Colchicine to Prevent Atrial Fibrillation Recurrence After Catheter Ablation: A Randomized, Placebo-Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Inflammation may promote atrial fibrillation (AF) recurrence after catheter ablation. This study aimed to evaluate a short-term anti-inflammatory treatment with colchicine following ablation of AF. METHODS: Patients scheduled for ablation were randomized to receive colchicine 0.6 mg twice daily or placebo for 10 days. The first dose of the study drug was administered within 4 hours before ablation. Atrial arrhythmia recurrence was defined as AF, atrial flutter, or atrial tachycardia >30 s on two 14-day Holters performed immediately and at 3 months following ablation. RESULTS: The modified intention-to-treat population included 199 patients (median age, 61 years; 22% female; 70% first procedure) who underwent radiofrequency (79%) or cryoballoon ablation (21%) of AF. Antiarrhythmic drugs were prescribed at discharge in 149 (75%) patients. Colchicine did not prevent atrial arrhythmia recurrence at 2 weeks (31% versus 32%; hazard ratio [HR], 0.98 [95% CI, 0.59–1.61]; P =0.92) or at 3 months following ablation (14% versus 15%; HR, 0.95 [95% CI, 0.45–2.02]; P =0.89). Postablation chest pain consistent with pericarditis was reduced with colchicine (4% versus 15%; HR, 0.26 [95% CI, 0.09–0.77]; P =0.02) and colchicine increased diarrhea (26% versus 7%; HR, 4.74 [95% CI, 1.95–11.53]; P <0.001). During a median follow-up of 1.3 years, colchicine did not reduce a composite of emergency department visit, cardiovascular hospitalization, cardioversion, or repeat ablation (29 versus 25 per 100 patient-years; HR, 1.18 [95% CI, 0.69–1.99]; P =0.55). CONCLUSIONS: Colchicine administered for 10 days following catheter ablation did not reduce atrial arrhythmia recurrence or AF-associated clinical events, but did reduce postablation chest pain and increase diarrhea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".