On the Significance of Exposure Time in Computational Blood Damage Estimation
The reliability of common stress-based power law models for hemolysis estimations in blood pumps is still not satisfying. Stress-based models are based on an instantaneous shear stress measure. Therefore, such models implicitly assume that red blood cells deform immediately due to the action of forces. In contrast, a strain-based model considers the entire deformation history of the cells. By applying a viscoelastic tensor equation for the stress computation, the effect of exposure time is represented as a biophysical phenomenon. Comparisons of stress-based and strain-based hemolysis models in a centrifugal blood pump show very significant differences. Stress peaks with short exposure time contribute to the overall hemolysis in the stress-based model, whereas regions with increased shear and long exposure time are responsible for damage in the strain-based model.
KeywordsComputational hemodynamics Hemolysis modeling Ventricular assist device Finite element method Blood damage
We like to thank Jaewook Nam and Matteo Pasquali for their contributions to previous implementations of the hemolysis models. In addition, we gratefully acknowledge the support by the DFG program GSC 111 (AICES Graduate School). Computing resources were provided by the RWTH Aachen University IT Center and by the Forschungszentrum Jülich John von Neumann Institute for Computing under the Jülich Aachen Research Alliance (JARA).
- 1.Arora, D., Behr, M., Coronado-Matutti, O., Pasquali, M.: Estimation of hemolysis in centrifugal blood pumps using morphology tensor approach. In: Bathe, K. (ed.) Proceedings of 3rd MIT Conference on Computational Fluid and Solid Dynamics, pp. 578–582. Elsevier Ltd. (2005)Google Scholar
- 4.Blackshear, P., Blackshear, G.: Mechanical hemolysis. In: Skalak, R., Chien, S. (eds.) Handbook of Bioengineering, p. 15.1–15.19. McGraw-Hill, New York (1987)Google Scholar
- 10.Evans, E., LaCelle, P.: Intrinsic material properties of the erythrocyte membrane indicated by mechanical analysis of deformation. Blood 45, 29–43 (1975)Google Scholar
- 12.Farinas, M., Garon, A., Lacasse, D., N’dri, D.: Asymptotically consistent numerical approximation of hemolysis. J. Biomed. Eng. 128, 688–696 (2006)Google Scholar
- 16.Giersiepen, M., Wurzinger, L., Opitz, R., Reul, H.: Estimation of shear stress-related blood damage in heart valve prostheses - in vitro comparison of 25 aortic valves. Int. J. Artif. Organs 13(5), 300–306 (1990)Google Scholar
- 21.Nicoud, F., Toda, H., Cabrit, O., Bose, S., Lee, J.: Using singular values to build a subgrid-scale model for large eddy simulations. Phys. Fluids (1994-present) 23(085106), 1–12 (2011)Google Scholar
- 24.Probst, M.: Robust Shape Optimization for Incompressible Flow of Shear-Thinning Fluids. Ph.D. thesis, RWTH Aachen University, Aachen, Germany (2013)Google Scholar
- 26.Riveros-Moreno, V., Wittenberg, J.: The self-diffusion coefficients of myoglobin and hemoglobin in concentrated solutions. J. Biol. Chem. 247(3), 895–901 (1972)Google Scholar
- 28.Stewart, S., Hariharan, P.: Computational round robin #2 (model blood pump), October 2013. https://fdacfd.nci.nih.gov/interlab_study_2_blood_pump
- 29.Wurzinger, L., Opitz, R., Eckstein, H.: Mechanical blood trauma: an overview. Angeiologie 38, 81–97 (1986)Google Scholar