Association of mortality and aspirin use for COVID-19 residents at VA Community Living Center Nursing Homes
Abul, Y.; Devone, F.; Bayer, T.; Halladay, C.; McConeghy, K.; Mujahid, N.; Singh, M.; Leeder, C.; Gravenstein, S.; Rudolph, J. L.
Show abstract
Background/ObjectivesCoronavirus 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 association1. 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. MethodsThis 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. ResultsWe 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) ConclusionChronic 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Association of Mortality and Aspirin Prescription for COVID-19 Patients at the Veterans Health Administration 96%
- Incidence and Risk of Post-COVID-19 Thromboembolic Disease and the Impact of Aspirin Prescription; Nationwide Observational Cohort at the US Department of Veteran Affairs 94%
- The usefulness of D-dimer as a predictive marker for mortality in patients with COVID-19 hospitalized during the first wave in Italy 94%
Similar papers in this journal
Similar papers in this journal
- Efficacy and safety of tirofiban following intravenous thrombolysis: A systematic review and meta-analysis of randomized controlled trials 91%
- L-arginine and aged garlic extract for the prevention of migraine: a study protocol for a randomised, double-blind, placebo-controlled, phase-II trial (LARGE trial) 90%
- Acute ischemic Stroke in Tsutsugamushi: Understanding the Underlying Mechanisms and Risk Factors 89%
Similar papers in this journal
- Venous Thromboembolism and the Effects of Statin and Hormone Therapy: A Case-Control Study of 250,000 Women 50-64 years of age 93%
- Score for Emergency Risk Prediction (SERP): An Interpretable Machine Learning AutoScore–Derived Triage Tool for Predicting Mortality after Emergency Admissions 92%
- COVID-SAFER: Deprescribing Guidance for Nirmatrelvir-ritonavir Drug Interactions in Older Adults 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.