Abstract 16700: Mineralocorticoid Receptor Antagonist Utilization in Eligible Patients Post St-elevation Myocardial Infarction
Bibliographic record
Abstract
Background: Mineralocorticoid receptor antagonists (MRAs) have been shown to reduce the morbidity and mortality in patients with reduced left ventricular ejection fraction (LVEF) post myocardial infarction (MI). Canadian guidelines recommend a MRA in patients post MI with an LVEF of ≤40% and documented heart failure or diabetes before hospital discharge, in the absence of any contraindications. We sought to examine if discrepancies between guideline-based therapy and actual prescribing rates exists in the use of MRAs in acute ST-elevation myocardial infarction (STEMI) patients. Methods: Retrospective analysis of utilization rates of MRAs in eligible patients enrolled in the Vancouver Coastal Health Authority STEMI database between October 2007 and October 2014. Inclusion criteria were based on the EPHESUS trial, which included an LVEF <40% and documented heart failure or history of diabetes. Patients on dialysis or with a serum Cr >221 were excluded. Results: 2583 patients had a STEMI during the study period. 192 (7.4%) patients were determined to be eligible for MRA prescription at discharge, 32 were excluded due missing discharge prescription information. Of the remaining 160 patients, the mean age was 67.9, 71.3% were male, 72.5% had an anterior MI and the mean LVEF was 30.4%. During hospitalization 51.6% had clinical evidence of HF and 21.3% were diagnosed with cardiogenic shock. PCI was performed in 75.6% of those eligible and 11.3% underwent CABG, 11.9% were medically managed and 1.25% underwent thrombolysis as a final revascularization strategy. On discharge 98% were on ASA, 81.3% on a second anti-platelet agent, 96% were on a beta-blocker, 75.6% were on an ACEi, 15.6% were on an ARB, 99.4% were on a statin. Only 22 (13.8%) of eligible patients were discharged on an MRA. No significant clinical difference existed amongst those that received an MRA compared to those that did not. Conclusions: Despite a Class IA recommendation for the use of MRAs in this patient population, our study demonstrates that the majority of patients are not prescribed an MRA after STEMI. This demonstrates a large care gap between evidence based guidelines and clinical practice. The reasons for this discrepancy in practice patterns are unclear and will be the focus of further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".