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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.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.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 (P＜0.05). Sex, age, and the interval time between blood collection and operation not significantly influenced Hb change (P＞0.05). The initially established predictive model by the machine learning (MAE 6.27) is superior to the theoretical predictive model (MAE 8.11).The predictive model established by the machine learning can provide a reference for more accurate evaluation of apheresis autologous red blood cells.单采自体红细胞患者采集前后血红蛋白变化预测模型的建立.分析单采自体红细胞后患者血红蛋白变化的影响因素, 初步建立单采自体红细胞采集后血红蛋白变化预测模型.收集本院2017年1月到2018年12月期间130例行术前单采自体红细胞择期手术患者的临床资料, 分析采集前后血红蛋白变化规律及影响因素, 通过机器学习算法初步建立单采自体红细胞血红蛋白变化预测模型并与理论预测模型进行比较.采集300、400 ml 自体红细胞组患者血红蛋白水平分别平均下降22.61±8.85和29.08±7.25 g/L。采集后血红蛋白变化值主要受采血前患者血红蛋白水平和外周血容量影响（P＜0.05）。患者的性别、年龄及采血-手术间隔时间对采集后血红蛋白变化值无明显影响（P＞0.05）。机器学习算法初步建立的预测模型（平均绝对误差6.27）优于理论预测模型（平均绝对误差8.11）.利用机器学习算法初步建立的单采自体红细胞预测模型可为更准确地评估自体红细胞的采集提供参考.

To investigate the factors affecting counting and collection efficiency of the final product- mononuclear cells (MNCs) in the collection of mononuclear cells for tumor cell biotherapy.The collected data of 142 tumor patients and healthy donors were analyzed, including age, sex, height, weight, BMI, the total blood volume, diagnostic category, vascular access, operator, final product volume, ACD anticoagulant usage, flow rate and circulation times, pre-apheresis Hb, RBC, Plt, WBC, lymphocyte count, monocyte count, neutrophil count, circulating blood volume without anticoagulant, final product MNC and collection efficiency of MNC. CE(collection efficiency)%= final product MNC×100/(pre-apheresis MNC×circulating blood volume without anticoagulant). The factors affecting final products MNC and CE of MNC were detected by T test and multiple linear regression analysis.The CE of tumor patients was higher than that of healthy donors （24.41±1.91,vs 20.01±0.99)，(P=0.043）, and CE of MNC was different among different operators (P=0.01, H=18.59). There was a positive correlation of the final MNC with the volume of final product, ACD anticoagulant usage and pre-apheresis lymphocyte count (P= 0.00, P= 0.01, P= 0.00， r=0.811); CE of MNC negatively correlated with flow rate and pre-apheresis RBC, but positively correlated with operator's working age and ACD anticoagulant usage (P=0.01, P=0.04, P=0.03, P= 0.00, r=0.495).more higher pre-apheresis lymphocyte , more amount of the final product and ACD anticoagulant usage, and more high the final MNC. During the collecting process, more ACD anticoagulant usage and more high operator's seniority, lead to the higher MNC'S CE; while more high pre-apheresis RBC and more fast flow rate, cause the lower the CE of MNC.肿瘤细胞生物治疗中单个核细胞采集的影响因素分析.探讨在肿瘤细胞生物治疗单个核细胞采集中，影响终产品单个核细胞计数（mononuclear cell, MNC）和采集效率（collection efficiency，CE）的因素.对共计142例肿瘤患者和健康供者的采集数据进行分析，包括年龄、性别、身高、体重、身体质量指数（body mass index, BMI）、全身血容量、诊断类别、血管通路、操作者、终产品量、ACD抗凝剂用量、流速、循环次数、采前血红蛋白（Hb）、红细胞计数（RBC）、血小板计数（Plt）、白细胞计数（WBC）、淋巴细胞计数、单核细胞计数、中性粒细胞计数、去掉抗凝剂的循环血量、终产品单个核细胞计数和单个核细胞的采集效率。其中单个核细胞CE（%）=终产品MNC×100/（采前MNC×去掉抗凝剂的循环血量）。采用T检验和多元线性回归分析研究影响终产品MNC和CE的因素.肿瘤患者的CE高于健康供者（24.41±1.91, 20.01±0.99，P=0.043），不同操作者之间CE不同（P=0.01，H=18.59）。终产品MNC与终产品容量、ACD抗凝剂用量、采前淋巴细胞计数呈正相关(P=0.00, P=0.01, P=0.00，r=0.811)；MNC的CE与流速、采前RBC计数呈负相关，与操作者工作年限、ACD抗凝剂用量呈正相关（P=0.01，P=0.04，P=0.03，P=0.00，r=0.495）.采前淋巴细胞计数越高、ACD抗凝剂用量和终产品容量越多，终产品MNC越多；ACD抗凝剂用量越多，操作者资历越高单个核细胞CE越高；采前RBC越高、采集流速越快，单个核细胞CE越低.