Antithrombotic treatment for newly diagnosed atrial fibrillation in relation to patient age: the GLORIA-AF registry programme
Bibliographic record
Abstract
AIMS: To assess antithrombotic therapy choices in relation to patient age in a large, global registry on atrial fibrillation (AF). METHODS AND RESULTS: Global Registry on Long-Term Oral Antithrombotic Treatment in Patients with Atrial Fibrillation (GLORIA-AF) is an international programme involving patients with newly diagnosed AF and ≥1 risk factors for stroke. We used Phase II data (from November 2011 through December 2014), which commenced immediately following first non-vitamin K antagonist oral anticoagulants (NOACs) approval in participating countries. Of 15 092 patients (mean age 70.5 ± 11.0 years), enrolled at 982 centres, 26.9% were aged <65 years, 33.9% 65-74, 30.5% 75-84, and 8.6% ≥85 years old. Oral anticoagulant (OAC) use was 73.5%, 81.4%, 83.3%, and 82.3% (overall NOACs use was 44.4%, 49.7%, 48.7%, and 45.6%) for those aged <65, 65-74, 75-84 and ≥85 years, respectively. Corresponding proportions for antiplatelet monotherapy and no treatment were: 16.2% and 10.2%; 11.2% and 7.3%; 10.0% and 6.5%; 10.5% and 7.0%, respectively. Of those aged 65-74, 75-84, and ≥85 years, respectively, 83.7, 86.8 and 85.4% received OAC unless bleeding risk was high (HAS-BLED ≥3), whereby 64.1%, 63.5%, and 64.5% were anticoagulated, and 31.1%, 30.3%, and 31.3% received antiplatelets only. Of patients ≥85 years, OAC use was 88.1% in Europe (NOAC 45.1%), 79.5% in North America (NOAC 44.8%), and 54.1% in Asia (NOAC 40.2%). CONCLUSION: Despite geographic differences in OAC use, neither OAC nor NOAC uptake was lower for patients ≥85 years old compared with younger patients. Although the majority of patients was prescribed OAC at all ages, nearly one-third received antiplatelet monotherapy when bleeding risk was increased. CLINICAL TRIAL REGISTRATION: http://www.clinicaltrials.gov. Unique identifier: NCT01468701.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".