Association of mortality and aspirin use for COVID-19 residents at VA Community Living Center Nursing Homes
Bibliographic record
Abstract
Abstract Background/Objectives Coronavirus disease 2019 (COVID-19) is associated with a hypercoagulable state and increased thrombotic risk in infected individuals. Several complex and varied coagulation abnormalities were proposed for this association 1 . Acetylsalicylic acid(ASA, aspirin) is known to have inflammatory, antithrombotic properties and its use was reported as having potency to reduce RNA synthesis and replication of some types of coronaviruses including human coronavirus-299E (CoV-229E) and Middle East Respiratory Syndrome (MERS)-CoV 2,3 . We hypothesized that chronic low dose aspirin use may decrease COVID-19 mortality relative to ASA non-users. Methods This is a retrospective, observational cohort analysis of residents residing at Veterans Affairs Community Living Centers from December 13, 2020, to September 18, 2021, with a positive SARS-CoV-2 PCR test. Low dose aspirin users had low dose (81mg) therapy (10 of 14 days) prior to the positive COVID date and were compared to aspirin non-users (no ASA in prior 14 days). The primary outcome was mortality at 30 and 56 days post positive test and hospitalization. Results We identified 1.823 residents who had SARS-CoV-2 infection and 1,687 residents were eligible for the study. Aspirin use was independently associated with a reduced risk of 30 days of mortality (adjusted HR, 0.60, 95% CI, 0.40-0.90) and 56 days of mortality (adjusted HR, 0.67, 95% CI, 0.47-0.95) Conclusion Chronic low dose aspirin use for primary or secondary prevention of cardiovascular events is associated with lower COVID-19 mortality. Although additional randomized controlled trials are required to understand these associations and the potential implications more fully for improving care, aspirin remains a medication with known side effects and clinical practice should not change based on these findings.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".