Infectious Diseases Society of America Guidelines on the Diagnosis of Coronavirus Disease 2019: Serologic Testing
Bibliographic record
Abstract
BACKGROUND: The role of serologic testing for SARS-CoV-2 has evolved during the pandemic as seroprevalence in global populations has increased. The Infectious Diseases Society of America (IDSA) convened an expert panel to perform a systematic review of the coronavirus disease 2019 (COVID-19) serology literature and construct updated best practice guidance related to SARS-CoV-2 serologic testing. This guideline is an update to the fourth in a series of rapid, frequently updated COVID-19 guidelines developed by IDSA. OBJECTIVE: To develop evidence-based recommendations and identify unmet research needs pertaining to the use of anti-SARS-CoV-2 antibody tests for diagnosis, decisions related to vaccination and administration of monoclonal antibodies or convalescent plasma in immunocompromised patients, and identification of a serologic correlate of immunity. METHODS: A multidisciplinary panel of infectious diseases clinicians, clinical microbiologists and experts in systematic literature reviewed, identified, and prioritized clinical questions related to the use of SARS-CoV-2 serologic 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 recommends against serologic testing to diagnose SARS-CoV-2 infection in the first two weeks after symptom onset (strong recommendations, low certainty of evidence). Serologic testing should not be used to provide evidence of COVID-19 in symptomatic patients with a high clinical suspicion and repeatedly negative nucleic acid amplification test results (strong recommendation, very low certainty of evidence). Serologic testing may assist with the diagnosis of multisystem inflammatory syndrome in children (strong recommendation, very low certainty of evidence). To seek evidence for prior SARS-CoV-2 infection, the panel suggests testing for IgG, IgG/IgM, or total antibodies to nucleocapsid protein three to five weeks after symptom onset (conditional recommendation, low certainty of evidence). In individuals with previous SARS-CoV-2 infection or vaccination, we suggest against routine serologic testing given no demonstrated benefit to improving patient outcomes (conditional recommendation, very low certainty of evidence.) The panel acknowledges further that a negative spike antibody test may be a useful metric to identify immunocompromised patients who are candidates for immune therapy. CONCLUSIONS: The high seroprevalence of antibodies against SARS-CoV-2 worldwide limits the utility of detecting anti-SARS CoV-2 antibody. The certainty of available evidence supporting the use of serology for diagnosis was graded as very low to low. Future studies should use serologic assays calibrated to a common reference standard.
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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 | 0.022 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".