Literature DB >> 12564835

Combination of analytical quality specifications based on biological within- and between-subject variation.

Per Hyltoft Petersen1, Callum G Fraser, Lone Jørgensen, Ivan Brandslund, Marta Stahl, Elizabeth Gowans, Jean-Claude Libeer, Carmen Ricós.   

Abstract

At a conference on 'Strategies to Set Global Analytical Quality Specifications in Laboratory Medicine' in Stockholm 1999, a hierarchy of models to set analytical quality specifications was decided. The consensus agreement from the conference defined the highest level as 'evaluation of the effect of analytical performance on clinical outcomes in specific clinical settings' and the second level as 'data based on components of biological variation'. Here, the many proposals for analytical quality specifications based on biological variation are examined and the outcomes of the different models for maximum allowable combined analytical imprecision and bias are illustrated graphically. The following models were investigated. (1) The Cotlove et al. (1970) model defining analytical imprecision (%CVA) in relation to the within-subject biological variation (%CV(W-S)) as: %CVA < or = 0.5 x %CV(W-S) (where %CV is percentage coefficient of variation). (2) The Gowans et al. (1988) concept, which defines a functional relationship between analytical imprecision and bias for the maximum allowable combination of errors for the purpose of sharing common reference intervals. (3) The European Group for the Evaluation of Reagents and Analytical Systems in Laboratory Medicine (EGE Lab) Working Group concept, which combines the Cotlove model with the Gowans concept using the maximal acceptable bias. (4) The External Quality Assessment (EQA) Organizers Working Group concept, which is close to the EGE Lab Working Group concept, but follows the Gowans et al. concept of imprecision up to the limit defined by the model of Cotlove et al. (5) The 'three-level' concept classifying analytical quality into three levels: optimum, desirable and minimum. The figures created clearly demonstrated that the results obtained were determined by the basic assumptions made. When %CV(W-S) is small compared with the population-based coefficient of variation [%CV(P) = (%CV2(W-S) +%CV2(B-S))(1/2)], the EGE Lab and EQA Organizers Working Group concepts become similar. Examples of analytical quality specifications based on biological variations are listed and an application on external quality control is illustrated for plasma creatinine.

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Year:  2002        PMID: 12564835     DOI: 10.1177/000456320203900601

Source DB:  PubMed          Journal:  Ann Clin Biochem        ISSN: 0004-5632            Impact factor:   2.057


  4 in total

1.  Analytical and biological variation of biomarkers of oxidative stress during the menstrual cycle.

Authors:  Richard W Browne; Michael S Bloom; Enrique F Schisterman; Kathy Hovey; Maurizio Trevisan; Chengqing Wu; Aiyi Liu; Jean Wactawski-Wende
Journal:  Biomarkers       Date:  2008-03       Impact factor: 2.658

2.  Clinical Application of Overlapping Confidence Intervals for Monitoring Changes in Serial Clinical Chemistry Test Results.

Authors:  Jooyoung Cho; Dong Min Seo; Young Uh
Journal:  Ann Lab Med       Date:  2020-05       Impact factor: 3.464

3.  Biological variability dominates and influences analytical variance in HPLC-ECD studies of the human plasma metabolome.

Authors:  Yevgeniya I Shurubor; Wayne R Matson; Walter C Willett; Susan E Hankinson; Bruce S Kristal
Journal:  BMC Clin Pathol       Date:  2007-11-12

4.  Evaluation of method performance for oxidative stress biomarkers in urine and biological variations in urine of patients with type 2 diabetes mellitus and diabetic nephropathy.

Authors:  Ergul Belge Kurutas; Yakup Gumusalan; Ali Cetinkaya; Ekrem Dogan
Journal:  Biol Proced Online       Date:  2015-02-02       Impact factor: 3.244

  4 in total

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