Effect of statins on mortality in patients with COVID-19: an updated meta-analysis of 147824 patients
Bibliographic record
Abstract
Abstract Background Coronavirus disease 2019 (COVID-19) remains a public health problem worldwide. There is conflicting evidence about the impact of statins use on clinical outcomes in patients with COVID-19. Purpose We performed a systematic review and meta-analysis to assess the effect of statins use on mortality in these patients. Methods We searched electronic databases from inception to March 3, 2021 for cohort studies evaluating the association between chronic and/or inpatient use of statins and mortality. Risk of bias was assessed using the Newcastle-Ottawa Scale. We pooled unadjusted and adjusted effect estimates with their 95% confidence intervals (95% CI) using random-effects models. Results A total of 25 cohort studies involving 147824 patients were included. The mean age ranged from 44.9 to 70.9 years and 57% of patients were men. The use of statins was not associated with mortality according to the unadjusted risk ratio (uRR, 1.16; 95% CI, 0.86–1.57, 19 studies). In contrast, meta-analyses of adjusted odds ratio (aOR, 0.67; 95% CI, 0.52–0.86, 11 studies) and adjusted hazard ratio (aHR, 0.73; 95% CI, 0.58–0.91, 10 studies) showed that the use of statins was independently associated with a significant reduction of mortality. Adjusted confounders included age, sex, and cardiovascular comorbidities in most of cohorts. Eighteen studies were scored as low risk of bias, six studies as moderate risk of bias, and one study as high risk of bias. Conclusion The use of statins was associated with lower mortality in patients with COVID-19 based on adjusted effects of cohort studies. However, randomized controlled trials are needed to confirm these findings. Funding Acknowledgement Type of funding sources: None.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".