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

Pilar Fernández-Calle (Lead / Corresponding author), Jorge Díaz-Garzón, William Bartlett, Sverre Sandberg, Federica Braga, Boned Beatriz, Anna Carobene, Abdurrahman Coskun, Elisabet Gonzalez-Lao, Fernando Marques, Carmen Perich, Margarida Simon, Aasne K. Aarsand, ,

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)483-493
Number of pages11
JournalClinical Chemistry and Laboratory Medicine (CCLM)
Volume60
Issue number4
Early online date15 Nov 2021
DOIs
Publication statusPublished - 4 Mar 2022

Keywords

  • Biological Variation Data Critical Appraisal Checklist (BIVAC)
  • biological variation
  • meta-analysis
  • thyroid biomarkers

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