Secondary Versus Primary Stroke Prevention in Atrial Fibrillation: Insights From the Darlington Atrial Fibrillation Registry
Bibliographic record
Abstract
Background and Purpose— Although patients with atrial fibrillation (AF) who experienced an acute stroke are at high risk for recurrence, many patients are untreated or treated suboptimally for stroke prevention. The objective of this study is to compare clinical outcomes of AF patients with versus without previous stroke in relation to guideline-adherent antithrombotic treatment in a contemporary primary care population. Methods— Community cohort of 105 000 patients from 11 general practices in Darlington, England, was used to assess AF stroke prevention strategies against 2014 National Institute for Health and Care Excellence guidelines. Results— Overall, 2259 (2.15%) patients with AF were identified, of which 18.9% constituted a secondary prevention cohort. For secondary prevention, antithrombotic treatment was guideline adherent in 56.3%, 18.9% were overtreated, and 24.8% undertreated; corresponding proportions for primary prevention were 49.5%, 11.7%, and 38.8%, respectively. One-year stroke rates were 8.6% and 1.6% for secondary and primary prevention, respectively ( P <0.001); corresponding all-cause mortality rates were 9.8% and 9.4%, respectively ( P =0.79). On multivariable analysis, lack of antithrombotic treatment guideline adherence was associated with increased stroke risk for primary prevention (odds ratio, 2.95; 95% confidence interval, 1.26–6.90; P =0.013 for undertreatment); for secondary prevention, lack of guideline adherence was associated with increased risk of recurrent stroke (odds ratio, 2.80; 95% confidence interval, 1.25–6.27; P =0.012 for overtreatment) and all-cause death (odds ratio, 2.75; 95% confidence interval, 1.33–5.69; P =0.006 for undertreatment). Conclusions— Only approximately half of eligible patients with AF are prescribed oral anticoagulation in line with guidelines. Guideline-adherent antithrombotic treatment significantly reduces the risk of stroke among primary prevention patients and both risk of recurrent stroke and death in patients with previous stroke.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".