Predictors of the Use of Mineralocorticoid Receptor Antagonists in Patients With Left Ventricular Dysfunction Post‐ST‐Segment–Elevation Myocardial Infarction
Bibliographic record
Abstract
Background Guidelines recommend mineralocorticoid receptor antagonist (MRA) use in patients with left ventricular ejection fraction ≤40% following a myocardial infarction plus heart failure or diabetes mellitus, based on mortality benefit in the EPHESUS (Eplerenone Post-Acute Myocardial Infarction Heart Failure Efficacy and Survival Study) trial. The objective of this study was to evaluate the real-world utilization of MRAs for patients with ST-segment-elevation myocardial infarction (STEMI) with left ventricular dysfunction. Methods and Results The prospective, population-based, Vancouver Coastal Health Authority STEMI database was linked with local outpatient cardiology records from 2007 to 2018. EPHESUS criteria were used to define post-STEMI MRA eligibility (left ventricular ejection fraction ≤40% plus clinical heart failure or diabetes mellitus, and no dialysis-dependent renal dysfunction). The primary outcome was MRA prescription among eligible patients at discharge and the secondary outcome was MRA prescription within 3 months postdischarge. Of 2691 patients with STEMI, 317 (12%) were MRA eligible, and 70 (22%) eligible patients were prescribed an MRA at discharge. Among eligible patients with no MRA at discharge, 12/126 (9.5%) with documented postdischarge follow-up were prescribed an MRA within 3 months. In multivariable analysis, left ventricular ejection fraction (odds ratio [OR], 1.55 per 5% left ventricular ejection fraction decrease; 95% CI, 1.26-1.90) and calendar year (OR, 1.23 per year, 95% CI, 1.11-1.37) were associated with MRA prescription at discharge. Other prespecified variables were not associated with MRA prescription. Conclusions In this contemporary STEMI cohort, only 1 in 4 MRA-eligible patients were prescribed an MRA within 3 months following hospitalization despite high-quality evidence for use. Novel decision-support tools are required to optimize pharmacotherapy decisions during hospitalization and follow-up to target this gap in post-STEMI care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".