Patients With Atrial Fibrillation and an Alternative Primary Diagnosis in the Emergency Department: A Description of their Characteristics and Outcomes
Bibliographic record
Abstract
OBJECTIVES: Atrial fibrillation is common in the emergency department (ED). Mortality rates at 30, 90, and 365 days for ED patients with a main diagnosis of atrial fibrillation are 4, 6, and 11%, respectively; there are no data on the characteristics and outcomes of ED patients with atrial fibrillation who have alternative primary ED diagnoses. METHODS: In this single-site, retrospective cohort study, all electrocardiograms (ECGs) with confirmed atrial fibrillation performed in the ED from April 2007 to March 2008 were identified. Repeat ED visits were excluded. ECGs associated with a primary ED diagnosis of atrial fibrillation were excluded, and from the remaining ECGs of patients with a different primary ED diagnosis, half were randomly selected for abstraction. The main outcome measure was all-cause mortality at 30, 90, and 365 days post-ED visit, derived from linkage to a provincewide mortality database. As a secondary analysis, logistic regression was used to compare 90-day mortality of these patients to those with primary ED diagnoses of atrial fibrillation seen during the same time period. RESULTS: Of 768 qualifying index ED visits, 416 charts were abstracted. Mean (± standard deviation [SD]) age was 80.3 (± 11.8) years, and 50.7% were female. Two-thirds had a previous history of atrial fibrillation/flutter, 300 (72.1%) had a CHADS2 score ≥ 2, one died in the ED, and 275 (66.1%) were admitted. The most common primary ED diagnoses were congestive heart failure (12%), pneumonia (6%), and chest pain not yet diagnosed (6%), while most common in-hospital diagnoses were congestive heart failure (15%), chronic obstructive pulmonary disease exacerbation (6%), atrial fibrillation (5%), and pneumonia (5%). Mortalities at 30, 90, and 365 days were 10.6% (95% confidence interval [CI] = 7.8% to 14.0%), 17.4% (95% CI = 13.9% to 21.5%), and 34.2% (95% CI = 29.6% to 39.0%), respectively. In the adjusted analysis, an alternative primary ED diagnosis was associated with an increased risk of death (odds ratio [OR] = 2.75; p = 0.01). CONCLUSIONS: Patients seen in the ED with atrial fibrillation and different primary ED diagnoses are older and have high short- and long-term mortality rates: mortality was three times higher than in patients with primary ED diagnoses of atrial fibrillation. Future studies of atrial fibrillation in the ED should distinguish between these two populations and the potential contribution of atrial fibrillation to mortality in the setting of other primary ED diagnoses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".