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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".