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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".