Literature DB >> 17867993

A plea for intra-laboratory reference limits. Part 1. General considerations and concepts for determination.

Rainer Haeckel1, Werner Wosniok, Farhad Arzideh.   

Abstract

Accurate results for quantitative procedures can be useless if the reference limits for the interpretation of laboratory results are unreliable. Recent concepts for quality management systems require that laboratories pay more attention to identification and verification of reference limits. Scientific recommendations often claim that each laboratory should determine intra-laboratory reference limits, which should be reviewed periodically. This recommendation is currently neglected by most laboratories; instead they use reference limits from external sources, despite various problems of transference. Prospective and retrospective methods either using or neglecting disease prevalences (polymodal or unimodal concepts, respectively) and applying different statistical approaches for determining reference limits have been described. The various procedures are reviewed with regard to their diagnostic sensitivity, specificity and (non-)efficiency. The present gold standard is the reference limit concept according to IFCC recommendations (a unimodal prospective approach). This concept, together with trueness-based standardization, is the most useful basis for harmonization of the decision-making process with laboratory results, despite complex problems of traceability and transference. This harmonization is at present only achieved for a limited number of analytes for which SI units and traceability can be technically realized. For the majority of measurands in laboratory medicine, much research is still required and results cannot be expected in the near future. For these measurands, a need remains for internal, efficient and simple identification of population-based reference limits. Therefore, newer retrospective concepts were developed that use large data sets from laboratory information systems to derive intra-laboratory reference limits. These approaches appear promising and should be further developed.

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Year:  2007        PMID: 17867993     DOI: 10.1515/CCLM.2007.249

Source DB:  PubMed          Journal:  Clin Chem Lab Med        ISSN: 1434-6621            Impact factor:   3.694


  4 in total

1.  An unsupervised learning method to identify reference intervals from a clinical database.

Authors:  Sarah Poole; Lee Frederick Schroeder; Nigam Shah
Journal:  J Biomed Inform       Date:  2015-12-19       Impact factor: 6.317

2.  Latent class distributional regression for the estimation of non-linear reference limits from contaminated data sources.

Authors:  Tobias Hepp; Jakob Zierk; Manfred Rauh; Markus Metzler; Andreas Mayr
Journal:  BMC Bioinformatics       Date:  2020-11-13       Impact factor: 3.169

3.  Mixture density networks for the indirect estimation of reference intervals.

Authors:  Tobias Hepp; Jakob Zierk; Manfred Rauh; Markus Metzler; Sarem Seitz
Journal:  BMC Bioinformatics       Date:  2022-07-29       Impact factor: 3.307

4.  Performance evaluation of presepsin using a Sysmex HISCL-5000 analyzer and determination of reference interval.

Authors:  Taewon Kang; Jeaeun Yoo; Hyunyu Choi; Seungok Lee; Dong Wook Jekarl; Yonggoo Kim
Journal:  J Clin Lab Anal       Date:  2022-07-23       Impact factor: 3.124

  4 in total

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