Variation in Critical Care Unit Admission Rates and Outcomes for Patients With Acute Coronary Syndromes or Heart Failure Among High‐ and Low‐Volume Cardiac Hospitals
Bibliographic record
Abstract
BACKGROUND: Little is known about cross-hospital differences in critical care units admission rates and related resource utilization and outcomes among patients hospitalized with acute coronary syndromes (ACS) or heart failure (HF). METHODS AND RESULTS: Using a population-based sample of 16,078 patients admitted to a critical care unit with a primary diagnosis of ACS (n=14,610) or HF (n=1467) between April 1, 2003 and March 31, 2013 in Alberta, Canada, we stratified hospitals into high (>250), medium (200 to 250), or low (<200) volume based on their annual volume of all ACS and HF hospitalization. The percentage of hospitalized patients admitted to critical care units varied across low, medium, and high-volume hospitals for both ACS and HF as follows: 77.9%, 81.3%, and 76.3% (P<0.001), and 18.0%, 16.3%, and 13.0% (P<0.001), respectively. Compared to low-volume units, critical care patients with ACS and HF admitted to high-volume hospitals had shorter mean critical care stays (56.6 versus 95.6 hours, P<0.001), more critical care procedures (1.9 versus 1.2 per patient, <0.001), and higher resource-intensive weighting (2.8 versus 1.5, P<0.001). No differences in in-hospital mortality (5.5% versus 6.2%, adjusted odds ratio 0.93; 95% CI, 0.61 to 1.41) were observed between high- and low-volume hospitals; however, 30-day cardiovascular readmissions (4.6% versus 6.8%, odds ratio 0.77; 95% CI, 0.60 to 0.99) and cardiovascular emergency-room visits (6.6% versus 9.5%, odds ratio 0.80; 95% CI, 0.69 to 0.94) were lower in high-volume compared to low-volume hospitals. Outcomes stratified by ACS or HF admission diagnosis were similar. CONCLUSIONS: Cardiac patients hospitalized in low-volume hospitals were more frequently admitted to critical care units and had longer hospitals stays despite lower resource-intensive weighting. These findings may provide opportunities to standardize critical care utilization for ACS and HF patients across high- and low-volume hospitals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".