The European Biological Variation Study (EuBIVAS): a summary report

Abstract

Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered the multicenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10 measurands (Non-HDL Cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.

Biological variation and reference change values of serum Mac‐2–binding protein glycosylation isomer (M2BPGi)

“…Biological variation can be estimated using statistical models of direct methods using prospective studies and indirect methods using retrospective studies with different strengths and limitations 12 . The Working group on Biological Variation of the EFLM generated a prospective study specifically designed to calculate biological variation estimates, the multicenter European Biological Variation Study (EuBIVAS) 13,14 . However, most clinical laboratories have limited resources to design and perform prospective studies to attain quality management using BV estimates 1,2 .…”

“…Variation Study (EuBIVAS). 13,14 However, most clinical laboratories have limited resources to design and perform prospective studies to attain quality management using BV estimates. 1,2 The strength of this study is the novelty of M2BPGi biological variation estimates in healthy subjects.…”

“…12 The Working group on Biological Variation of the EFLM generated a prospective study specifically designed to calculate biological variation estimates, the multicenter European Biological Variation Study (EuBIVAS). 13 , 14 However, most clinical laboratories have limited resources to design and perform prospective studies to attain quality management using BV estimates. 1 , 2 The strength of this study is the novelty of M2BPGi biological variation estimates in healthy subjects.…”

Moving average procedures as an additional tool for real-time analytical quality control

“…When we talk about selecting TEa from different sources, we used CLIA data because we have already used it for calculating Sigma metrics in our laboratory. There are, of course, more stringent specifications of analytical performance goals, but other researchers have already concluded that MA procedures cannot detect biological variation-based TEa ( 9 , 15 , 17 ). Nevertheless, we checked this, and found that minimum TEa based on biological variation could be detected by our optimized MA procedures within the daily test production – always for AST and cholesterol and in about 50% of cases for albumin, total protein and sodium (for the last two, only for positive bias).…”

“…Additionally, the ability of an optimized MA procedure to detect bias the size of minimum TEa based on biological variation was read from MA validation charts. Minimum TEa was calculated using the formula: TE a = 1.65 (0.75 x CV i ) + 0.375 (CV i 2 + CV g 2 ) 1/2 , where CV i is intraindividual variation and CV g interindividual variation, for which data were taken from the EuBIVAS summary report ( 17 ). Due to the complexity of calculations that are necessary if a laboratory decides to use this type of quality control, a software is needed which performs all the calculations for each MA procedure, then allows their comparison to select the optimal one, and also provides the possibility of MA procedure validation ( 4 , 18 ).…”