Prior Statin Use and Risk of Mortality and Severe Disease From Coronavirus Disease 2019: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background Statins up-regulate angiotensin-converting enzyme 2, the receptor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while also exhibiting pleiotropic antiviral, antithrombotic, and anti-inflammatory properties. Uncertainties exist about their effect on the course of SARS-CoV-2 infection. We sought to systematically review the literature and perform a meta-analysis to examine the association between prior statin use and outcomes of patients with coronavirus disease 2019 (COVID-19). Methods We searched Ovid Medline, Web of Science, Scopus, and the preprint server medRxiv from inception to December 2020. We assessed the quality of eligible studies with the Newcastle-Ottawa quality scale. We pooled adjusted relative risk (aRRs) of the association between prior statin use and outcomes of patients with COVID-19 using the DerSimonian-Laird random-effects model and assessed heterogeneity using the I2 index. Results Overall, 19 (16 cohorts and 3 case-control) studies were eligible, with a total of 395 513 patients. Sixteen of 19 studies had low or moderate risk of bias. Among 109 080 patients enrolled in 13 separate studies, prior statin use was associated with a lower risk of mortality (pooled aRR, 0.65 [95% confidence interval {CI}, .56–.77], I2 = 84.1%) and a reduced risk of severe COVID-19 was also observed in 48 110 patients enrolled in 9 studies (pooled aRR, 0.73 [95% CI, .57–.94], I2 = 82.8%), with no evidence of publication bias. Conclusions Cumulative evidence suggests that prior statin use is associated with lower risks of mortality or severe disease in patients with COVID-19. These data support the continued use of statins medications in patients with an indication for lipid-lowering therapy during the COVID-19 pandemic.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".