Biological variation and analytical goals of four thyroid function biomarkers in healthy European volunteers

Abstract

Background Interpretation of thyroid function tests by means of biological variation (BV) data is essential to identify significant changes between serial measurements at an individual level. Data on thyroid parameters in adults are limited. Objectives We aimed at determining the BV of four thyroid function test (thyroid‐stimulating hormone (TSH), free thyroxin (FT4), free triiodothyronine (FT3) and thyroglobulin (Tg)) by applying recent recommendations to acquire BV data on a latest generation of immunoassay. Methods Nineteen healthy volunteers (8 males and 11 females) were drawn every week during 5 consecutive weeks. Samples were analysed in duplicate on the Cobas 602 analyzer (Roche Diagnostics). After normality assessment, outlier exclusion and homogeneity of variance analysis, analytical variation (CVA), within‐subject biological variation (CVI) and between‐subject biological variation (CVG) were determined using nested ANOVA. Results CVA, CVI and CVG were 0.9%, 19.7% and 37.6% for TSH; 3.6%, 4.6% and 10.8% for FT4; 2.2%, 6.0% and 8.6% for FT3; and 0.9%, 15.4% and 84.9% for Tg. Index of individuality (II) for all parameters was between 0.2 and 0.7. The percentage above which the change between two measures is truly significant (reference change value) was 54.7% for TSH, 16.2% for FT4, 17.7% for FT3 and 42.8% for Tg. Conclusion Based on recent international recommendations, our study provides updated BV data for four thyroid function tests in European healthy volunteers. Reliable BV characteristics, and especially RCV, can facilitate the interpretation of consecutive thyroid function tests in an individual and therefore have the potential to efficiently support clinical decisions regarding thyroid diseases.

European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum thyroid biomarkers based on weekly samplings from 91 healthy participants

“…Moreover, increased serum TG concentrations can also be found in subjects with other thyroid diseases, such as thyroiditis, increased thyroid activity, goiter, or Graves' disease [4,34]. The EuBIVAS CV I estimate for TG (all subjects: 10.3% (95% CI; 9.8-10.9)) is lower than the estimates recently reported by Mairesse et al (15.4%,95% CI;3) [33] and by Feldt-Rasmussen, even if for the latter, 12.8%, is not significantly different based on evaluation of the 95% CI; 10.0-17.1 [35]. EuBIVAS, being highly powered and fully BIVAC-compliant, delivers lower RCVs and stricter APS than those based on non-BIVAC-compliant studies with clinical implications both for monitoring patients and ensuring that analytical methods are fit for purpose.…”

“…Until now 10, seven, and five papers have been published and included in the EFLM database for TSH, FT4, and FT3, respectively [7]. In addition, another study with BV estimates for TSH, FT4, FT3, and TG has recently been published by Mairesse et al [33]. The EuBIVAS provides lower BV estimates than those previously published, which delivers not only stricter APS, but also smaller RCV, which would identify smaller changes as being within the expected variation caused by analytical and biological variation.…”

Suitability System of Microbiological Method for Nystatin Potency Determination in the Routine Analysis Using Agar Diffusion Method

“…Importantly, the validity of the potencies obtained from the assay determinations must be verified through checking tests sources of variability quantitatively of which two of them are pharmacopeial, namely parallelism and regression analysis [25]. Accordingly, a spreadsheet has been adopted for the presumed assay design to be used for regular assay of Nystatin from which there are no outlier variances could be detected and the treatment groups passed the Cochran test [34]. The variance ratios (F-test) for the sources of the test variabilities were tested against a limiting value to evaluate the system suitability for the assay.…”

Artificial intelligence and thyroid disease management

“…Thyroid function tests variability within and between subjects is high with the example of TSH concentrations which can change over the long run inside a person because of different internal and outer elements ( 24 ). Artificial intelligence tools could be seen also as additional options to better estimate and derive biological variability by diving into clinical databases to identify sources of fluctuations for TFT and therefore adjust decision limit accordingly ( 25 ).…”