Simulated effects of ferritin screening on C-reactive protein levels in recruited blood donors.

BACKGROUND AND OBJECTIVES: Ferritin is commonly measured to evaluate iron stores in the body. Some countries have added or considered adding ferritin lower bounds to donor eligibility criteria. Ferritin is also elevated by inflammation. The main goal of this study is to estimate how different ferritin cut-offs would affect the proportion of donors with a C-reactive protein (CRP) level over 3 mg/L, which is the decision limit of the highest chronic cardiovascular risk. MATERIALS AND METHODS: To simulate recruitment of new blood donors, we selected participants from two Finnish general population cohorts, namely FINRISK 1997 (n = 5369) and Health 2000 (n = 3278), that would likely fulfil the selection criteria of blood donation. We then calculated the proportion of individuals with high-sensitivity CRP values above 3 mg/L, over a range of ferritin values. RESULTS: We found that for several ferritin cut-offs the proportion of potential donors with CRP > 3 mg/L would rise by a statistically significant amount. The trend was significant and similar for all subgroups but weaker for non-menstruating women as well as men. CONCLUSION: Our results show that screening a population of potential blood donors with ferritin cut-offs raises the number of people with CRP > 3 mg/L within the blood donor population.

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.

Prediction and impact of personalized donation intervals.

BACKGROUND AND OBJECTIVES: Deferral of blood donors due to low haemoglobin (Hb) is demotivating to donors, can be a sign for developing anaemia and incurs costs for blood establishments. The prediction of Hb deferral has been shown to be feasible in a number of studies based on demographic, Hb measurement and donation history data. The aim of this paper is to evaluate how state-of-the-art computational prediction tools can facilitate nationwide personalized donation intervals. MATERIALS AND METHODS: Using donation history data from the last 20 years in Finland, FinDonor blood donor cohort data and blood service Biobank genotyping data, we built linear and non-linear predictors of Hb deferral. Based on financial data from the Finnish Red Cross Blood Service, we then estimated the economic impacts of deploying such predictors. RESULTS: We discovered that while linear predictors generally predict Hb relatively well, they have difficulties in predicting low Hb values. Overall, we found that non-linear or linear predictors with or without genetic data performed only slightly better than a simple cutoff based on previous Hb. However, if any of our deferral prediction methods are used to assign temporary prolongations of donation intervals for females, then our calculations indicate cost savings while maintaining the blood supply. CONCLUSION: We find that even though the prediction accuracy is not very high, the actual use of any of our predictors in blood collection is still likely to bring benefits to blood donors and blood establishments alike.