Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Greaves, Ronda / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter


IMPACT FACTOR 2018: 3.638

CiteScore 2018: 2.44

SCImago Journal Rank (SJR) 2018: 1.191
Source Normalized Impact per Paper (SNIP) 2018: 1.205

Online
ISSN
1437-4331
See all formats and pricing
More options …
Volume 57, Issue 12

Issues

A new indirect estimation of reference intervals: truncated minimum chi-square (TMC) approach

Werner Wosniok / Rainer Haeckel
  • Corresponding author
  • Bremer Zentrum für Laboratoriumsmedizin, Klinikum Bremen Mitte, 28305 Bremen, Germany, Phone: +49 412 273446
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2019-07-04 | DOI: https://doi.org/10.1515/cclm-2018-1341

Abstract

All known direct and indirect approaches for the estimation of reference intervals (RIs) have difficulties in processing very skewed data with a high percentage of values at or below the detection limit. A new model for the indirect estimation of RIs is proposed, which can be applied even to extremely skewed data distributions with a relatively high percentage of data at or below the detection limit. Furthermore, it fits better to some simulated data files than other indirect methods. The approach starts with a quantile-quantile plot providing preliminary estimates for the parameters (λ, μ, σ) of the assumed power normal distribution. These are iteratively refined by a truncated minimum chi-square (TMC) estimation. The finally estimated parameters are used to calculate the 95% reference interval. Confidence intervals for the interval limits are calculated by the asymptotic formula for quantiles, and tolerance limits are determined via bootstrapping. If age intervals are given, the procedure is applied per age interval and a spline function describes the age dependency of the reference limits by a continuous function. The approach can be performed in the statistical package R and on the Excel platform.

Keywords: age partitioning; data mining; indirect reference limits; reference intervals

References

  • 1.

    Haeckel R, Wosniok W, Arzideh F. A plea for intra-laboratory decision limits. Part 1 General consideration and concepts for determining decision limits. Clin Chem Lab Med 2007;45:1033–42.Google Scholar

  • 2.

    Jones GR, Haeckel R, Loh TP, Sikaris K, Streichert Th, Katayev A, et al. Indirect methods for reference interval determination – review and recommendations. Clin Chem Lab Med 2019;57:20–9.Web of ScienceGoogle Scholar

  • 3.

    Ozarda Y. Reference intervals: current status, recent developments and future considerations. Biochem Med 2016;26:5–16.Google Scholar

  • 4.

    Hoffmann RG. Statistics in the practice of medicine. J Am Med Assoc 1963;185:864–73.CrossrefGoogle Scholar

  • 5.

    Hoffmann G, Lichtinghagen R, Wosniok W. Simple estimation of reference intervals from routine laboratory tests. J Lab Med 2016; advanced online publication: DOI: 10.1515/labmed-2015-0104.Google Scholar

  • 6.

    Bhattacharya CG. A simple method of resolution of a distribution into Gaussian components. Biometrics 1967; 23:115–35.PubMedCrossrefGoogle Scholar

  • 7.

    Arzideh F, Wosniok W, Gurr E, Hinsch W, Schumann G, Weinstock N, et al. A plea for intra-laboratory reference limits. Part 2. A bimodal retrospective concept for determining reference intervals from intra-laboratory data bases demonstrated by catalytic activity concentrations of enzymes. Clin Chem Lab Med 2007;45:1043–57.Google Scholar

  • 8.

    Kristiansen S, Friis-Hansen L, Jensen CA, Hansen SI. Validation study on the NORIP LDH reference intervals with a proposed new upper limit. Scand J Clin Lab Invest 2018; in printGoogle Scholar

  • 9.

    Lykkeboe S, Nielsen CG, Christensen PA. Indirect method for validating transference of reference intervals. Clin Chem Lab Med 2018;56:463–70.CrossrefWeb of SciencePubMedGoogle Scholar

  • 10.

    Den Elzen WP, Brouwer A, CobbaerThelen MH, Haagen IA, Cobbaert CM. Number: standardized reference intervals in the Netherlands using a ‘big data’ approach. Clin Chem Lab Med 2019;57:42–56.Web of ScienceGoogle Scholar

  • 11.

    Ozcurumez M, Haeckel R. Biological variables influencing the estimation of reference limits. Scand J Clin Invest Lab Med 2018;78:337–45.CrossrefGoogle Scholar

  • 12.

    Zierk J, Arzideh F, Haeckel R, Rauh M, Metzlerr M, Ganslandt Th, et al. Indirect determination of hematology reference intervals in adult patients on Beckman Coulter UniCell DxH 800 and Abbott CELLDyn Sapphire devices. Clin Chem Lab Med 2019;57:730–9.CrossrefGoogle Scholar

  • 13.

    Sonntag O. Is this normal? – This is normal implication and interpretation of the so-called normal values. J Lab Med 2003;8:302–10.Google Scholar

  • 14.

    Shah SA, Ishihara K, Dherah AJ, Ashavald TF. Reference intervals for 33 biochemical analytes in a healthy Indian population: C-RIDL IFCC initiative. Clin Chem Med Lab 2018;56:2093–103.Web of ScienceCrossrefGoogle Scholar

  • 15.

    Reed AH, Cannon DC, Winkelman JW, Bhasin YP, Henry RJ, Pileggi VJ. Estimation of normal ranges from a controlled sample. Clin Chem 1978;18:57–61.Google Scholar

  • 16.

    Grossi E, Columbo R, Cavuto S, Franzini C. The REALAB project: a new method for the formulation of reference intervals based on current data. Clin Chem 2005;51:1232–40.CrossrefPubMedGoogle Scholar

  • 17.

    Ceriotti F, Henny J, Queralto J, Ziyu S, Özarda Y, Chen B, et al. Common reference intervals for aspartate aminotransferase (AST), alanine aminotransferase (ALT) and γ-glutamyl transferase (GGT) in serum: results from an IFCC multicentre study. Clin Chem Lab Med 2010;48:1593–601.PubMedGoogle Scholar

  • 18.

    Bakan E, Polat H, Ozarda Y, Ozturk N, Baygutalp NK, Umudum FZ, et al. A reference interval study for common biochemical analytes in Eastern Turkey: a comparison of a reference population with laboratory data mining. Biochem Med 2016;26:210–23.Google Scholar

  • 19.

    Biino G, Balduni CL, Casula L, Cavallo P, Vaccargiu S, Parracciani D, et al. Analysis of 12517 inhabitants of a Sardinian geographic isolate reveals that predisposition to thrombocytopenia and thrombocytosis are inherited traits. Haematologia 2011;96:96–101.CrossrefGoogle Scholar

  • 20.

    Balduini CL, Noris P. Platelet count and aging. Haematologica 2014;99:953–5.Web of SciencePubMedCrossrefGoogle Scholar

  • 21.

    Masuch A, Ittermann T, Greinacher A, Lubenow N, Kohlmann Th, Nauck M, et al. High-sensitivity cardiac troponin T: association of creatine kinase with the 99th percentile. Clin Chem 2018;64:973–7.CrossrefWeb of SciencePubMedGoogle Scholar

  • 22.

    Monneret D, Gellerstedt M, Bonnefont-Rousellot D. Determination of age- and sex-specific 99th percentiles for high-sensitive troponin T from patients: an analytical imprecision- and partitioning-based approach. Clin Chem Lab Med 2018;56:818–29.Web of ScienceGoogle Scholar

  • 23.

    Zierk J, Arzideh F, Haeckel R, Rascher W, Rauh M, Metzler M. Indirect determination of pediatric blood count reference intervals. Clin Chem Lab Med 2013;51:863–72.PubMedWeb of ScienceGoogle Scholar

  • 24.

    Haeckel R. The influence of age and other biological variables on the estimation of reference limits of cardiac troponin T. Clin Chem Lab Med 2018;56:685–7.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 25.

    Freeman J, Modarres R. Inverse Box-Cox: the power-normal distribution. Stat Probabil Lett 2006;76:764–72.CrossrefGoogle Scholar

  • 26.

    R Core Team (2017). R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org.

About the article

Received: 2018-12-18

Accepted: 2019-05-19

Published Online: 2019-07-04

Published in Print: 2019-11-26


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

Research funding: None declared.

Employment or leadership: None declared.

Honorarium: None declared.

Competing interests: The funding organization(s) 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.


Citation Information: Clinical Chemistry and Laboratory Medicine (CCLM), Volume 57, Issue 12, Pages 1933–1947, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/cclm-2018-1341.

Export Citation

© 2019 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in