AI Article Synopsis

  • A study assessed various automated immunoassays for detecting anti S1-RBD SARS-CoV-2 antibodies following the introduction of a new WHO standard (NIBSC 20/136) aimed at reducing measurement discrepancies.
  • Four different assays were analyzed using samples from vaccinated health workers who had no previous SARS-CoV-2 infections, demonstrating significant differences in antibody concentration measurements across these assays despite standardization efforts.
  • Although using the NIBSC standard improved the comparability of results, it did not eliminate the variability between the different testing methods, indicating ongoing challenges in achieving total measurement consistency.

Article Abstract

Objectives: A few CLIA automated immunoassays for the recognition of anti S1-RBD SARS-CoV-2 antibodies have recently been placed on the market. Preliminary data demonstrate a high correlation between methods but wide differences in absolute concentrations. A new WHO international standard for anti-SARS-CoV-2 immunoglobulin, NIBSC code 20/136, has been recently introduced to reduce the differences. The aim of this study is thus to verify the harmonization made by NIBSC 20/136 on Ab anti S1-RBD measurement on real samples.

Methods: The following assays were studied: LIAISON SARS-CoV-2 TrimericS IgG (DiaSorin); Elecsys anti-SARS-CoV-2 S (ROCHE); Atellica IM SARS-CoV-2 IgG (sCOVG) (Siemens); MAGLUMI SARS-CoV-2 S-RBD IgG (Snibe), measuring 210 samples from 70 health workers with no previous SARS-CoV2 infection, during their Pfizer-BioNTech's BNT162b2 vaccination period.

Results: The recalculation of concentrations based on the NIBSC 20/136 standardization improve the analytical and diagnostic comparability but do not cancel this variability between methods: recalibrated results remain different across methods, both in terms of tendency and single data.

Conclusions: The recalculation of concentrations based on the NIBSC 20/136 standardization improves the analytical and diagnostic comparability but does not cancel the differences between methods: recalibrated results remain different across methods, both in terms of tendency and single data.

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Source
http://dx.doi.org/10.1515/dx-2021-0126DOI Listing

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