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Licensed Unlicensed Requires Authentication Published by De Gruyter February 28, 2017

An approach for estimating measurement uncertainty in medical laboratories using data from long-term quality control and external quality assessment schemes

  • Andrea Padoan EMAIL logo , Giorgia Antonelli , Ada Aita , Laura Sciacovelli and Mario Plebani ORCID logo

Abstract

Background:

The present study was prompted by the ISO 15189 requirements that medical laboratories should estimate measurement uncertainty (MU).

Methods:

The method used to estimate MU included the: a) identification of quantitative tests, b) classification of tests in relation to their clinical purpose, and c) identification of criteria to estimate the different MU components. Imprecision was estimated using long-term internal quality control (IQC) results of the year 2016, while external quality assessment schemes (EQAs) results obtained in the period 2015–2016 were used to estimate bias and bias uncertainty.

Results:

A total of 263 measurement procedures (MPs) were analyzed. On the basis of test purpose, in 51 MPs imprecision only was used to estimate MU; in the remaining MPs, the bias component was not estimable for 22 MPs because EQAs results did not provide reliable statistics. For a total of 28 MPs, two or more MU values were calculated on the basis of analyte concentration levels. Overall, results showed that uncertainty of bias is a minor factor contributing to MU, the bias component being the most relevant contributor to all the studied sample matrices.

Conclusions:

The model chosen for MU estimation allowed us to derive a standardized approach for bias calculation, with respect to the fitness-for-purpose of test results. Measurement uncertainty estimation could readily be implemented in medical laboratories as a useful tool in monitoring the analytical quality of test results since they are calculated using a combination of both the long-term imprecision IQC results and bias, on the basis of EQAs results.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

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Received: 2016-10-6
Accepted: 2017-1-18
Published Online: 2017-2-28
Published in Print: 2017-10-26

©2017 Walter de Gruyter GmbH, Berlin/Boston

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