Atrial fibrillation in older inpatients: are there any differences in clinical characteristics and pharmacological treatment between the frail and the non‐frail?
Bibliographic record
Abstract
BACKGROUND: Frailty is common in patients with atrial fibrillation and may impact on antithrombotic and anti-arrhythmic treatment. AIM: To describe differences in clinical characteristics, prescription of antithrombotic and anti-arrhythmic medications and incidence of haemorrhage and stroke, between frail and non-frail older inpatients. METHODS: Prospective observational study in patients aged ≥65 years with atrial fibrillation admitted to a teaching hospital in Sydney, Australia. Frailty was assessed using the Reported Edmonton Frail Scale, stroke risk with CHA2DS2-VASc score and bleeding risk with HAS-BLED score. Participants were followed after 6 months for haemorrhages and strokes. RESULTS: We recruited 302 patients (mean age 84.7 ± 7.1 years, 53.3% frail, 50% female, mean CHA2DS2-VASc 4.61 ± 1.44, mean HAS-BLED 2.97 ± 1.04). Frail participants were older and had more co-morbidities and higher risk of stroke but not haemorrhage. Upon discharge, 55.7% participants were prescribed with anticoagulants (49.3% frail, 62.6% non-frail, P = 0.02). Thirty-three per cent received antiplatelets only and 11.1% no antithrombotics, with no difference by frailty status. For anti-arrhythmics, 52.6% received rate-control drugs only, 11.8% rhythm-control drugs only and 13.5% both and 22.1% were not prescribed either, with no difference by frailty status. On univariate logistic regression, frailty decreased the likelihood of anticoagulant prescription (odds ratio (OR) 0.58, 95%CI 0.36-0.93), but this was not significant on multivariate analysis (OR 0.66, 95%CI 0.40-1.11). After 6 months, overall incidence of ischaemic stroke was 2.1%, and in patients taking anticoagulants, incidence of major/severe bleeding was 6.3%, with no significant difference between frailty groups. CONCLUSIONS: Frailty status had little impact on antithrombotic prescription and no impact on anti-arrhythmic prescription.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".