Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2022
Historique:
received: 27 06 2021
accepted: 13 10 2022
entrez: 11 11 2022
pubmed: 12 11 2022
medline: 16 11 2022
Statut: epublish

Résumé

In the last decade, Australia has experienced an overall decline in red cell demand, but there has been an increased need for phenotyped matched red cells. Lifeblood and mathematicians from Queensland universities have developed a probabilistic model to determine the percentage of the donor panel that would need extended antigen typing to meet this increasing demand, and an estimated timeline to achieve the optimum required phenotyped (genotyped) panel. Mathematical modelling, based on Multinomial distributions, was used to provide guidance on the percentage of typed donor panel needed, based on recent historical blood request data and the current donor panel size. Only antigen combinations determined to be uncommon, but not rare, were considered. Simulations were run to attain at least 95% success percentage. Modelling predicted a target of 38% of the donor panel, or 205,000 donors, would need to be genotyped to meet the current demand. If 5% of weekly returning donors were genotyped, this target would be reached within 12 years. For phenotyping, 35% or 188,000 donors would need to be phenotyped to meet Lifeblood's demand. With the current level of testing, this would take eight years but could be performed within three years if testing was increased to 9% of weekly returning donors. An additional 26,140 returning donors need to be phenotyped annually to maintain this panel. This mathematical model will inform business decisions and assist Lifeblood in determining the level of investment required to meet the desired timeline to achieve the optimum donor panel size.

Identifiants

pubmed: 36367895
doi: 10.1371/journal.pone.0276780
pii: PONE-D-21-20968
pmc: PMC9651592
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0276780

Informations de copyright

Copyright: © 2022 Best et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

Transfus Med Hemother. 2014 Oct;41(5):338-41
pubmed: 25538534
Vox Sang. 2009 Jul;97(1):61-8
pubmed: 19490581
Transfusion. 2019 Feb;59(2):582-592
pubmed: 30451305
Ther Adv Hematol. 2017 Oct;8(10):277-291
pubmed: 29051799
Vox Sang. 2008 Oct;95(3):236-53
pubmed: 19121189
Transfus Med Hemother. 2018 Oct;45(5):331-340
pubmed: 30498411
Transfusion. 2015 Nov;55(11):2610-5; quiz 2609
pubmed: 26094790

Auteurs

Denisse Best (D)

Australian Red Cross Lifeblood, Brisbane, Queensland, Australia.

Kevin Burrage (K)

School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

Pamela Burrage (P)

School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

Diane Donovan (D)

School of Mathematics and Physics, University of Queensland, Brisbane, Queensland, Australia.

Shamila Ginige (S)

Australian Red Cross Lifeblood, Brisbane, Queensland, Australia.

Tanya Powley (T)

Australian Red Cross Lifeblood, Brisbane, Queensland, Australia.

Bevan Thompson (B)

School of Mathematics and Physics, University of Queensland, Brisbane, Queensland, Australia.

James Daly (J)

Australian Red Cross Lifeblood, Brisbane, Queensland, Australia.

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Classifications MeSH