Dronedarone vs. placebo in patients with atrial fibrillation or atrial flutter across a range of renal function: a<i>post hoc</i>analysis of the ATHENA trial
Bibliographic record
Abstract
AIMS: Use of antiarrhythmic drugs (AADs) in patients with chronic kidney disease (CKD) is challenging owing to issues with renal clearance, drug accumulation, and increased proarrhythmic risks. Because CKD is a common comorbidity in patients with atrial fibrillation/atrial flutter (AF/AFL), it is important to establish the efficacy and safety of AAD treatment in patients with CKD. METHODS AND RESULTS: Dronedarone efficacy and safety in individuals with AF/AFL and varying renal functionality [estimated glomerular filtration rate (eGFR): ≥60, ≥45 and <60, and <45 mL/min] was investigated in a post hoc analysis of ATHENA (NCT00174785), a randomized, double-blind trial of dronedarone vs. placebo in patients with paroxysmal or persistent AF/AFL plus additional cardiovascular (CV) risk factors. Log-rank testing and Cox regression were used to compare the incidence of endpoints between treatments. Overall, 4588 participants were enrolled from the trial. There was no interaction between treatment group and baseline eGFR assessed as a continuous variable (P = 0.743) for the first CV hospitalization or death from any cause (primary outcome). This outcome was lower with dronedarone vs. placebo across a wide range of renal function. First CV hospitalization and first AF/AFL recurrence were both lower in the two least renally impaired subgroups with dronedarone vs. placebo. Treatment emergent adverse events leading to treatment discontinuation were more frequent with dronedarone vs. placebo and occurred more often in patients with severe renal impairment. CONCLUSION: Dronedarone is an effective AAD in patients with AF/AFL and CV risk factors across a wide range of renal function.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".