Direct oral anticoagulant use in under‐represented populations with atrial fibrillation: a survey of Australian pharmacist practice
Bibliographic record
Abstract
Abstract Direct oral anticoagulants (DOACs) are recommended as first‐line anticoagulation therapy for stroke prevention in patients with non‐valvular atrial fibrillation (NVAF). However, real‐world data on pharmacists' practice patterns in under‐represented populations, such as patients with advanced age, obesity, and end‐stage kidney disease (ESKD), are lacking. This study aimed to explore Australian pharmacists' perceptions in the use of DOACs among patients with NVAF and advanced age, obesity, or ESKD using a cross‐sectional, self‐administered survey. The survey was distributed to members of the Society of Hospital Pharmacists of Australia, with members of Specialty Practice (SP) groups targeted specifically. A total of 45 responses were received with 78% ( n = 35) coming from SP groups. Of the 45 responses, apixaban ( n = 36, 80%) was indicated to be the preferred anticoagulant for use with advanced age. Thirty‐seven (84%) out of 44 pharmacists indicated that DOAC doses were reduced in accordance with product information (PI) in their daily practice, and seven (16%) of 45 indicated they would recommend reducing a DOAC dose outside of PI recommendations. Regarding obesity, most of the 39 responses indicated that warfarin was the anticoagulant of choice ( n = 20, 51%). Twelve (29%) of the 41 responses indicated pharmacists would recommend a dose reduction outside of the PI for patients with renal disease, while from the 40 responses received for haemodialysis, 57.5% ( n = 23) of pharmacists preferred to use warfarin. Our findings highlighted opportunities for targeted education to promote evidence‐directed DOAC utilisation in under‐represented populations, especially in certain obesity subgroups now supported by growing evidence and updated guidelines. Ethical approval was granted by the Grampians Health Human Research Committee (Reference no: LNR/95233/GHSJOG‐2023‐364 599) and the study conforms to the Australian National statement on ethical conduct in human research . Informed consent was obtained from all participants. Potential participants were given project information indicating their participation was voluntary and anonymous. Participants provided their consent by completing the survey.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".