Statin Use and COVID-19 Infectivity and Severity in South Korea: Two Population-Based Nationwide Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Basic studies suggest that statins as add-on therapy may benefit patients with COVID-19; however, real-world evidence of such a beneficial association is lacking. OBJECTIVE: We investigated differences in SARS-CoV-2 test positivity and clinical outcomes of COVID-19 (composite endpoint: admission to intensive care unit, invasive ventilation, or death) between statin users and nonusers. METHODS: Two independent population-based cohorts were analyzed, and we investigated the differences in SARS-CoV-2 test positivity and severe clinical outcomes of COVID-19, such as admission to the intensive care unit, invasive ventilation, or death, between statin users and nonusers. One group comprised an unmatched cohort of 214,207 patients who underwent SARS-CoV-2 testing from the Global Research Collaboration Project (GRCP)-COVID cohort, and the other group comprised an unmatched cohort of 74,866 patients who underwent SARS-CoV-2 testing from the National Health Insurance Service (NHIS)-COVID cohort. RESULTS: The GRCP-COVID cohort with propensity score matching had 29,701 statin users and 29,701 matched nonusers. The SARS-CoV-2 test positivity rate was not associated with statin use (statin users, 2.82% [837/29,701]; nonusers, 2.65% [787/29,701]; adjusted relative risk [aRR] 0.97; 95% CI 0.88-1.07). Among patients with confirmed COVID-19 in the GRCP-COVID cohort, 804 were statin users and 1573 were matched nonusers. Statin users were associated with a decreased likelihood of severe clinical outcomes (statin users, 3.98% [32/804]; nonusers, 5.40% [85/1573]; aRR 0.62; 95% CI 0.41-0.91) and length of hospital stay (statin users, 23.8 days; nonusers, 26.3 days; adjusted mean difference -2.87; 95% CI -5.68 to -0.93) than nonusers. The results of the NHIS-COVID cohort were similar to the primary results of the GRCP-COVID cohort. CONCLUSIONS: Our findings indicate that prior statin use is related to a decreased risk of worsening clinical outcomes of COVID-19 and length of hospital stay but not to that of SARS-CoV-2 infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; 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".