Evidence map›Paper›PMID 26207259›Full record

ArticleThe Lancet. Haematology2015

Integration of red cell genotyping into the blood supply chain: a population-based study.

Willy A Flegel, Jerome L Gottschall, Gregory A Denomme

Open access · greenAbstract read
In one paragraph

Article in The Lancet. Haematology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
6.5field-weighted citation impact, top 3% of its field
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

28 citing papers in PubMed, 69 citations in OpenAlex.

  1. When the available blood supply mismatches the needs of the patient.Blood transfusion = Trasfusione del sangue · 2023
    Article
  2. Article
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  4. Review
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  7. Review
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  10. Next-Generation Sequencing Technologies in Blood Group Typing.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2020
    Review
  11. Article
  12. Red Cell Transfusions in the Genomics Era.Seminars in hematology · 2019
    Review
  13. Article
  14. CompleteBlood advances · 2018
    Article
  15. Article
  16. Extended Donor Typing by Pooled Capillary Electrophoresis: Impact in a Routine Setting.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2018
    Article
  17. Optimized Antigen Matching - Chances and Challenges in Molecular Times.Transfusion medicine and hemotherapy : offizielles Organ der Deutschen Gesellschaft fur Transfusionsmedizin und Immunhamatologie · 2018
    Article
  18. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 3 institutions in 1 country.

Willy A Flegel
Jerome L Gottschall
Gregory A Denomme
Medical College of Wisconsin · USNational Institutes of Health · USVersiti Blood Center of Wisconsin · US

Funding

Intramural NIH HHS Z99 CL999999
6 · The paper itself

Abstract

backgroundWhen problems with compatibility arise, transfusion services often use time-consuming serological tests to identify antigen-negative red cell units for safe transfusion. New methods have made red cell genotyping possible for all clinically relevant blood group antigens. We did mass-scale genotyping of donor blood and provided hospitals with access to a large red cell database to meet the demand for antigen-negative red cell units beyond ABO and Rh blood typing.

methodsWe established a red cell genotype database at the BloodCenter of Wisconsin on July 17, 2010. All self-declared African American, Asian, Hispanic, and Native American blood donors were eligible irrespective of their ABO and Rh type or history of donation. Additionally, blood donors who were groups O, A, and B, irrespective of their Rh phenotype, were eligible for inclusion only if they had a history of at least three donations in the previous 3 years, with one donation in the previous 12 months at the BloodCenter of Wisconsin. We did red cell genotyping with a nanofluidic microarray system, using 32 single nucleotide polymorphisms to predict 42 blood group antigens. An additional 14 antigens were identified via serological phenotype. We monitored the ability of the red cell genotype database to meet demand for compatible blood during 3 years. In addition to the central database at the BloodCenter of Wisconsin, we gave seven hospitals online access to a web-based antigen query portal on May 1, 2013, to help them to locate antigen-negative red cell units in their own inventories.

findingsWe analysed genotype data for 43,066 blood donors. Requests were filled for 5661 (99.8%) of 5672 patient encounters in which antigen-negative red cell units were needed. Red cell genotyping met the demand for antigen-negative blood in 5339 (94.1%) of 5672 patient encounters, and the remaining 333 (5.9%) requests were filled by use of serological data. Using the 42 antigens represented in our red cell genotype database, we were able to fill 14,357 (94.8%) of 15,140 requests for antigen-negative red cell units from hospitals served by the BloodCenter of Wisconsin. In the pilot phase, the seven hospitals identified 71 units from 52 antigen-negative red cell unit requests.

interpretationRed cell genotyping has the potential to transform the way antigen-negative red cell units are provided. An antigen query portal could reduce the need for transportation of blood and serological screening. If this wealth of genotype data can be made easily accessible online, it will help with the supply of affordable antigen-negative red cell units to ensure patient safety.

fundingBloodCenter of Wisconsin Diagnostic Laboratories Strategic Initiative and the NIH Clinical Center Intramural Research Program.

Indexed as

Blood Grouping and CrossmatchingGenotyping TechniquesBlood BanksBlood DonorsBlood Group AntigensErythrocytesGenotypeHumansPolymorphism, Single NucleotideWisconsinBlood Group Antigens

Identifiers

PMID26207259
PMCPMC4508019
OpenAlexW1822814480

What OpenQuestion holds

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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.