Indirect determination of biochemistry reference intervals using outpatient data

Luisa Martinez-Sanchez, Christa M Cobbaert, Raymond Noordam, Nannette Brouwer, Albert Blanco-Grau, Yolanda Villena-Ortiz, Marc Thelen, Roser Ferrer-Costa, Ernesto Casis, Francisco Rodríguez-Frias and Wendy P J den Elzen

PLOS ONE, 2022, vol. 17, issue 5, 1-18

Abstract: The aim of this study was to determine reference intervals in an outpatient population from Vall d’Hebron laboratory using an indirect approach previously described in a Dutch population (NUMBER project). We used anonymized test results from individuals visiting general practitioners and analysed during 2018. Analytical quality was assured by EQA performance, daily average monitoring and by assessing longitudinal accuracy between 2018 and 2020 (using trueness verifiers from Dutch EQA). Per test, outliers by biochemically related tests were excluded, data were transformed to a normal distribution (if necessary) and means and standard deviations were calculated, stratified by age and sex. In addition, the reference limit estimator method was also used to calculate reference intervals using the same dataset. Finally, for standardized tests reference intervals obtained were compared with the published NUMBER results. Reference intervals were calculated using data from 509,408 clinical requests. For biochemical tests following a normal distribution, similar reference intervals were found between Vall d’Hebron and the Dutch study. For creatinine and urea, reference intervals increased with age in both populations. The upper limits of Gamma-glutamyl transferase were markedly higher in the Dutch study compared to Vall d’Hebron results. Creatine kinase and uric acid reference intervals were higher in both populations compared to conventional reference intervals. Medical test results following a normal distribution showed comparable and consistent reference intervals between studies. Therefore a simple indirect method is a feasible and cost-efficient approach for calculating reference intervals. Yet, for generating standardized calculated reference intervals that are traceable to higher order materials and methods, efforts should also focus on test standardization and bias assessment using commutable trueness verifiers.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0268522

DOI: 10.1371/journal.pone.0268522

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