Safety of Alpha-Adrenergic Receptor Antagonists in Heart Failure
Bibliographic record
Abstract
OBJECTIVES: This study evaluated whether alpha-blocker (AB) use following an admission for heart failure (HF) was associated with an increased risk of HF readmission or death. BACKGROUND: ABs, found to increase the risk of HF in the ALLHAT (Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial) trial, are commonly used for prostatic hypertrophy, including in those with or at risk for HF. METHODS: This propensity score-matched cohort study included patients discharged from a Veterans Affairs hospital between January 2002 and September 2015 with a primary diagnosis of HF and ascertained AB use at discharge. Cox proportional hazards models were constructed to compare time to first HF readmission and death at 2 years between groups. Secondary analyses assessed effects by AB dose and type and by beta-blocker (BB) use. RESULTS: Of 169,911 HF patients, 47,638 (28%) were prescribed an AB. Propensity score matching resulted in 35,713 matched pairs. In the propensity score-matched cohort, AB use was associated with fewer HF readmissions (39.8% vs. 41.7% at 2 years; hazard ratio: 0.95; 95% confidence interval [CI]: 0.92 to 0.97; p < 0.0001) and death (42.8% vs. 46.5%; hazard ratio: 0.93; 95% CI: 0.91 to 0.94; p < 0.0001). Nonselective ABs had fewer deaths and HF readmissions (p < 0.0001), while higher AB doses reduced mortality (p < 0.0001). AB treatment was associated with reduced deaths in both BB-treated and untreated patients, with no increase in HF. CONCLUSIONS: Treatment of HF patients with an AB was associated not with a higher but instead with a lower rate of HF readmission and death. Higher doses and nonselective ABs were also associated with lower mortality, regardless of BB use. ABs may be used safely in HF patients where clinically indicated. The finding of improved outcomes with ABs may warrant further study.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".