Pre-treatment with antiarrhythmic drugs for elective electrical cardioversion of atrial fibrillation: a systematic review and network meta-analysis
Bibliographic record
Abstract
AIMS: Our objective was to compare the efficacy of pre-treatment with different classes of anti-arrhythmic drugs (AADs) in patients with atrial fibrillation (AF) undergoing electrical cardioversion. METHODS AND RESULTS: We performed a systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) comparing different AADs in patients with AF undergoing electrical cardioversion. We grouped AADs into five network nodes: no treatment or rate control, Class Ia, Class Ic, Class III, and amiodarone. Outcomes were (i) acute restoration and (ii) maintenance of sinus rhythm. We searched MEDLINE and EMBASE from inception until June 2020. We used Python 3.8.3 and R 3.6.2 for data analysis. We evaluated the overall certainty of evidence with the GRADE framework. We included 28 RCTs. Compared with no treatment or rate control, Class III AADs [odds ratio (OR): 2.41; 95% credible interval (CrI): 1.37 to 4.62, high certainty] and amiodarone (OR: 2.58; 95% CrI: 1.54 to 4.37, high certainty) improved restoration of sinus rhythm. Amiodarone improved long-term maintenance of sinus rhythm when compared with no treatment or rate control (OR: 5.37; 95% CrI: 4.00-7.39, high certainty), Class Ic (OR: 1.89; 95% CrI: 1.05-3.45, moderate certainty) and Class III AADs (OR: 2.19; 95% CrI: 1.39-3.26, high certainty). CONCLUSION: Before electrical cardioversion of AF, treatment with Class III AADs or amiodarone improves the acute restoration of sinus rhythm. Amiodarone is most likely to improve the maintenance of sinus rhythm after electrical cardioversion, but Class Ic and Class III AADs are also effective.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".