Comparison of β-blocker agents and mortality in maintenance hemodialysis patients: an international cohort study
Bibliographic record
Abstract
Background: Despite a lack of clinical trial data, β-blockers are widely prescribed to dialysis patients. Whether specific β-blocker agents are associated with improved long-term outcomes compared with alternative β-blocker agents in the dialysis population remains uncertain. Methods: We analyzed data from an international cohort study of 10 125 patients on maintenance hemodialysis across 18 countries that were newly prescribed a β-blocker medication within the Dialysis Outcomes and Practice Patterns Study (DOPPS). The following β-blocker agents were compared: metoprolol, atenolol, bisoprolol and carvedilol. Multivariable Cox proportional hazards models were used to estimate the association between the newly prescribed β-blocker agent and all-cause mortality. Stratified analyses were performed on patients with and without a prior history of cardiovascular disease. Results: The mean (standard deviation) age in the cohort was 63 (15) years and 57% of participants were male. The most commonly prescribed β-blocker agent was metoprolol (49%), followed by carvedilol (29%), atenolol (11%) and bisoprolol (11%). Compared with metoprolol, atenolol {adjusted hazard ratio (HR) 0.77 [95% confidence interval (CI) 0.65-0.90]} was associated with a lower mortality risk. There was no difference in mortality risk with bisoprolol [adjusted HR 0.99 (95% CI 0.82-1.20)] or carvedilol [adjusted HR 0.95 (95% CI 0.82-1.09)] compared with metoprolol. These results were consistent upon stratification of patients by presence or absence of a prior history of cardiovascular disease. Conclusions: Among patients on maintenance hemodialysis who were newly prescribed β-blocker medications, atenolol was associated with the lowest mortality risk compared with alternative agents.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".