{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:44:05Z","timestamp":1791909845390,"version":"4.3.4"},"reference-count":22,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transfusion"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Despite a fingerstick hemoglobin requirement and 56\u2010day minimum donation interval, repeat blood donation continues to cause and exacerbate iron deficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study design and methods<\/jats:title>\n                    <jats:p>Using data from the REDS\u2010II 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 \u201cindividual risk trajectories\u201d: estimates of how each donors' risk developed as a function of the time interval until their next donation attempt.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>With standard biomarkers, the top model had a multiclass area under the receiver operator characteristic curve (AUC) of 77.6% (95% CI [77.3%\u201377.8%]). With extra biomarkers, multiclass AUC increased to 82.8% (95% CI [82.5%\u201383.1%]). In the extra biomarkers model, ferritin was the single most important variable, followed by the donation interval. We identified three risk archetypes: \u201cfast recoverers\u201d (&lt;10% risk of any adverse outcome on post\u2010donation day 56), \u201cslow recoverers\u201d (&gt;60% adverse outcome risk on day 56 that declines to &lt;35% by day 250), and \u201cchronic high\u2010risk\u201d (&gt;85% risk of the adverse outcome on day 250).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>A longer donation interval reduced the estimated risk of iron\u2010related 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/trf.16740","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T07:46:09Z","timestamp":1637048769000},"page":"116-124","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Individualized risk trajectories for iron\u2010related adverse outcomes in repeat blood donors"],"prefix":"10.1111","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1780-4470","authenticated-orcid":false,"given":"W. Alton","family":"Russell","sequence":"first","affiliation":[{"name":"Department of Management Science and Engineering Stanford University  Stanford California USA"},{"name":"Epidemiology and Health Policy Science Vitalant Research Institute  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Scheinker","sequence":"additional","affiliation":[{"name":"Department of Management Science and Engineering Stanford University  Stanford California USA"},{"name":"Systems Design and Collaborative Research Lucile Packard Children's Hospital Stanford  Palo Alto California USA"},{"name":"Pediatric Endocrinology Stanford School of Medicine  Palo Alto California USA"},{"name":"Clinical Excellence Research Center Stanford School of Medicine  Palo Alto California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-366X","authenticated-orcid":false,"given":"Brian","family":"Custer","sequence":"additional","affiliation":[{"name":"Epidemiology and Health Policy Science Vitalant Research Institute  San Francisco California USA"},{"name":"Department of Laboratory Medicine University of California  San Francisco California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1537-2995.2011.03401.x"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.12647"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.15133"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1537-2995.2012.03956.x"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.12518"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.15179"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1537-2995.2007.01292.x"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.15136"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1423-0410.2010.01382.x"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1537-2995.2012.03655.x"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/trf.13659"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"RussellWA ScheinkerD CusterB.Altonrus\/iron_trajectories: code repository for individualized risk trajectories for iron\u2010related adverse outcomes in repeat blood donors. 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