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1000 Titel
  • Clinical accuracy of instrument-based SARS-CoV-2 antigen diagnostic tests: a systematic review and meta-analysis
1000 Autor/in
  1. Manten, Katharina |
  2. Katzenschlager, Stephan |
  3. Brümmer, Lukas E. |
  4. Schmitz, Stephani |
  5. Gaeddert, Mary |
  6. Erdmann, Christian |
  7. Grilli, Maurizio |
  8. Pollock, Nira R. |
  9. Macé, Aurélien |
  10. Erkosar, Berra |
  11. Carmona, Sergio |
  12. Ongarello, Stefano |
  13. Johnson, Cheryl C. |
  14. Sacks, Jilian A. |
  15. Faehling, Verena |
  16. Bornemann, Linus |
  17. Weigand, Markus A. |
  18. Denkinger, Claudia M. |
  19. Yerlikaya, Seda |
1000 Verlag BioMed Central
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-04-29
1000 Erschienen in
1000 Quellenangabe
  • 21(1):99
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12985-024-02371-5 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11059670/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>During the COVID-19 pandemic, antigen diagnostic tests were frequently used for screening, triage, and diagnosis. Novel instrument-based antigen tests (iAg tests) hold the promise of outperforming their instrument-free, visually-read counterparts. Here, we provide a systematic review and meta-analysis of the SARS-CoV-2 iAg tests’ clinical accuracy.</jats:p> </jats:sec><jats:sec> <jats:title>Methods</jats:title> <jats:p>We systematically searched MEDLINE (via PubMed), Web of Science, medRxiv, and bioRxiv for articles published before November 7th, 2022, evaluating the accuracy of iAg tests for SARS-CoV-2 detection. We performed a random effects meta-analysis to estimate sensitivity and specificity and used the QUADAS-2 tool to assess study quality and risk of bias. Sub-group analysis was conducted based on Ct value range, IFU-conformity, age, symptom presence and duration, and the variant of concern.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>We screened the titles and abstracts of 20,431 articles and included 114 publications that fulfilled the inclusion criteria. Additionally, we incorporated three articles sourced from the FIND website, totaling 117 studies encompassing 95,181 individuals, which evaluated the clinical accuracy of 24 commercial COVID-19 iAg tests. The studies varied in risk of bias but showed high applicability. Of 24 iAg tests from 99 studies assessed in the meta-analysis, the pooled sensitivity and specificity compared to molecular testing of a paired NP swab sample were 76.7% (95% CI 73.5 to 79.7) and 98.4% (95% CI 98.0 to 98.7), respectively. Higher sensitivity was noted in individuals with high viral load (99.6% [95% CI 96.8 to 100] at Ct-level ≤ 20) and within the first week of symptom onset (84.6% [95% CI 78.2 to 89.3]), but did not differ between tests conducted as per manufacturer’s instructions and those conducted differently, or between point-of-care and lab-based testing.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>Overall, iAg tests have a high pooled specificity but a moderate pooled sensitivity, according to our analysis. The pooled sensitivity increases with lower Ct-values (a proxy for viral load), or within the first week of symptom onset, enabling reliable identification of most COVID-19 cases and highlighting the importance of context in test selection. The study underscores the need for careful evaluation considering performance variations and operational features of iAg tests.</jats:p> </jats:sec>
1000 Sacherschließung
lokal COVID-19/diagnosis [MeSH]
gnd 1206347392 COVID-19
lokal COVID-19/virology [MeSH]
lokal Humans [MeSH]
lokal Systematic review
lokal COVID-19 Serological Testing/methods [MeSH]
lokal Meta-analysis
lokal COVID-19
lokal Antigens, Viral/immunology [MeSH]
lokal Antigen test
lokal Sensitivity and Specificity [MeSH]
lokal Instrument-based
lokal SARS-CoV-2/immunology [MeSH]
lokal Research
lokal Antigens, Viral/analysis [MeSH]
lokal COVID-19 Testing/methods [MeSH]
lokal SARS-CoV-2
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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  1. Universität Heidelberg |
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    1000 Förderer Universität Heidelberg |
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