The Infectious Diseases Society of America Guidelines on the Diagnosis of COVID-19: Antigen Testing (June 2021)
Bibliographic record
Abstract
BACKGROUND: Immunoassays designed to detect SARS-CoV-2 protein antigens are now commercially available. The most widely used tests are rapid lateral flow assays that generate results in approximately 15 minutes for diagnosis at the point-of-care. Higher throughput, laboratory-based SARS-CoV-2 antigen (Ag) assays have also been developed. The overall accuracy of SARS-CoV-2 Ag tests, however, is not well defined. The Infectious Diseases Society of America (IDSA) convened an expert panel to perform a systematic review of the literature and develop best practice guidance related to SARS-CoV-2 Ag testing. This guideline is the third in a series of rapid, frequently updated COVID-19 diagnostic guidelines developed by IDSA. OBJECTIVE: IDSA's goal was to develop evidence-based recommendations or suggestions that assist clinicians, clinical laboratories, patients, public health authorities, administrators and policymakers in decisions related to the optimal use of SARS-CoV-2 Ag tests in both medical and non-medical settings. METHODS: A multidisciplinary panel of infectious diseases clinicians, clinical microbiologists and experts in systematic literature review identified and prioritized clinical questions related to the use of SARS-CoV-2 Ag tests. Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make testing recommendations. RESULTS: The panel agreed on five diagnostic recommendations. These recommendations address antigen testing in symptomatic and asymptomatic individuals as well as assess single versus repeat testing strategies. CONCLUSIONS: Data on the clinical performance of U.S. Food and Drug Administration SARS-CoV-2 Ag tests with Emergency Use Authorization is mostly limited to single, one-time testing versus standard nucleic acid amplification testing (NAAT) as the reference standard. Rapid Ag tests have high specificity and low to modest sensitivity compared to reference NAAT methods. Antigen test sensitivity is heavily dependent on viral load, with differences observed between symptomatic compared to asymptomatic individuals and the time of testing post onset of symptoms. Based on these observations, rapid RT-PCR or laboratory-based NAAT remain the diagnostic methods of choice for diagnosing SARS-CoV-2 infection. However, when molecular testing is not readily available or is logistically infeasible, Ag testing can help identify some individuals with SARS-CoV-2 infection. The overall quality of available evidence supporting use of Ag testing was graded as very low to moderate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".