Abstract 11497: Mineralocorticoid Receptor Antagonists and Atrial Fibrillation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Medical therapies to prevent atrial fibrillation (AF) episodes and to reduce heart failure (HF) or cardiovascular (CV) death in patients with AF are limited. The role of mineralocorticoid receptor antagonists (MRAs) in this population is unclear. Objectives: We aimed to assess whether the effect of MRAs (e.g. spironolactone, eplerenone, finerenone) in reducing cardiovascular events differs in patients with and without AF, and to evaluate the efficacy of MRAs in reducing AF events. Methods: We searched MEDLINE, Embase, and CENTRAL to March 2022 for randomized controlled trials (RCT) comparing an MRA to placebo or usual care in patients with established cardiovascular disease or risk factors. Pairs of reviewers systematically screened the eligible studies and used random-effects models to combine data. Results: We identified 6 RCTs including 7,245 participants (1,791 with AF and 5,454 without AF) that assessed the effect of MRAs on a composite of HF hospitalization or CV death (Figure). MRAs had similar efficacy for reducing HF hospitalization/CV death in patients with a history of AF (HR 0.86, 95% CI: 0.52-1.42; I 2 =71%) as compared to those without a history of AF (HR 0.77, 95% CI: 0.62-0.96; I 2 =58%) - P for subgroup differences=0.69. There was no evidence of difference in the efficacy of MRAs in reducing HF hospitalization (P for subgroup differences=0.93) and CV death (P for subgroup differences=0.41) in patients with and without AF. We identified 21 RCTs including 22,126 participants that reported on the occurrence of AF events. MRAs significantly reduced AF events (RR 0.78, 95% CI: 0.71-0.87; I 2 =0%). This effect was similar for reducing both new-onset AF (RR 0.81, 95% CI: 0.64-1.01; I 2 =32%) and recurrent AF episodes (RR 0.77, 95% CI: 0.67-0.88; I 2 =0%) - P for subgroup differences=0.71. Conclusions: MRAs reduce HF hospitalization or CV death to a similar extent in patients with and without AF. In addition, MRAs may prevent new-onset and recurrent AF events.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".