Efficacy and Safety of Corticosteroid Treatment in Patients With COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: COVID-19 is a type of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection that was identified in December 2019. Corticosteroid therapy was empirically used for clinical treatment in the early stage of the disease outbreak; however, data regarding its efficacy and safety are controversial. The aim of this study was to evaluate the efficacy and safety of corticosteroid therapy in patients with COVID-19. Methods: The PubMed, Cochrane Library, EMBASE, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang, and China Science and Technology Journal (VIP) databases were searched for studies. Data on clinical improvement, mortality, virus clearance time, adverse events (AEs), utilization of mechanical ventilation, length of intensive care unit (ICU) hospitalization and hospital stay were extracted by two authors independently. Study quality was assessed by the Newcastle Ottawa Scale (cohort studies). The pooled data were meta-analyzed using a random effects model, and the quality of evidence was rated using the GRADE approach. Results: Eleven cohort studies (corticosteroid group vs control group), two retrospective cohort studies (without control group), and seven case studies were identified. A total of 2840 patients were included. Compared with the control treatments, corticosteroid therapy was associated with clinical recovery (RR=1.30, 95% CI [0.98, 1.72]) and a significantly shortened length of ICU hospitalization (RR=-6.50, 95% CI [-7.63, -5.37]), but it did not affect the mortality ((RR=1.59, 95% CI [0.69, 3.66], I2=93.5%), utilization of mechanical ventilation (RR=0.35, 95% CI [0.10, 1.18]), duration of symptoms (WMD=1.69, 95% CI [-0.24, 3.62]) or virus clearance time (RR=1.01, 95% CI [-0.91, 2.92], I2=57%) in COVID-19 patients. Treatment with corticosteroids in patients with COVID-19 may cause mild adverse outcomes. The quality of evidence was low or very low for all outcomes. Conclusion: The findings of our study indicate that corticosteroid therapy is not highly effective, but it appears to improve prognosis and promote clinical recovery in patients with severe COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".