Incremental costs of high intensive care utilisation in patients hospitalised with heart failure
Bibliographic record
Abstract
Aims: Registries have reported large inter-hospital differences in intensive care unit admission rates for patients with acute heart failure, but little is known about the potential economic impact of over-admission of low-risk patients with heart failure to higher cost intensive care units. We described the variability in intensive care unit admission practices, the provision of critical care therapies, and estimated the potential national cost savings if all hospitals adopted low intensive care unit admission practices for patients admitted with heart failure. Methods: Using a national population health dataset, we identified 349,693 heart failure admission hospitalisations with a primary diagnosis of heart failure between 2007 and 2016. Hospitals were categorised as low (first quartile), medium (second and third quartile) and high (fourth quartiles) intensive care unit utilisation. Results: The mean intensive care unit admission rate was 16.4% (inter-hospital range 0.3–51%) including 5.4% in low, 14.5% in medium and 30% in high utilisation hospitals. Intensive care unit therapies in low, medium and high intensive care unit utilisation hospitals were 54.5%, 45.1% and 24.1% ( P<0.001), respectively and the inhospital mortality rate was not significantly different. The proportion of hospital costs incurred by intensive care unit care was 7.8% in low, 19.8% in medium and 28.2% in high ( P<0.001) admission hospitals. The potential cost savings of altering intensive care unit utilisation practices for patients with heart failure was CAN$234.8m over the study period. Conclusions: In a national cohort of patients hospitalised with heart failure, we observed that low intensive care unit utilisation centres had lower hospital costs with no differences in mortality rates. The development of standardised admission criteria for high-cost and high acuity intensive care unit beds could reduce costs to the healthcare system.
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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 | 0.001 |
| 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".