Outcomes of Non-ST Elevation Myocardial Infarction Patients by Presentation Site: Rural, Urban Community, or Specialized Cardiac Hospital
Bibliographic record
Abstract
Background Although delays in treatment are known to worsen outcomes in ST-elevation myocardial infarction, their effect in non-ST-elevation myocardial infarction (NSTEMI) is less clear. Care quality and timely revascularization should be comparable across presentation sites to optimize patient outcomes. Methods Using the Manitoba Centre for Health Policy data, we retrospectively analyzed adult NSTEMI patients who underwent cardiac catheterization and revascularization from January 2001 to March 2021. Patients were grouped by initial presentation site—rural hospital, urban noncardiac hospital, or specialized cardiac centre. We assessed in-hospital, 1-year, and long-term outcomes. Results Of 30,817 NSTEMI patients, 19,482 underwent catheterization, and 12,567 received revascularization. Distribution by site was as follows: 44% at cardiac centres, 28.5% at urban noncardiac hospitals, and 27.5% at rural hospitals. Urban noncardiac hospital patients experienced significantly higher cardiovascular mortality in-hospital (hazard ratio [HR] 1.64; 95% confidence interval [CI] 1.09-2.47), at 1 year (HR 1.30; 95% CI 1.11-1.53), and over an average 6.65-year follow-up period (HR 1.15; 95% CI 1.07-1.24). Rural hospital patients showed a lower mortality incidence, potentially due to selection bias if critically ill patients did not survive the transfer. Both rural and urban noncardiac cohorts had elevated rates of major adverse cardiovascular events at all follow-up intervals. Time to catheterization was notably delayed for nonspecialized sites (cardiac centre, 0.83 ± 1.90 vs urban noncardiac 3.20 ± 3.05 vs rural, 3.09 ± 2.56 days; P < 0.001). Conclusions NSTEMI patients presenting to rural and urban nonspecialized hospitals experience worse short- and long-term outcomes, including increased incidence of major adverse cardiovascular events and mortality. These findings highlight the need for strategies to reduce disparities in access to specialized cardiac 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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".