Literature DB >> 29220884

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

Aldo Clerico1, Lucia Belloni2, Cinzia Carrozza3, Mario Correale4, Ruggero Dittadi5, Claudio Dotti2, Antonio Fortunato6, Giulio Vignati7, Gian Carlo Zucchelli8, Marco Migliardi9.   

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.

Entities:  

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

Mesh:

Year:  2018        PMID: 29220884     DOI: 10.1515/cclm-2017-0881

Source DB:  PubMed          Journal:  Clin Chem Lab Med        ISSN: 1434-6621            Impact factor:   3.694


  3 in total

Review 1.  Errors within the total laboratory testing process, from test selection to medical decision-making - A review of causes, consequences, surveillance and solutions.

Authors:  Cornelia Mrazek; Giuseppe Lippi; Martin H Keppel; Thomas K Felder; Hannes Oberkofler; Elisabeth Haschke-Becher; Janne Cadamuro
Journal:  Biochem Med (Zagreb)       Date:  2020-06-15       Impact factor: 2.313

2.  The Selectivity of Molecularly Imprinted Polymers.

Authors:  Gergely Becskereki; George Horvai; Blanka Tóth
Journal:  Polymers (Basel)       Date:  2021-05-28       Impact factor: 4.329

3.  HMGB1 concentration measurements in trauma patients: assessment of pre-analytical conditions and sample material.

Authors:  William Ottestad; Ingrid N Rognes; Erlend Skaga; Cassandra Frisvoll; Guttorm Haraldsen; Torsten Eken; Peter Lundbäck
Journal:  Mol Med       Date:  2019-12-31       Impact factor: 6.354

  3 in total

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