RESUMO
【Objective】 To explore the effects of blood routine parameters on the peripheral blood hematopoietic stem cell collection of healthy donors, and predict collection timing based on these parameters. 【Methods】 The blood routine parameters pre-donation and the total number of mononuclear cells post-donation of 249 donors who applied blood cell separator to collect peripheral blood hematopoietic stem cells in our hospital from January 2018 to August 2020 were collected. Taking total nucleated cells of circulating blood per litre as the main evaluation index, and its collection with blood routine parameters pre-collection was analyzed. The relevant influencing factors were analyzed using multiple linear regression analysis. The blood routine parameters of healthy donors who donated peripheral blood hematopoietic stem cells in our hospital from September 2020 to October 2020 were substituted into the equation to obtain the predicted values, which were then compared with the actual values obtained from actual product using t test for verification. 【Results】 The analysis showed that the parameters of Hb, RBC, Hct, leukocyte count, neutrophil, lymphocyte, monocyte and Plt were statistically correlated with the total number of mononuclear cells of circulating blood per liter volume (P<0.05). There was a linear relationship between lymphocyte, monocyte, Plt and leukocyte count and the total number of mononuclear cells of circulating blood per liter. The total number of mononuclear cells of circulating blood per liter was set to (Y), and the variables such as lymphocyte (X1), monocyte (X2), Plt (X3), leukocyte count (X), and neutrophil were used as dependent variables for multiple linear regression, and the equation was: Y=9.814+ 3.131X1+ 1.666X2+ 0.020X3+ 0.124X4. There was no statistical difference between the predicted value and the calculated value (P>0.05). 【Conclusion】 The blood routine parameters of lymphocyte, monocyte, platelet count and leukocyte count of donors before collection can effectively predict the collection efficiency, therefore help predict the collection time.