Individualized risk trajectories for iron-related adverse outcomes in repeat blood donors

Abstract

Background: Despite a fingerstick hemoglobin requirement and 56-day minimum donation interval, repeat blood donation continues to cause and exacerbate iron deficiency. Study design and methods: Using data from the REDS-II Donor Iron Status Evaluation study, we developed multiclass prediction models to estimate the competing risk of hemoglobin deferral and collecting blood from a donor with sufficient hemoglobin but low or absent underlying iron stores. We compared models developed with and without two biomarkers not routinely measured in most blood centers: ferritin and soluble transferrin receptor. We generated and analyzed individual risk trajectories: estimates of how each donors' risk developed as a function of the time interval until their next donation attempt. Results: With standard biomarkers, the top model had a multiclass area under the receiver operator characteristic curve (AUC) of 77.6% (95% CI 77.3% - 77.8%). With extra biomarkers, multiclass AUC increased to 82.8% (95% CI 82.5% - 83.1%). In the extra biomarkers model, ferritin was the single most important variable, followed by the donation interval. We identified three risk archetypes: fast recoverers (<10% risk of any adverse outcome on post-donation day 56), slow recoverers (>60% adverse outcome risk on day 56 that declines to <35% by day 250), and chronic high-risk (>85% risk of adverse outcome on day 250). Discussion: A longer donation interval reduced estimated risk of iron-related adverse events for most donors, but risk remained high for some. Tailoring safeguards to individual risk estimates could reduce blood collections from donors with low or absent iron stores.

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

“…Currently maximum donation frequencies are three and five per year, which implies that such a target would have a substantial impact on donor availability. The most noticeable difference between the model described in this paper and models that have been (recently) developed for predicting donor outcomes and associated deferral rates, 28,[39][40][41][42] is that the latter are primarily data driven, whereas our predictions are based on the physiological dynamics of the processes driving the change in blood parameters. As a result, the outcome of our model will be likely to provide predictions that are more robust when exploring proposed changes in deferral policy.…”

“…10,43 A model such as presented in this paper could play an important role in preventing low ferritin levels in whole blood donors. Interestingly, Russell et al 41 recently reported that in whole blood donors, the ferritin level was one of the most important biomarkers for predicting low iron status at a subsequent visit, and that this risk may be reduced by increasing donation intervals, further indicating that such an approach may prove successful.…”

Individualized risk trajectories for iron‐related adverse outcomes in repeat blood donors

“…We assessed the models' predictive performance, compared performance with and without the inclusion of two non-routine biomarkers (ferritin and soluble transferrin receptor [STfR]) as features for prediction, and generated and analyzed individual risk profiles for each donor's likelihood of iron-related adverse donation outcomes at their next visit as a function of their donation interval (how long until the donor returns). We have shared all code in a public repository 12 and provide the TRIPOD checklist (Table S4). 13…”

Iron status of blood donors

“…Development of personalized donation intervals requires a balance between the maximal units collected that occur with shorter intervals and low hemoglobin deferrals that decrease with longer intervals. Three recent articles developed computational techniques to determine personal interdonation intervals [69 ▪ ,70,71]. They found considerable variability among donors, justifying this approach.…”