Drug treatments for covid-19: living systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the effects of treatments for coronavirus disease 2019 (covid-19). DESIGN: Living systematic review and network meta-analysis. DATA SOURCES: WHO covid-19 database, a comprehensive multilingual source of global covid-19 literature, up to 3 December 2021 and six additional Chinese databases up to 20 February 2021. Studies identified as of 1 December 2021 were included in the analysis. STUDY SELECTION: Randomised clinical trials in which people with suspected, probable, or confirmed covid-19 were randomised to drug treatment or to standard care or placebo. Pairs of reviewers independently screened potentially eligible articles. METHODS: After duplicate data abstraction, a bayesian network meta-analysis was conducted. Risk of bias of the included studies was assessed using a modification of the Cochrane risk of bias 2.0 tool, and the certainty of the evidence using the grading of recommendations assessment, development, and evaluation (GRADE) approach. For each outcome, interventions were classified in groups from the most to the least beneficial or harmful following GRADE guidance. RESULTS: 463 trials enrolling 166 581 patients were included; 267 (57.7%) trials and 89 814 (53.9%) patients are new from the previous iteration; 265 (57.2%) trials evaluating treatments with at least 100 patients or 20 events met the threshold for inclusion in the analyses. Compared with standard care, three drugs reduced mortality in patients with mostly severe disease with at least moderate certainty: systemic corticosteroids (risk difference 23 fewer per 1000 patients, 95% credible interval 40 fewer to 7 fewer, moderate certainty), interleukin-6 receptor antagonists when given with corticosteroids (23 fewer per 1000, 36 fewer to 7 fewer, moderate certainty), and Janus kinase inhibitors (44 fewer per 1000, 64 fewer to 20 fewer, high certainty). Compared with standard care, two drugs probably reduce hospital admission in patients with non-severe disease: nirmatrelvir/ritonavir (36 fewer per 1000, 41 fewer to 26 fewer, moderate certainty) and molnupiravir (19 fewer per 1000, 29 fewer to 5 fewer, moderate certainty). Remdesivir may reduce hospital admission (29 fewer per 1000, 40 fewer to 6 fewer, low certainty). Only molnupiravir had at least moderate quality evidence of a reduction in time to symptom resolution (3.3 days fewer, 4.8 fewer to 1.6 fewer, moderate certainty); several others showed a possible benefit. Several drugs may increase the risk of adverse effects leading to drug discontinuation; hydroxychloroquine probably increases the risk of mechanical ventilation (moderate certainty). CONCLUSION: Corticosteroids, interleukin-6 receptor antagonists, and Janus kinase inhibitors probably reduce mortality and confer other important benefits in patients with severe covid-19. Molnupiravir and nirmatrelvir/ritonavir probably reduce admission to hospital in patients with non-severe covid-19. SYSTEMATIC REVIEW REGISTRATION: This review was not registered. The protocol is publicly available in the supplementary material. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This is the fifth version of the original article published on 30 July 2020 (BMJ 2020;370:m2980), and previous versions can be found as data supplements. When citing this paper please consider adding the version number and date of access for clarity.
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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 | 0.039 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".