COVID-19: Rapid Antigen detection for SARS-CoV-2 by lateral flow assay: a national systematic evaluation for mass-testing
UK COVID-19 Lateral Flow Oversight Team, ; Peto, T.
Show abstract
Lateral flow device (LFD) viral antigen immunoassays have been developed around the world as diagnostic tests for SARS-CoV-2 infection. They have been proposed to deliver an infrastructure-light, cost-economical solution giving results within half an hour. Here we report on standardised laboratory evaluations of LFDs, and for those that met the published criteria, field testing in the Falcon-C19 research study and UK pilots (UK COVID-19 testing centres, hospital, schools, armed forces). 4/64 LFDs so far have desirable performance characteristics (Orient Gene, Deepblue, Abbott and Innova SARS-CoV-2 Antigen Rapid Qualitative Test). All these LFDs have a viral antigen detection of >90% at 100,000 RNA copies/ml. 8951 Innova LFD tests were performed with a kit failure rate of 5.6% (502/8951, 95% CI: 5.1-6.1), false positive rate of 0.32% (22/6954, 95% CI: 0.20-0.48). Viral antigen detection/sensitivity across the sampling cohort when performed by laboratory scientists (156/198, 95% CI 72.4-84.3) was 78.8%. Our results suggest LFDs have promising performance characteristics for mass population testing and can be used to identify infectious positive individuals. The Innova LFD shows good viral antigen detection/sensitivity with excellent specificity, although kit failure rates and the impact of training are potential issues. These results support the expanded evaluation of LFDs, and assessment of greater access to testing on COVID-19 transmission. FundingDepartment of Health and Social Care. University of Oxford. Public Health England Porton Down, Manchester University NHS Foundation Trust, National Institute of Health Research.
Matching journals
The top 10 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SARS-CoV-2 antibody testing in a UK population: detectable IgG for up to 20 weeks post infection 94%
- Meta-Analysis of Robustness of COVID-19 Diagnostic Kits During Early Pandemic 93%
- Screening for SARS-CoV-2 infection in asymptomatic individuals using the Panbio™ COVID-19 Antigen Rapid Test (Abbott) compared to RT-qPCR 92%
Similar papers in this journal
- Performance characteristics of five antigen-detecting rapid diagnostic test (Ag-RDT) for SARS-CoV-2 asymptomatic infection: a head-to-head benchmark comparison 94%
- A highly effective reverse-transcription loop-mediated isothermal amplification (RT-LAMP) assay for the rapid detection of SARS-CoV-2 infection 94%
- Clinical accuracy of SARS-CoV-2 rapid antigen testing in screening children and adolescents in comparison to RT-qPCR, November 2020 to September 2022 93%
Similar papers in this journal
- Implementation and extended evaluation of the Euroimmun Anti-SARS-CoV-2 IgG assay and its contribution to the United Kingdom’s COVID-19 public health response 96%
- Performance of the Cue COVID-19 Molecular Test for Point of Care: Insights from a multi-site clinic service model 95%
- Investigating sensitivity of nasal or throat (ISNOT): A combination of both swabs increases sensitivity of SARS-CoV-2 rapid antigen tests 95%
Similar papers in this journal
- Comparative performance of SARS CoV-2 lateral flow antigen tests demonstrates their utility for high sensitivity detection of infectious virus in clinical specimens 94%
- Comparison of seven commercial SARS-CoV-2 rapid Point-of-Care Antigen tests 92%
- Air and surface sampling for monkeypox virus in UK hospitals 92%
Similar papers in this journal
- Clinical performance evaluation of SARS-CoV-2 rapid antigen testing in point of care usage in comparison to RT-qPCR 94%
- Accuracy of four lateral flow immunoassays for anti SARS-CoV-2 antibodies: a head-to-head comparative study 94%
- SARS-CoV-2 Antigen Rapid Detection Tests: test performance during the COVID-19 pandemic and the impact of COVID-19 vaccination 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.