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Updated: Aug 2, 2025
Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
Published on: February 16, 2022
Multicenter clinical evaluation of a novel transcription-mediated amplification assay for SARS-CoV-2 molecular
Miguel Fernández-Huerta1, Paula Salmerón2, Yolanda Hernández-Hermida1
1Microbiology Department, Hospital Universitari Bellvitge, University of Barcelona-IDIBELL, L'Hospitalet de Llobregat, Spain.
Introduction:
The onset and spread of COVID-19 pandemic has forced clinical laboratories to rapidly expand testing capacity for SARS-CoV-2. This study evaluates the clinical performance of the TMA Procleix SARS-CoV-2 assay in comparison to the RT-PCR assay Allplex™ SARS-CoV-2 for the qualitative detection of SARS-CoV-2 RNA.
Methods:
Between November 2020 and February 2021, 610 upper-respiratory specimens received for routine SARS-CoV-2 molecular testing were prospectively collected and selected at the Hospital Universitari Vall d'Hebron and the Hospital Universitari Bellvitge in Barcelona, Spain. All samples were processed in parallel with the TMA and the RT-PCR assays, and results were compared. Discrepancies were retested by an additional RT-PCR method and the clinical history of these patients was reviewed.
Results:
Overall, the level of concordance between both assays was 92.0% (κ, 0.772). Most discordant results (36/38, 94.7%) corresponded to samples testing positive with the TMA assay and negative with the RT-PCR method. Of these discrepant cases, most (28/36, 77.8%) were finally classified as confirmed or probable SARS-CoV-2 cases according to the discrepant analysis.
Conclusion:
In conclusion, the TMA Procleix SARS-CoV-2 assay performed well for the qualitative detection of SARS-CoV-2 RNA in a multisite clinical setting. This novel TMA assay demonstrated a greater sensitivity in comparison to RT-PCR methods for the molecular detection of SARS-CoV-2. This higher sensitivity but also the qualitative feature of this detection of SARS-CoV-2 should be considered when making testing algorithm decisions.
