Statin use and clinical outcomes in patients with COVID-19: An updated systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose Observations studies have shown that prior use of statins is associated with a reduced risk of adverse clinical outcomes in patients with COVID-19. However, the available data are limited, inconsistent and conflicting. Besides, no randomised controlled trial exists in this regard. Hence, the present meta-analysis was conducted to provide an updated summary and collate the effect of statin use on clinical outcomes in COVID-19 using unadjusted and adjusted risk estimates. Methods PubMed, Scopus and Web of Science databases were systematically searched using appropriate keywords till December 18 2020, to identify observational studies reporting clinical outcomes in COVID-19 patients using statins versus those not using statins. Prior and in-hospital use of statins were considered. Study quality was assessed using the Newcastle-Ottawa Scale. Unadjusted and adjusted pooled odds ratio (OR) with 95% CIs were calculated. Results We included 14 observational studies pooling data retrieved from 19 988 patients with COVID-19. All the studies were of high/moderate quality. Pooled analysis of unadjusted data showed that statin use was not associated with improved clinical outcomes (OR 1.02; 95% CI 0.69 to 1.50, p=0.94, I2=94%, random-effects model). However, on pooling adjusted risk estimates, the use of statin was found to significantly reduce the risk of adverse outcomes (OR 0.51; 95% CI 0.41 to 0.63, p<0.0005, I2=0%, fixed-effects model). Conclusions Statin use is associated with improved clinical outcomes in patients with COVID-19. Individuals with multiple comorbidities on statin therapy should be encouraged to continue the drug amid the ongoing pandemic.
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 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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; 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".