Beta-Blocker Use and Outcomes in Patients with Heart Failure and Mildly Reduced and Preserved Ejection Fraction
Bibliographic record
Abstract
ABSTRACT Aims In the absence of randomized trial evidence, we performed a large observational analysis of the association between beta-blocker (BB) use and clinical outcomes in patients with heart failure (HF) and mildly reduced (HFmrEF) and preserved ejection fraction (HFpEF). Methods and results We pooled individual patient data from four large HFmrEF/HFpEF trials (I-Preserve, TOPCAT, PARAGON-HF, and DELIVER). The primary outcome was the composite of cardiovascular death or HF hospitalization. Among the 16 951 patients included, the mean left ventricular ejection fraction (LVEF) was 56.8%, and 13 400 (79.1%) had HFpEF (LVEF ≥50%). Overall, 12 812 patients (75.6%) received a BB. The median bisoprolol-equivalent dose of BB was 5.0 (Q1–Q3: 2.5–5.0) mg with BB continuation rates of 93.1% at 2 years (in survivors). The unadjusted hazard ratio (HR) for the primary outcome did not differ between BB users and non-users (HR 0.98, 95% confidence interval [CI] 0.91–1.05), but the adjusted HR was lower in BB users than non-users (0.81, 95% CI 0.74–0.88), and this association was maintained across LVEF (pinteraction = 0.88). In subgroup analyses, the adjusted risk of the primary outcome was similar in BB users and non-users with or without a history of myocardial infarction, hypertension, or a baseline heart rate <70 bpm. By contrast, a better outcome with BB use was seen in patients with atrial fibrillation compared to those without atrial fibrillation (pintreraction = 0.02). Conclusions In this observational analysis of non-randomized BB treatment, there was no suggestion that BB use was associated with worse HF outcomes in HFmrEF/HFpEF, even after extensive adjustment for other prognostic variables.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| 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".