Guo Kai, Song Shanshan, Qiu Lijuan, Wang Xiaohuan, Ma Shuxuan
Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Front Med (Lausanne). 2022 May 19;9:706284. doi: 10.3389/fmed.2022.706284. eCollection 2022.
Red blood cells (RBCs) are an essential factor to consider for modern medicine, but planning the future collection of RBCs and supply efforts for coping with fluctuating demands is still a major challenge.
This study aimed to explore the feasibility of the time-series model in predicting the clinical demand of RBCs for pediatric patients each month.
Our study collected clinical RBC transfusion data from years 2014 to 2019 in the National Center for Children's Health (Beijing) in China, with the goal of constructing a time-series, autoregressive integrated moving average (ARIMA) model by fitting the monthly usage of RBCs from 2014 to 2018. Furthermore, the optimal model was used to forecast the monthly usage of RBCs in 2019, and we subsequently compared the data with actual values to verify the validity of the model.
The seasonal multiplicative model SARIMA (0, 1, 1) (1, 1, 0) (normalized BIC = 8.740, = 0.730) was the best prediction model and could better fit and predict the monthly usage of RBCs for pediatric patients in this medical center in 2019. The model residual sequence was white noise (Ljung-Box Q = 15.127, > 0.05), and its autocorrelation function (ACF) and partial autocorrelation function (PACF) coefficients also fell within the 95% confidence intervals (CIs). The parameter test results were statistically significant ( < 0.05). 91.67% of the actual values were within the 95% CIs of the forecasted values of the model, and the average relative error of the forecasted and actual values was 6.44%, within 10%.
The SARIMA model can simulate the changing trend in monthly usage of RBCs of pediatric patients in a time-series aspect, which represents a short-term prediction model with high accuracy. The continuously revised SARIMA model may better serve the clinical environments and aid with planning for RBC demand. A clinical study including more data on blood use should be conducted in the future to confirm these results.
红细胞（RBCs）是现代医学需要考虑的一个重要因素，但规划未来红细胞的采集以及应对需求波动的供应工作仍是一项重大挑战。
本研究旨在探讨时间序列模型预测儿科患者每月红细胞临床需求的可行性。
我们的研究收集了中国国家儿童医学中心（北京）2014年至2019年的临床红细胞输血数据，目的是通过拟合2014年至2018年红细胞的月使用量构建一个时间序列自回归积分滑动平均（ARIMA）模型。此外，使用最优模型预测2019年红细胞的月使用量，随后将数据与实际值进行比较以验证模型的有效性。
季节性乘法模型SARIMA(0, 1, 1)(1, 1, 0)(归一化BIC = 8.740, = 0.730)是最佳预测模型，能够更好地拟合和预测该医学中心2019年儿科患者红细胞的月使用量。模型残差序列为白噪声（Ljung-Box Q = 15.127, > 0.05），其自相关函数（ACF）和偏自相关函数（PACF）系数也落在95%置信区间（CIs）内。参数检验结果具有统计学意义（ < 0.05）。91.67%的实际值在模型预测值的95%置信区间内，预测值与实际值的平均相对误差为6.44%，在10%以内。
SARIMA模型能够从时间序列角度模拟儿科患者红细胞月使用量的变化趋势，是一种高精度的短期预测模型。不断修订的SARIMA模型可能会更好地服务于临床环境，并有助于规划红细胞需求。未来应开展纳入更多用血数据的临床研究以证实这些结果。
