<span class="word">Full <span class="word"><span class="changedDisabled">Hematocrit-<span class="word"><span class="changedDisabled">Viscosity <span class="word"><span class="changedDisabled">Curve <span class="word"><span class="changedDisabled">Identification <span class="word"><span class="changedDisabled">Using <span class="word"><span class="changedDisabled">Three-<span class="word"><span class="changedDisabled">Dataset <span class="word">Krieger-<span class="word">Dougherty <span class="word"><span class="changedDisabled">Regression

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. Hematocrit of each blood is measured using a micro hemocytometer. Simultaneously, blood viscosity of each blood is measured using coflowing streams method. The proposed method is evaluated sequentially using reference datasets and hematocrit-viscosity datasets of control blood. According to results, full hematocrit–viscosity curve obtained from selected three datasets is in well agreement with the experimental data and yields lower root-mean-square error than conventional method using all datasets. The exponent of K–D model is strongly influenced by the midpoint dataset whereas μ0 is mainly affected by suspending medium (dextran solution). In contrast, GA-induced rigidified RBCs do not significantly affect μ0. In conclusion, the proposed method provides simple, efficient, and reliable approach for estimating the full hematocrit–viscosity curve.