P4601Differences in two-year outcomes according to type of atrial fibrillation: results from the GARFIELD-AF registry
Bibliographic record
Abstract
Purpose: Atrial fibrillation (AF) burden and type of AF have not been established as major differential predictors of stroke and death. The aim of this work was to analyse outcomes by type of AF and by antithrombotic therapy. Methods: 28,628 adults (≥18 yrs) with nonvalvular AF and ≥1 investigator-defined stroke risk factor were enrolled in the ongoing, prospective GARFIELD-AF registry from 32 countries in Mar 2010–Oct 2014. Patients classified as having paroxysmal (n=10,473, 48.5%), persistent (n=6020, 27.9%), or permanent AF (n=5117, 23.7%) by 4 mos were included in the analysis of baseline characteristics, antithrombotic therapy, and 2yr incidence of outcomes. Results: Patients with permanent AF had slightly higher CHA2DS2-VASc (3.5 vs both 3.1) and HAS-BLED (1.6 vs both 1.4) vs those with paroxysmal or persistent AF, and they were most likely to be ≥75 yrs (48.3% vs 33.6% vs 34.3%). Compared to patients with other AF types, those with paroxysmal AF were less likely to be obese (26.7% vs 30.9% vs 33.2%) or to have LVEF<40% (6.0% vs 12.0% vs 14.4%) or severe HF (NYHA Class III/IV; 25.3% vs 33.0% vs 38.8%), but they were as likely to have history of vascular disease: stroke/transient ischaemic attack 12.2% vs 10.7% vs 13.5%; carotid occlusive disease 2.9% vs 2.8% vs 4.1%; ACS 9.4% vs 8.3% vs 9.6%. Patients with paroxysmal AF were less likely to receive anticoagulant (AC) therapy (±antiplatelets, AP) vs those with persistent or permanent AF and more likely to receive AP only or no antithrombotics (Tab). Compared to patients with paroxysmal AF, those with persistent or permanent AF had higher risks of all-cause mortality, stroke/systemic embolism (SE) and major bleeding. However, only the difference in mortality persisted after adjustment (Fig). Adjusted HRs also showed higher mortality for non-paroxysmal vs paroxysmal AF and for permanent vs paroxysmal/persistent AF (Fig). We found no interaction between type of AF and AC therapy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".