The Impact Exerted on Clinical Outcomes of Patients With Chronic Heart Failure by Aldosterone Receptor Antagonists: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Aldosterone receptor antagonists (ARAs) have been associated with improved clinical outcomes in patients with heart failure with reduced left ventricular ejection fraction (HFREF), but not in those with heart failure with preserved left ventricular ejection fraction (HFpEF). With the aim to study this topic more deeply, we carried out a meta-analysis of selective and non-selective ARAs in HFREF and HFpEF. METHODS: We searched PubMed and Scopus databases. We decided to incorporate in the meta-analysis only randomized controlled trials (RCTs) of ARAs in patients with chronic heart failure (CHF) if they met the following criteria: experimental groups included patients with CHF treated with ARAs in addition to the conventional therapy; control groups included patients with CHF receiving conventional therapy without ARAs. Outcomes of interest were all-cause death, hospitalizations from cardiovascular cause, hyperkalemia, or gynecomastia. RESULTS: We detected 15 studies representing 15,671 patients. ARAs were associated with a reduced odds of all-cause death (odds ratio (OR): 0.79; 95% confidence interval (CI): 0.73 - 0.87) and hospitalizations from cardiovascular cause (OR: 0.73; 95% CI: 0.61 - 0.89). However, subgroup analysis showed that these advantages were limited to HFREF (all-cause death: OR: 0.77, 95% CI: 0.69 - 0.84; hospitalizations from cardiovascular cause: OR: 0.66, 95% CI: 0.51 - 0.85), but they did not affect the HFpEF group (all-cause death: OR: 0.91, 95% CI: 0.76 - 1.1; hospitalizations from cardiovascular cause: OR: 0.85, 95% CI: 0.7 - 1.09). ARAs increased the risk of hyperkalemia (OR: 2.17; 95% CI: 1.88 - 2.5). Non-selective ARAs, but not selective ARAs, increased the risk of gynecomastia (OR: 8.22, 95% CI: 4.9 - 13.81 vs. OR: 0.74, 95% CI: 0.43 - 1.27). CONCLUSIONS: ARAs reduced the risk of adverse cardiac events in HFREF but not HFpEF. In particular, ARA use in HFpEF patients is questionable, since in this CHF type, no significant improvement in all-cause death and cardiovascular hospitalizations was demonstrated with ARA treatment, in the face of the well-known risks of hyperkalemia and/or gynecomastia that chronic ARA therapy entails. Selective ARAs were equally effective as non-selective ARAs, without the risk of gynecomastia.
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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 | 0.030 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.062 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".