The Admit-AF risk score: A clinical risk score for predicting hospital admissions in patients with atrial fibrillation
Bibliographic record
Abstract
AIMS: To develop and externally validate a risk score for all-cause hospital admissions in patients with atrial fibrillation. METHODS AND RESULTS: We used a prospective cohort of 2387 patients with established atrial fibrillation as derivation cohort. Independent risk factors were selected from a broad range of variables using the least absolute shrinkage and selection operator method fit to a Cox model. The risk score was validated in a separate prospective cohort of 1300 atrial fibrillation patients. The incidence of all-cause hospital admission was 19.1 per 100 person-years in the derivation cohort and it was 26.1 per 100 person-years in the validation cohort. The most important predictors for admission were age (75-79 years: adjusted hazard ratio (aHR), 1.34; 95% confidence interval (CI), 1.01-1.78; 80-84 years: aHR, 1.50; 95% CI, 1.11-2.03; ≥85 years: aHR, 1.88; 95% CI, 1.36-2.62), prior pulmonary vein isolation (aHR, 0.72; 95% CI, 0.58-0.88), hypertension (aHR, 1.16; 95% CI, 0.99-1.36), diabetes (aHR, 1.38; 95% CI, 1.17-1.62), coronary heart disease (aHR, 1.17; 95% CI, 1.02-1.36), prior stroke/transient ischaemic attack (aHR, 1.26; 95% CI, 1.18-1.47), heart failure (aHR, 1.19; 95% CI, 1.03-1.39), peripheral artery disease (aHR, 1.35; 95% CI, 1.08-1.67), cancer (aHR, 1.33; 95% CI, 1.12-1.57), renal failure (aHR, 1.17; 95% CI, 0.99-1.37) and previous falls (aHR, 1.40; 95% CI, 1.13-1.74). A risk score with these variables was well calibrated, and achieved a C-index of 0.64 in the derivation and 0.59 in the validation cohort. CONCLUSIONS: Multiple risk factors were associated with hospital admissions in atrial fibrillation patients. This prediction tool selects high-risk patients who may benefit from preventive interventions.
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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 | 0.003 | 0.011 |
| 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".