The association between statin and COVID-19 adverse outcomes: national COVID-19 cohort in South Korea
Bibliographic record
Abstract
BACKGROUND: There currently exist limited and conflicting clinical data on the use of statins in coronavirus disease 2019 (COVID-19) patients. The aim of this paper was to compare hospitalized patients with COVID-19 who did and did not receive statins. METHODS: We performed a population-based retrospective cohort study using South Korea's nationwide healthcare claim database. We identified consecutive patients hospitalized with COVID-19 and aged 40 years or older. Statin users were individuals with inpatient and outpatient prescription records of statins in the 240 days before cohort entry to capture patients who are chronic statin users and, therefore, receive statin prescriptions as infrequently as every 8 months. Our primary endpoint was a composite of all-cause death, intensive care unit (ICU) admission, mechanical ventilation use and cardiovascular outcomes [myocardial infarction (MI), transient cerebral ischemic attacks (TIA) or stroke]. We compared the risk of outcomes between statin users and non-users using logistic regression models after inverse probability of treatment weighting (IPTW) adjustment. RESULTS: Of 234,427 subjects in the database, 4,349 patients were hospitalized with COVID-19 and aged 40+ years. In total, 1,115 patients were statin users (mean age =65.9 years; 60% female), and 3,234 were non-users (mean age =58.3 years; 64% female). Pre-hospitalization statin use was not significantly associated with increased risk of the primary endpoint [IPTW odds ratio (OR) 0.82; 95% confidence interval (CI): 0.60-1.11]. Subgroup analysis showed a protective role of antecedent statin use for individuals with hypertension (IPTW OR 0.40; 95% CI: 0.23-0.69, P for interaction: 0.0087). CONCLUSIONS: Pre-hospitalization statin use is not detrimental and may be beneficial amongst hypertensive COVID-19 patients. Further investigation into statin is needed for more conclusive effects of statins for treatment of COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".