P5132External validation and improvement of EHMRG risk model using a population-based cohort of patients with heart failure
Bibliographic record
Abstract
Background: Emergency Heart Failure Mortality Risk Grade (EHMRG) is a 10-item risk score that was developed to assess the risk of dying in the next 7 days for patients with acute heart failure (AHF) in the emergency department (ED). However, it lacks key variables including natriuretic peptide (NP) values and widely used triage scores. Purpose: We aimed to externally validate and refine the EHMRG risk model using a cohort of patients who presented to ED via ambulance with AHF. Methods: Cohort study using administrative data of all ambulance-transported patients from Alberta (2012 - 2016) presenting to the ED with a primary diagnosis of acute HF (ICD-10 I50.x). Data were linked to laboratory data for EHMRG variables. The C-index and Net reclassification improvement (NRI) were used to assess overall model quality. Results: The cohort consisted of 6,708 patients with AHF. The 7-day mortality was 0.9%, 2.8%, 4.2%, 4.6%, and 13.3%, across the 1st to 5th quintiles. The EHMRG score had a c-index of 0.73 (95% CI 0.71 to 0.76) and 0.71 (95% CI 0.70 to 0.73) for identifying patients at risk of 7-day and 30-day mortality. Addition of NP (BNP or NT-proBNP) to the EHMRG model improved the net re-classification index of patients (p<0.01) for 7-day mortality as did the addition of the Canadian Triage & Acuity Scale (CTAS) (p<0.02). The EHMRG model had a reduced discriminatory performance without inclusion of the troponin component with an NRI of -0.27 (95% CI -0.36 to -0.17, p<0.01) for predicting 7-day mortality. There was no association between the use of metolazone and 7-day mortality, and its removal did not alter the model's predictive ability (p=0.9).
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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 | 0.037 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".