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

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 the adverse outcome on day 250). Discussion A longer donation interval reduced the estimated risk of iron-related adverse outcomesfor 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.

Automated summary

This study developed multiclass prediction models to estimate the risk of iron deficiency in repeat blood donors, comparing the use of extra biomarkers like ferritin and soluble transferrin receptor. Results showed that ferritin was the most important variable in predicting adverse outcomes, and identified three risk archetypes based on individual risk trajectories.