Oral anticoagulant treatment in rheumatoid arthritis patients with atrial fibrillation results of an international audit
Bibliographic record
Abstract
To describe the prevalence of atrial fibrillation (AF) in patients with rheumatoid arthritis (RA), and to evaluate the proportion of patients with AF receiving guideline-recommended anticoagulation for prevention of stroke, based on data from a large international audit. The cohort was derived from the international audit SUrvey of cardiovascular disease Risk Factors in patients with Rheumatoid Arthritis (SURF-RA) which collected data from 17 countries during 2014–2019. We evaluated the prevalence of AF across world regions and explored factors associated with the presence of AF with multivariable logistic regression models. The proportion of AF patients at high risk of stroke (CHA2DS2-VASc ≥ 2 in males and ≥ 3 in females) receiving anticoagulation was examined. Of the total SURF-RA cohort (n = 14,503), we included RA cases with data on whether the diagnosis of AF was present or not (n = 7,665, 75.1% women, mean (SD) age 58.7 (14.1) years). A total of 288 (3.8%) patients had a history of AF (4.4% in North America, 3.4% in Western Europe, 2.8% in Central and Eastern Europe and 1.5% in Asia). Factors associated with the presence of AF were older age, male sex, atherosclerotic cardiovascular disease, heart failure and hypertension. Two-hundred and fifty-five (88.5%) RA patients had a CHA2DS2-VASc score indicating recommendation for oral anticoagulant treatment, and of them, 164 (64.3%) were anticoagulated. Guideline-recommended anticoagulant therapy for prevention of stroke due to AF may not be optimally implemented among RA patients, and requires special attention.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".