Country and health expenditure are major predictors of withholding anticoagulation in atrial fibrillation patients at high risk of stroke
Bibliographic record
Abstract
Background Guidelines for patients with atrial fibrillation (AF) at high thromboembolic risk recommend oral anticoagulants (OACs) for preventing stroke and systemic embolism (SE). The reasons for guideline non-adherence are still unclear. Aim The aim is to identify clinical, demographic and non-patient characteristics associated with withholding OAC in patients with AF at high stroke risk. Methods Patients in the Global Anticoagulant Registry in the FIELD-AF, newly diagnosed with AF between March 2010 and August 2016, and with CHA 2 DS 2 -VASc Score≥2 (excluding sex), were grouped by OAC treatment at enrolment. Factors associated with OAC non-use were analysed by multivariable logistic regression. Results Of 40 416 eligible patients, 12 126 (30.0%) did not receive OACs at baseline. Globally, OAC prescription increased over time, from 60.4% in 2010–2011 to 74.7% in 2015–2016. Country of enrolment was the major predictor for OAC withholding (χ 2 −df=2576). Clinical predictors of OAC non-use included type of AF (χ 2 −df=404), history of bleeding (χ 2 −df=263) and vascular disease (χ 2 −df=99). OACs were used most frequently around the age of 75 years and decreasingly with younger as well as older age beyond 75 years (χ 2 −df=148). Non-cardiologists (χ 2 −df=201) and emergency room physicians (χ 2 −df=14) were less likely to prescribe OACs. OAC prescription correlated positively with country health expenditure. Conclusions Approximately one out of three AF patients did not receive OAC, while eligible according to the guidelines. Country of enrolment was the major determinant of anticoagulation strategy, while higher country health expenditure was associated with lower likelihood of withholding anticoagulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".