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Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection

2025· article· en· W4416791286 on OpenAlexaff
Jacqueline Dinnes, Sarah Berhane, Jennifer Walsh, Paul Reidy, Aaron Doherty, Bethany Hillier, Katie Scandrett, D. Hettiarachchi, Fahmida Islam, Yasith Mathangasinghe, Nicholas Nyaaba, Mélissa Taylor, Praveen Weeratunga, Dakshitha Wickramasinghe, Susanna S van Wyk, Jane Cunningham, Clare Davenport, Sabine Dittrich, Devy Emperador, Lotty Hooft, Mariska Leeflang, Matthew D. F. McInnes, René Spijker, Jan Y. Verbakel, Yemisi Takwoingi, Taylor Phillips, Jonathan J Deeks

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2025
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of Ottawa
FundersDepartment of Health and Social CareNational Institute for Health and Care ResearchBirmingham Biomedical Research CentreUniversity Hospitals Birmingham NHS Foundation TrustWorld Health Organization
KeywordsAntigenProtocol (science)Confidence intervalMEDLINEClinical trial

Abstract

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BACKGROUND: Accurate rapid diagnostic tests for SARS-CoV-2 infection could help manage the COVID-19 pandemic by potentially increasing access to testing and speed detection of infection, as well as informing clinical and public health management decisions to reduce transmission. Previous iterations of this review provided clear and conclusive evidence of superior test performance in those experiencing possible signs and symptoms of Covid-19. However, test performance in asymptomatic individuals and sensitivity by setting and indication for testing remains unclear. This is the fourth iteration of this review, first published in 2020. OBJECTIVES: To assess the diagnostic accuracy of rapid, point-of-care antigen tests (Ag-RDTs) for diagnosis of SARS-CoV-2 infection in asymptomatic population groups. SEARCH METHODS: We searched the COVID-19 Open Access Project living evidence database from the University of Bern (which includes daily updates from MEDLINE and Embase and preprints from medRxiv and bioRxiv) on 17 February 2022. We included independent evaluations from national reference laboratories, FIND and the Diagnostics Global Health website. We did not apply language restrictions. SELECTION CRITERIA: We included test accuracy studies of any design that evaluated commercially produced, rapid antigen tests in asymptomatic people tested because of known or suspected contact with SARS-CoV-2 infection, known SARS-CoV-2 infection or known absence of infection, or those who were being screened for infection. We included evaluations of single applications of a test (one test result reported per person). Reference standards for presence or absence of infection were any laboratory-based molecular test (primarily reverse transcription polymerase chain reaction (RT-PCR)). DATA COLLECTION AND ANALYSIS: We used standard screening procedures with three reviewers. Two reviewers independently carried out quality assessment (using the QUADAS-2 tool) and extracted study results. Other study characteristics were extracted by one review author and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test, and pooled data using the bivariate model. We investigated heterogeneity by including indicator variables in the random-effects logistic regression models. We tabulated results by test manufacturer and compliance with manufacturer instructions for use and according to symptom status. MAIN RESULTS: We included 146 study cohorts (described in 130 study reports). The main results relate to 164 evaluations of single test applications including 144,250 unique samples (7104 with confirmed SARS-CoV-2) obtained from asymptomatic or mainly asymptomatic populations. Studies were mainly conducted in Europe (85/146, 58%), and evaluated 41 different commercial antigen assays (test kit). Only six studies compared two or more brands of test. Nearly all studies (96%) used RT-PCR alone to define presence or absence of infection. Risk of bias was high because of participant selection (13, 9%); interpretation of the index test (3, 2%); weaknesses in the reference standard for absence of infection (3, 2%); and participant flow and timing (46, 32%). Characteristics of participants (11, 8%) and index test delivery (117, 80%) differed from the way in which and in whom the test was intended to be used. Estimates of sensitivity varied considerably between studies, with consistently high specificities. Average sensitivity was 55.0% (95% CI 50.9%, 59.0%) and average specificity was 99.5% (95% CI 99.5%, 99.6%) across the 147 evaluations of Ag-RDTs reporting both sensitivity and specificity (149,251 samples, 7636 cases). Average sensitivity was higher when epidemiological exposure to SARS-CoV-2 was suspected (58.6%, 95% CI 51.4% to 65.5%; 43 evaluations; 15,516 samples, 1483 cases) compared to where COVID-19 testing was reported to be widely available to anyone on presentation for testing (53.0%, 95% CI 48.4% to 57.5%; 103 evaluations; 129,032 samples, 5660 cases); however CIs overlapped, limiting the inference that can be drawn from these data. Average specificity was similarly high for both groups (99.4% and 99.6%). Sensitivity was generally lower when used in a screening context (summary values from 40.6% to 42.1% for three of four screening settings) compared to testing asymptomatic individuals at Covid-19 test centres (56.7%) or emergency departments (54.7%). We observed a decline in summary sensitivities as measures of sample viral load decreased. Sensitivity varied between brands. When tests were used according to manufacturer instructions, average sensitivities by brand ranged from 36.3% to 78.8% in asymptomatic participants (14 assays with sufficient data for pooling). None of the assays met the WHO acceptable performance standard for sensitivity (of 80%) based on meta-analysis; however, sensitivities from individual studies (where meta-analysis was not possible) exceeded 80% for three assays. The WHO acceptable performance criterion of 97% specificity was met by all but four assays (based on individual studies or meta-analysis) when tests were used according to manufacturer instructions. At 0.5% prevalence using summary data for asymptomatic people, where testing was widely available and where epidemiological exposure to COVID-19 was suspected, resulting PPVs would be 40% and 33%, meaning that 3 in 5 or 2 in 3 positive results will be false positives, and between 1 in 2 and 2 in 5 cases will be missed. AUTHORS' CONCLUSIONS: Evidence for antigen testing in asymptomatic cohorts has increased considerably since the publication of the previous update of this review. Average sensitivities remain lower for testing of asymptomatic when compared to symptomatic individuals; however, there is an indication that sensitivities may be higher where epidemiological exposure to SARS-CoV-2 is suspected compared to testing any asymptomatic individual regardless of indication. Sensitivities were particularly low when antigen tests were used in screening settings. Assays from different manufacturers also vary in sensitivity, indicating the need for appropriate clinical validation of a particular antigen test in a given intended use setting prior to more widespread deployment. Further research is needed to evaluate the effectiveness of screening programmes at reducing transmission of infection, whether mass screening or targeted approaches, including schools, healthcare setting and traveller screening. FUNDING: This paper presents independent research supported by the NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, and the University of Birmingham. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. REGISTRATION: Protocol (2020) doi: 10.1002/14651858.CD013596.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.131
GPT teacher head0.405
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
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