Association Between Nonsteroidal Antiinflammatory Drug Use and Adverse Clinical Outcomes Among Adults Hospitalized With Coronavirus 2019 in South Korea: A Nationwide Study
Bibliographic record
Abstract
BACKGROUND: Nonsteroidal antiinflammatory drugs (NSAIDs) may exacerbate coronavirus disease 2019 (COVID-19) and worsen associated outcomes by upregulating the enzyme that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds to in order to enter cells. METHODS: We conducted a cohort study using South Korea's nationwide healthcare database, which contains data for all individuals who received a COVID-19 test (n = 69 793) as of 8 April 2020. We identified adults hospitalized with COVID-19, where cohort entry was the date of hospitalization. NSAID users were those prescribed NSAIDs in the 7 days before and including cohort entry, and nonusers were those not prescribed NSAIDs during this period. Our primary outcome was a composite of in-hospital death, intensive care unit admission, mechanical ventilation use, and sepsis; our secondary outcomes were cardiovascular complications and acute renal failure. We conducted logistic regression analysis to estimate odds ratio (OR) with 95% confidence intervals (CIs) using inverse probability of treatment weighting to minimize confounding. RESULTS: Of 1824 adults hospitalized with COVID-19 (mean age, 49.0 years; female, 59%), 354 were NSAID users and 1470 were nonusers. Compared with nonuse, NSAID use was associated with increased risks of the primary composite outcome (OR, 1.54; 95% CI, 1.13-2.11) but insignificantly associated with cardiovascular complications (OR, 1.54; 95% CI, 0.96-2.48) or acute renal failure (OR, 1.45; 95% CI, 0.49-4.14). CONCLUSIONS: While awaiting the results of confirmatory studies, we suggest NSAIDs be used with caution for COVID-19 patients as the harms associated with their use may outweigh their benefits.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".