Effects of Dronedarone on Clinical Outcomes in Patients with Lone Atrial Fibrillation: Pooled Post Hoc Analysis from the ATHENA/EURIDIS/ADONIS Studies
Bibliographic record
Abstract
INTRODUCTION: Dronedarone has been shown to reduce cardiovascular hospitalizations or death in patients with atrial fibrillation (AF) and additional risk factors. This post hoc exploratory analysis examines its effects in the subgroup of lone AF patients. METHODS AND RESULTS: Individual data from patients with lone AF enrolled in the EURIDIS, ADONIS, and ATHENA trials were entered in a central database. The effects of dronedarone compared to placebo on the composite endpoint of cardiovascular hospitalizations or death, and their individual components, were evaluated. A total of 432 (192 placebo and 240 dronedarone) patients (7% of the total population) were classified as having lone AF (69.4% male patients, mean age 64 ± 13 years). The patients were followed for 13.8 ± 7.2 months. The risk for first cardiovascular hospitalizations or death from any cause in the placebo group after 1 year was 25% in the lone AF group compared to 29% the rest of the population. For patients with lone AF, dronedarone led to a 44% reduction of cardiovascular hospitalizations or death (hazard ratio (HR) 0.56; 95%CI 0.36-0.88, P = 0.004) and to a 46% reduction in cardiovascular hospitalizations alone (HR 0.54; 95%CI 0.34-0.87, P = 0.004) compared to placebo. HR for all-cause mortality was 1.02 (95%CI 0.31-3.34, P = 0.885). All findings were homogeneous across the 3 studies and similar to those observed in the overall population. CONCLUSION: According to this post hoc analysis, patients with lone AF have a high risk for cardiovascular hospitalization within 1 year. Dronedarone when added to standard of care reduces the risk of cardiovascular hospitalizations in this population.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".