Anticoagulation in Elderly Ischemic Stroke Patients With Atrial Fibrillation: Perspective From a Tertiary Neuroscience Center
Bibliographic record
Abstract
Background: Oral anticoagulation reduces the risk of cardioembolic (CE) stroke due to atrial fibrillation (AF). Despite guideline recommendations, anticoagulation use remains underutilized, especially in elderly patients. Methods: This was a retrospective cross-sectional study to assess the oral anticoagulation use among elderly patients (≥ 65 years old) with AF-related CE strokes admitted to Brunei Neuroscience Stroke & Rehabilitation Centre (BNSRC) from January 2020 to December 2021. This study aimed to: 1) determine the incidence of AF-related CE stroke in the elderly; 2) describe the patients’ demographic and clinical profiles; 3) describe the pattern of anticoagulation use; and 4) describe the stroke recurrence, bleeding, and mortality within the 2 years follow-up. The data were analyzed using IBM SPSS Statistics version 26.0. Results: Of all ischemic stroke patients admitted within the study period, 74 (23.8%) were elderly with AF-related CE stroke. The annual incidence of AF-related CE strokes in the elderly was 35.3% in 2020 and 22.3% in 2021. The median age was 77.0 years (interquartile range (IQR): 13.0) and 54.1% were males. The median CHA2DS2-VASc score was 4.0, with hypertension (79.7%) being the most common co-morbidity. The majority (75.7%) received anticoagulation, mostly direct oral anticoagulants (DOACs) (89.3%), specifically dabigatran (62.5%). There were higher mortality (72.2%, P = 0.001) and bleeding (38.9%, P = 0.032) in non-anticoagulated patients. However, there was no significant stroke recurrence between the groups on DOACs, warfarin and no anticoagulation (P = 0.557). Subtherapeutic anticoagulation showed a higher trend but was not statistically significant in terms of mortality (42.9%, P = 0.165), bleeding (21.4%, P = 0.350), and stroke recurrence (14.3%, P = 0.590). Among patients ≥ 80 years of age, there was also no significant increase in bleeding, stroke recurrence, or mortality with anticoagulation. Conclusion: There was a high incidence of CE ischemic strokes in elderly patients with AF in BNSRC, Brunei Darussalam. Majority of our patients received DOACs. Anticoagulated patients had lower bleeding and mortality risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".