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High-resolution pediatric reference intervals for 15 biochemical analytes described using fractional polynomials

  • Jakob Zierk EMAIL logo , Hannsjörg Baum , Alexander Bertram , Martin Boeker , Armin Buchwald , Holger Cario , Jürgen Christoph , Michael C. Frühwald , Hans-Jürgen Groß , Arndt Groening , Thomas Gscheidmeier , Torsten Hoff , Reinhard Hoffmann , Rainer Klauke , Alexander Krebs , Ralf Lichtinghagen , Sabine Mühlenbrock-Lenter , Michael Neumann , Peter Nöllke , Charlotte M. Niemeyer , Hans-Georg Ruf , Udo Steigerwald , Thomas Streichert , Antje Torge , Ayami Yoshimi-Nöllke , Hans-Ulrich Prokosch , Markus Metzler and Manfred Rauh
Published/Copyright: February 10, 2021
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 59 Issue 7

Abstract

Objectives

Assessment of children’s laboratory test results requires consideration of the extensive changes that occur during physiological development and result in pronounced sex- and age-specific dynamics in many biochemical analytes. Pediatric reference intervals have to account for these dynamics, but ethical and practical challenges limit the availability of appropriate pediatric reference intervals that cover children from birth to adulthood. We have therefore initiated the multi-center data-driven PEDREF project (Next-Generation Pediatric Reference Intervals) to create pediatric reference intervals using data from laboratory information systems.

Methods

We analyzed laboratory test results from 638,683 patients (217,883–982,548 samples per analyte, a median of 603,745 test results per analyte, and 10,298,067 test results in total) performed during patient care in 13 German centers. Test results from children with repeat measurements were discarded, and we estimated the distribution of physiological test results using a validated statistical approach (kosmic).

Results

We report continuous pediatric reference intervals and percentile charts for alanine transaminase, aspartate transaminase, lactate dehydrogenase, alkaline phosphatase, γ-glutamyl-transferase, total protein, albumin, creatinine, urea, sodium, potassium, calcium, chloride, anorganic phosphate, and magnesium. Reference intervals are provided as tables and fractional polynomial functions (i.e., mathematical equations) that can be integrated into laboratory information systems. Additionally, Z-scores and percentiles enable the normalization of test results by age and sex to facilitate their interpretation across age groups.

Conclusions

The provided reference intervals and percentile charts enable precise assessment of laboratory test results in children from birth to adulthood. Our findings highlight the pronounced dynamics in many biochemical analytes in neonates, which require particular consideration in reference intervals to support clinical decision making most effectively.

Keywords: continuous reference intervals; data mining; indirect reference intervals; pediatric reference intervals; percentile charts
Corresponding author: Dr. Jakob Zierk, Department of Pediatrics and Adolescent Medicine, University Hospital Erlangen, Loschgestr. 15, 91054Erlangen, Germany; and Center of Medical Information and Communication Technology, University Hospital Erlangen, Erlangen, Germany, Phone: +49 9131/85 33731, Fax: +49 9131/85 35742, E-mail:

Acknowledgments

We thank the members of the German Society for Clinical Chemistry and Laboratory Medicine’s working group on guide limits (“AG Richtwerte der DGKL”) for their valuable input.

  1. Research funding: None declared.

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

  3. Competing interests: Authors state no conflict of interest.

  4. Ethical approval: The study was approved by the Institutional Review Board of the University Hospital Erlangen (reference number 97_17 Bc).

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2020-1371).

Received: 2020-09-09
Accepted: 2021-01-28
Published Online: 2021-02-10
Published in Print: 2021-06-25

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/cclm-2020-1371
Keywords for this article
continuous reference intervals; data mining; indirect reference intervals; pediatric reference intervals; percentile charts

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Machine learning and coagulation testing: the next big thing in hemostasis investigations?
  4. Reviews
  5. Updates on liquid biopsy: current trends and future perspectives for clinical application in solid tumors
  6. The underestimated issue of non-reproducible cardiac troponin I and T results: case series and systematic review of the literature
  7. Opinion Paper
  8. Benefits, limitations and controversies on patient-based real-time quality control (PBRTQC) and the evidence behind the practice
  9. Genetics and Molecular Diagnostics
  10. ctDNA from body fluids is an adequate source for EGFR biomarker testing in advanced lung adenocarcinoma
  11. General Clinical Chemistry and Laboratory Medicine
  12. Incidence, characteristics and outcomes among inpatient, outpatient and emergency department with reported high critical serum potassium values
  13. Clinical usefulness of drug-laboratory test interaction alerts: a multicentre survey
  14. Integrating quality assurance in autoimmunity: the changing face of the automated ANA IIF test
  15. Plasma thiol/disulphide homeostasis changes in patients with restless legs syndrome
  16. Reference Values and Biological Variations
  17. High-resolution pediatric reference intervals for 15 biochemical analytes described using fractional polynomials
  18. Continuous reference intervals for leukocyte telomere length in children: the method matters
  19. Hematology and Coagulation
  20. Using machine learning to identify clotted specimens in coagulation testing
  21. Cardiovascular Diseases
  22. Long term pronostic value of suPAR in chronic heart failure: reclassification of patients with low MAGGIC score
  23. Infectious Diseases
  24. Monocyte distribution width (MDW) parameter as a sepsis indicator in intensive care units
  25. A low level of CD16pos monocytes in SARS-CoV-2 infected patients is a marker of severity
  26. Thrombin generation in patients with COVID-19 with and without thromboprophylaxis
  27. Corrigendum
  28. Applying the concept of uncertainty to the sFlt-1/PlGF cut-offs for diagnosis and prognosis of preeclampsia
  29. Letters to the Editors
  30. Additional approaches for identifying non-reproducible cardiac troponin results
  31. Paediatric reference intervals for ionised calcium – a data mining approach
  32. A case of interference in testosterone, DHEA-S and progesterone measurements by second generation immunoassays
  33. Lack of cross-reactivity between anti-A IgG isoagglutinins and anti-SARS-CoV-2 IgG antibodies
  34. Artefactual bands on urine protein immunofixation gels
  35. A case of methaemoglobinaemia interference on the WDF channel on Sysmex XN-Series analysers
  36. Soluble fms-like tyrosine kinase-1: a potential early predictor of respiratory failure in COVID-19 patients
  37. Serendipitous detection of α1-antitrypsin deficiency: a single institution’s experience over a 32 month period
  38. The activated partial thromboplastin time may not reveal even severe fibrinogen deficiency
  39. Influence of C-reactive protein on thrombin generation assay
  40. Inappropriate extrapolations abound in fecal microbiota research
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