Long term health outcomes in patients with a history of myocardial infarction: A population based cohort study
Bibliographic record
Abstract
BACKGROUND: Myocardial infarction (MI) is associated with high morbidity and mortality, particularly in the first 12 months post-event. Interventions such as dual antiplatelet therapy can reduce the risk of major adverse cardiovascular events (MACE), but the duration of the high-risk time interval and the optimal prescription time frame for these interventions remains unknown. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: We performed a retrospective cohort study using data from medical services and hospitalizations in Manitoba, Canada for patients admitted with a MI between April 2006 and March 2010, and followed until Nov 30, 2014. We used survival analysis to determine the cumulative incidence of death, subsequent MI, or stroke, and used Cox proportional hazards models to assess factors associated with these endpoints. RESULTS: There were 8,493 patients in Manitoba admitted to hospital for a MI during the study period. Of those, 6,749 (79.5%) survived for at least 1 year without a recurrent MI or stroke. In the following year, this population remained at high risk, with 372 (5.5%) of the remaining patients dying in the next twelve months (48.1% cardiovascular deaths), 244 (3.6%) having a recurrent MI, and 74 (1.1%) having a stroke. Older age, male sex, diabetes, prior stroke, prior heart failure, prior unstable angina, and absence of revascularization were associated with worse long-term prognosis. CONCLUSIONS: The risk of MACE remains elevated among post-MI patients after the first year. Interventions to more intensively monitor, evaluate, and treat these patients should be considered beyond the first year following myocardial infarction.
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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 | 0.000 |
| 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".