Factors contributing to high-cost hospital care for patients with COPD
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital admission, the fifth leading cause of death in North America, and is estimated to cost $49 billion annually in North America by 2020. The majority of COPD care costs are attributed to hospitalizations; yet, there are limited data to understand the drivers of high costs among hospitalized patients with COPD. In this study, we aimed to determine the patient and hospital-level factors associated with high-cost hospital care, in order to identify potential targets for the reorganization and planning of health services. We conducted a retrospective cohort study at a Canadian academic hospital between September 2010 and 2014, including adult patients with a first-time admission for COPD exacerbation. We calculated total costs, ranked patients by cost quintiles, and collected data on patient characteristics and health service utilization. We used multivariable regression to determine factors associated with highest hospital costs. Among 1,894 patients included in the study, the mean age was 73±12.6 years, median length of stay was 5 (interquartile range 3-9) days, mortality rate was 7.8% (n=147), and 9% (n=170) required intensive care. Hospital spending totaled $19.8 million, with 63% ($12.5 million) spent on 20% of patients. Factors associated with highest costs for COPD care included intensive care unit admission (odds ratio [OR] 32.4; 95% confidence interval [CI] 20.3, 51.7), death in hospital (OR 2.6; 95% CI 1.3, 5.2), discharge to long-term care facility (OR 5.7; 95% CI 3.5, 9.2), and use of the alternate level of care designation during hospitalization (OR 23.5; 95% CI 14.1, 39.2). High hospital costs are driven by two distinct groups: patients who require acute medical treatment for severe illness and patients with functional limitation who require assisted living facilities upon discharge. Improving quality of care and reducing cost in this high-needs population require a strong focus on early recognition and management of functional impairment for patients living with chronic disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".