360Role of cardioversion in the management of non-valvular atrial fibrillation: insights from the GARFIELD-AF registry
Bibliographic record
Abstract
Introduction: In atrial fibrillation (AF), a strategy of rhythm control based on cardioversion, by restoring sinus rhythm, may reduce the risk of stroke/systemic embolization (SE) and improve quality of life. However, randomized trials conducted so far have failed to show a benefit of cardioversion on hard endpoints. This study aims to investigate the prevalence of cardioversion and its association with clinical outcomes in patients from GARFIELD-AF registry. Methods: GARFIELD-AF is a prospective, global registry of patients with recent-onset (<6 weeks) non-valvular AF (NVAF). Patients were enrolled in 32 countries between 2010 and 2016, patients with paroxysmal AF were excluded from the analysis. Comparisons were made between those receiving cardioversion at baseline and patients who had no cardioversion. Clinical endpoints, evaluated over 1 year, were: all-cause mortality, stroke or systemic embolism (SE) and major bleeding. An adjusted Cox proportional hazard model was utilized. Results: The study cohort consisted of 23,919 patients; 2856 received cardioversion (11.9%). Patients who were treated with cardioversion were younger (65.5±11.7 years vs 70.6±11.3 years; p≤0.001), had a shorter time since AF diagnosis (1.7±1.6 weeks vs 2.0±1.7 weeks; p≤0.001), and were more often treated in a cardiology setting (73.9% vs 63.5%; p≤0.001). Event rates per 100 person years (95% CI) for all-cause mortality were 3.26% (2.65–4.01) (n=89) for cardioversion vs 5.40% (5.09–5.76) (n=1072) for no cardioversion, (Fig.1); stroke/SE rate 0.96% (0.65–1.40) (n=26) for cardioversion and 1.48% (1.32–1.65) (n=291) no cardioversion and major bleed 0.66% (0.42–1.05) (n=18) cardioversion vs 0.93% (0.80–1.07) (n=183) no cardioversion. Adjusted hazard ratios (95% CI) for cardioversion were 0.69 (0.53–0.90) for all-cause mortality, 0.92 (0.58–1.44) for stroke/SE and 0.82 (0.46–1.47) for major bleed.
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 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".