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Allergy: Evaluation of 16 years (2007–2022) results of the shared external quality assessment program in Belgium, Finland, Portugal and The Netherlands

  • Michiel Heron ORCID logo EMAIL logo , Marco W.J. Schreurs , Inez-Anne Haagen , Bernard China , Ana Paula Faria , Anna-Riitta Vanhanen , Marc Thelen ORCID logo and Cas W. Weykamp
Published/Copyright: November 28, 2023
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 62 Issue 9

Abstract

Objectives

This paper evaluates 16 year results of the Allergy EQA program shared by EQA organisers in Belgium, Finland, Portugal, and The Netherlands.

Methods

The performance of Thermo Fisher and Siemens user groups (in terms of concordance between both groups, between laboratory CV, prevalence of clinically significant errors) and suitability of samples (stability and validity of dilution of patient samples) are evaluated using data of 192 samples in the EQA programs from 2007 to 2022. Measurands covered are total IgE, screens and mixes, specific IgE extracts and allergen components.

Results

There is perfect (53 %), acceptable (40 %) and poor (6 %) concordance between both method groups. In case of poor concordance the best fit with clinical data is seen for Thermo Fisher (56 %) and Siemens (26 %) respectively. The between laboratory CV evolves from 7.8 to 6.6 % (Thermo Fisher) and 7.3 to 7.7 % (Siemens). The prevalence of blunders by individual laboratories is stable for Siemens (0.4 %) and drops from 0.4 to 0.2 % for Thermo Fisher users. For IgE, the between year CV of the mean of both user groups is 1 %, and a fifteen-fold dilution of a patient sample has an impact of 2 and 4 % on the recovery of Thermo Fisher and Siemens user groups.

Conclusions

The analytical performance of Thermo Fisher is slightly better than that of Siemens users but the clinical impact of this difference is limited. Stability of the sample and the low impact of dilution on the recovery of measurands demonstrates the suitability for purpose of the EQA program.

Keywords: allergy; between laboratory CV; external quality assessment; Siemens; sample quality; Thermo Fisher
Corresponding author: Michiel Heron, Medical Microbiology and Immunology, Diakonessenhuis, Utrecht, 3582 KE, The Netherlands, E-mail:

  1. Research ethics: The local Institutional Review Board deemed the study exempt from review.

  2. Informed consent: Informed consent was obtained from all individuals included in this study.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: None declared.

  6. Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2023-0862).

Received: 2023-08-07
Accepted: 2023-11-03
Published Online: 2023-11-28
Published in Print: 2024-08-27

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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  2. Editorials
  3. EFLM European Urinalysis Guideline
  4. Clinical Chemistry Laboratory Medicine in the post-acute COVID-19 era
  5. EFLM Guideline
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  30. Biological matrices, reagents and turnaround-time: the full-circle of artificial intelligence in the pre-analytical Phase. Comment on Turcic A, et al., Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. CCLM 2024;62:436–41
https://doi.org/10.1515/cclm-2023-0862
Keywords for this article
allergy; between laboratory CV; external quality assessment; Siemens; sample quality; Thermo Fisher

Articles in the same Issue

  1. Frontmatter
  2. Editorials
  3. EFLM European Urinalysis Guideline
  4. Clinical Chemistry Laboratory Medicine in the post-acute COVID-19 era
  5. EFLM Guideline
  6. The EFLM European Urinalysis Guideline 2023
  7. Review
  8. Approaching sustainability in Laboratory Medicine
  9. Opinion Papers
  10. New reimbursement models to promote better patient outcomes and overall value in laboratory medicine and healthcare
  11. Screening for sickle cell disease: focus on newborn investigations
  12. Genetics and Molecular Diagnostics
  13. The role of Killer immunoglobulin-like receptors (KIRs) in the genetic susceptibility to non-celiac wheat sensitivity (NCWS)
  14. General Clinical Chemistry and Laboratory Medicine
  15. Understanding the limitations of your assay using EQA data with serum creatinine as an example
  16. Add-on testing: stability assessment of 63 biochemical analytes in centrifuged and capped samples stored at 16 °C
  17. Smartphone swabs as an emerging tool for toxicology testing: a proof-of-concept study in a nightclub
  18. Allergy: Evaluation of 16 years (2007–2022) results of the shared external quality assessment program in Belgium, Finland, Portugal and The Netherlands
  19. Cancer Diagnostics
  20. Monoclonal whole IgG impairs both fibrin and thrombin formation: hemostasis and surface plasmon resonance studies
  21. Cardiovascular Diseases
  22. Patients with antiphospholipid syndrome and a first venous or arterial thrombotic event: clinical characteristics, antibody profiles and estimate of the risk of recurrence
  23. Letters to the Editor
  24. Familial dysalbuminemic hyperthyroxinemia coexisting with a Grave’s disease: a Belgian case report
  25. Diagnostic challenge between a frequent polygenic hypocholesterolemia and an unusual Smith Lemli Opitz syndrome related to bi-allelic DHCR7 mutations
  26. First reported co-occurrence of Philadelphia chromosome-positive B-cell acute lymphoblastic leukemia with pseudo Chediak-Higashi anomaly and complex karyotype
  27. Facing the new IVD Regulation 2017/746: Contract Research Organizations (CROs), key partners of IVDs manufacturers for compliance
  28. Comprehensive analysis and clinical case studies on pseudoeosinophilia: insights and implications – unraveling the complexity: analytical approaches and clinical significance
  29. Misdiagnosis of type 2B von Willebrand disease as immune thrombocytopenia in a thrombocytopenic patient
  30. Biological matrices, reagents and turnaround-time: the full-circle of artificial intelligence in the pre-analytical Phase. Comment on Turcic A, et al., Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. CCLM 2024;62:436–41
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