Effect of ivabradine in heart failure: a meta-analysis of heart failure patients with reduced versus preserved ejection fraction
Bibliographic record
Abstract
In clinical trials of heart failure reduced ejection fraction (HFrEF), ivabradine seemed to be an effective heart rate lowering agent associated with lower risk of cardiovascular death. In contrast, ivabradine failed to improve cardiovascular outcomes in heart failure preserved ejection fraction (HFpEF) despite the significant effect on heart rate. This meta-analysis is the first to compare the effects of ivabradine on heart rate and mortality parameters in HFpEF versus HFrEF. We screened three databases: PubMed, Embase, and Cochrane Library. The outcomes of these studies were mortality, reduction in heart rate, and left ventricular function improvement. We compared the efficacy of ivabradine treatment in HFpEF versus HFrEF. Heart rate analysis of pooled data showed decrease in both HFrEF (–17.646 beats/min) and HFpEF (–11.434 beats/min), and a tendency to have stronger bradycardic effect in HFrEF (p = 0.094) in randomized clinical trials. Left ventricular ejection fraction analysis revealed significant improvement in HFrEF (5.936, 95% CI: [4.199–7.672], p < 0.001) when compared with placebo (p < 0.001). We found that ivabradine significantly improves left ventricular performance in HFrEF, at the same time it exerts a tendency to have improved bradycardic effect in HFrEF. These disparate effects of ivabradine and the higher prevalence of non-cardiac comorbidities in HFpEF may explain the observed beneficial effects in HFrEF and the unchanged outcomes in HFpEF patients after ivabradine treatment.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".