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Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size
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 th...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651592/ https://www.ncbi.nlm.nih.gov/pubmed/36367895 http://dx.doi.org/10.1371/journal.pone.0276780 |
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author | Best, Denisse Burrage, Kevin Burrage, Pamela Donovan, Diane Ginige, Shamila Powley, Tanya Thompson, Bevan Daly, James |
author_facet | Best, Denisse Burrage, Kevin Burrage, Pamela Donovan, Diane Ginige, Shamila Powley, Tanya Thompson, Bevan Daly, James |
author_sort | Best, Denisse |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9651592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-96515922022-11-15 Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size Best, Denisse Burrage, Kevin Burrage, Pamela Donovan, Diane Ginige, Shamila Powley, Tanya Thompson, Bevan Daly, James PLoS One Research Article 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. Public Library of Science 2022-11-11 /pmc/articles/PMC9651592/ /pubmed/36367895 http://dx.doi.org/10.1371/journal.pone.0276780 Text en © 2022 Best et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Best, Denisse Burrage, Kevin Burrage, Pamela Donovan, Diane Ginige, Shamila Powley, Tanya Thompson, Bevan Daly, James Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size |
title | Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size |
title_full | Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size |
title_fullStr | Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size |
title_full_unstemmed | Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size |
title_short | Probabilistic mathematical modelling to predict the red cell phenotyped donor panel size |
title_sort | probabilistic mathematical modelling to predict the red cell phenotyped donor panel size |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651592/ https://www.ncbi.nlm.nih.gov/pubmed/36367895 http://dx.doi.org/10.1371/journal.pone.0276780 |
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