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Record W4408803358 · doi:10.1002/ejhf.3647

Impact of Mineralocorticoid Receptor Antagonists on the Risk of Sudden Cardiac Death Across Cardio-Kidney-Metabolic Diseases: A Meta-Analysis of Randomized Clinical Trials

2025· article· en· W4408803358 on OpenAlexaff
Pedro Marques, Faïez Zannad, João Pedro Ferreira

Bibliographic record

VenueEuropean Journal of Heart Failure · 2025
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineEjection fractionInternal medicineMeta-analysisOdds ratioPlaceboMineralocorticoid receptorCardiologyHeart failureRandomized controlled trialSudden cardiac deathConfidence intervalKidney diseaseAldosteronePathology

Abstract

fetched live from OpenAlex

Aims Sudden cardiac death (SCD) is a prevalent cause of mortality among patients with cardio-kidney-metabolic (CKM) diseases. Mineralocorticoid receptor antagonists (MRAs) reduce the risk of SCD in patients with left ventricular dysfunction, but it is unclear if similar effects are expected across different CKM risk populations irrespective of ejection fraction. Methods and results A random-effects meta-analysis of randomized clinical trials (RCTs) of MRA (vs. placebo) on the occurrence of SCD was performed. The number of reported SCD events were collected from the individual RCTs and the respective odds ratio (OR) and 95% confidence intervals (95% CI) calculated. Data from five major RCTs were collected including over 31 000 patients. Overall, MRAs, compared with placebo, reduced the occurrence of SCD (OR 0.79, 95% CI 0.70–0.89, p < 0.001). No significant heterogeneity was found across trials (I2 = 0%, Q = 0.38, p = 0.98). Conclusion This meta-analysis supports the benefit of MRAs for SCD risk reduction across CKM risk and irrespective of ejection fraction.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.016
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.085
GPT teacher head0.413
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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