Alpha‐1 adrenergic antagonists and the risk of hospitalization or death in non‐hospitalized patients with COVID‐19: A population‐based study
Bibliographic record
Abstract
BACKGROUND: Alpha-1 receptor antagonists may interfere with IL-6 signaling and could therefore be a potential treatment for COVID-19. However, the effectiveness of these drugs in mitigating the risk of clinical deterioration among non-hospitalized patients with COVID-19 is unknown. OBJECTIVES: The aim of this study is to examine the association between alpha-1 antagonist exposure and the 30-day risk of a hospital encounter or death in nonhospitalized patients with COVID-19. METHODS: We conducted a population-based cohort study of Ontario residents aged 35 years and older who were eligible for public drug coverage and who had a positive test for SARS-CoV-2 between January 1, 2020, and March 1, 2021. We matched each individual receiving an alpha-1 antagonist at the time of their positive test with two non-exposed individuals using propensity scores. Our outcome was a composite of a hospital admission, emergency department visit, or death, 1 to 30 days following the positive test. RESULTS: We matched 3289 alpha-1 antagonist exposed patients to 6189 unexposed patients. Overall, there was no difference in the 30-day risk of the primary outcome among patients exposed to alpha-1 antagonists at the time of their diagnosis relative to unexposed individuals (28.8% vs. 28.0%; OR 1.00, 95% CI 0.91 to 1.11). In a secondary analysis, individuals exposed to alpha-1 antagonists had a lower risk of death in the 30 days following a COVID diagnosis (OR 0.79; 95% CI 0.66 to 0.93). CONCLUSION: Alpha-1 antagonists did not mitigate the 30-day risk of clinical deterioration in non-hospitalized patients with COVID-19. Our findings do not support the general repurposing of alpha-1 antagonists as a treatment for such patients, although there may be subgroups of patients in whom further research is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".