Evidence map›Paper›PMID 34825380›Full record

ArticleVox sanguinis2022

Prediction and impact of personalized donation intervals.

Jarkko Toivonen, Yrjö Koski, Esa Turkulainen, Femmeke Prinsze, Pietro Della Briotta Parolo, Markus Heinonen, Mikko Arvas

Abstract read
In one paragraph

Article in Vox sanguinis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Study on Predictive Methods for Delayed Blood Donation Due to Low Hemoglobin: Evidence Based on Blood Donation Data in Hangzhou, China.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2026
    Article
  2. Article
  3. Article
  4. Article
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

7 authors.

Jarkko ToivonenFinnish Red Cross Blood Service (FRCBS), Helsinki, Finland.ORCID https://orcid.org/0000-0002-6843-5831
Yrjö KoskiFinnish Red Cross Blood Service (FRCBS), Helsinki, Finland.
Esa TurkulainenFinnish Red Cross Blood Service (FRCBS), Helsinki, Finland.
Femmeke PrinszeSanquin Research, Amsterdam, The Netherlands.
Pietro Della Briotta ParoloInstitute for Molecular Medicine Finland, Helsinki, Finland.
Markus HeinonenDepartment of Computer Science, Aalto University, Helsinki, Finland.
Mikko ArvasFinnish Red Cross Blood Service (FRCBS), Helsinki, Finland.ORCID https://orcid.org/0000-0002-6902-8488

Funding

European Blood Alliance 2019-02Finnish Red Cross Blood Service (FRCBS)
6 · The paper itself

Abstract

BACKGROUND AND

objectivesDeferral of blood donors due to low haemoglobin (Hb) is demotivating to donors, can be a sign for developing anaemia and incurs costs for blood establishments. The prediction of Hb deferral has been shown to be feasible in a number of studies based on demographic, Hb measurement and donation history data. The aim of this paper is to evaluate how state-of-the-art computational prediction tools can facilitate nationwide personalized donation intervals. MATERIALS AND

methodsUsing donation history data from the last 20 years in Finland, FinDonor blood donor cohort data and blood service Biobank genotyping data, we built linear and non-linear predictors of Hb deferral. Based on financial data from the Finnish Red Cross Blood Service, we then estimated the economic impacts of deploying such predictors.

resultsWe discovered that while linear predictors generally predict Hb relatively well, they have difficulties in predicting low Hb values. Overall, we found that non-linear or linear predictors with or without genetic data performed only slightly better than a simple cutoff based on previous Hb. However, if any of our deferral prediction methods are used to assign temporary prolongations of donation intervals for females, then our calculations indicate cost savings while maintaining the blood supply.

conclusionWe find that even though the prediction accuracy is not very high, the actual use of any of our predictors in blood collection is still likely to bring benefits to blood donors and blood establishments alike.

Indexed as

AnemiaHematologic DiseasesBlood DonorsFemaleHematologic TestsHemoglobinsHumansHemoglobinsblood collectiondonor healthhaemoglobin measurement

Identifiers

PMID34825380
PMCPMC9299493

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.