Abstract
BackgroundTo optimize the planning of blood donations but also to continue motivating the volunteers it is important to streamline the practical organization of the timing of donations. While donors are asked to return for donation after a suitable period, still a relevant proportion of blood donors is deferred from donation each year due to a too low hemoglobin level. Rejection of donation may demotivate the candidate donor and implies an inefficient planning of the donation process. Hence, it is important to predict the future hemoglobin level to improve the planning of donors’ visits to the blood bank.MethodsThe development of the hemoglobin prediction rule is based on longitudinal (panel) data from blood donations collected by Sanquin (the only blood product collecting and supplying organization in the Netherlands). We explored and contrasted two popular statistical models, i.e. the transition (autoregressive) model and the mixed effects model as plausible models to account for the dependence among subsequent hemoglobin levels within a donor.ResultsThe predictors of the future hemoglobin level are age, season, hemoglobin levels at the previous visits, and a binary variable indicating whether a donation was made at the previous visit. Based on cross-validation, the areas under the receiver operating characteristic curve (AUCs) for male donors are 0.83 and 0.81 for the transition model and the mixed effects model, respectively; for female donors we obtained AUC values of 0.73 and 0.72 for the transition model and the mixed effects model, respectively.ConclusionWe showed that the transition models and the mixed effects models provide a much better prediction compared to a multiple linear regression model. In general, the transition model provides a somewhat better prediction than the mixed effects model, especially at high visit numbers. In addition, the transition model offers a better trade-off between sensitivity and specificity when varying the cut-off values for eligibility in predicted values. Hence transition models make the prediction of hemoglobin level more precise and may lead to less deferral from donation in the future.
Highlights
To optimize the planning of blood donations and to continue motivating the volunteers it is important to streamline the practical organization of the timing of donations
We start with a multiple linear regression model (Model LR) that includes age, season, and donation at previous visit (DPV) as covariates
In addition to the multiple linear regression model, a mixed effects model (Model linear mixed effects (LME)) and transition models of different orders are fitted to the training data set
Summary
To optimize the planning of blood donations and to continue motivating the volunteers it is important to streamline the practical organization of the timing of donations. While donors are asked to return for donation after a suitable period, still a relevant proportion of blood donors is deferred from donation each year due to a too low hemoglobin level. It is important to predict the future hemoglobin level to improve the planning of donors’ visits to the blood bank. There may be several reasons for the ineligibility of a blood donor for donation, a common reason being low hemoglobin level of the donor [1,2]. While donors are asked to return for donation after a suitable period, a relevant proportion of blood donors are temporarily deferred from donation each year due to low
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