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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".