Paper Number
PO113 

Session
Poster Session

Title
Determination of statistically significant correlations between physiological and rheological model parameters of human blood

Presentation Date and Time
October 23, 2019 (Wednesday) 6:30

Track / Room
Poster Session / Ballroom C on 4th floor

Authors (Click on name to view author profile)

  1. Armstrong, Matthew J. (United States Military Academy)
  2. Deegan, Michael (United States Military Academy, Chemistry and Life Science)
  3. Barnhill, Jason (United States Military Academy, Chemistry and Life Science)
  4. Wickiser, Ken (United States Military Academy, Chemistry and Life Science)
  5. Clark, Nick (United States Military Academy, Mathematical Sciences)
  6. Baker, Jeff (Keller Army Community Hospital, Lab)

Author and Affiliation Lines (in printed abstract book)
Matthew J. Armstrong1, Michael Deegan1, Jason Barnhill1, Ken Wickiser1, Nick Clark2, and Jeff Baker3
1Chemistry and Life Science, United States Military Academy, West Point, NY 10996; 2Mathematical Sciences, United States Military Academy, west point, NY 10996; 3Lab, Keller Army Community Hospital, west point, NY 10996

Speaker / Presenter 
Armstrong, Matthew J.

Text of Abstract
Recent work modeling the rheological behavior of human blood indicates that blood has all of the hallmark features of a complex material, including shear-thinning, viscoelastic behavior, a yield stress and thixotropy. After decades of modeling steady state blood data, and the development of steady state models, like the Casson, Carreau-Yasuda, Herschel-Bulkley, etc. the advancement and evolution of blood modeling to transient flow conditions now has a renewed interest [1,2,5,11]. Using recently collected steady state human blood rheological data we show and compare modeling efforts with several new models including the new modified Horner-Armstrong-Wagner-Beris (mHAWB), the viscoelastic enhanced Modified Delaware Thixotropic Model (MDTM), and Bautista-Monera-Puig, and Blackwell TEVP model. We will then use the best fit rheological model parameters to compare with physiological blood parameters such as hematocrit, glucose, triglycerides, LDL and HDL. To meet this end we will incorporate the correlation matrix, looking for statistically significant correlations by interpretation of the correlation coefficients and associated p-values. Lastly we will graphically show the most meaningful correlations and compare to similar work from literature. To fit the rheological data to the models a stochastic, global optimization algorithm is used.