Effect of Bucindolol on Heart Failure Outcomes and Heart Rate Response in Patients with Reduced Ejection Fraction Heart Failure and Atrial Fibrillation
Bibliographic record
Abstract
AIMS: There is little evidence of beta-blocker treatment benefit in patients with heart failure and reduced left ventricular ejection fraction (HFREF) and atrial fibrillation (AF). We investigated the effects of bucindolol in HFREF patients with AF enrolled in the Beta-blocker Evaluation of Survival Trial (BEST). METHODS AND RESULTS: A post-hoc analysis of patients in BEST with and without AF was performed to estimate the effect of bucindolol on mortality and hospitalization. Patients were also evaluated for treatment effects on heart rate and the influence of beta1-adrenergic receptor position 389 (β(1)389) arginine (Arg) vs. glycine (Gly) genotypes. In the 303/2708 patients in AF, patients receiving bucindolol were more likely to achieve a resting heart rate ≤ 80 b.p.m. at 3 months (P < 0.005) in the absence of treatment-limiting bradycardia. In AF patients and sinus rhythm (SR) patients who achieved a resting heart rate ≤ 80 b.p.m., there were beneficial treatment effects on cardiovascular mortality/cardiovascular hospitalization [hazard ratio (HR) 0.61, P = 0.025, and 0.79, P = 0.002]. Without achieving a resting heart rate ≤ 80 b.p.m., there were no treatment effects on events in either group. β(1)389-Arg/Arg AF patients had nominally significant reductions in all-cause mortality/HF hospitalization and cardiovascular mortality/hospitalization with bucindolol (HR 0.23, P = 0.037 and 0.28, P = 0.039), whereas Gly carriers did not. There was no evidence of diminished heart rate response in β(1)389-Arg homozygotes. CONCLUSION: In HFREF patients with AF, bucindolol was associated with reductions in composite HF endpoints in those who achieved a resting heart rate ≤ 80 b.p.m. and nominally in those with the β(1)389-Arg homozygous genotype.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".