The Effect of Mineralocorticoid Receptor Antagonists on Heart Failure with Nonreduced Ejection Fraction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The recommendations for mineralocorticoid receptor antagonists (MRAs) in patients with heart failure with nonreduced ejection fraction (HFnrEF), defined as heart failure with left ventricular ejection fraction > 40%, are not clear. This systematic review and meta-analysis aims to evaluate the effect of MRAs on patient-important outcomes in HFnrEF. Methods: We searched MEDLINE, Embase, Cochrane Database/Register from inception to September 6, 2024, for all randomized controlled trials comparing MRAs to placebo/standard of care in HFnrEF. Fixed and random effects models pooled estimates for mortality (all-cause and cardiovascular), HF hospitalization (HFH), functional capacity, health-related quality of life, and adverse outcomes. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach informed certainty-of-evidence assessments. Results: Eight RCTs reported on 10,313 patients with HFnrEF. Compared to placebo or standard of care, MRAs result in a reduction in HFH (risk ratio [RR] 0.83, 95% confidence interval [CI] 0.76-0.91; risk difference [RD] 29 fewer per 1000, 95% CI 31 fewer to 15 fewer; high certainty). Moderate-certainty evidence suggests that MRAs probably result in a slight reduction in all-cause mortality (RR 0.93, 95% CI 0.85-1.02; RD 11 fewer per 1000, 95% CI 23 fewer to 3 more) and cardiovascular mortality (RR 0.92, 95% CI 0.81-1.05; RD 7 fewer per 1000, 95% CI 16 fewer to 5 more). MRA use is associated with more hyperkalemia and worsening renal function, with no difference in withdrawal of the drug due to adverse events. compared to placebo. Conclusions: Among patients with HFnrEF, MRAs reduce HFH. Although MRAs increase the risk of hyperkalemia and worsening renal function, this does not lead to higher rates of drug discontinuation.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".