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Prediction of Red Blood Cell Demand for Pediatric Patients Using a Time-Series Model: A Single-Center Study in China
Kai Guo1, Shanshan Song1, Lijuan Qiu1
1Department of Transfusion Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Insights
Accurate forecasting of pediatric red blood cell (RBC) demand is crucial. A seasonal autoregressive integrated moving average (SARIMA) model accurately predicted monthly RBC usage in children, aiding supply chain management.
Area of Science:
- Hematology
- Biostatistics
- Healthcare Management
Background:
- Red blood cells (RBCs) are vital in modern medicine, yet predicting demand and managing supply remains challenging.
- Fluctuating demand for RBCs complicates collection and distribution efforts.
Purpose of the Study:
- To assess the feasibility of using time-series models for predicting monthly clinical demand of pediatric red blood cells.
- To develop and validate a predictive model for RBC usage in pediatric patients.
Main Methods:
- Collected clinical RBC transfusion data from 2014-2019 at a children's hospital in China.
- Developed and fitted a seasonal autoregressive integrated moving average (SARIMA) model using data from 2014-2018.
- Validated the model by forecasting RBC usage for 2019 and comparing it with actual data.
Main Results:
- The SARIMA (0, 1, 1) (1, 1, 0)12 model demonstrated the best fit and predictive accuracy (R2 = 0.730).
- The model's predictions showed a low average relative error of 6.44%, with 91.67% of actual values falling within the 95% confidence intervals.
- Model residuals were white noise, indicating statistical validity.
Conclusions:
- The SARIMA model effectively simulates and predicts monthly pediatric RBC usage trends with high accuracy.
- This time-series approach offers a valuable tool for short-term clinical RBC demand planning.
- Further clinical studies with larger datasets are recommended to confirm these findings and enhance RBC management strategies.
Background:
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.
Objectives:
This study aimed to explore the feasibility of the time-series model in predicting the clinical demand of RBCs for pediatric patients each month.
Methods:
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.
Results:
The seasonal multiplicative model SARIMA (0, 1, 1) (1, 1, 0)12 (normalized BIC = 8.740, R 2 = 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(18) = 15.127, P > 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 (P < 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%.
Conclusions:
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.
