Mineralocorticoid Receptor Antagonists for Heart Failure: A Real-Life Observational Study
Bibliographic record
Abstract
Abstract Aims Mineralocorticoid receptor antagonists (MRAs) have been demonstrated to improve outcomes in reduced ejection fraction heart failure (HFrEF) patients. However, MRAs added to conventional treatment may lead to worsening of renal function and hyperkalaemia. We investigated, in a population-based analysis, the long-term effects of MRA treatment in HFrEF patients. Methods and results We analysed data of 6046 patients included in the Metabolic Exercise Cardiac Kidney Index score dataset. Analysis was performed in patients treated (n = 3163) and not treated (n = 2883) with MRA. The study endpoint was a composite of cardiovascular death, urgent heart transplantation, or left ventricular assist device implantation. Ten years' survival was analysed through Kaplan–Meier, compared by log-rank test and propensity score matching. At 10 years' follow-up, the MRA-untreated group had a significantly lower number of events than the MRA-treated group (P < 0.001). MRA-treated patients had more severe heart failure (higher New York Heart Association class and lower left ventricular ejection fraction, kidney function, and peak VO2). At a propensity-score-matching analysis performed on 1587 patients, MRA-treated and MRA-untreated patients showed similar study endpoint values. Conclusions In conclusion, MRA treatment does not affect the composite of cardiovascular death, urgent heart transplantation or left ventricular assist device implantation in a real-life setting. A meticulous patient follow-up, as performed in trials, is likely needed to match the positive MRA-related benefits observed in clinical trials.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".