Efficacy and Safety of Dronedarone in Patients Previously Treated With Other Antiarrhythmic Agents
Bibliographic record
Abstract
BACKGROUND: Currently available antiarrhythmic drugs (AADs) for the prevention of atrial fibrillation (AF)/atrial flutter (AFL) suffer from incomplete efficacy and poor tolerability. HYPOTHESIS: Dronedarone could represent an effective and safe option in patients previously treated with AADs, especially class Ic AADs and sotalol. METHODS: Retrospective analysis of 2 double-blind, parallel-group trials (EURIDIS [European Trial in Atrial Fibrillation or Flutter Patients Receiving Dronedarone for the Maintenance of Sinus Rhythm] and ADONIS [American-Australian-African Trial With Dronedarone in Atrial Fibrillation or Flutter Patients for the Maintenance of Sinus Rhythm]) comparing the efficacy and safety of dronedarone with placebo over 12 months. The primary end point was AF/AFL recurrence in patients previously treated with another AAD that was discontinued for whatever reason prior to randomization. RESULTS: In patients previously treated with any AADs, dronedarone decreased the risk of AF recurrence by 30.4% vs placebo (hazard ratio [HR]: 0.70; 95% confidence interval [CI]: 0.59-0.82; P < 0.001). In patients previously treated with a class Ic agent, dronedarone decreased the risk of recurrence by 31.4% (HR: 0.69; 95% CI: 0.53-0.89; P = 0.004), whereas in patients previously treated with sotalol, dronedarone showed a trend toward a decrease of risk of recurrence (HR: 0.86; 95% CI: 0.67-1.11; P = 0.244). Dronedarone was equally effective irrespective of whether class Ic or sotalol were stopped for lack of efficacy or adverse events (AEs). Discontinuation rates were similar in the 2 groups (55.9% vs 43.1%), as were incidence of AEs and serious AEs. CONCLUSIONS: Dronedarone seems to be effective in preventing AF recurrences in patients without permanent AF previously treated with other AADs, even if those were discontinued for lack of efficacy. Dronedarone appears to be well tolerated even in patients who already had tolerability issues with AADs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".