], 2021.

  63. Calmet, H., et al. Flow features and micro-particle deposition in a human respiratory system during sniffing. J. Aerosol Sci. 123:171–184, 2018.

    Article  ADS  CAS  Google Scholar 

  64. Calmet, H., et al. Subject-variability effects on micron particle deposition in human nasal cavities. J. Aerosol Sci. 115:12–28, 2018.

    Article  ADS  CAS  Google Scholar 

  65. Rayz, V. L., et al. Flow residence time and regions of intraluminal thrombus deposition in intracranial aneurysms. Ann. Biomed. Eng. 38(10):3058–3069, 2010.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Maud, B. G., and V. S. Michael. Biomaterial-associated thrombosis: roles of coagulation factors, complement, platelets and leukocytes. Biomaterials. 25(26):5681–5703, 2004.

    Article  Google Scholar 

  67. Reza, M. M. S., and A. Arzani. A critical comparison of different residence time measures in aneurysms. J. Biomech. 88:122–129, 2019.

    Article  PubMed  Google Scholar 

  68. Rossini, L., et al. A clinical method for mapping and quantifying blood stasis in the left ventricle. J. Biomech. 49(11):2152–2161, 2016.

    Article  PubMed  Google Scholar 

  69. Bludszuweit, C. Model for a general mechanical blood damage prediction. Artif. Org. 19:583–589, 1995.

    Article  CAS  Google Scholar 

  70. Thygesen, K., et al. Fourth universal definition of myocardial infarction (2018). Circulation. 138(20):e618–e651, 2018.

    Article  PubMed  Google Scholar 

  71. Rosseel, L., O. De Backer, and L. Søndergaard. Clinical valve thrombosis and subclinical leaflet thrombosis following transcatheter aortic valve replacement: is there a need for a patient-tailored antithrombotic therapy? Front. Cardiovasc. Med. 6:44, 2019.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Khodaee, F., M. Barakat, M. Abbasi, D. Dvir, and A. N. Azadani. Incomplete expansion of transcatheter aortic valves is associated with propensity for valve thrombosis. Interact. CardioVasc. Thorac. Surg. 30(1):39–46, 2020.

    Article  PubMed  Google Scholar 

  73. Niederer, S. A., et al. Creation and application of virtual patient cohorts of heart models. Philos. Trans. R. Soc. A. 378(2173):20190558, 2020.

    Article  ADS  CAS  Google Scholar 

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Funding

This project was supported by NIH-NIBIB U01EB026414 (DB), has received funding from “la Caixa” Foundation (fellowship ID: LCF/BQ/DI18/11660044), from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No.713673., and from the project CompBioMed2 (H2020-EU.1.4.1.3. Grant No. 823712).

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Author notes

  1. David Oks and Symon Reza have contributed equally and share the first authorship.

Authors and Affiliations

  1. Barcelona Supercomputing Center, Computer Applications in Science and Engineering, Barcelona, Spain

    David Oks, Mariano Vázquez, Guillaume Houzeaux & Cristóbal Samaniego

  2. Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794-8084, USA

    Symon Reza, Brandon Kovarovic & Danny Bluestein

  3. ELEM Biotech SL, Barcelona, Spain

    Mariano Vázquez

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  1. David Oks
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Contributions

SR and DO conducted the study and prepared the manuscript. GH and CS contributed significantly to the design and implementation of algorithms. CS, MV, BK, and DB contributed to the critical review, interpretation of results, and the writing.

Corresponding author

Correspondence to Danny Bluestein.

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Competing interests

Author D.B. has an equity interest in Polynova Cardiovascular Inc. Author B.K. is a consultant of Polynova Cardiovascular Inc. Author M.V. is the CTO/CSO of ELEM Biotech S.L. Author D.O. is a Business Development Engineer at ELEM Biotech S.L. Author G.H. and C.S. have equity interests in ELEM Biotech S.L. The other authors declare that they have no competing interests.

Ethical Approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Stony Brook University (2013-2357-R5, 2 October 2022).

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Associate Editor Umberto Morbiducci oversaw the review of this article.

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Oks, D., Reza, S., Vázquez, M. et al. Effect of Sinotubular Junction Size on TAVR Leaflet Thrombosis: A Fluid–Structure Interaction Analysis. Ann Biomed Eng 52, 719–733 (2024). https://doi.org/10.1007/s10439-023-03419-3

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