The association of beta-blocker use with mortality in elderly patients with congestive heart failure and advanced chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: Whether the survival benefit of β-blockers in congestive heart failure (CHF) from randomized trials extends to patients with advanced chronic kidney disease (CKD) [estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m2 but not receiving dialysis] is uncertain. METHODS: This was a retrospective cohort study using administrative datasets. Older adults from Ontario, Canada, with incident CHF (median age 79 years) from April 2002 to March 2014 were included. We matched new users of β-blockers to nonusers on age, sex, eGFR categories (>60, 30-60, <30), CHF diagnosis date and a high-dimensional propensity score. Using Cox proportional hazards models, we examined the association of β-blocker use versus nonuse with all-cause mortality. RESULTS: We matched 5862 incident β-blocker users (eGFR >60, n = 3136; eGFR 30-60, n = 2368; eGFR <30, n = 358). There were 2361 mortality events during follow-up. β-Blocker use was associated with reduced all-cause mortality [adjusted hazard ratio (HR) 0.58, 95% confidence interval (CI) 0.54-0.64]. This result was consistent across all eGFR categories (>60: adjusted HR 0.55, 95% CI 0.49-0.62; 30-60: adjusted HR 0.63, 95% CI 0.55-0.71; <30: adjusted HR 0.55, 95% CI 0.41-0.73; interaction term, P = 0.30). The results were consistent in an intention-to-treat analysis and with β-blocker use treated as a time-varying exposure. CONCLUSIONS: β-Blocker use is associated with reduced all-cause mortality in elderly patients with CHF and CKD, including those with an eGFR <30. Randomized trials that examine β-blockers in patients with CHF and advanced CKD are needed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".