Abstract
Objectives
Laboratory information systems typically contain hundreds or even thousands of reference limits stratified by sex and age. Since under these conditions a manual plausibility check is hardly feasible, we have developed a simple algorithm that facilitates this check. An open-source R tool is available as a Shiny application at github.com/SandraKla/Zlog_AdRI.
Methods
Based on the zlog standardization, we can possibly detect critical jumps at the transitions between age groups, regardless of the analytical method or the measuring unit. Its advantage compared to the standard z-value is that means and standard deviations are calculated from the reference limits rather than from the underlying data itself. The purpose of the tool is illustrated by the example of reference intervals of children and adolescents from the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER).
Results
The Shiny application identifies the zlog values, lists them in a colored table format and plots them additionally with the specified reference intervals. The algorithm detected several strong and rapid changes in reference intervals from the neonatal period to puberty. Remarkable jumps with absolute zlog values of more than five were seen for 29 out of 192 reference limits (15.1%). This might be attenuated by introducing shorter time periods or mathematical functions of reference limits over age.
Conclusions
Age-partitioned reference intervals will remain the standard in laboratory routine for the foreseeable future, and as such, algorithmic approaches like our zlog approach in the presented Shiny application will remain valuable tools for testing their plausibility on a wide scale.
-
Research funding: None declared.
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Informed consent: Not applicable.
-
Ethical approval: Not applicable.
References
1. Cadamuro, J, Hillarp, A, Unger, A, von Meyer, A, Bauca, J, Plekhanova, O, et al.. Presentation and formatting of laboratory results: a narrative review on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group “postanalytical phase” (WG-POST). Crit Rev Clin Lab Sci 2021;58:329–53. https://doi.org/10.1080/10408363.2020.1867051.Search in Google Scholar PubMed
2. CLSI. Defining, establishing, and verifying reference intervals in the clinical laboratory; Approved Guideline—Third Edition. CLSI document C28-A3c. Wayne, PA: Clinical and Laboratory Standards Institute; 2008.Search in Google Scholar
3. Zierk, J, Arzideh, F, Rechenauer, T, Haeckel, R, Rascher, W, Metzler, M, et al.. Age- and sex-specific dynamics in 22 hematologic and biochemical analytes from birth to adolescence. Clin Chem 2015;61:964–73. https://doi.org/10.1373/clinchem.2015.239731.Search in Google Scholar PubMed
4. Palm, J, Hoffmann, G, Klawonn, F, Tutarel, O, Palm, H, Holdenrieder, S, et al.. Continuous, complete and comparable NT-proBNP reference ranges in healthy children. Clin Chem Lab Med 2020;58:1509–151. https://doi.org/10.1515/cclm-2019-1185.Search in Google Scholar PubMed
5. Asgari, S, Higgins, V, McCudden, C, Adeli, K. Continuous reference intervals for 38 biochemical markers in healthy children and adolscents: comparison to traditionally partitioned reference intervals. Clin Biochem 2019;73:82–9. https://doi.org/10.1016/j.clinbiochem.2019.08.010.Search in Google Scholar PubMed
6. Hoffmann, G, Klawonn, F, Lichtinghagen, R, Orth, M. The zlog value as a basis for the standardization of laboratory results. J Lab Med 2017;41:23–31. https://doi.org/10.1515/labmed-2017-0135.Search in Google Scholar
7. Colantonio, D, Kyriakopoulou, L, Chan, M, Daly, C, Brinc, D, Venner, A, et al.. Closing the gaps in pediatric laboratory reference intervals: a CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children. Clin Chem 2012;58:854–68. https://doi.org/10.1373/clinchem.2011.177741.Search in Google Scholar PubMed
8. Gibb, S. zlog: z(log) Transformation for Laboratory Measurements. R package version 1.0.0. CRAN; 2021.Search in Google Scholar
9. Hoffmann, G, Lichtinghagen, R, Wosniok, W. Simple estimation of reference intervals from routine laboratory data. J Lab Med 2016;39: English translation pp. 000010151520150104.10.1515/labmed-2015-0104Search in Google Scholar
10. Haeckel, R, Wosniok, W, Postma, T. Quantity quotient reporting. Comparison of various models. Clin Chem Lab Med 2015;53:1921–6. https://doi.org/10.1515/cclm-2015-0101.Search in Google Scholar PubMed
11. Wright, E, Royston, P. A comparison of statistical methods for age-related reference intervals. J Roy Stat Soc: Ser A (Stat Soc) 2008;160:47–69. https://doi.org/10.1111/1467-985X.00045.Search in Google Scholar
12. Zierk, J, Hirschmann, J, Toddenroth, D, Arzideh, F, Haeckel, R, Bertram, A, et al.. Next-generation reference intervals for pediatric hematology. Clin Chem Lab Med 2019;57:1595–607. https://doi.org/10.1515/cclm-2018-1236.Search in Google Scholar PubMed
13. Yang, Q, Lew, H, Peh, R, Metz, M, Loh, T. An automated and objective method for age partitioning of reference intervals based on continuous centile curves. J Pathol 2016;48:581–5. https://doi.org/10.1016/j.pathol.2016.07.002.Search in Google Scholar PubMed
14. Haeckel, R, Wosniok, W, Streichert, T, Members of the Section Guide Limits of the DGKL. Review of potentials and limitations of indirect approaches for estimating reference limits/intervals of quantitative procedures in laboratory medicine. J Lab Med 2021;45:35–53. https://doi.org/10.1515/labmed-2020-0131.Search in Google Scholar
Supplementary Material
The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2022-0688).
© 2022 Walter de Gruyter GmbH, Berlin/Boston
