Evaluation of measurement uncertainty of 11 serum proteins measured by immunoturbidimetric methods according to ISO/TS 20914: a 1-year laboratory data analysis
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Emine Feyza Yurt
,
Medine Alpdemir
und
Mehmet Şeneş
Abstract
Objectives
Measurement uncertainty (MU) plays an important role in the interpretation of laboratory results, but data on serum proteins analyzed by immunoturbidimetry according to ISO/TS 20914 are limited.
Methods
MU of 11 serum proteins, including CRP, RF, ASO, IgG, IgA, IgM, C3, C4, ceruloplasmin, transferrin, and β2-microglobulin, were estimated using 1-year internal quality control (IQC) data obtained from Roche Cobas analyzers. MU was calculated using uncertainty and calibrator uncertainty according to ISO/TS 20914, assuming negligible deviation from external quality assessment data. Analytical performance specification (APS) models were selected according to the EFLM APS selection criteria, and maximum allowable uncertainty (MAU) values were determined based on sources such as EFLM models and literature.
Results
IgA and RF were the only two analytes that met the required and minimum MAU values, respectively, at both IQC levels. MU values for CRP, ceruloplasmin, transferrin, and β2-microglobulin exceeded targets at both levels. MU for C3, C4, IgG, and IgM exceeded the minimum MAU at IQC1 but remained acceptable at IQC2. MU values for ASO were calculated as 10.01 and 7.22 % but could not be evaluated due to a lack of reference data. Assay precision should be improved for CRP, IgG, IgM, ceruloplasmin, transferrin, and β2-microglobulin. Use of updated calibration materials for CRP may help reduce MU.
Conclusions
Maintaining acceptable precision over a long period remains a challenge for serum proteins analyzed by immunoturbidimetry, highlighting the need for methodological improvements and stricter quality monitoring. In this context, MU assessment is crucial.
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Research ethics: The study received approval from the clinical research ethics committee of Ankara Training and Research Hospital (Decision Date and Number: Feb 05, 2025/386).
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: Chat GPT (Open AI) was used for language correction.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2025-0654).
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