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This Article Full Text Full Text (PDF) 086371.Supplemental Data All Versions of this Article: clinchem.2007.086371v1 53/7/1343    most recent Submit an electronic Letter to the Editor about this paper View responses Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Rynning, M. Articles by Bolann, B. J. Search for Related Content PubMed PubMed Citation Articles by Rynning, M. Articles by Bolann, B. J. Related Collections Laboratory Management General Clinical Chemistry Informatics and Statistics

A Model for an Uncertainty Budget for Preanalytical Variables in Clinical Chemistry Analyses

¹ Laboratory of Clinical Biochemistry and ² Centre for Clinical Research, Haukeland University Hospital, Helse Bergen HF, Bergen, Norway.
³ Institute of Medicine, University of Bergen, Bergen, Norway.

^aAddress correspondence to this author at: Laboratory of Clinical Biochemistry, Haukeland University Hospital, Helse Bergen HF, Postbox 1, N-5021 Bergen, Norway. Fax 47-55973115; e-mail marit.rynning{at}helse-bergen.no.

Background: We sought a practical method to calculate preanalytical uncertainties. In clinical chemistry measurements, the combined preanalytical uncertainty is a function of the magnitude and probability distribution of the different uncertainty sources and the number of such sources.

Methods: Results from an optimal practice for handling of the blood samples (termed the standard method) were compared with alternative methods that deviate from the standard method but are used in current practice. For categorically distributed uncertainty sources (e.g., use of different kinds of blood tubes), alternative treatments were modeled discretely using a known probability distribution for each alternative. For continuously distributed sources (e.g., clotting time), we assumed a rectangular distribution. We calculated the expectation, variance, and SD of differences between results from current practice and the standard method. We tabulated uncertainty budgets for the differences between current practice and the standard method for each uncertainty source. The expected individual biases and variances were summed to obtain the combined expected bias and variance.

Results: The combined expected bias (SD) for glucose was –0.15 (0.130) mmol/L, with prolonged clotting time giving the greatest contribution. The combined expected bias (SD) for calcium was –0.011 (0.0182) mmol/L, for magnesium 0.006 (0.026) mmol/L, and for creatinine 0.5 (1.81) µmol/L.

Conclusion: By comparing a standard method for preanalytical sample handling to alternative methods used in current practice, and considering the distribution of alternative methods, our modeling approach allows the development of an uncertainty budget for preanalytical variables in clinical chemistry analyses.

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