National trends in coronary intensive care unit admissions, resource utilization, and outcomes
Bibliographic record
Abstract
Background: Emerging evidence suggests that coronary intensive care units are evolving into intensive care environments with an increasing burden of non-cardiovascular illness, but previous studies have been limited to older populations or single center experiences. Methods: Canadian national health-care data was used to identify all patients ≥18 years admitted to dedicated coronary intensive care units (2005–2015) and admissions were categorized as primary cardiac or non-cardiac. The outcomes of interest included longitudinal trends in admission diagnoses, critical care therapies, and all-cause in-hospital mortality. Results: Among the 373,992 patients admitted to a coronary intensive care unit, minimal changes in the proportion of patients admitted with a primary cardiac (88.2% to 86.9%; p<0.001) and non-cardiac diagnoses (11.8% to 13.1%; p<0.001) were observed. Among cardiac admissions, a temporal increase in the proportion of ST-segment elevation myocardial infarction (19.4% to 24.1%, p<0.001), non-ST-segment elevation myocardial infarction (14.6% to 16.2%, p<0.001), heart failure (7.3% to 8.4%, p<0.001), shock (4.9% to 5.7%, p<0.001), and decline in unstable angina (4.9% to 4.0%, p<0.001) and stable coronary diseases (21.3% to 12.4%, p<0.001) was observed. The proportion of patients requiring critical care therapies (57.8% to 63.5%, p<0.001) including mechanical ventilation (9.6% to 13.1%, p<0.001) increased. In-hospital mortality rates for patients with primary cardiac (4.9% to 4.4%; adjusted odds ratio 0.71, 95% confidence interval 0.63–0.79) and non-cardiac (17.8% to 16.1%; adjusted odds ratio 0.84, 0.73–0.97) declined; results were consistent when stratified by academic vs community hospital, and by the presence of on-site percutaneous coronary intervention. Conclusion: In a national dataset we observed a changing case-mix among patients admitted to a coronary intensive care unit, though the proportion of patients with a primary cardiac diagnosis remained stable. There was an increase in clinical acuity highlighted by critical care therapies, but in-hospital mortality rates for both primary cardiac and non-cardiac conditions declined across all hospitals. Our findings confirm the changing coronary intensive care unit case-mix and have implications for future coronary intensive care unit training and staffing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".