Extensive red blood cell matching considering patient alloimmunization risk.

BACKGROUND AND OBJECTIVES: Red blood cell (RBC) transfusions pose a risk of alloantibody development in patients. For patients with increased alloimmunization risk, extended preventive matching is advised, encompassing not only the ABO-D blood groups but also the most clinically relevant minor antigens: C, c, E, e, K, Fya, Fyb, Jka, Jkb, S and s. This study incorporates patient-specific data and the clinical consequences of mismatching into the allocation process. MATERIALS AND METHODS: We have redefined the MINimize Relative Alloimmunization Risks (MINRAR) model to include patient group preferences in selecting RBC units from a finite supply. A linear optimization approach was employed, considering both antigen immunogenicity and the clinical impact of mismatches for specific patient groups. We also explore the advantages of informing the blood bank about scheduled transfusions, allowing for a more strategic blood distribution. The model is evaluated using historical data from two Dutch hospitals, measuring shortages and minor antigen mismatches. RESULTS: The updated model, emphasizing patient group-specific considerations, achieves a similar number of mismatches as the original, yet shifts mismatches among patient groups and antigens, reducing expected alloimmunization consequences. Simultaneous matching for multiple hospitals at the distribution centre level, considering scheduled demands, led to a 30% decrease in mismatches and a 92% reduction in shortages. CONCLUSION: The reduction of expected alloimmunization consequences by incorporating patient group preferences demonstrates our strategy's effectiveness for patient health. Substantial reductions in mismatches and shortages with multi-hospital collaboration highlights the importance of sharing information in the blood supply chain.

The added value of ferritin levels and genetic markers for the prediction of haemoglobin deferral.

BACKGROUND AND OBJECTIVES: On-site haemoglobin deferral for blood donors is sometimes necessary for donor health but demotivating for donors and inefficient for the blood bank. Deferral rates could be reduced by accurately predicting donors' haemoglobin status before they visit the blood bank. Although such predictive models have been published, there is ample room for improvement in predictive performance. We aim to assess the added value of ferritin levels or genetic markers as predictor variables in haemoglobin deferral prediction models. MATERIALS AND METHODS: Support vector machines with and without this information (the full and reduced model, respectively) are compared in Finland and the Netherlands. Genetic markers are available in the Finnish data and ferritin levels in the Dutch data. RESULTS: Although there is a clear association between haemoglobin deferral and both ferritin levels and several genetic markers, predictive performance increases only marginally with their inclusion as predictors. The recall of deferrals increases from 68.6% to 69.9% with genetic markers and from 79.7% to 80.0% with ferritin levels included. Subgroup analyses show that the added value of these predictors is higher in specific subgroups, for example, for donors with minor alleles on single-nucleotide polymorphism 17:58358769, recall of deferral increases from 73.3% to 93.3%. CONCLUSION: Including ferritin levels or genetic markers in haemoglobin deferral prediction models improves predictive performance. The increase in overall performance is small but may be substantial for specific subgroups. We recommend including this information as predictor variables when available, but not to collect it for this purpose only.

Predicting vasovagal reactions to needles with anticipatory facial temperature profiles.

Around one-third of adults are scared of needles, which can result in adverse emotional and physical responses such as dizziness and fainting (e.g. vasovagal reactions; VVR) and consequently, avoidance of healthcare, treatments, and immunizations. Unfortunately, most people are not aware of vasovagal reactions until they escalate, at which time it is too late to intervene. This study aims to investigate whether facial temperature profiles measured in the waiting room, prior to a blood donation, can be used to classify who will and will not experience VVR during the donation. Average temperature profiles from six facial regions were extracted from pre-donation recordings of 193 blood donors, and machine learning was used to classify whether a donor would experience low or high levels of VVR during the donation. An XGBoost classifier was able to classify vasovagal groups from an adverse reaction during a blood donation based on this early facial temperature data, with a sensitivity of 0.87, specificity of 0.84, F1 score of 0.86, and PR-AUC of 0.93. Temperature fluctuations in the area under the nose, chin and forehead have the highest predictive value. This study is the first to demonstrate that it is possible to classify vasovagal responses during a blood donation using temperature profiles.

Predicting Vasovagal Reactions to Needles from Facial Action Units.

BACKGROUND: Merely the sight of needles can cause extreme emotional and physical (vasovagal) reactions (VVRs). However, needle fear and VVRs are not easy to measure nor prevent as they are automatic and difficult to self-report. This study aims to investigate whether a blood donors' unconscious facial microexpressions in the waiting room, prior to actual blood donation, can be used to predict who will experience a VVR later, during the donation. METHODS: The presence and intensity of 17 facial action units were extracted from video recordings of 227 blood donors and were used to classify low and high VVR levels using machine-learning algorithms. We included three groups of blood donors as follows: (1) a control group, who had never experienced a VVR in the past (n = 81); (2) a 'sensitive' group, who experienced a VVR at their last donation (n = 51); and (3) new donors, who are at increased risk of experiencing a VVR (n = 95). RESULTS: The model performed very well, with an F1 (=the weighted average of precision and recall) score of 0.82. The most predictive feature was the intensity of facial action units in the eye regions. CONCLUSIONS: To our knowledge, this study is the first to demonstrate that it is possible to predict who will experience a vasovagal response during blood donation through facial microexpression analyses prior to donation.

Determining optimal locations for blood distribution centers.

BACKGROUND: Blood banks have to be thoughtful about supply chain decisions to effectively satisfy the blood product demand of hospitals. These decisions include the number and locations of distribution centers (DC), as this has a strong impact on both transportation cost and the ability to deliver emergency orders in time. STUDY DESIGN AND METHODS: We propose a mixed-integer linear programming approach to find optimal DC locations for supplying individual hospitals. The model maximizes the number of hospitals reachable from a DC within a given time-limit, and minimizes transportation cost. The minimal amount of data required is a set of hospital locations. The model can be further attuned to the user's needs by adding various model extensions. The model's use is demonstrated by two case studies, considering the blood banks of the Netherlands and Finland. RESULTS: For both case studies re-locating the DCs would result in a reduction of transportation cost of about 10% without affecting the reliability of delivery. In addition, to save facility exploitation costs, the number of DCs may be reduced in both countries while maintaining the reliability of delivery. The model was also shown to be robust against changes in hospital ordering behavior. DISCUSSION: We demonstrated the general usability and added value of the model by successfully optimizing the blood supply chains of the Netherlands and Finland, which differ substantially. Nonetheless, in both countries potential savings in both transportation and facility exploitation cost could be shown. The model code is open source and freely accessible online.

Generating synthetic mixed discrete-continuous health records with mixed sum-product networks.

OBJECTIVE: Privacy is a concern whenever individual patient health data is exchanged for scientific research. We propose using mixed sum-product networks (MSPNs) as private representations of data and take samples from the network to generate synthetic data that can be shared for subsequent statistical analysis. This anonymization method was evaluated with respect to privacy and information loss. MATERIALS AND METHODS: Using a simulation study, information loss was quantified by assessing whether synthetic data could reproduce regression parameters obtained from the original data. Predictors variable types were varied between continuous, count, categorical, and mixed discrete-continuous. Additionally, we measured whether the MSPN approach successfully anonymizes the data by removing associations between background and sensitive information for these datasets. RESULTS: The synthetic data generated with MSPNs yielded regression results highly similar to those generated with original data, differing less than 5% in most simulation scenarios. Standard errors increased compared to the original data. Particularly for smaller datasets (1000 records), this resulted in a discrepancy between the estimated and empirical standard errors. Sensitive values could no longer be inferred from background information for at least 99% of tested individuals. DISCUSSION: The proposed anonymization approach yields very promising results. Further research is required to evaluate its performance with other types of data and analyses, and to predict how user parameter choices affect a bias-privacy trade-off. CONCLUSION: Generating synthetic data from MSPNs is a promising, easy-to-use approach for anonymization of sensitive individual health data that yields informative and private data.

Why the majority of on-site repeat donor deferrals are completely unwarranted .

BACKGROUND: On-site deferral for low hemoglobin (Hb) is common in most countries and deferral rates commonly vary between 1% and 20%. Blood banks continuously strive to reduce deferral rates as these imply an immediate loss of products, a waste of materials, a waste of staff and donor time, and potential loss of donors. Despite many efforts, the main cause of donor deferral-the variability in hemoglobin measurement outcomes-remains largely unaddressed. STUDY DESIGN AND METHODS: Repeated hemoglobin measurements obtained at donor intake were used to estimate the variability in measurement outcomes (measurement variability). This information is incorporated in a new algorithm for donor deferral where the mean hemoglobin level of a donor is used to determine both donor eligibility and the deviance of individual measurement outcomes. The algorithm was tested on a cohort of new Dutch donors that started between 2012 and 2022 to evaluate its impact on the donor deferral rate. RESULTS: Historical data from 439,376 new donors with a deferral rate of 5.3% were analyzed by applying the new donor deferral algorithm. It was found that 92% of all deferrals were unnecessary as Hb levels were within the range of expected measurement variability. Contrarily, it appeared that 460 donors (0.10%) made 704 donations (0.06%) whilst not complying with donor eligibility criteria. DISCUSSION: Not accounting for measurement variability can be shown to not only result in unnecessary on-site deferrals but also results in donations by donors that can be shown not to comply with the legally required minimum Hb levels.

Explainable haemoglobin deferral predictions using machine learning models: Interpretation and consequences for the blood supply.

BACKGROUND AND OBJECTIVES: Accurate predictions of haemoglobin (Hb) deferral for whole-blood donors could aid blood banks in reducing deferral rates and increasing efficiency and donor motivation. Complex models are needed to make accurate predictions, but predictions must also be explainable. Before the implementation of a prediction model, its impact on the blood supply should be estimated to avoid shortages. MATERIALS AND METHODS: Donation visits between October 2017 and December 2021 were selected from Sanquin's database system. The following variables were available for each visit: donor sex, age, donation start time, month, number of donations in the last 24 months, most recent ferritin level, days since last ferritin measurement, Hb at nth previous visit (n between 1 and 5), days since the nth previous visit. Outcome Hb deferral has two classes: deferred and not deferred. Support vector machines were used as prediction models, and SHapley Additive exPlanations values were used to quantify the contribution of each variable to the model predictions. Performance was assessed using precision and recall. The potential impact on blood supply was estimated by predicting deferral at earlier or later donation dates. RESULTS: We present a model that predicts Hb deferral in an explainable way. If used in practice, 64% of non-deferred donors would be invited on or before their original donation date, while 80% of deferred donors would be invited later. CONCLUSION: By using this model to invite donors, the number of blood bank visits would increase by 15%, while deferral rates would decrease by 60% (currently 3% for women and 1% for men).

Mapping anticipated advantages and disadvantages of implementation of extensive donor genotyping: A focus group approach.

BACKGROUND AND OBJECTIVES: Current genotyping techniques allow typing of all relevant red cell, human leukocyte and platelet antigens in a single analysis. Even genetic markers related to donor health can be added. Implementation of this technology will affect various stakeholders within the transfusion chain. This study aims to systematically map the anticipated advantages and disadvantages of a national rollout of blood group genotyping of donors, which will affect the availability of rare donors and the implementation of an extensively typed blood transfusion policy. MATERIALS AND METHODS: Two focus-group sessions were held with a wide representation of stakeholders, including representatives of donor and patient organisations. A dedicated software tool was used to collect the reflections of participants on genotyping for blood group antigens and extensive matching. Additionally, stakeholders and experts discussed various prepared propositions. All information collected was categorised. RESULTS: From 162 statements collected, 59 statements (36%) were labelled as 'hopes' and 77 (48%) as 'fears'. Twenty-six (16%) statements remained unlabelled. The statements were divided in 18 categories under seven main themes: patient health, genotyping, privacy issues and ethical aspects, donor management, inventory management and logistics, hospital and transfusion laboratory and general aspects. The discussion on the propositions was analysed and summarised. CONCLUSION: Stakeholders believe that a genotyped donor pool can result in a reduction of alloimmunization and higher availability of typed blood products. There are concerns regarding logistics, costs, consent for extended typing, data sharing, privacy issues and donor management. These concerns need to be carefully addressed before further implementation.

A robust autonomous method for blood demand forecasting.

BACKGROUND: Blood supply chain management requires estimates about the demand of blood products. The more accurate these estimates are, the less wastage and fewer shortages occur. While the current literature demonstrates tangible benefits from statistical forecasting approaches, it highlights issues that discourage their use in blood supply chain optimization: there is no single approach that works everywhere, and there are no guarantees that any favorable method performance continues into the future. STUDY DESIGN AND METHODS: We design a novel autonomous forecasting system to solve the aforementioned issues. We show how possible changes in blood demand could affect prediction performance using partly synthetic demand data. We use these data then to investigate the performances of different method selection heuristics. Finally, the performances of the heuristics and single method approaches were compared using historical demand data from Finland and the Netherlands. The development code is publicly accessible. RESULTS: We find that a shift in the demand signal behavior from stochastic to seasonal would affect the relative performances of the methods. Our autonomous system outperforms all examined individual methods when forecasting the synthetic demand series, exhibiting meaningful robustness. When forecasting with real data, the most accurate methods in Finland and in the Netherlands are the autonomous system and the method average, respectively. DISCUSSION: Optimal use of method selection heuristics, as with our autonomous system, may overcome the need to constantly supervise forecasts in anticipation of changes in demand while being sufficiently accurate in the absence of such changes.

Associations Between Symptoms, Donor Characteristics and IgG Antibody Response in 2082 COVID-19 Convalescent Plasma Donors.

Many studies already reported on the association between patient characteristics on the severity of COVID-19 disease outcome, but the relation with SARS-CoV-2 antibody levels is less clear. To investigate this in more detail, we performed a retrospective observational study in which we used the IgG antibody response from 11,118 longitudinal antibody measurements of 2,082 unique COVID convalescent plasma donors. COVID-19 symptoms and donor characteristics were obtained by a questionnaire. Antibody responses were modelled using a linear mixed-effects model. Our study confirms that the SARS-CoV-2 antibody response is associated with patient characteristics like body mass index and age. Antibody decay was faster in male than in female donors (average half-life of 62 versus 72 days). Most interestingly, we also found that three symptoms (headache, anosmia, nasal cold) were associated with lower peak IgG, while six other symptoms (dry cough, fatigue, diarrhoea, fever, dyspnoea, muscle weakness) were associated with higher IgG concentrations.

Predicting vasovagal reactions to a virtual blood donation using facial image analysis.

BACKGROUND: People with needle fear experience not only anxiety and stress but also vasovagal reactions (VVR), including nausea, dizziness, sweating, pallor changes, or even fainting. However, the mechanism behind needle fear and the VVR response are not yet well understood. The aim of our study was to explore whether fluctuations in facial temperature in several facial regions are related to the level of experienced vasovagal reactions, in a simulated blood donation. STUDY DESIGN AND METHODS: We recruited 45 students at Tilburg University and filmed them throughout a virtual blood donation procedure using an Infrared Thermal Imaging (ITI) camera. Participants reported their fear of needles and level of experienced vasovagal reactions. ITI data pre-processing was completed on each video frame by detecting facial landmarks and image alignment before extracting the mean temperature from the six regions of interest. RESULTS: Temperatures of the chin and left and right cheek areas increased during the virtual blood donation. Mixed-effects linear regression showed a significant association between self-reported vasovagal reactions and temperature fluctuations in the area below the nose. DISCUSSION: Our results suggest that the area below the nose may be an interesting target for measuring vasovagal reactions using video imaging techniques. This is the first in a line of studies, which assess whether it is possible to automatically detect levels of fear and vasovagal reactions using facial imaging, from which the development of e-health solutions and interventions can benefit.

Extended red blood cell matching for all transfusion recipients is feasible.

OBJECTIVE: To demonstrate the feasibility and effectiveness of extended matching of red blood cells (RBC) in practice. BACKGROUND: At present, alloimmunisation preventing matching strategies are only applied for specific transfusion recipient groups and include a limited number of RBC antigens. The general assumption is that providing fully matched RBC units to all transfusion recipients is not feasible. In this article we refute this assumption and compute the proportion of alloimmunisation that can be prevented, when all donors and transfusion recipients are typed for A, B, D plus twelve minor blood group antigens (C, c, E, e, K, Fya , Fyb , Jka , Jkb , M, S and s). METHODS: We developed a mathematical model that determines the optimal sequence for antigen matching. The model allows for various matching strategies, issuing policies and inventory sizes. RESULTS: For a dynamic inventory composition (accounting for randomness in the phenotypes supplied and requested) and an antigen identical issuing policy 97% and 94% of alloimmunisation events can be prevented, when respectively one and two RBC units per recipient are requested from an inventory of 1000 units. Although this proportion decreases with smaller inventory sizes, even for an inventory of 60 units almost 50% of all alloimmunisation events can be prevented. CONCLUSION: In case antigen of both donors and recipients are comprehensively typed, extended preventive matching is feasible for all transfusion recipients in practice and will significantly reduce transfusion-induced alloimmunisation and (alloantibody-induced) haemolytic transfusion reactions.

Preventing alloimmunization using a new model for matching extensively typed red blood cells.

BACKGROUND AND OBJECTIVES: Alloimmunization is a well-known adverse event associated with red blood cell (RBC) transfusions, caused by phenotype incompatibilities between donor and patient RBCs that may lead to haemolytic transfusion reactions on subsequent transfusions. Alloimmunization can be prevented by transfusing fully matched RBC units. Advances in RBC genotyping render the extensive typing of both donors and patients affordable in the foreseeable future. However, the exponential increase in the variety of extensively typed RBCs asks for a software-driven selection to determine the 'best product for a given patient'. MATERIALS AND METHODS: We propose the MINimize Relative Alloimmunization Risks (MINRAR) model for matching extensively typed RBC units to extensively typed patients to minimize the risk of alloimmunization. The key idea behind this model is to use antigen immunogenicity to represent the clinical implication of a mismatch. Using simulations of non-elective transfusions in Caucasian donor and patient populations, the effect on the alloimmunization rate of the MINRAR model is compared with that of a baseline model that matches antigens A, B and RhD only. RESULTS: Our simulations show that with the MINRAR model, even for small inventories, the expected number of alloimmunizations can be reduced by 78.3% compared with a policy of only matching on antigens A, B and RhD. Furthermore, a reduction of 93.7% can be achieved when blood is issued from larger inventories. CONCLUSION: Despite an exponential increase in phenotype variety, matching of extensively typed RBCs can be effectively implemented using our MINRAR model, effectuating a substantial reduction in alloimmunization risk without introducing additional outdating or shortages.

Evaluating privacy of individuals in medical data.

Although data protection is compulsory when personal data is shared, there is no systematic method available to evaluate to what extent each individual is at risk of a privacy breach. We use a collection of measures that quantify how much information is needed to uncover sensitive information. Combined with visualization techniques, our approach can be used to perform a detailed privacy analysis of medical data. Because privacy is evaluated per variable, these adjustments can be made while incorporating how likely it is that these variables will be exploited to uncover sensitive information in practice, as is mandatory in the European Union. Additionally, the analysis of privacy can be used to evaluate to what extent knowledge on specific variables in the data can contribute to privacy breaches, which can subsequently guide the use of anonymization techniques, such as generalization.

Not a crystal ball: Mapping opportunities and threats for the future demand of red blood cells in the Netherlands using a scenario approach.

BACKGROUND: As Western blood transfusion practices are changing, there is interest and need in anticipating the future demand of blood products and how a blood establishment can actively prepare for various long-term developments. This article provides an overview of how a scenario approach was used to prioritize key categories of drivers for the future demand of red blood cells and the organizational implications thereof for Sanquin, the Dutch national blood establishment. STUDY DESIGN AND METHODS: Based on previously identified drivers from interviews and a literature review (Step 1), we conducted scenario sessions and a survey to rank a list of drivers ("themes") with its related opportunities and threats (Step 2), to identify mitigating measures per theme through focus groups (Step 3). RESULTS: In Step 2, 10 themes were found that were classified in terms of importance and uncertainty. These were plotted on a two-dimensional graph with an ellipse to indicate the interquartile ranges per theme. Experts rated the top three most important themes to be the blood supply organization, precision medicine, and red blood cell replacements. In Step 3, focus groups identified specific mitigating measures per theme. These measures had parallel ideas, such as the need for an innovative mentality, internal and external communication and collaboration, and building Sanquin's reputation and trust with the public. CONCLUSION: Having identified the most important themes with suggestions for mitigating measures, Sanquin can take steps to become adaptive and proactive. Other blood establishments may also use a scenario approach to create contextualized long-term strategies.

West Nile virus and blood transfusion safety: A European perspective.

BACKGROUND AND OBJECTIVES: There is a growing concern for the transmission of arboviral infections by blood transfusion in Europe. However, no assessment of the risk of transmission through all European blood supplies has been reported. Risk regulations at a European level should take differences in local transmission risk and the risk of transmission by travelling donors into consideration. MATERIALS AND METHODS: A risk model and publicly available tool were developed to calculate the risk of transmission by all European blood supplies for arboviral outbreaks within Europe. Data on individual European blood supplies from Council of Europe reports and inter-European travel data from EUROSTAT were used to populate this model. RESULTS: Each neuroinvasive case of WNV reported in Europe will on average result in 0·43 (95%CI: 0·32-0·55) infected blood product by locally infected donors and 0·010 (95%CI: 0·006-0·015) infected products by travelling donors. On basis of the 1373 neuroinvasive human WNV cases reported in the outbreak of 2018, it is estimated that without safety interventions this outbreak would have resulted in 708 (95%CI: 523-922) infected components derived from resident donors. Noncompliance to European regulations, which requires donor deferral or testing of donors who visited WNV-infected areas, would have resulted in 7.4 (95%CI: 4·7-11·1) infected blood components derived from infectious travelling donors exposed in outbreak areas throughout Europe. CONCLUSION: The risk of WNV transmission by a local outbreak is on average 113 times (95%CI: 95-139), so two orders of magnitude higher than the risk of transmission by travelling donors in Europe.

Reduction of anti-K-mediated hemolytic disease of newborns after the introduction of a matched transfusion policy: A nation-wide policy change evaluation study in the Netherlands.

BACKGROUND: During pregnancy, maternal red blood cell (RBC) antibodies can lead to life-threatening fetal hemolysis and anemia. Women can become immunized by a pregnancy or an unmatched transfusion. Our aim was to quantify the effect of a nationwide K-matched transfusion policy for women of childbearing age potential to prevent K-immunization in pregnancy. STUDY DESIGN AND METHODS: In this nation-wide policy change evaluation study we determined the occurrence of RBC antibodies before and after introduction of a K-matched transfusion policy and evaluated the cause K alloimmunization 10 years after introduction of this measure. K-matched transfusion for females under 45 years of age is advised in the Dutch transfusion guideline since 2004. We used laboratory data from pregnancies with RBC antibodies identified in the period 1999-2018 obtained as part of a population-based screening program in the Netherlands. RESULTS: Tests of 36 286 pregnancies produced a positive antibody screening result which concerned anti-K in 1550 pregnancies. The occurrence of anti-K decreased from 67.9 to 20.2 per 100 000 pregnancies. The relative risk reduction was 0.70 which largely exceeded the relative risk reduction of 0.27 for antibodies against RBC antigens for which no preventive matching is required. The number of pregnancies at risk for anti-K-mediated disease decreased from 9.7 to 4.2 per 100 000 pregnancies. CONCLUSIONS: A K-matched transfusion policy is associated with a major decrease in a number of pregnant women with anti-K and pregnancies at risk for anti-K-mediated disease. A relatively simple measure is now shown to impact prevention of hemolytic disease in the fetus and newborn.

A WHO tool for risk-based decision making on blood safety interventions.

BACKGROUND: Risk-based decision making is increasingly recognized as key to support national blood policy makers and blood operators concerning the implementation of safety interventions, especially to address emerging infectious threats and new technology opportunities. There is an urgent need for practical decision support tools, especially for low- and middle-income countries that may not have the financial or technical capability to develop risk models. WHO supported the development of such a tool for blood safety. The tool enables users to perform both a quantitative Multi-Criteria Decision Assessment and a novel step-by-step qualitative assessment. STUDY DESIGN AND METHODS: This paper summarizes the content, functionalities, and added value of the new WHO tool. A fictitious case study of a safety intervention to reduce the risk of HIV transmission by transfusion was used to demonstrate the use and usefulness of the tool. RESULTS: Application of the tool highlighted strengths and weaknesses of both the quantitative and qualitative approaches. The quantitative approach facilitates assessment of the robustness of the decision but lacks nuances and interpretability especially when multiple constraints are taken into consideration. Conversely, while unable to provide an assessment of robustness, the step-by-step qualitative approach helps structuring the thought process and argumentation for a preferred intervention in a systematic manner. CONCLUSION: The relative strengths and weaknesses of the quantitative and step-by-step qualitative approach to risk-based decision making are complementary and mutually enhancing. A combination of the two approaches is therefore advisable to support the selection of appropriate blood safety interventions for a particular setting.

First results of a ferritin-based blood donor deferral policy in the Netherlands.

BACKGROUND: Whole blood donors are at risk of becoming iron deficient. To monitor iron stores, Sanquin implemented a new deferral policy based on ferritin levels, in addition to the traditional hemoglobin measurements. METHODS: Ferritin levels are determined in every fifth donation, as well as in all first-time donors. Donors with ferritin levels <15 ng/mL (WHO threshold) are deferred for 12 months; those ≥15 and ≤30 ng/mL for 6 months. The first results were analyzed and are presented here. RESULTS: The results show that 25% of women (N = 20151, 95% CI 24%-25%) and 1.6% of men (N = 10391, 95% CI 1.4%-1.8%) have ferritin levels ≤30 ng/mL at their first blood center visit. For repeat (non-first-time) donors, these proportions are higher: 53% of women (N = 28329, 95% CI 52%-54%) and 42% of men (N = 31089, 95% CI 41%-43%). After a 6-month deferral, in 88% of returning women (N = 3059, 95% CI 87%-89%) and 99% of returning men (N = 3736, 95% CI 98%-99%) ferritin levels were ≥15 ng/mL. After a 12-month deferral, in 74% of returning women (N = 486, 95% CI 70%-78%) and 95% of returning men (N = 479, 95% CI 94%-97%) ferritin levels increased to ≥15 ng/mL. CONCLUSION: Deferral of donors whose pre-donation ferritin levels were ≤30 ng/mL might prevent donors from returning with ferritin levels <15 ng/mL. This policy is promising to mitigate effects of repeated donations on iron stores.