Inhaled corticosteroids’ effect on COVID-19 patients: A systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Background More than six million people died due to COVID-19, and 10-15% of infected individuals suffer from post-covid syndrome. Corticosteroids are widely used in the management of severe COVID-19 and post-acute COVID-19 symptoms. This study synthesizes current evidence of the effectiveness of inhaled corticosteroids (ICS) on mortality, hospital length-of-stay (LOS), and improvement of smell scores in patients with COVID-19. Methods We searched Embase, Web of Science, PubMed, Cochrane Library, and Scopus until Aug 2022. The Cochrane risk of bias tool was used to assess the quality of studies. We evaluated the effectiveness of ICS in COVID-19 patients through measures of mortality, LOS, alleviation of post-acute COVID-19 symptoms, time to sustained self-reported cure, and sense of smell (visual analog scale (VAS)). Results Ten studies were included in the meta-analysis. Our study showed a significant decrease in the LOS in ICS patients over placebo (MD = -1.52, 95% CI [-2.77 to -0.28], p-value = 0.02). Patients treated with intranasal corticosteroids (INC) showed a significant improvement in VAS smell scores from week three to week four (MD =1.52, 95% CI [0.27 to 2.78], p-value = 0.02), and alleviation of COVID-related symptoms after 14 days (RR = 1.17, 95% CI [1.09 to 1.26], p-value < 0.0001). No significant differences were detected in mortality (RR= 0.69, 95% CI [0.36 to 1.35], p-value = 0.28) and time to sustained self-reported cure (MD = -1.28, 95% CI [-6.77 to 4.20], p-value = 0.65). Conclusion We concluded that the use of ICS decreased patient LOS and improved COVID-19-related symptoms. INC may have a role in improving the smell score. Therefore, using INC and ICS for two weeks or more may prove beneficial. Current data do not demonstrate an effect on mortality or time to sustained self-reported cure. However, the evidence is inconclusive, and more studies are needed for more precise data.
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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 | 0.052 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.100 | 0.027 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".