Antirheumatic Disease Therapies for the Treatment of COVID‐19: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: Antirheumatic disease therapies have been used to treat coronavirus disease 2019 (COVID-19) and its complications. We conducted a systematic review and meta-analysis to describe the current evidence. METHODS: A search of published and preprint databases in all languages was performed. Included studies described ≥1 relevant clinical outcome for ≥5 patients who were infected with severe acute respiratory syndrome coronavirus 2 and were treated with antirheumatic disease therapy between January 1, 2019 and May 29, 2020. Pairs of reviewers screened articles, extracted data, and assessed risk of bias. A meta-analysis of effect sizes using random-effects models was performed when possible. RESULTS: The search identified 3,935 articles, of which 45 were included (4 randomized controlled trials, 29 cohort studies, and 12 case series). All studies evaluated hospitalized patients, and 29 of the 45 studies had been published in a peer-reviewed journal. In a meta-analysis of 3 cohort studies with a low risk of bias, hydroxychloroquine use was not significantly associated with mortality (pooled hazard ratio [HR] 1.41 [95% confidence interval (95% CI) 0.83, 2.42]). In a meta-analysis of 2 cohort studies with some concerns/higher risk of bias, anakinra use was associated with lower mortality (pooled HR 0.25 [95% CI 0.12, 0.52]). Evidence was inconclusive with regard to other antirheumatic disease therapies, and the majority of other studies had a high risk of bias. CONCLUSION: In this systematic review and meta-analysis, hydroxychloroquine use was not associated with benefit or harm regarding COVID-19 mortality. The evidence supporting the effect of other antirheumatic disease therapies in COVID-19 is currently inconclusive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".