Older adults with heart failure treated with carvedilol, bisoprolol, or metoprolol tartrate: risk of mortality
Bibliographic record
Abstract
PURPOSE: The long-term use of β-blockers has been shown to improve clinical outcomes among patients with heart failure (HF). However, a lack of data persists in assessing whether carvedilol or bisoprolol are superior to metoprolol tartrate in clinical practice. We endeavored to compare the effectiveness of β-blockers among older adults following a primary hospital admission for HF. METHODS: We conducted a cohort study using Quebec administrative databases to identify patients who were using β-blockers, carvedilol, bisoprolol, or metoprolol tartrate after the diagnosis of HF. We characterized the patients by the type of β-blocker prescribed at discharge of their first HF hospitalization. An adjusted multivariate Cox proportional hazards model was used to compare the primary outcome of all-cause mortality. We also conducted analyses by matching for a propensity score for initiation of β-blocker therapy and assessed the effect on primary outcome. RESULTS: Among 3197 patients with HF with a median follow-up of 2.8 years, the crude annual mortality rates (per 100 person-years) were at 16, 14.9, and 17.7 for metoprolol tartrate, carvedilol, and bisoprolol, respectively. Adjusted hazard ratios of carvedilol (hazard ratio 0.92; 0.78-1.09) and bisoprolol (hazard ratio 1.04; 0.93-1.16) were not significantly different from that of metoprolol tartrate in improving survival. After matching for propensity score, carvedilol and bisoprolol showed no additional benefit with respect to all-cause mortality compared with metoprolol tartrate. CONCLUSIONS: Our evidence suggests no differential effect of β-blockers on all-cause mortality among older adults with HF. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".