The Infectious Diseases Society of America Guidelines on the Diagnosis of COVID-19: Antigen Testing (January 2023)
Bibliographic record
Abstract
Immunoassays designed to detect SARS-CoV-2 protein antigens (Ag) are commonly used to diagnose COVID-19. The most widely used tests are lateral flow assays that generate results in approximately 15 minutes for diagnosis at the point-of-care. Higher throughput, laboratory-based SARS-CoV-2 Ag assays have also been developed. The number of commercially available SARS-CoV-2 Ag detection tests has increased rapidly, as has the COVID-19 diagnostic literature. 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 an update to the third in a series of frequently updated COVID-19 diagnostic guidelines developed by the IDSA. 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 nonmedical settings. 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. A review of relevant, peer-reviewed published literature was conducted through 1 April 2022. Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology was used to assess the certainty of evidence and make testing recommendations. The panel made 10 diagnostic recommendations that address Ag testing in symptomatic and asymptomatic individuals and assess single versus repeat testing strategies. US Food and Drug Administration (FDA) SARS-CoV-2 Ag tests with Emergency Use Authorization (EUA) have high specificity and low to moderate sensitivity compared with nucleic acid amplification testing (NAAT). Ag test sensitivity is dependent on the presence or absence of symptoms and, in symptomatic patients, on timing of testing after symptom onset. In most cases, positive Ag results can be acted upon without confirmation. Results of point-of-care testing are comparable to those of laboratory-based testing, and observed or unobserved self-collection of specimens for testing yields similar results. Modeling suggests that repeat Ag testing increases sensitivity compared with testing once, but no empirical data were available to inform this question. Based on these observations, rapid RT-PCR or laboratory-based NAAT remain the testing methods of choice for diagnosing SARS-CoV-2 infection. However, when timely molecular testing is not readily available or is logistically infeasible, Ag testing helps identify individuals with SARS-CoV-2 infection. Data were insufficient to make a recommendation about the utility of Ag testing to guide release of patients with COVID-19 from isolation. 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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 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".