Computational Fluid Dynamics (CFD) for blood flow in cardiovascular medical devices and blood damage prediction

Document Type : Original Article

Author

Department of Mathematics and Actuarial Sciences, Kenyatta University, Nairobi, Kenya

Abstract

Background: The hemodynamic performance of cardiovascular medical devices and their potential to cause blood damage are critical factors in ensuring patient safety and device efficacy. Computational Fluid Dynamics (CFD) has emerged as a valuable tool for simulating blood flow within these devices and predicting the risk of blood damage.
Objectives: This study aims to utilize CFD simulations to evaluate the local hemodynamic performance of a particular implantable device and to provide precise predictions about likely adverse clinical effects, cutting-edge techniques like laser doppler anemometry (LDA) or particle image velocimetry (PIV) must be accessible.
Methods: A patient-specific CFD model of the cardiovascular system and medical devices was developed based on medical imaging data. Hemodynamic parameters such as shear stress and flow recirculation were computed to identify regions of potential blood damage. The simulations were validated against data.
Results: The CFD simulations revealed intricate flow patterns and areas of concern within the medical devices. Elevated shear stresses and prolonged residence times were identified in certain regions, indicating a risk of blood damage. By quantifying these parameters, the study provided a comprehensive assessment of potential blood damage locations and severity levels.
Conclusion: CFD proved to be a robust approach for evaluating blood flow within cardiovascular devices and predicting potential blood damage. The study highlighted specific design modifications that could mitigate the risk of blood damage, thus contributing to the improvement of device safety. The integration of CFD with patient-specific data offers clinicians and engineers a powerful tool for optimizing cardiovascular device design and minimizing patient risk.

Keywords

Main Subjects


  1. Grunkemeier GL, Jin R, Starr A. Prosthetic heart valves: objective performance criteria versus randomized clinical trial. Ann Thorac Surg. 2006;82(3):776-80. doi:10.1016/j.athoracsur.2006.06.037 PMid:16928482
  2. Sun JC, Davidson MJ, Lamy A, Eikelboom JW. Antithrombotic management of patients with prosthetic heart valves: current evidence and future trends. The Lancet. 2009;374(9689):565-76.
    doi:10.1016/S0140-6736(09)60780-7 PMid:19683642
  3. Salem DN, O'Gara PT, Madias C, Pauker SG. Valvular and structural heart disease: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2008;133(6): 593S-629S. doi:10.1378/chest.08-0724 PMid:18574274
  4. Mendez Rojano R, Zhussupbekov M, Antaki JF. Multi‐constituent simulation of thrombus formation at LVAD inlet cannula connection: Importance of Virchow’s triad. Artificial organs. 2021;45(9):1014-23.. doi:10.1111/aor.13949 PMid:33683718 PMCid:PMC9987618
  5. Lamson TC. Relative blood damage in the three phases of a prosthetic heart valve flow cycle. The Pennsylvania State University; 1993. doi:10.1097/00002480-199339030-00091
  6. Herbertson LH. Evaluation of fluid mechanics and cavitation generated by mechanical heart valves during the closing phase. The Pennsylvania State University; 2009.
  7. Seltzer JH, Heise T, Carson P, Canos D, Hiatt JC, Vranckx P, et al. Use of endpoint adjudication to improve the quality and validity of endpoint assessment for medical device development and post marketing evaluation: Rationale and best practices. A report from the cardiac safety research consortium. Am Heart J. 2017;190:76-85. doi:10.1016/j.ahj.2017.05.009 PMid:28760216
  8. Arjunon S, Saikrishnan N, Yoganathan AP. Cardiac Valve Prostheses. InMolecular, Cellular, and Tissue Engineering 2018 (pp. 77-1). CRC Press.
  9. Ozturk M, O'Rear EA, Papavassiliou DV. Hemolysis related to turbulent eddy size distributions using comparisons of experiments to computations. Artificial organs. 2015;39(12):E227-39. doi:10.1111/aor.12572 PMid:26412190
  10. Sutera SP. Flow-induced trauma to blood cells. Circulation Rse. 1977;41(1):2-8. doi:10.1161/01.RES.41.1.2 PMid:324656
  11. Grande Gutiérrez N, Alber M, Kahn AM, Burns JC, Mathew M, McCrindle BW, et al. Computational modeling of blood component transport related to coronary artery thrombosis in Kawasaki disease. PLoS computational Biol. 2021;17(9):e1009331. doi:10.1371/journal.pcbi.1009331 PMid:34491991 PMCid:PMC8448376
  12. Guglietta F, Behr M, Falcucci G, Sbragaglia M. Loading and relaxation dynamics of a red blood cell. Soft Matter. 2021;17(24): 5978-90. doi:10.1039/D1SM00246E PMid:34048527
  13. Nugent AH. Fluid dynamical investigation of a ventricular assist device (Doctoral dissertation, UNSW Sydney).
  14. Yen JH, Chen SF, Chern MK, Lu PC. The effect of turbulent viscous shear stress on red blood cell hemolysis. J Artificial Organs. 2014;17:178-85. doi:10.1007/s10047-014-0755-3 PMid:24619800
  15. Ozturk M, O’Rear EA, Papavassiliou DV. Reynolds stresses and hemolysis in turbulent flow examined by threshold analysis. Fluids. 2016 21;1(4):42.doi:10.3390/fluids1040042
  16. Leo H, Simon HA, Dasi LP, Yoganathan AP. Effect of hinge gap width on the microflow structures in 27-mm bileaflet mechanical heart valves. J Heart Valve Dis. 2006;15(6):800.
  17. Sutera SP, Mehrjardi MH. Deformation and fragmentation of human red blood cells in turbulent shear flow. Biophysical J. 1975; 15(1):1-0. doi:10.1016/S0006-3495(75)85787-0 PMid:1174639
  18. Xu S, Lazarian A. Turbulent dynamo in a conducting fluid and a partially ionized gas. Astrophysical J. 2016 ;833(2):215. doi:10.3847/1538-4357/833/2/215
  19. Quinlan NJ, Dooley PN. Models of flow-induced loading on blood cells in laminar and turbulent flow, with application to cardiovascular device flow. Ann biomedical engineering. 2007; 35:1347-56. doi:10.1007/s10439-007-9308-8 PMid:17458700
  20. Maymir JC. Effects of tilting disk heart valve gap width on mean velocity and Reynolds stress fields in regurgitant flow. The Pennsylvania State University; 1996.
  21. Hund SJ, Antaki JF, Massoudi M. On the representation of turbulent stresses for computing blood damage. Int J Engineering Sci. 2010;48(11):1325-31. doi:10.1016/j.ijengsci.2010.09.003 PMid:21318093 PMCid:PMC3037028
  22. Morbiducci U, D Avenio G, Del Gaudio C, Grigioni M. Testing requirements for steroscopic Particle Image Velocimetry measurements of mechanical heart valves fluid dynamics. RAPPORTI ISTISAN. 2005;46:21.
  23. Lowe GD. Virchow’s triad revisited: abnormal flow. Pathophysiology haemostasis thrombosis. 2003;33(5-6):455-7. doi:10.1159/000083845 PMid:15692260
  24. James ME, Papavassiliou DV, O’Rear EA. Use of computational fluid dynamics to analyze blood flow, hemolysis and sublethal damage to red blood cells in a bileaflet artificial heart valve. Fluids. 2019;4(1):19. doi:10.3390/fluids4010019
  25. Thamsen B, Blümel B, Schaller J, Paschereit CO, Affeld K, Goubergrits L, et al. Numerical analysis of blood damage potential of the HeartMate II and HeartWare HVAD rotary blood pumps. Artificial organs. 2015;39(8):651-9. doi:10.1111/aor.12542 PMid:26234447
  26. Alemu Y, Bluestein D. Flow‐induced platelet activation and damage accumulation in a mechanical heart valve: numerical studies. Artificial organs. 2007;31(9):677-88. doi:10.1111/j.1525-1594.2007.00446.x PMid:17725695
  27. Sun W, Wang S, Chen Z, Zhang J, Li T, Arias K, et al. Impact of high mechanical shear stress and oxygenator membrane surface on blood damage relevant to thrombosis and bleeding in a pediatric ECMO circuit. Artificial organs. 2020; 44 (7):717-26. doi:10.1111/aor.13646 PMid:31970795 PMCid:PMC7308201
  28. Ge L, Dasi LP, Sotiropoulos F, Yoganathan AP. Characterization of hemodynamic forces induced by mechanical heart valves: Reynolds vs. viscous stresses. Ann Biomedical Eng. 2008;36:276-97. doi:10.1007/s10439-007-9411-x PMid:18049902