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Updated: Sep 12, 2025
09:12
Measuring Deformability and Red Cell Heterogeneity in Blood by Ektacytometry
Published on: January 12, 2018
14.8K
Measurement uncertainty for practical use - applied in hematology.
Wytze P Oosterhuis1, Abdurrahman Coskun2, Sverre Sandberg3
1EFLM Committee: Practical Guide to Implement Measurement Uncertainty, Milan, Italy; Reinier Haga Medical Diagnostic Center, Delft, the Netherlands.
Summary
This study presents a practical approach to calculating measurement uncertainty (MU) for routine hematological parameters in clinical laboratories. The findings show that MU can be effectively determined and improved within a laboratory conglomerate, ensuring reliable diagnostic testing.
Area of Science:
- Clinical Laboratory Science
- Medical Diagnostics
- Quality Management in Healthcare
Background:
- Accreditation standards mandate monitoring and reporting of measurement uncertainty (MU) in clinical laboratories.
- Current guidelines for MU calculations can be complex, necessitating pragmatic approaches.
- Internal quality control (IQC) data offers a viable basis for MU estimation.
Purpose of the Study:
- To evaluate an alternative, utilitarian approach for calculating MU in a multi-laboratory setting.
- To assess MU for routine hematological parameters using IQC data.
- To identify and address factors contributing to result variability across different measurement systems.
Main Methods:
- Applied a pragmatic MU calculation method utilizing internal quality control (IQC) results.
- Analyzed routine hematological parameters across eleven measurement systems in seven laboratories.
- Employed graphical and variance component analysis to pinpoint sources of divergence.
- Compared calculated MU against established limits based on biological variation (MU-APS).
Main Results:
- Measurement uncertainty (MU) for most hematological tests fell within the MU-APS limits.
- Cellular counting parameters, excluding indices, met biological variation specifications.
- A correction procedure successfully improved MU for a deviating measurement system.
- Factors causing divergent results within the laboratory conglomerate were identified and mitigated.
Conclusions:
- A pragmatic MU calculation approach is feasible for hematological tests in a laboratory conglomerate.
- Internal quality control data provides a reliable basis for MU estimation in routine hematology.
- Identified sources of variation can be addressed to improve measurement system performance and reliability.
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