Predisposing Factors Associated With Development of Persistent Compared With Paroxysmal Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Once atrial fibrillation (AF) progresses to sustained forms, adverse outcomes increase and treatment success rates decrease. Therefore, identification of risk factors predisposing to persistence of AF may have a significant impact on AF morbidity. METHODS AND RESULTS: We prospectively examined the differential associations between traditional, lifestyle, and biomarker AF risk factors and development of paroxysmal versus nonparoxysmal AF (persistent/permanent) among 34 720 women enrolled in the Women's Health Study who were free of cardiovascular disease and AF at baseline. AF patterns were defined based on current guidelines and classified according to the most sustained form of AF within 2 years of diagnosis. During a median follow-up of 16.4 years, 690 women developed paroxysmal AF and 349 women developed nonparoxysmal AF. In multivariable time-varying competing risk models, increasing age (hazard ratio [HR] 1.11, 95% CI 1.10 to 1.13, versus HR 1.08, 1.07 to 1.09, per year), body mass index (HR 1.07, 1.05 to 1.09, versus HR 1.03, 1.02 to 1.05, per kg/m(2)), and weight (HR 1.30, 1.22 to 1.39, versus HR 1.14, 1.08 to 1.20, per 10 kg) were more strongly associated with the development of nonparoxysmal AF compared with paroxysmal AF. Hemoglobin A1c levels at baseline were directly related to the development of nonparoxysmal AF but inversely associated with paroxysmal AF in multivariable competing risk models (P for nonequal association=0.01). CONCLUSIONS: In women without AF or CVD at baseline, increasing age, adiposity, and higher hemoglobin A1c levels were preferentially associated with the early development of nonparoxysmal AF. These data raise the hypothesis that efforts aimed at weight reduction or glycemic control may affect the proportion of the population with sustained AF.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".