Characteristics and outcomes of atrial fibrillation in patients without traditional risk factors: an RE-LY AF registry analysis
Bibliographic record
Abstract
AIMS: Data on patient characteristics, prevalence, and outcomes of atrial fibrillation (AF) patients without traditional risk factors, often labelled 'lone AF', are sparse. METHODS AND RESULTS: The RE-LY AF registry included 15 400 individuals who presented to emergency departments with AF in 47 countries. This analysis focused on patients without traditional risk factors, including age ≥60 years, hypertension, coronary artery disease, heart failure, left ventricular hypertrophy, congenital heart disease, pulmonary disease, valve heart disease, hyperthyroidism, and prior cardiac surgery. Patients without traditional risk factors were compared with age- and region-matched controls with traditional risk factors (1:3 fashion). In 796 (5%) patients, no traditional risk factors were present. However, 98% (779/796) had less-established or borderline risk factors, including borderline hypertension (130-140/80-90 mmHg; 47%), chronic kidney disease (eGFR < 60 mL/min; 57%), obesity (body mass index > 30; 19%), diabetes (5%), excessive alcohol intake (>14 units/week; 4%), and smoking (25%). Compared with patients with traditional risk factors (n = 2388), patients without traditional risk factors were more often men (74% vs. 59%, P < 0.001) had paroxysmal AF (55% vs. 37%, P < 0.001) and less AF persistence after 1 year (21% vs. 49%, P < 0.001). Furthermore, 1-year stroke occurrence rate (0.6% vs. 2.0%, P = 0.013) and heart failure hospitalizations (0.9% vs. 12.5%, P < 0.001) were lower. However, risk of AF-related re-hospitalization was similar (18% vs. 21%, P = 0.09). CONCLUSION: Almost all patients without traditionally defined AF risk factors have less-established or borderline risk factors. These patients have a favourable 1-year prognosis, but risk of AF-related re-hospitalization remains high. Greater emphasis should be placed on recognition and management of less-established or borderline risk factors.
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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".