47Characteristics and outcomes of atrial fibrillation in patients without conventional risk factors: A RE-LY AF registry analysis
Bibliographic record
Abstract
Funding Acknowledgements: Heart and Stroke Foundation of Ontario Mid-Career Award (MC7450), and CVON 2014-9 with support of the Dutch Heart Foundation Background: Lone atrial fibrillation (AF) lacks a widely accepted definition, and data on prevalence, risk factors, and prognosis outside of North America and Western Europe are sparse. Purpose: To study prevalence, characteristics, and outcomes in patients with lone AF from multiple regions of the world included in the RE-LY AF registry. Methods: The RE-LY AF registry prospectively enrolled 15400 patients, between December 2007 and October 2011, who presented to an emergency department with AF from 164 sites in 47 countries. We identified patients with classically-defined lone AF: age<60 years, without hypertension, coronary artery disease, heart failure, left ventricular hypertrophy, congenital heart disease, pulmonary disease, valve disease, hyperthyroidism, or recent cardiac surgery. All patients with lone AF as secondary diagnosis were manually screened to additionally exclude those with triggered AF (e.g. pericarditis, myocarditis). Results: Classically-defined lone AF was present in 796 patients (5%). In 779 (98%) patients additional risk factors were present, including borderline hypertension (130-140/80-90 mmHg; 47%), kidney disease (eGFR<60 ml/min; 57%), obesity (BMI>30; 19%), diabetes (5%), excessive alcohol intake (>14 units/week; 4%), smoking (25%), and sleep apnea (2%). ‘’Truly lone AF’’, excluding these additional risk factors was extremely rare (17 patients [0.1%]). In North America/Western Europe obesity was common (30%), while excessive alcohol was in Africa (22%), and diabetes in the Middle East (11%). During one year follow-up, patients returned to the emergency department 1.2±1.3 times. Hospitalization for AF was required in 141 (18%) patients, 7 (0.9%) patients were hospitalized for heart failure, of which 3 (8%) in Africa. A total of 13 patients died (1.6%), 5 (0.6%) suffered a stroke, and 3 patients (0.4%) experienced major bleeding without differences between world regions. Conclusions: Classically-defined lone AF is uncommon among patients presenting to the emergency department. It is typically associated with additional risk factors that vary between regions. True lone AF is very rare. During 1 year follow-up lone AF poses a short-term risk of stroke and death, but a high rate of recurrent AF hospitalizations.
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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) | 0.000 | 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".