Association Between the Emergency Heart Failure Mortality Risk Grade (EHMRG30-ST) and Costs of Care
Bibliographic record
Abstract
Background: The Emergency Heart Failure Mortality Risk Grade (EHMRG30-ST) is a clinically validated model that predicts 30-day mortality in heart failure patients presenting to the emergency department (ED). However, the relationship between the EHMRG30-ST score and costs of care remains unclear. In this study, we explored the relationship between the EHMRG30-ST score and costs of care at 90 and 365 days after the index ED visit, and identified predictors of costs at 90 days. Methods: We combined 2 chart review databases, including 11,407 patients presenting to the ED with heart failure between 2004-2007. We estimated the costs from administrative databases. We stratified patients into quintiles (Q) of EHMRG30-ST scores (Q1 = lowest risk; Q5 = highest risk), and compared the total costs in 2021 Canadian dollars at 90 and 365 days by score quintiles. We further examined the cost breakdown by categories. Finally, we used a generalized linear model to identify predictors of 90-day costs. Results: < 0.0001). A positive correlation between EHMRG30-ST risk quintile and cost was observed for subcategories of hospital costs, long-term-care, and home care. Major predictors of 90-day costs were ejection fraction measurement and troponin levels. Conclusions: Higher mortality risk as determined by the EHMRG30-ST score was associated with higher costs for up to 1 year after the initial ED presentation. Our study suggests that costs of care may be another potential dimension of the utility of prognostic risk scores.
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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 | 0.006 |
| 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 | 0.000 |
| 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".