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Background: Red blood cells are essential for modern medicine but managing their collection and supply to cope with fluctuating demands represents a major challenge. As deterministic models based on predicted population changes have been problematic, there remains a need for more precise and reliable prediction of use. Here, we develop three new time-series methods to predict red cell use 4 to 52 weeks ahead.Study Design and Methods: From daily aggregates of red blood cell (RBC) units issued from 2005 to 2011 from the NHS Blood and Transplant, we generated a new set of non-overlapping weekly data by summing the daily data over 7 days and derived the average blood use per week over 4-week and 52-week periods. We used three new methods for linear prediction of blood use by computing the coefficients using Minimum Mean Squared Error (MMSE) algorithm.Results: We optimized the time-window size, order of the prediction, and order of the polynomial fit for our data set. By exploiting the annual periodicity of the data, we achieved significant improvements in long-term predictions, as well as modest improvements in short-term predictions. The new methods predicted mean RBC use with a standard deviation of the percentage error of 2.5% for 4 weeks ahead and 3.4% for 52 weeks ahead.Conclusion: This paradigm allows short- and long-term prediction of RBC use and could provide reliable and precise prediction up to 52 weeks ahead to improve the efficiency of blood services and sufficiency of blood supply with reduced costs. [ABSTRACT FROM AUTHOR]

Background: Blood products are essential for modern medicine, but managing their collection and supply in the face of fluctuating demands represents a major challenge. As deterministic models based on predicted changes in population have been problematic, there remains a need for more precise and reliable prediction of demands. Here, we propose a paradigm incorporating four different time-series methods to predict red blood cell (RBC) issues 4 to 24 weeks ahead.Study Design and Methods: We used daily aggregates of RBC units issued from 2005 to 2011 from the National Health Service Blood and Transplant. We generated a new set of nonoverlapping weekly data by summing the daily data over 7 days and derived the average blood issues per week over 4-week periods. We used four methods for linear prediction of blood demand by computing the coefficients with the minimum mean squared error and weighted least squares error algorithms.Results: We optimized the time-window size, order of the prediction, and order of the polynomial fit for our data set. The four time-series methods, essentially using different weightings to data points, gave very similar results and predicted mean RBC issues with a standard deviation of the percentage error of 3.0% for 4 weeks ahead and 4.0% for 24 weeks ahead.Conclusion: This paradigm allows prediction of demand for RBCs and could be developed to provide reliable and precise prediction up to 24 weeks ahead to improve the efficiency of blood services and sufficiency of blood supply with reduced costs. [ABSTRACT FROM AUTHOR]