Evidence map›Paper›PMID 34393596›Full record

ArticleBiochemia medica2021

Estimation of inter-laboratory reference change values from external quality assessment data.

Michael Paal, Katharina Habler, Michael Vogeser

Abstract read
In one paragraph

Article in Biochemia medica, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Michael PaalInstitute of Laboratory Medicine, University Hospital, LMU Munich, Germany.
Katharina HablerInstitute of Laboratory Medicine, University Hospital, LMU Munich, Germany.
Michael VogeserInstitute of Laboratory Medicine, University Hospital, LMU Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIt is common for patients to switch between several healthcare providers. In this context, the long-term follow-up of medical conditions based on laboratory test results obtained from different laboratories is a challenge. The measurement uncertainty in an inter-laboratory context should also be considered in data mining research based on routine results from randomly selected laboratories. As a proof-of-concept study, we aimed at estimating the inter-laboratory reference change value (IL-RCV) for exemplary analytes from publicly available data on external quality assessment (EQA) and biological variation. MATERIALS AND

methodsExternal quality assessment data of the Reference Institute for Bioanalytics (RfB, Bonn, Germany) for serum creatinine, calcium, aldosterone, PSA, and of whole blood HbA1c from campaigns sent out in 2019 were analysed. The median CVs of all EQA participants were calculated based on 8 samples from 4 EQA campaigns

resultsWe estimated IL-RCVs for all exemplary analytes, ranging from 13.3% to 203% for the positive IL-RCV and - 11.8% to - 67.0% for the negative IL-RCV (serum calcium - serum aldosterone), respectively.

conclusionExternal quality assessment data together with data on the biological variation - both freely available - allow the estimation of inter-laboratory RCVs. These differ substantially between different analytes and can help to assess the boundaries of interoperability in laboratory medicine.

Indexed as

Clinical Laboratory TechniquesAldosteroneBlood Chemical AnalysisCalciumCreatinineData CollectionData MiningDecision MakingEquipment DesignGlycated HemoglobinHumansModels, TheoreticalProstate-Specific AntigenQuality ControlReference ValuesReproducibility of ResultsAldosteroneCalciumCreatinineGlycated Hemoglobinhemoglobin A1c protein, humanProstate-Specific Antigenbiological variationexternal quality assessmentinter-laboratorymeasurement uncertaintyreference change values

Identifiers

PMID34393596
PMCPMC8340502

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