Home Medicine NUMBER: standardized reference intervals in the Netherlands using a ‘big data’ approach
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NUMBER: standardized reference intervals in the Netherlands using a ‘big data’ approach

  • Wendy P.J. den Elzen EMAIL logo , Nannette Brouwer , Marc H. Thelen , Saskia Le Cessie , Inez-Anne Haagen and Christa M. Cobbaert
Published/Copyright: September 15, 2018
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 57 Issue 1

Abstract

Background

External quality assessment (EQA) programs for general chemistry tests have evolved from between laboratory comparison programs to trueness verification surveys. In the Netherlands, the implementation of such programs has reduced inter-laboratory variation for electrolytes, substrates and enzymes. This allows for national and metrological traceable reference intervals, but these are still lacking. We have initiated a national endeavor named NUMBER (Nederlandse UniforMe Beslisgrenzen En Referentie-intervallen) to set up a sustainable system for the determination of standardized reference intervals in the Netherlands.

Methods

We used an evidence-based ‘big-data’ approach to deduce reference intervals using millions of test results from patients visiting general practitioners from clinical laboratory databases. We selected 21 medical tests which are either traceable to SI or have Joint Committee for Traceability in Laboratory Medicine (JCTLM)-listed reference materials and/or reference methods. Per laboratory, per test, outliers were excluded, data were transformed to a normal distribution (if necessary), and means and standard deviations (SDs) were calculated. Then, average means and SDs per test were calculated to generate pooled (mean±2 SD) reference intervals. Results were discussed in expert meetings.

Results

Sixteen carefully selected clinical laboratories across the country provided anonymous test results (n=7,574,327). During three expert meetings, participants found consensus about calculated reference intervals for 18 tests and necessary partitioning in subcategories, based on sex, age, matrix and/or method. For two tests further evaluation of the reference interval and the study population were considered necessary. For glucose, the working group advised to adopt the clinical decision limit.

Conclusions

Using a ‘big-data’ approach we were able to determine traceable reference intervals for 18 general chemistry tests. Nationwide implementation of these established reference intervals has the potential to improve unequivocal interpretation of test results, thereby reducing patient harm.

Keywords: big data approach; reference intervals; standardization

Acknowledgments

We thank the members of the NUMBER working group for providing anonymous clinical laboratory data and technical information on the laboratory tests, for their active participation in expert meetings and advice on the project outcomes.

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

  2. Research funding: This project is supported by a grant from Stichting Kwaliteitsbewaking Medische Laboratoriumdiagnostiek.

  3. Employment of leadership: None declared.

  4. Honorarium: None declared.

  5. Competing interests: The funding organization 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.

  6. NUMBER working group: Dr. G. den Besten (Isala); Dr. V.M. Bruinenberg (Certe locatie Martini Ziekenhuis); Dr. M.W.H.J. Demmers (Gelre Apeldoorn); Dr. I.M. Dijkstra (Antonius Ziekenhuis); Dr. M.A.M. Frasa (LangeLand Ziekenhuis); Dr.ir. R.L.J.M. Herpers (Bernhoven); Dr.ir. M.B. Kok (Saltro); Dr. J.G. Krabbe (MEDLON); Dr. E. Kusters (HMC Bronovo); Dr. A. Leyte (OLVG); Dr. D. van Loon (Antonius Ziekenhuis); C.H. Meijer-Huissen (SALT); Dr. K. Mohrmann (Star-SHL); T.L. Njo (Saltro); Dr. W.P. Oosterhuis (Zuyderland locatie Heerlen); Dr. M.C.F.J. de Rotte (SCAL Medische Diagnostiek); Dr. R.C. Rotteveel (Atalmedial); Dr. C.H.H. Schoenmakers (Elkerliek Ziekenhuis); Dr. B.A. Wevers (Atalmedial); Dr.ir. J.J. van Zanden (Certe); Dr. J.C. Zant (Zuyderland locatie Heerlen).

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2018-0462).

Received: 2018-04-30
Accepted: 2018-05-21
Published Online: 2018-09-15
Published in Print: 2018-12-19

©2019 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. The Post-Analytical Phase
  3. Terminology, units and reporting – how harmonized do we need to be?
  4. A pragmatic bottom-up approach to harmonize the units of clinical chemistry tests among Belgian clinical laboratories, focusing on immunoassays
  5. Indirect methods for reference interval determination – review and recommendations
  6. 10.1515/cclm-2018-0059
  7. An update report on the harmonization of adult reference intervals in Australasia
  8. 10.1515/cclm-2018-0462
  9. Pediatric and adult reference interval harmonization in Canada: an update
  10. Report formatting in laboratory medicine – a call for harmony
  11. Harmonization of interpretative comments in laboratory hematology reporting: the recommendations of the Working Group on Diagnostic Hematology of the Italian Society of Clinical Chemistry and Clinical Molecular Biology (WGDH-SIBioC)
  12. 10.1515/cclm-2017-1080
  13. An evidence- and risk-based approach to a harmonized laboratory alert list in Australia and New Zealand
  14. Harmonization of units and reference intervals of plasma proteins: state of the art from an External Quality Assessment Scheme
  15. Harmonization activities of Noklus – a quality improvement organization for point-of-care laboratory examinations
  16. Towards harmonization of external quality assessment/proficiency testing in hemostasis
  17. The Post-Post-Analytical Phase
  18. Extra-analytical quality indicators – where to now?
  19. Role of laboratory medicine in collaborative healthcare
  20. 10.1515/cclm-2018-2393
https://doi.org/10.1515/cclm-2018-0462
Keywords for this article
big data approach; reference intervals; standardization

Articles in the same Issue

  1. Frontmatter
  2. The Post-Analytical Phase
  3. Terminology, units and reporting – how harmonized do we need to be?
  4. A pragmatic bottom-up approach to harmonize the units of clinical chemistry tests among Belgian clinical laboratories, focusing on immunoassays
  5. Indirect methods for reference interval determination – review and recommendations
  6. 10.1515/cclm-2018-0059
  7. An update report on the harmonization of adult reference intervals in Australasia
  8. 10.1515/cclm-2018-0462
  9. Pediatric and adult reference interval harmonization in Canada: an update
  10. Report formatting in laboratory medicine – a call for harmony
  11. Harmonization of interpretative comments in laboratory hematology reporting: the recommendations of the Working Group on Diagnostic Hematology of the Italian Society of Clinical Chemistry and Clinical Molecular Biology (WGDH-SIBioC)
  12. 10.1515/cclm-2017-1080
  13. An evidence- and risk-based approach to a harmonized laboratory alert list in Australia and New Zealand
  14. Harmonization of units and reference intervals of plasma proteins: state of the art from an External Quality Assessment Scheme
  15. Harmonization activities of Noklus – a quality improvement organization for point-of-care laboratory examinations
  16. Towards harmonization of external quality assessment/proficiency testing in hemostasis
  17. The Post-Post-Analytical Phase
  18. Extra-analytical quality indicators – where to now?
  19. Role of laboratory medicine in collaborative healthcare
  20. 10.1515/cclm-2018-2393
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