Characteristics of patients presenting to emergency department for primary atrial fibrillation or flutter at an academic medical center
Bibliographic record
Abstract
OBJECTIVE: In the United States, atrial fibrillation (AF) accounts for over 400,000 hospitalizations annually. Emergency Department (ED) physicians have few resources available to guide AF/AFL (atrial flutter) patient triage, and the majority of these patients are subsequently admitted. Our aim is to describe the characteristics and disposition of AF/AFL patients presenting to the University of North Carolina (UNC) ED with the goal of developing a protocol to prevent unnecessary hospitalizations. METHODS: We performed a retrospective electronic medical chart review of AF/AFL patients presenting to the UNC ED over a 15-month period from January 2015 to March 2016. Demographic and ED visit variables were collected. Additionally, patients were designated as either having primary or secondary AF/AFL where primary AF/AFL patients were those in whom AF/AFL was the primary reason for ED presentation. These primary AF/AFL patients were categorized by AF symptom severity score according to the Canadian Cardiovascular Society Severity of Atrial Fibrillation (CCS-SAF) Scale. RESULTS: A total of 935 patients presented to the ED during the study period with 202 (21.5%) having primary AF/AFL. Of the primary AF/AFL patients, 189 (93.6%) had mild-moderate symptom severity (CCS-SAF ≤ 3). The majority of primary AF/AFL patients were hemodynamically stable, with a mean (SD) SBP of 123.8 (21.3), DBP of 76.6 (14.1), and ventricular rate of 93 (21.9). Patients with secondary AF/AFL were older 76 (13.1), p < 0.001 with a longer mean length of stay 6.1 (7.7), p = 0.31. Despite their mild-moderate symptom severity and hemodynamic stability, nearly 2/3 of primary AF/AFL patients were admitted. CONCLUSION: Developing a protocol to triage and discharge hemodynamically stable AF/AFL patients without severe AF/AFL symptoms to a dedicated AF/AFL clinic may help to conserve healthcare resources and potentially deliver more effective care.
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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 | 0.001 |
| 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) | 0.002 | 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 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".