Improving donor management by data-driven individualized donation and boostering strategies for anti-RhD donors

Abstract

Background: Anti-RhD immunoglobulins are used in immunoprophylaxis products to prevent RhD haemoltyic disease of the newborn. However, the number of available donors is decreasing structurally, mainly as a result of the success of the treatment. Therefore, this study aims to optimize the production of the donor population by finding individualized donor management strategies that allow fulfilling the anti-RhD demand and minimize the overall effort of donors in terms of exposure to RhD-antigens (booster events) and donations. Study Design and Methods: The boostering effect and decline rate are estimated for each donor by fitting a Bayesian random effects model with informative data-driven priors to titers and booster events of the anti-RhD donor population of Sanquin. Two optimization problems were solved to find optimal boundaries to assign donors to four donor subsets, and to derive boostering and donation frequencies for each subset. Results: Several optimal strategies were found prescribing a boostering and donation frequency of each donor subset. The strategies reveal that additional boosters of a small group of donors with high boostering effects and low decline rates (HB-LD donors) could potentially provide the main part of the production. Conclusion: In this study, optimal donation and boostering strategies were derived for individual donors that minimize the effort of donors and allow meeting the anti-RhD demand. The procedure is applicable to any anti-RhD donor population, and allows policymakers to take into account constraints on boostering and donation frequencies, donor subset sizes, and risk and impact of not fulfilling the demand.