Prevalence and risk of inappropriate dosing of direct oral anticoagulants in two Swiss atrial fibrillation registries
Bibliographic record
Abstract
Background Direct oral anticoagulants (DOACs) have a favourable risk-benefit profile compared to vitamin K-antagonists (VKAs) in atrial fibrillation (AF). Dosing is based on age, weight and renal function, without need of routine monitoring. Methods and results In two prospective, multicentre AF cohorts (Swiss-AF, BEAT-AF) patients were stratified as receiving VKAs or adequately-, under- or overdosed DOACs, according to label. Primary outcome was a composite of major adverse clinical events (MACE), defined as cardiovascular death, myocardial infarction (MI), ischaemic stroke and systemic embolism. Secondary outcomes included major bleeding . Adjustment for confounding was performed. Median follow-up was 4 years. Of 3236 patients, 1875 (58%) were on VKAs and 1361 (42%) were on DOACs, of which 1137 (83%) were adequately-, 134 (10%) over- and 90 (7%) under-dosed. Compared to adequately dosed individuals, overdosed patients were more likely to be older and female. Underdosing correlated with concomitant aspirin therapy and coronary artery disease . Both groups had higher CHA 2 DS 2 -VASc scores. Patients on overdosed DOACs had higher incidence of MACE (HR 1.75; CI 1.10–2.79; adjusted-HR: 1.22) and major bleeding (HR 1.99; CI 1.14–3.48; adjusted-HR: 1.51). Underdosing was not associated with a higher incidence of MACE (HR 0.94; CI 0.46–1.92; adjusted-HR 0.61) or major bleeding (HR 1.07; CI 0.46–2.46; adjusted-HR 0.82). After adjustment, all CIs crossed 1.0. Conclusion Inappropriate DOAC-dosing was more prevalent in multimorbid patients, but did not correlate with higher risks of adverse events after adjusting for confounders. DOAC prescription should follow label.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".