Hospital treatment costs and length of stay associated with hypertension and multimorbidity after hemorrhagic stroke
Bibliographic record
Abstract
BACKGROUND: Previous studies have identified various treatment and patient characteristics that may be associated with higher hospital cost after spontaneous intracerebral hemorrhage (ICH); a devastating type of stroke. Patient morbidity is perhaps the least understood of these cost-driving factors. We describe how hypertension and other patient morbidities affect length of stay, and hospital treatment costs after ICH using primary and simulated data. We also describe the relationship between cost and length of stay within these patients. METHODS: We used a cohort design; evaluating 987 consecutive ICH patients across one decade in a Canadian center. Economic, treatment, and patient data were obtained from clinical and administrative sources. Multimorbidity was defined as the presence of one or more diagnoses at hospital admission in addition to a primary diagnosis of ICH. RESULTS: Hypertension was the most frequent (67%) morbidity within these patients, as well as the strongest predictor of longer stay (adjusted RR for >7 days: 1.31, 95% CI: 1.07-1.60), and was significantly associated with higher cost per visit when accounting for other morbidities (adjusted cost increase for hypertension $8123.51, 95% CI: $4088.47 to $12,856.72 USD). A Monte Carlo simulation drawing one million samples of patients estimated for a generation (100 years) assuming 0.94% population growth per year, and a hospitalization rate of 12 per 100,000 inhabitants, supported these findings (p = 0.516 for the difference in unadjusted cost: simulated vs primary). Using a restricted cubic spline, we observed that the rate of change in overall cost for all patients was greatest for the first 3 weeks (p < 0.001) compared to subsequent weeks. CONCLUSION: Patient multimorbidity, specifically hypertension, is a strong predictor of longer stay and cost after ICH. The non-linear relationship between cost and time should also be considered when forecasting healthcare spending in these patients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".