Full Hematocrit-Viscosity Curve Identification Using Three-Dataset Krieger-Dougherty Regression

Area of Science:

• Biomedical Engineering
• Rheology
• Hematology

Background:

• Blood viscosity is crucial for understanding blood flow dynamics.
• The relationship between hematocrit and blood viscosity is complex and vital for physiological and pathological states.
• Current methods for determining the full hematocrit-viscosity curve are time-consuming, requiring numerous measurements.

Purpose of the Study:

• To develop a simplified method for reconstructing the complete hematocrit-viscosity curve.
• To validate a three-point Krieger-Dougherty (K-D) regression model for this purpose.
• To offer an efficient alternative to conventional multi-measurement techniques.

Main Methods:

• Preparation of blood samples: suspended, RBC-rich, and RBC-depleted blood.
• Measurement of hematocrit using a micro-hemocytometer.
• Measurement of blood viscosity using the coflowing streams method.
• Application of a three-dataset K-D regression model: μ=μ0(1-ϕ/ϕm)⁻ᵅ.

Main Results:

• The reconstructed hematocrit-viscosity curves closely matched experimental data.
• The proposed three-point method achieved a lower root-mean-square error compared to conventional methods.
• The K-D model's exponent was sensitive to the midpoint dataset, while μ0 was influenced by the suspending medium.

Conclusions:

• The proposed three-dataset K-D regression method is a simple, efficient, and reliable approach for estimating the full hematocrit-viscosity curve.
• This method reduces the number of required measurements for blood rheology analysis.
• The findings contribute to a better understanding of blood rheological properties.