Evidence map›Paper›PMID 33930540›Full record

ArticleInternational journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases2021

Comparative diagnostic performance of rapid antigen detection tests for COVID-19 in a hospital setting.

Bianca Bruzzone, Vanessa De Pace, Patrizia Caligiuri, Valentina Ricucci, Giulia Guarona, Beatrice M Pennati, Simona Boccotti, Andrea Orsi, Alexander Domnich, Giorgio Da Rin and 1 more

Abstract readComparative Study
In one paragraph

Article in International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection.The Cochrane database of systematic reviews · 2022
    Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021
    Article
  16. Article
  17. Review
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Bianca BruzzoneHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy.
Vanessa De PaceHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy.
Patrizia CaligiuriHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy.
Valentina RicucciHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy.
Giulia GuaronaHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy.
Beatrice M PennatiDepartment of Health Sciences, University of Genoa, Genoa, Italy.
Simona BoccottiHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy; Department of Health Sciences, University of Genoa, Genoa, Italy.
Andrea OrsiHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy; Department of Health Sciences, University of Genoa, Genoa, Italy.
Alexander DomnichHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy. Electronic address: alexander.domnich@hsanmartino.it.
Giorgio Da RinLaboratory Medicine, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, 16132 Genoa, Italy.
Giancarlo IcardiHygiene Unit, San Martino Policlinico Hospital - IRCCS for Oncology and Neurosciences, Genoa, Italy; Department of Health Sciences, University of Genoa, Genoa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe availability of accurate and rapid diagnostic tools for COVID-19 is essential for tackling the ongoing pandemic. Our study aimed to quantify the performance of available antigen-detecting rapid diagnostic tests (Ag-RDTs) in a real-world hospital setting.

methodsIn this retrospective analysis, the diagnostic performance of 7 Ag-RDTs was compared with real-time reverse transcription quantitative polymerase chain reaction assay in terms of sensitivity, specificity and expected predictive values.

resultsA total of 321 matched Ag-RDTreal-time reverse transcription quantitative polymerase chain reaction samples were analyzed retrospectively. The overall sensitivity and specificity of the Ag-RDTs was 78.7% and 100%, respectively. However, a wide range of sensitivity estimates by brand (66.0%-93.8%) and cycle threshold (Ct) cut-off values (Ct <25: 96.2%; Ct 30-35: 31.1%) was observed. The optimal Ct cut-off value that maximized sensitivity was 29.

conclusionsThe routine use of Ag-RDTs may be convenient in moderate-to-high intensity settings when high volumes of specimens are tested every day. However, the diagnostic performance of the commercially available tests may differ substantially.

Indexed as

Antigens, ViralCOVID-19COVID-19 Nucleic Acid TestingCOVID-19 Serological TestingHospitalsHumansRetrospective StudiesSARS-CoV-2Sensitivity and SpecificityAntigens, ViralCOVID-19ItalyRapid antigen detection testSARS-CoV-2

Identifiers

PMID33930540
PMCPMC8078031

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.