Adjustment of Antiplatelet Therapy in Patients With Myocardial Infarction Treated Without Revascularization: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Although a substantial proportion of patients with myocardial infarction (MI) are treated without revascularization, no randomized controlled trial has evaluated the optimal antiplatelet strategy in this vulnerable population and practice patterns may be heterogeneous. This study aims to describe postdischarge antiplatelet therapy (APT) practice patterns in medically managed patients with MI. Methods: A retrospective cohort study was conducted at the Montreal Heart Institute (July 31, 2020-July 31, 2023). Patients aged ≥18 years hospitalized for MI and discharged without revascularization were included, and discharge antiplatelet patterns were documented. Results: A total of 365 patients were included, comprising 156 women (42.7%) (median age: 71.4 years [interquartile range: 61-83]). Reasons for being treated without revascularization include MI without obstructive coronary artery disease (n=139; 38%), no angiography performed (n=118; 32%), severe disease not amenable to revascularization (n=71; 20%), small branch disease (n=21; 6%), and spontaneous coronary dissection (n=16; 4%). At discharge, 41.9% (n=153) received dual APT (DAPT), 38.4% (n=140) received single APT, and 19.7% (n=72) received no antiplatelet agent. The most common DAPT regimen prescribed was clopidogrel-acetylsalicylic acid (aspirin) (34.0%; n=124), and the most frequently prescribed antiplatelet monotherapy was aspirin (25.8%; n=94). Among patients treated with DAPT, duration of prescription was 12 months in 91.5% of cases. Postdischarge antiplatelet strategy varied depending on the underlying MI etiology. Conclusion: Postdischarge antiplatelet strategies prescribed in patients with an MI treated without revascularization are heterogeneous, whereas the preferred strategy is DAPT for 12 months. This variability reflects current clinical equipoise in this understudied population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".