An Experimental-Computational Approach to Quantify Blood Rheology in Sickle Cell Disease

Marisa S. Bazzi, José M. Valdez, Victor H. Barocas, David K. Wood

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


In sickle cell disease, aberrant blood flow due to oxygen-dependent changes in red cell biomechanics is a key driver of pathology. Most studies to date have focused on the potential role of altered red cell deformability and blood rheology in precipitating vaso-occlusive crises. Numerous studies, however, have shown that sickle blood flow is affected even at high oxygen tensions, suggesting a potentially systemic role for altered blood flow in driving pathologies, including endothelial dysfunction, ischemia, and stroke. In this study, we applied a combined experimental-computation approach that leveraged an experimental platform that quantifies sickle blood velocity fields under a range of oxygen tensions and shear rates. We computationally fitted a continuum model to our experimental data to generate physics-based parameters that capture patient-specific rheological alterations. Our results suggest that sickle blood flow is altered systemically, from the arterial to the venous circulation. We also demonstrated the application of this approach as a tool to design patient-specific transfusion regimens. Finally, we demonstrated that patient-specific rheological parameters can be combined with patient-derived vascular models to identify patients who are at higher risk for cerebrovascular complications such as aneurysm and stroke. Overall, this study highlights that sickle blood flow is altered systemically, which can drive numerous pathologies, and this study demonstrates the potential utility of an experimentally parameterized continuum model as a predictive tool for patient-specific care.

Original languageEnglish (US)
Pages (from-to)2307-2315
Number of pages9
JournalBiophysical journal
Issue number11
StatePublished - Dec 1 2020

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health ( U01-HL139471 ; R01-HL132906 ). Portions of this work were conducted in the Minnesota Nano Center, which was supported by the National Science Foundation through the National Nano Coordinated Infrastructure Network under Award No. ECCS-1542202 .

Publisher Copyright:
© 2020 Biophysical Society


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