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

Abstract

Blood viscosity is strongly dependent on hematocrit, and the hematocrit-viscosity relationship is an important determinant of blood rheology under physiological and pathological conditions. However, obtaining a full hematocrit-viscosity curve requires multiple measurements over a wide hematocrit range. In this study, a simple method is proposed to reconstruct the full hematocrit-viscosity curve using only three-dataset Krieger-Dougherty (K-D) regression as μ=μ0(1-ϕϕm)-α ϕm. Based on suspended blood, RBC-rich blood and RBC-depleted blood are prepared after centrifugation. The hematocrit of each type of blood is measured using a micro-hemocytometer. Simultaneously, the blood viscosity of each type of blood is measured using the coflowing streams method. The proposed method is evaluated sequentially using reference datasets and hematocrit-viscosity datasets of control blood. According to results, the full hematocrit-viscosity curve obtained from three selected datasets is in good agreement with the experimental data and yields a lower root-mean-square error than conventional methods using all datasets. The exponent of the K-D model is strongly influenced by the midpoint dataset, whereas μ₀ is mainly affected by the suspending medium (dextran solution). In contrast, GA-induced rigidified RBCs do not significantly affect μ₀ within a 0.15% concentration. In conclusion, the proposed method provides a simple, efficient, and reliable approach for estimating the full hematocrit-viscosity curve.