Prediction and impact of personalized donation intervals

Predicting haemoglobin deferral using machine learning models: Can we use the same prediction model across countries?

BACKGROUND AND OBJECTIVES

Personalized donation strategies based on haemoglobin (Hb) prediction models may reduce Hb deferrals and hence costs of donation, meanwhile improving commitment of donors. We previously found that prediction models perform better in validation data with a high Hb deferral rate. We therefore investigate how Hb deferral prediction models perform when exchanged with other blood establishments.

MATERIALS AND METHODS

Donation data from the past 5 years from random samples of 10,000 donors from Australia, Belgium, Finland, the Netherlands and South Africa were used to fit random forest models for Hb deferral prediction. Trained models were exchanged between blood establishments. Model performance was evaluated using the area under the precision-recall curve (AUPR). Variable importance was assessed using SHapley Additive exPlanations (SHAP) values.

RESULTS

Across the validation datasets and exchanged models, the AUPR ranged from 0.05 to 0.43. Exchanged models performed similarly within validation datasets, irrespective of the origin of the training data. Apart from subtle differences, the importance of most predictor variables was similar in all trained models.

CONCLUSION

Our results suggest that Hb deferral prediction models trained in different blood establishments perform similarly within different validation datasets, regardless of the deferral rate of their training data. Models learn similar associations in different blood establishments.

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.

An international comparison of haemoglobin deferral prediction models for blood banking

BACKGROUND AND OBJECTIVES

Blood banks use a haemoglobin (Hb) threshold before blood donation to minimize donors' risk of anaemia. Hb prediction models may guide decisions on which donors to invite, and should ideally also be generally applicable, thus in different countries and settings. In this paper, we compare the outcome of various prediction models in different settings and highlight differences and similarities.

MATERIALS AND METHODS

Donation data of repeat donors from the past 5 years of Australia, Belgium, Finland, the Netherlands and South Africa were used to fit five identical prediction models: logistic regression, random forest, support vector machine, linear mixed model and dynamic linear mixed model. Only donors with five or more donation attempts were included to ensure having informative data from all donors. Analyses were performed for men and women separately and outcomes compared.

RESULTS

Within countries and overall, different models perform similarly well. However, there are substantial differences in model performance between countries, and there is a positive association between the deferral rate in a country and the ability to predict donor deferral. Nonetheless, the importance of predictor variables across countries is similar and is highest for the previous Hb level.

CONCLUSION

The limited impact of model architecture and country indicates that all models show similar relationships between the predictor variables and donor deferral. Donor deferral is found to be better predictable in countries with high deferral rates. Therefore, such countries may benefit more from deferral prediction models than those with low deferral rates.

Validity of donor-reported iron supplementation practices obtained at the time of donation

BACKGROUND

Iron supplementation (IS) improves blood donors' iron stores and allows more frequent blood donation. Understanding the accuracy of self-reported IS is helpful for potential application of IS practices to donor eligibility or donation intervals.

METHODS

Successful whole blood and red cell apheresis donors completed a survey at donation on the use of select dietary supplements. Respondents reporting use of either iron pills (IP) or multivitamins (MV) were invited by email to complete a similar follow-up survey 6-8 weeks later and to provide the quantitative iron content of IS by referring the donor to the pill bottle label. Consistency between baseline and follow-up responses was assessed overall and by pill type and demographic variables.

RESULTS

Of 2444 donors answering the baseline survey, 40% (978) reported MV or IP at donation, 354 of whom completed the follow-up survey. A majority of survey respondents (56%-61%) reported taking iron across the two surveys, and 21%-24% took MV but were uncertain if their pills contained iron. Of 215 reporting IS at baseline, overall concordance at follow-up was 68% and was higher for donors who were female, ≥50-years old, and taking iron as an iron pill rather than in a multivitamin.

CONCLUSION

Consistency of donor responses may be insufficient for use in guiding donor eligibility. Referring donors to their pill bottles was unsuccessful in improving the high frequency of uncertain responses. Incorporating IS into donor eligibility determinations is a complex endeavor that will benefit from careful planning and from post-implementation monitoring.

Donor and non-donor perspectives on receiving information from routine genomic testing of donor blood

BACKGROUND

Genomic testing is already used by blood collection agencies (BCAs) to identify rare blood types and ensure the best possible matching of blood. With ongoing technological developments, broader applications, such as the identification of genetic markers relevant to blood donor health, will become feasible. However, the perspectives of blood donors (and potential blood donors) on routine genomic testing of donor blood are under-researched.

STUDY DESIGN AND METHODS

Eight online Focus Groups were conducted: four with donors and four with non-donors. Participants were presented with three hypothetical scenarios about the current and possible future applications of genomic testing: Performing rare blood type testing; identifying donors with genetic markers associated with iron metabolism; and identifying donors with genetic markers associated with bowel cancer.

RESULTS

Testing to identify rare blood types was perceived to be an appropriate application for the BCA to undertake, while identifying markers associated with iron metabolism and cancer genetic markers were only partially supported. Participants raised concerns about the boundaries of acceptable testing and the implications of testing for privacy, data security, and health insurance. Perspectives of donors and non-donors on all scenarios were similar.

DISCUSSION

The principles of who benefits from genomic testing and the perceived role of BCAs were key in shaping participants' perspectives. Participants generally agreed that testing should be directly related to blood donation or be of benefit to the recipient or donor. Findings indicate that consent and communication are key to the acceptability of current and expanded genomic testing.

Iron status of blood donors

PURPOSE OF REVIEW

This review examines recent research on the prevalence and importance of iron deficiency in blood donors, and on efforts to mitigate it.

RECENT FINDINGS

Premenopausal females, teenagers, and high-frequency donors are at the highest risk for donation-induced iron deficiency, in both high-resource and low-resource settings. The physiology relating iron stores to hemoglobin levels and low hemoglobin deferral is well elucidated in blood donor populations, yet the clinical effects attributable to iron loss in the absence of anemia are challenging to identify. Expanded adoption of ferritin testing is improving donor management but may cause decreases in the blood supply from temporary donor loss. The potential for personalized donor management is emerging with development of computational models that predict individual interdonation intervals that aim to optimize blood collected from each donor while minimizing low hemoglobin deferrals.

SUMMARY

Measures to reduce iron deficiency are available that can be deployed on a standardized or, increasingly, personalized basis. Blood centers, regulators, and donors should continue to evaluate different tactics for addressing this problem, to obtain a balanced approach that is optimal for maintaining adequate collections while safeguarding donor health.

A computational model for prediction of ferritin and haemoglobin levels in blood donors

Blood donors are at risk of iron deficiency anaemia. While this risk is decreased through ferritin-based deferral, ideally ferritin monitoring should also aid in optimising donation frequencies. We extended an existing model of haemoglobin (Hb) synthesis with iron homeostasis and validated the model on a cohort of 300 new donors whose ferritin levels were measured from stored blood samples collected over a 2-year period. We then used the donor's gender, body weight, height, and baseline Hb and ferritin levels to predict subsequent Hb and ferritin levels. The prediction error was within measurement variability in 88% of Hb level predictions and 64% of ferritin level predictions. A sensitivity analysis of the model revealed that baseline ferritin level was the most important in predicting future ferritin levels. Finally, we used the model to calculate the annual donation frequency at which donors would keep their ferritin level >15 ng/ml when measured after donating for 2 years. The mean annual donation frequency would then be 1.9 for women and 4.1 for men. The computational model, requiring baseline values only, can predict future Hb and ferritin levels remarkably well. This enables determination of optimal donation frequencies for individual donors at the start of their donation career.

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.