RESUMO
OBJECTIVE: To analyze the affecting factors of hemoglobin changes in apheresis red blood cells (RBCs), and to establish a predictive model for the evaluation of apheresis. METHODS: The clinical data of 130 patients undergoing selective surgery for apheresis autologous RBCs from January 2017 to December 2018 were collected. The change of hemoglobin and its affecting factors before and after apheresis were analyzed. The predictive model of the hemoglobin change was established by machine learning algorithm and compared with the theoretical predictive model. RESULTS: The average Hb level in the 300 ml autologous RBC group decreased by 22.61±8.85 g/L, and the average Hb in 400 ml group decreased by 29.08±7.25 g/L. The change of Hb was mainly affected by Hb level before apheresis and peripheral circulation blood volume (Pï¼0.05). Sex, age, and the interval time between blood collection and operation not significantly influenced Hb change (Pï¼0.05). The initially established predictive model by the machine learning (MAE 6.27) is superior to the theoretical predictive model (MAE 8.11). CONCLUSION: The predictive model established by the machine learning can provide a reference for more accurate evaluation of apheresis autologous red blood cells.