JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE) ›› 2020, Vol. 40 ›› Issue (08): 1113-1119.doi: 10.3969/j.issn.1674-8115.2020.08.019

• Original article (Public health) • Previous Articles     Next Articles

Time series analysis and prediction on clinical usage demand of red blood cells

LIU Yun-nan1*, PENG Rong-rong1*, YANG Dong-yan2, ZHAO Ming-feng1, WANG Han-rou1, YANG Xiao-li1   

  1. 1. Innovation Center for Social Risk Governance in Health, Research Center for Medical and Social Development, College of Public Health and Management, Chongqing Medical University, Chongqing 400016, China; 2. Department of Operations, Chongqing Blood Center, Chongqing 400015, China
  • Online:2020-08-28 Published:2020-08-28
  • Supported by:
    Foundation of Decision-making Consultation and Management Innovation of Chongqing (cstc2016jccxBX0064).

Abstract: Objective · To establish the clinical usage demand prediction model of red blood cells (RBCs) based on the autoregressive integrated moving average (ARIMA) model, and provide scientific basis for plans of blood collection and blood donor recruitment. Methods · Clinical RBCs outflow data of Chongqing Blood Center from January, 2006 to June, 2016 were collected, and SPSS software was used to establish ARIMA model of ABO blood groups. The clinical usage demand of ABO blood groups of RBCs from July to December, 2016 were predicted by using the established model and the prediction effect was verified by comparing with the actual value. Results · The optimal models of ABO blood groups were established successfully, i.e. blood group A was ARIMA(3,1,0)(1,1,0)12, blood group B was ARIMA(3,1,0)(0,1,1)12, blood group O was ARIMA(3,1,0)(1,0,1)12, and blood group AB was ARIMA(3,1,0)(0,1,1)12. There was no statistical significance of Ljung-Box Q statistics in each model. The residual sequences were white noise with good fitting effects. The dynamic trend of the fitted value and the actual value from January, 2006 to June, 2016 of each ARIMA model was approximately the same, the actual values of clinical usage demand from July to December, 2016 were within the 95%CI of predicted values, and the average relative errors were less than 10%. Conclusion · The ARIMA model has a high prediction accuracy, which can fit the trend of clinical usage demand of RBCs well, and is suitable for short-term prediction of clinical usage demand of ABO blood groups of RBCs.

Key words: time series analysis, clinical red blood cell, demand prediction, ABO blood group

CLC Number: