Statin Use and Stroke Rate in Older Adults With Atrial Fibrillation: A Population‐Based Cohort Study
Bibliographic record
Abstract
Background Atherosclerotic disease is an important contributor to adverse outcomes in patients with atrial fibrillation (AF). There is limited recognition of the association between statin use and stroke rates in AF. We aimed to quantify the association between statin use and stroke rate in AF. Methods and Results Using linked administrative databases in Ontario, Canada, we conducted a population‐based retrospective cohort study of patients, aged ≥66 years, diagnosed with AF between 2009 and 2019. We used cause‐specific hazard regression to determine the association of statin use with stroke rate. We developed a second model to further adjust for lipid levels in the subset of patients with available measurements in the year before AF diagnosis. Both models adjusted for age, sex, heart failure, hypertension, diabetes, stroke/transient ischemic attack, vascular disease, and P2Y12 inhibitors at baseline, plus anticoagulation as a time‐varying covariate. We studied 261 659 qualifying patients (median age, 78 years; 49% women). Statins were used in 142 834 (54.6%) patients, and 145 673 (55.7%) had lipid measurement(s) in the preceding year. Statin use was associated with lower stroke rates, with adjusted hazard ratios of 0.83 (95% CI, 0.77–0.88; P <0.001) in the full cohort and 0.87 (95% CI, 0.78–0.97; P =0.01) when adjusting for lipid data. Stroke rates increased in a near‐linear manner as low‐density lipoprotein values increased >1.5 mmol/L. Conclusions Statins were associated with lower stroke rates in patients with AF, whereas higher low‐density lipoprotein levels were associated with higher stroke rates, highlighting the importance of vascular risk factor treatment in AF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".