The association between patterns of atrial fibrillation, anticoagulation, and cardiovascular events
Bibliographic record
Abstract
AIMS: Guidelines do not recommend to take pattern of atrial fibrillation (AF) into account for the indication of anticoagulation (AC). We assessed AF pattern and the risk of cardiovascular events during 2-years of follow-up. METHODS AND RESULTS: We categorized AF as paroxysmal, persistent, or permanent in 29 181 patients enrolled (2010-15) in the Global Anticoagulant Registry In the FIELD of AF (GARFIELD-AF). We used multivariable Cox regression to assess the risks of stroke/systemic embolism (SE) and death across patterns of AF, and whether this changed with AC on outcomes. Atrial fibrillation pattern was paroxysmal in 14 344 (49.2%), persistent in 8064 (27.6%), and permanent 6773 (23.2%) patients. Median CHA2DS2-VASc, GARFIELD-AF, and HAS-BLED scores assessing the risk of stroke/SE and/or bleeding were similar across AF patterns, but the risk of death, as assessed by the GARFIELD-AF risk calculator, was higher in non-paroxysmal than in paroxysmal AF patterns. During 2-year follow-up, after adjustment, non-paroxysmal AF patterns were associated with significantly higher rates of all-cause death, stroke/SE, and new/worsening congestive heart failure (CHF) than paroxysmal AF in non-anticoagulated patients only. In anticoagulated patients, a significantly higher risk of death but not of stroke/SE and new/worsening CHF persisted in non-paroxysmal compared with paroxysmal AF patterns. CONCLUSION: In non-anticoagulated patients, non-paroxysmal AF patterns were associated with higher risks of stroke/SE, new/worsening HF and death than paroxysmal AF. In anticoagulated patients, the risk of stroke/SE and new/worsening HF was similar across all AF patterns. Thus AF pattern is no longer prognostic for stroke/SE when patients are treated with anticoagulants. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".