Antibody and cellular therapies for treatment of covid-19: a living systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy and safety of antiviral antibody therapies and blood products for the treatment of novel coronavirus disease 2019 (covid-19). DESIGN: Living systematic review and network meta-analysis, with pairwise meta-analysis for outcomes with insufficient data. DATA SOURCES: WHO covid-19 database, a comprehensive multilingual source of global covid-19 literature, and six Chinese databases (up to 21 July 2021). STUDY SELECTION: Trials randomising people with suspected, probable, or confirmed covid-19 to antiviral antibody therapies, blood products, or standard care or placebo. Paired reviewers determined eligibility of trials independently and in duplicate. METHODS: After duplicate data abstraction, we performed random effects bayesian meta-analysis, including network meta-analysis for outcomes with sufficient data. We assessed risk of bias using a modification of the Cochrane risk of bias 2.0 tool. The certainty of the evidence was assessed using the grading of recommendations assessment, development, and evaluation (GRADE) approach. We meta-analysed interventions with ≥100 patients randomised or ≥20 events per treatment arm. RESULTS: As of 21 July 2021, we identified 47 trials evaluating convalescent plasma (21 trials), intravenous immunoglobulin (IVIg) (5 trials), umbilical cord mesenchymal stem cells (5 trials), bamlanivimab (4 trials), casirivimab-imdevimab (4 trials), bamlanivimab-etesevimab (2 trials), control plasma (2 trials), peripheral blood non-haematopoietic enriched stem cells (2 trials), sotrovimab (1 trial), anti-SARS-CoV-2 IVIg (1 trial), therapeutic plasma exchange (1 trial), XAV-19 polyclonal antibody (1 trial), CT-P59 monoclonal antibody (1 trial) and INM005 polyclonal antibody (1 trial) for the treatment of covid-19. Patients with non-severe disease randomised to antiviral monoclonal antibodies had lower risk of hospitalisation than those who received placebo: casirivimab-imdevimab (odds ratio (OR) 0.29 (95% CI 0.17 to 0.47); risk difference (RD) -4.2%; moderate certainty), bamlanivimab (OR 0.24 (0.06 to 0.86); RD -4.1%; low certainty), bamlanivimab-etesevimab (OR 0.31 (0.11 to 0.81); RD -3.8%; low certainty), and sotrovimab (OR 0.17 (0.04 to 0.57); RD -4.8%; low certainty). They did not have an important impact on any other outcome. There was no notable difference between monoclonal antibodies. No other intervention had any meaningful effect on any outcome in patients with non-severe covid-19. No intervention, including antiviral antibodies, had an important impact on any outcome in patients with severe or critical covid-19, except casirivimab-imdevimab, which may reduce mortality in patients who are seronegative. CONCLUSION: In patients with non-severe covid-19, casirivimab-imdevimab probably reduces hospitalisation; bamlanivimab-etesevimab, bamlanivimab, and sotrovimab may reduce hospitalisation. Convalescent plasma, IVIg, and other antibody and cellular interventions may not confer any meaningful benefit. SYSTEMATIC REVIEW REGISTRATION: This review was not registered. The protocol established a priori is included as a data supplement. FUNDING: This study was supported by the Canadian Institutes of Health Research (grant CIHR- IRSC:0579001321). READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Interim updates and additional study data will be posted on our website (www.covid19lnma.com).
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".