Frailty and Anticoagulant Therapy in Patients Aged 65 Years or Older with Atrial Fibrillation
Bibliographic record
Abstract
Background: Elderly adults with atrial fibrillation (AF) are at increased risk of frailty and thromboembolic complications. However, studies on the prevalence of frailty in AF patients and data on the relationship between frailty and the use of anticoagulants are limited. Methods: We conducted a cross-sectional study involving 500 participants. Patients aged 65 years or older were consecutively selected from the Chinese Atrial Fibrillation Registry study. The patient’s frailty status was assessed with use of the Canadian Study of Health and Aging Clinical Frailty Scale. We assessed the prevalence of and factors associated with frailty, and how frailty affects anticoagulant therapy. Results: In 500 elderly adults with AF (age 75.2±6.7 years; 51.6% female), 201 patients (40.2%) were frail. The prevalence of frailty was higher in females (P=0.002) and increased with age and CHA2DS2-VASc score (P for trend less than 0.001 for both). The factors associated with frailty were a history of heart failure (odds ratio [OR] 2.40, 95% confidence interval [CI] 1.39–4.14), female sex (OR 2.09, 95% CI 1.27–3.43), and advanced age (OR 1.13, 95% CI 1.09–1.17). Frail patients were significantly less likely to have ever been prescribed anticoagulants compared with nonfrail patients (81.7 vs. 54.9%, P<0.001). Conclusions: Frailty is prevalent in elderly adults with AF, especially in females, those of advanced age, and those with heart failure. Frailty status has a significant impact on prescription of anticoagulants for high-risk AF patients.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".