Evidence map›Paper›PMID 36367895›Full record

ArticlePloS one2022

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

Denisse Best, Kevin Burrage, Pamela Burrage, Diane Donovan, Shamila Ginige, Tanya Powley, Bevan Thompson, James Daly

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Denisse BestAustralian Red Cross Lifeblood, Brisbane, Queensland, Australia.
Kevin BurrageSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
Pamela BurrageSchool of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.ORCID 0000-0002-6612-3084
Diane DonovanSchool of Mathematics and Physics, University of Queensland, Brisbane, Queensland, Australia.ORCID 0000-0002-1329-3514
Shamila GinigeAustralian Red Cross Lifeblood, Brisbane, Queensland, Australia.ORCID 0000-0002-6513-2114
Tanya PowleyAustralian Red Cross Lifeblood, Brisbane, Queensland, Australia.
Bevan ThompsonSchool of Mathematics and Physics, University of Queensland, Brisbane, Queensland, Australia.
James DalyAustralian Red Cross Lifeblood, Brisbane, Queensland, Australia.ORCID 0000-0002-6008-0386

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Blood DonorsErythrocytesGenotypeHumansModels, TheoreticalPhenotype

Identifiers

PMID36367895
PMCPMC9651592

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.