Abstract 14227: Development of a Tool To Improve Risk Stratification of Patients With Atrial Fibrillation in the Emergency Department
Bibliographic record
Abstract
Introduction: Currently, no tool exists to help emergency department (ED) physicians determine which patients with atrial fibrillation (AF) may be low risk for adverse outcomes and potential candidates for expedited discharge. Hypothesis: A simple decision aid can assess 90-day risk for a combined outcome of mortality, stroke, or re-presentation to the ED in AF patients. Methods: Through multi-disciplinary meetings, clinically relevant and easily attainable factors were selected to risk stratify AF patients in the ED, including: primary ED diagnosis (AF or other), age, AF symptom severity (Canadian Cardiovascular Society - Symptom of AF [CCS-SAF]), heart rate, blood pressure, and laboratory assessment. Logistic regression analyses were used to develop a scoring tool. Results: Over 15 months (Jan 2015 - Mar 2016), 935 consecutive patients presenting to the ED of an academic medical center hospital (University of North Carolina) with AF were included in the cohort. Ninety days from presentation, there were 275 (29.4%) adverse events. In multivariable analyses, 4 factors were identified which independently associated events among all patients with AF: non-AF primary ED diagnosis, age, heart rate, and severe AF symptoms (CCS-SAF 4). Using a scoring tool with these 4 factors (range 0-18), a score ≥ 14 predicted events with 72% accuracy. Including only the 223 patients with a primary diagnosis of AF, using a scoring tool with 3 factors (age ≥ 70, heart rate ≥ 105, severe AF symptoms) a score ≥ 10 (range 0-13) predicted events with 78% accuracy (25% sensitivity, 95% specificity). Conclusions: A simple risk stratification scoring tool may be useful to determine patient disposition for AF patients in the ED. In patients with a primary diagnosis of AF, the scoring tool was particularly useful (95% specific) in identifying patients unlikely to have an event. Further analyses of larger data sets are required for validation.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".