Prevalence and predictors of atrial fibrillation type among individuals with recent onset of atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: Atrial fibrillation (AF) is considered to be a progressive disease, starting with intermittent episodes that progress over time to more sustained events. However, little is known about the prevalence of and predictors for AF type among patients with recent-onset AF. We aimed to address these issues among a selected population of patients with AF. METHODS: The Basel atrial fibrillation cohort (BEAT-AF) study is an ongoing prospective multicentre cohort study among patients with AF. At baseline, we obtained information on the date of AF diagnosis, AF type, comorbidities, medication and lifestyle factors. For this analysis, 486 (31.4%) out of 1550 participants with recent-onset AF (defined as AF duration <24 months) were included. Predictors for AF type (non-paroxysmal vs paroxysmal) were obtained using multivariable adjusted logistic regression models. RESULTS: Mean age was 67 (59-75) years and 136 (28%) were women. Recent-onset paroxysmal AF was observed in 301 (62%) participants, 185 (38%) had non-paroxysmal AF - persistent AF in 148 (30.4%) and permanent AF in 37 (7.6%). In multivariable models, odds ratios for having non-paroxysmal AF around AF diagnosis were 1.03 per year increasing in age (95% confidence interval [CI] 1.01-1.05, p = 0.01); 2.70 (1.5-4.68, p = 0.0004) for history of heart failure; 3.82 (1.05-13.87, p = 0.04) for a history of hyperthyroidism and 1.04 (1.02-1.05, p <0.0001) per beat increase in heart rate. CONCLUSION: We found a substantial proportion of AF patients with the non-paroxysmal form shortly after diagnosis. Predictors for non-paroxysmal AF were increasing age, history of heart failure or hyperthyroidism, and a higher heart rate.
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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 | 0.001 | 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 | 0.000 | 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 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".