A Black Swan in clinical laboratory practice: the analytical error due to interferences in immunoassay methods

Aldo Clerico; Lucia Belloni; Cinzia Carrozza; Mario Correale; Ruggero Dittadi; Claudio Dotti; Antonio Fortunato; Giulio Vignati; Gian Carlo Zucchelli; Marco Migliardi; a document endorsed by the Italian Section of the European Ligand Assay Society (ELAS)

doi:10.1515/cclm-2017-0881

Article

A Black Swan in clinical laboratory practice: the analytical error due to interferences in immunoassay methods

Aldo Clerico , Lucia Belloni , Cinzia Carrozza , Mario Correale , Ruggero Dittadi , Claudio Dotti , Antonio Fortunato , Giulio Vignati , Gian Carlo Zucchelli , Marco Migliardi and a document endorsed by the Italian Section of the European Ligand Assay Society (ELAS)

Published/Copyright: December 7, 2017

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From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 56 Issue 3

Abstract

It is well known that the results of immunoassay methods can be affected by specific or non-specific interferences, ranging from 0.4% to 4.0%. The presence of interference may greatly compromise the accuracy of immunoassay analyses causing an error in the measurement, producing false-positive or false-negative results. From a clinical point of view, these analytical errors may have serious implications for patient care because they can cause misdiagnosis or inappropriate treatment. Unfortunately, it is a very difficult task to identify the irregular analytical errors related to immunoassay methods because they are not detectable by normal laboratory quality control procedures, are reproducible within the test system, may be clinically plausible and are relatively rare. The first line of defense against erroneous results is to use in laboratory practice only immunoassay systems with the highest level of robustness against interference. The second line of defense is always taking into account the possibility of interference in immunoassay results. A correct approach should be addressed on identification of samples at high risk of interference. The attainment of this goal requires a critical review of the test result in relation to patient’s clinical conditions and literature data, taking into account the analytical characteristics of the immunoassay system. The experts in immunoassay systems should make every effort to find some specific and reliable quality indicators for irregular analytical errors in order to better detect and monitor erroneous immunoassay results due to specific or non-specific interferences.

Keywords: Bayesian inference analysis; immunoassay methods; interference; patient-centered process; quality control programs; quality control indicators; statistical analysis for quality control

Corresponding author: Prof. Aldo Clerico, MD, Laboratory of Cardiovascular Endocrinology and Cell Biology, Department of Laboratory Medicine, Fondazione CNR Toscana G. Monasterio, Scuola Superiore Sant’Anna, Via Trieste 41, 56126 Pisa, Italy

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Received: 2017-9-29

Accepted: 2017-10-22

Published Online: 2017-12-7

Published in Print: 2018-2-23

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Articles in the same Issue

https://doi.org/10.1515/cclm-2017-0881

Keywords for this article

Bayesian inference analysis; immunoassay methods; interference; patient-centered process; quality control programs; quality control indicators; statistical analysis for quality control