Providing optimal regional care for ST-segment elevation myocardial infarction: a prospective cohort study of patients in the Hamilton Niagara Haldimand Brant Local Health Integration Network
Bibliographic record
Abstract
BACKGROUND: Although considered the evidence-based best therapy for ST-segment elevation myocardial infarction (STEMI), many patients do not receive primary percutaneous coronary intervention (PCI) because of health care resource distribution and constraints. This study describes the clinical management and outcomes of all patients identified with STEMI within a region, including those who did not receive primary PCI. METHODS: This study used a prospective cohort design. Patients presenting with STEMI to PCI- and non-PCI-capable hospitals in one integrated health region in Ontario were included in the study. The primary objective was to examine use of reperfusion strategies and timeliness of care. Secondary objectives included determining (through regression models) which variables were associated with mortality within 90 days, and describing patient uptake of risk-reducing therapies and activities post-STEMI. RESULTS: Between Apr. 1, 2010, and Mar. 31, 2013, data were collected on 2247 consecutive patients presenting with STEMI. Patients presenting to the PCI-capable hospital were more likely to receive primary PCI (82.5% v. 65.2%, p < 0.001) and be treated within optimal treatment times. However, there was no appreciable difference in mortality at 90 days post-STEMI between patients presenting to PCI- and non-PCI-capable hospitals (7.8% v. 7.5%, p = 0.82), even after adjustment for acuity on presentation. Despite recognized risk factors, many patients were not taking evidence-based medications for risk factor modification before STEMI. INTERPRETATION: A systematic approach to regional STEMI care focusing on timely access to the best available therapies, rather than the type of reperfusion provided alone, can yield favourable outcomes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".