Comparison of age- and sex-dependent reference limits derived from distinct sources for metabolic measurands in basic liver diagnostics

Abstract

Background Reference intervals for basic liver laboratory diagnostic rely on manufacturers’ information, remaining unchanged for more than 20 years. This ignores known age and sex dependencies.

Methods We performed a retrospective cross-sectional study to compare the age-dependent distribution of flagged and non-flagged laboratory findings between reference limits from 3 distinct sources: manufacturer, published reference study, and the truncated maximum likelihood method applied on a cohort of inpatients aged 18–100 years. Discordance rates adjusted for the permissible analytical uncertainty are reported for serum levels of albumin (n= 150,550), alkaline phosphatase (n= 433,721), gamma-GT (n=580,012), AST (n= 510,620), and ALT (n= 704,546).

Results The number of flagged findings differed notably between reference intervals compared, except for alkaline phosphatase. AST and alkaline phosphatase increased with age in women. Overall discordance for AP, AST, and ALT remained below 10%, respectively, in both sexes. Albumin decreased with age which led to discordant flags in up to 22% in patients ≥70 years. GGT and ALT peaked in 50–59-year-old men with up to 23.5% and 22.8% discordant flags, respectively.

Conclusion We assessed the impact of different reference limits on liver related laboratory results and found up to 25 % discordant flags. We suggest to further analyse the diagnostic and economic effects of reference limits adapted to the population of interest even for well-established basic liver diagnostics.

Zusammenfassung

Hintergrund Referenzintervalle für die Basis-Leberlabordiagnostik beruhen auf seit über 20 Jahren unveränderten Angaben der Hersteller. Dabei werden bekannte Abhängigkeiten von Alter und Geschlecht ignoriert.

Methoden Eine retrospektive Querschnittsstudie wurde durchgeführt, um die altersabhängige Verteilung von normwertigen/nicht normwertigen Laborbefunden zwischen 3 unterschiedlichen Referenzintervallen zu vergleichen: Herstellerangaben, Referenzstudie und Truncated Maximum-Likelihood-Methode, angewandt auf eine Kohorte stationärer Patienten (18–100 Jahre). Diskordanzraten, angepasst an die zulässige Messunsicherheit, werden für die Serumspiegel von Albumin (n=150.550), alkalische Phosphatase (n=433.721), gamma-GT (n=580.012), AST (n= 510.620) und ALT (n= 704.546) angegeben.

Ergebnisse Die Anzahl nicht normwertiger Befunde unterschied sich deutlich zwischen den verglichenen Referenzintervallen, außer bei alkalischer Phosphatase. AST und alkalische Phosphatase nahmen bei Frauen mit dem Alter zu. Die Gesamtdiskordanz blieb bei beiden Geschlechtern unter 10%. Albumin nahm mit dem Alter ab, was bei Patienten ≥70 Jahren zu einer Diskordanz von bis zu 22% führte. GGT und ALT erreichten ihren Höhepunkt bei 50–59-jährigen Männern mit bis zu 23,5% Diskordanz.

Schlussfolgerung Verwendung unterschiedlicher Referenzgrenzen auf leberbezogene Laborergebnisse ergab bis zu 25% nicht übereinstimmende Befunde. Diagnostische und wirtschaftliche Auswirkungen von an die Zielpopulation angepassten Referenzwerten sollten für die Basisdiagnostik der Leber näher analysiert werden.

Schlüsselwörter

Referenzgrenzen - Querschnittsstudie - Leberdiagnostik - Diskordanzraten

Keywords

Reference limits - cross-sectional study - liver diagnostics - discordance rates

Publication History

Received: 22 September 2022

Accepted after revision: 05 December 2022

Article published online:
09 January 2023

• References

• 1 Clinical and Laboratory Standards Institute. Defining, establishing, and verifying reference intervals in the clinical laboratory; Approved Guideline. EP28-A3, Third edition. In: Clinical and Laboratory Standards. PA: Wayne; 2010
• 2 Jones GRD, Haeckel R, Loh TP. et al. Reference Intervals and Decision Limits. Indirect methods for reference interval determination – review and recommendations. Clin Chem Lab Med 2019; 57: 20-29 DOI: 10.1515/cclm-2018-0073.
• 3 Haeckel R, Wosniok W, Streichert T. 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 DOI: 10.1515/labmed-2020-0131.
• 4 Arzideh F, Wosniok W, Gurr E. et al. A plea for intra-laboratory reference limits. Part 2. A bimodal retrospective concept for determining reference limits from intra-laboratory databases demonstrated by catalytic activity concentrations of enzymes. Clin Chem Lab Med 2007; 45: 1043-1057 DOI: 10.1515/CCLM.2007.250.
• 5 Bundesärztekammer. Richtlinie der Bundesärztekammer zur Qualitätssicherung laboratoriumsmedizinischer Untersuchungen – Rili-BÄK. Dtsch Ärzteblatt Online. 2019; DOI: 10.3238/arztebl.2019.rili_baek_qs_labor20192312.
• 6 Roche Diagnostics GmbH. Package inserts/method sheets. 2022 Accessed November 08, 2022 at: https://dialog1.roche.com
• 7 Rustad P, Simonsson P, Felding P. et al. Nordic Reference Interval Project Bio - bank and Database ( NOBIDA ): a source for future estimation and retrospective evaluation of reference intervals Nordic Reference Interval Project Bio-bank and Database ( NOBIDA ): a source for future estimation and. Scand J Clin Lab Invest 2004; 64: 431-438 DOI: 10.1080/00365510410006694. (PMID: 15223705)
• 8 Arzideh F, Özcürümez M, Albers E. et al. Indirect estimation of reference intervals using first or last results and results from patients without repeated measurements. J Lab Med 2021; 45 DOI: 10.1515/labmed-2020-0149.
• 9 Zierk J, Arzideh F, Rechenauer T. et al. Age- and sex-specific dynamics in 22 hematologic and biochemical analytes from birth to adolescence. Clin Chem 2015; 61: 964-973 DOI: 10.1373/clinchem.2015.239731.
• 10 Yang Q, Lew HY, Peh RHH. et al. An automated and objective method for age partitioning of reference intervals based on continuous centile curves. Pathology 2016; 48: 581-585 DOI: 10.1016/j.pathol.2016.07.002. (PMID: 27567225)
• 11 Haeckel R, Wosniok W, Torge A. et al. Age- and sex-dependent reference intervals for uric acid estimated by the truncated minimum chi-square (TMC) approach, a new indirect method. J Lab Med 2020; 44: 157-163 DOI: 10.1515/labmed-2019-0164.
• 12 Haeckel R, Wosniok W, Arzideh F. Equivalence limits of reference intervals for partitioning of population data. Relevant differences of reference limits. LaboratoriumsMedizin 2016; 40 DOI: 10.1515/labmed-2016-0002.
• 13 Ozarda Y, Ichihara K, Jones G. et al. Comparison of reference intervals derived by direct and indirect methods based on compatible datasets obtained in Turkey. Clin Chim Acta 2021; 520: 186-195 DOI: 10.1016/j.cca.2021.05.030.
• 14 Martinez-Sanchez L, Marques-Garcia F, Ozarda Y. et al. Big data and reference intervals: rationale, current practices, harmonization and standardization prerequisites and future perspectives of indirect determination of reference intervals using routine data. Adv Lab Med / Av en Med Lab 2020; 2: 9-16 DOI: 10.1515/almed-2020-0034.
• 15 International Organization for Standardization (ISO). Medical laboratories – Particular requirements for quality and competence. ISO15189: 2012.
• 16 Hyohdoh Y, Hatakeyama Y, Okuhara Y. A simple method to identify real-world clinical decision intervals of laboratory tests from clinical data. Informatics Med Unlocked 2021; 23: 100512 DOI: 10.1016/j.imu.2021.100512.
• 17 Özçürümez MK, Haeckel R, Gurr E. et al. Determination and verification of reference interval limits in clinical chemistry. Recommendations for laboratories on behalf of the Working Group Guide Limits of the DGKL with respect to ISO Standard 15189 and the Guideline of the German Medical Association on Quality Assurance in Medical Laboratory Examinations (Rili-BAEK). J Lab Med 2019; 43: 127-133 DOI: 10.1515/labmed-2018-0500.
• 18 Jónsson JJ, Kairisto V, Bökman F. et al. Nordic Reference Interval Project(NORIP). 2004 Accessed November 08, 2022 at: http://nyenga.net/norip/index.htm
• 19 R Core Team. R: A Language and Environment for Statistical Computing. 2020 Accessed November 08, 2022 at: https://www.r-project.org/
• 20 Allaire JJ, Gandrud C, Russell K. et al. networkD3: D3 JavaScript Network Graphs from R. 2017 Accessed August 25, 2022 at: https://cran.r-project.org/package=networkD3
• 21 Ma C, Wang X, Wu J. et al. Real-world big-data studies in laboratory medicine: Current status, application, and future considerations. Clin Biochem 2020; 84: 21-30 DOI: 10.1016/j.clinbiochem.2020.06.014.
• 22 Dati F, Schumann G, Thomas L. et al. Consensus of a group of professional societies and diagnostic companies on guidelines for interim reference ranges for 14 proteins in serum based on the standardization against the IFCC/BCR/CAP Reference Material (CRM 470). Eur J Clin Chem Clin Biochem 1996; 34: 517-520 (PMID: 8831057)
• 23 Thomas L, Müller M, Schumann G. et al. Consensus of DGKL and VDGH for interim reference intervals on enzymes in serum. LaboratoriumsMedizin 2005; 29: 301-308 DOI: 10.1515/JLM.2005.041.
• 24 Berg J, Lane V. Pathology Harmony; a pragmatic and scientific approach to unfounded variation in the clinical laboratory. Ann Clin Biochem Int J Lab Med 2011; 48: 195-197 DOI: 10.1258/acb.2011.011078. (PMID: 33023299)
• 25 Tate JR, Sikaris KA, Jones GRD. et al. Harmonising adult and paediatric reference intervals in Australia and New Zealand: an evidence-based approach for establishing a first panel of chemistry analytes. Clin Biochem Rev 2014; 35: 213 (PMID: 25678727)
• 26 Yamamoto Y, Hosogaya S, Osawa S. et al. Nationwide multicenter study aimed at the establishment of common reference intervals for standardized clinical laboratory tests in Japan. Clin Chem Lab Med 2013; 51: 1663-1672 DOI: 10.1515/cclm-2012-0413. (PMID: 23612542)
• 27 Özçürümez MK, Haeckel R. Biological variables influencing the estimation of reference limits. Scand J Clin Lab Invest 2018; 78: 337-345 DOI: 10.1080/00365513.2018.1471617. (PMID: 29764232)
• 28 Ceriotti F, Henny J. “Are my Laboratory Results Normal?” Considerations to be Made Concerning Reference Intervals and Decision Limits. EJIFCC 2008; 19: 106-114 (PMID: 27683305)
• 29 The European Parliament and the Council of the European Union. Regulation (EU) 2017/746 on in vitro diagnostic medical devices. Off J Eur Union. 2017 Accessed August 19, 2022 at: http://data.europa.eu/eli/reg/2017/746/oj
• 30 Ceriotti F, Henny J, Queraltó J. et al. Common reference intervals for aspartate aminotransferase ({AST}), alanine aminotransferase ({ALT}) and $\upgamma$-glutamyl transferase ({GGT}) in serum: results from an {IFCC} multicenter study. Clin Chem Lab Med 2010; 48 DOI: 10.1515/cclm.2010.315.
• 31 Jassam N, Luvai A, Narayanan D. et al. Albumin and calcium reference interval using healthy individuals and a data-mining approach. Ann Clin Biochem 2020; 57: 373-381 DOI: 10.1177/0004563220944204. (PMID: 32646226)
• 32 Coley-Grant D, Herbert M, Cornes MP. et al. The impact of change in albumin assay on reference intervals, prevalence of `hypoalbuminaemia’ and albumin prescriptions. Ann Clin Biochem Int J Lab Med 2015; 53: 112-116 DOI: 10.1177/0004563215599560. (PMID: 26195486)
• 33 Adeli K, Higgins V, Seccombe D. et al. National Survey of Adult and Pediatric Reference Intervals in Clinical Laboratories across Canada: A Report of the CSCC Working Group on Reference Interval Harmonization. Clin Biochem 2017; 50: 925-935 DOI: 10.1016/j.clinbiochem.2017.06.006. (PMID: 28647526)
• 34 Tella SH, Gallagher JC. Prevention and treatment of postmenopausal osteoporosis. J Steroid Biochem Mol Biol 2014; 142: 155-170 DOI: 10.1016/j.jsbmb.2013.09.008. (PMID: 24176761)
• 35 Kim KJ, Hong N, Yu MH. et al. Elevated gamma-glutamyl transpeptidase level is associated with an increased risk of hip fracture in postmenopausal women. Sci Rep 2022; 12 DOI: 10.1038/s41598-022-18453-9.
• 36 Matsui S, Yasui T, Kasai K. et al. Changes of liver enzymes and triglyceride during the menopausal transition in Japanese women. J Obstet Gynaecol (Lahore) 2016; 36: 806-811 DOI: 10.3109/01443615.2016.1154516.
• 37 Brady CW. Liver disease in menopause. World J Gastroenterol 2015; 21: 7613 DOI: 10.3748/wjg.v21.i25.7613. (PMID: 26167064)
• 38 Cieslak KP, Baur O, Verheij J. et al. Liver function declines with increased age. HPB 2016; 18: 691-696 DOI: 10.1016/j.hpb.2016.05.011.
• 39 Kälsch J, Bechmann LP, Heider D. et al. Normal liver enzymes are correlated with severity of metabolic syndrome in a large population based cohort. Sci Rep 2015; 5 DOI: 10.1038/srep13058.