Biological variation estimates of thyroid related measurands – meta-analysis of BIVAC compliant studies

Abstract

Objectives Testing for thyroid disease constitutes a high proportion of the workloads of clinical laboratories worldwide. The setting of analytical performance specifications (APS) for testing methods and aiding clinical interpretation of test results requires biological variation (BV) data. A critical review of published BV studies of thyroid disease related measurands has therefore been undertaken and meta-analysis applied to deliver robust BV estimates. Methods A systematic literature search was conducted for BV studies of thyroid related analytes. BV data from studies compliant with the Biological Variation Data Critical Appraisal Checklist (BIVAC) were subjected to meta-analysis. Global estimates of within subject variation (CVI) enabled determination of APS (imprecision and bias), indices of individuality, and indicative estimates of reference change values. Results The systematic review identified 17 relevant BV studies. Only one study (EuBIVAS) achieved a BIVAC grade of A. Methodological and statistical issues were the reason for B and C scores. The meta-analysis derived CVI generally delivered lower APS for imprecision than the mean CVA of the studies included in this systematic review. Conclusions Systematic review and meta-analysis of studies of BV of thyroid disease biomarkers have enabled delivery of well characterized estimates of BV for some, but not all measurands. The newly derived APS for imprecision for both free thyroxine and triiodothyronine may be considered challenging. The high degree of individuality identified for thyroid related measurands reinforces the importance of RCVs. Generation of BV data applicable to multiple scenarios may require definition using “big data” instead of the demanding experimental approach.

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

“…The results of our study may not be generalizable to other ethnic populations because all subjects within this study were Korean adults. However, this study includes the use of an asymmetric RCV approach proposed by a recent EFLM TG‐BVD publication that identifies different RCVs for rising and falling values of a measurand 11 . The BV estimates of M2BPGi assessed with different statistical models and methods using prospective and retrospective studies could be compared through future studies performed in different ethnic populations 16 .…”

“…However, this study includes the use of an asymmetric RCV approach proposed by a recent EFLM TG‐BVD publication that identifies different RCVs for rising and falling values of a measurand. 11 The BV estimates of M2BPGi assessed with different statistical models and methods using prospective and retrospective studies could be compared through future studies performed in different ethnic populations. 16 Considering that some biological variation may depend on health status and may change in a short time period, future studies with a prospective design and detailed clinical information in association with liver fibrosis are needed to understand more accurate implications of changes in M2BPGi.…”

“…Statistical analysis on data dependency on sex or age was tested using t ‐test and Pearson correlation coefficient. In addition to one‐way ANOVA, asymmetric RCV was calculated according to following formulae from a recent publication of the European Federation of Clinical Chemistry and Laboratory Medicine Working Group on Biological Variation and Task Group for the Biological Variation Database (EFLM TG‐BVD): RCV = 100% (exp (± Z 2 1/2 (CV 2 LnA + CV 2 LnI ) 1/2 )‐1), where CV Ln = (ln (1+CV 2 )) 1/2 and Z = 1.64 (probability level 95%) 11 …”

“…, where CV Ln = (ln (1+CV 2 )) 1/2 and Z = 1.64 (probability level 95%). 11 Analytical performance specifications for M2BPGi measurement derived from biological variation data were investigated using the following calculations. For optimal performance specifications: imprecision CV A <0.25 CV I , bias <0.125…”

Bias in Laboratory Medicine: The Dark Side of the Moon

“…The EFLM BV Working Group has greatly contributed to laboratory test BV. In the last decade, the group has developed checklists [58] and standards [59] for BV studies, measured the BV of numerous analytes [60] using a strict protocol [61], performed meta-analyses of BV data of numerous analytes published in the literature [62][63][64][65][66][67][68][69], and launched a BV database for most laboratory tests [70]. The database is dynamic and is updated when a new paper on BV is published.…”