Ranolazine Added to Amiodarone Facilitates Earlier Conversion of Atrial Fibrillation Compared to Amiodarone‐Only Therapy
Bibliographic record
Abstract
BACKGROUND: Amiodarone (AMIO) is for many years effectively used to control ventricular rate during atrial fibrillation (AF) and to convert it into sinus rhythm. However, due to its delayed onset of action, ranolazine (RAN), a new antianginal agent with atrial-selective electrophysiologic properties, has recently been attempted as add-on therapy with AMIO to facilitate AF conversion. METHODS: To establish the role of this combination therapy, we enrolled 173 consecutive patients (68 ± 10 years, 54% male) with recent-onset (<48-hour duration) AF who were eligible for pharmacologic cardioversion. Patients were randomized to intravenous AMIO (loading dose 5 mg/kg in 1 hour followed by 50 mg/h; n = 81), or AMIO plus a single oral dose of RAN 1 g (n = 92). RESULTS: Mean left atrial diameter did not significantly differ between groups, AMIO and AMIO + RAN (4.2 ± 0.5 cm vs 4.1 ± 0.4 cm, P = 0.18). The AMIO + RAN group compared with the AMIO-only group showed significantly shorter time to conversion (8.6 ± 2.8 hours vs 19.4 ± 4.4 hours, P < 0.0001) and higher conversion rate at 24 hours (98% vs 58%, P < 0.001). Left ventricular ejection fraction did not markedly vary between the two groups and ranged within moderately reduced values. No serious clinically evident adverse effects were observed in any of the patients receiving either AMIO or the combination treatment. CONCLUSIONS: Our data demonstrate faster sinus rhythm restoration and enhanced conversion rate of AF after AMIO plus RAN in patients with preserved ejection fraction and left atrial size, implicating a synergistic effect of the two agents.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".