Clinical Outcomes in Older Patients with Atrial Fibrillation: Insights from the GARFIELD-AF Registry
Bibliographic record
Abstract
BACKGROUND: Oral anticoagulants (OAC) are underutilized in older patients with atrial fibrillation, despite proven clinical benefits. Our objective was to investigate baseline characteristics, treatment patterns, and impact of anticoagulation upon clinical outcomes with respect to age. METHODS: Adults with newly diagnosed atrial fibrillation were recruited into the prospective observational registry, GARFIELD-AF, and followed up for 24 months. Adjusted hazard ratios (HR) were obtained via Cox proportional-hazards models with applied weights, to quantify the association of age with clinical outcomes. Comparative effectiveness of OAC vs No OAC and non-vitamin K oral anticoagulants (NOAC) vs vitamin K antagonists (VKA) were assessed using a propensity score with an overlap weighting scheme. RESULTS: Of 52,018 patients, 32.6% were 65-74 years of age, 29.3% were 75-84 years, and 7.9% were ≥85 years. OAC treatment was associated with a numerical reduction in all-cause mortality among those aged 65-74 years (HR; 95% confidence interval) (0.86; 0.69-1.06) and aged 75-84 years (0.89; 0.75-1.05) and a significant reduction in patients ≥85 years (0.77; 0.63-0.95) vs no OAC. Similarly, OACs were associated with a decrease in stroke: 65-74 (0.51; 0.35-0.76) and ≥85 years (0.58; 0.34-0.99) and a numerical decrease in 75-84 years (0.84; 0.59-1.18). No increase in major bleeding was observed in patients aged ≥85 treated with OACs. Compared with VKA, NOACs were associated with a significant reduction in all-cause mortality in patients aged <65 and 65-74, with numerical reductions in those aged 75-84 and ≥85 years. CONCLUSIONS: Older patients using OACs saw lower all-cause mortality and stroke risk; NOACs had less mortality and major bleeding compared with VKAs.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".