Risk prediction of atrial fibrillation progression in patients with paroxysmal atrial fibrillation: data from the RACE V study
Bibliographic record
Abstract
Background: Atrial Fibrillation (AF) may progress from paroxysmal AF (PAF) to more sustained forms, but predicting which patients progress remains a challenge. The RACE V study is a prospective, observational study aiming to characterize phenotypical differences between patients with and without AF progression. Based on interim data of the RACE V study, a clinical risk prediction model for AF progression in patients with PAF was previously developed. The aim of the current analysis is to assess its performance in the complete cohort over extended follow-up. Methods: In the RACE V study, 612 patients with PAF were extensively phenotyped and continuously monitored using implantable loop recorders to track AF recurrences. AF progression was the primary outcome, defined as (1) progression to persistent or permanent AF, or (2) AF burden increase > 3%, during complete follow-up. The risk score incorporates five clinical predictors at baseline: sex, PR interval duration, left atrial contractile function, waist circumference, and presence of mitral valve regurgitation. Prediction model performance was assessed using receiver operating characteristic (ROC) curve-derived area under the curve (AUC). Results: in the full cohort, compared with 0.709 (95% CI: 0.617-0.801) in the interim cohort (DeLong's unpaired test, p = 0.656). Conclusions: The model may help clinicians identify patients at risk of progression, showing stable performance in the whole cohort over extended follow-up.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".