The association of stroke rate with low density lipoprotein and statin exposure in patients with atrial fibrillation (AF)
Bibliographic record
Abstract
Abstract Background There are limited data on the association between cholesterol levels and stroke risk in atrial fibrillation (AF). Objective To quantify the association of stroke rate in AF with low-density lipoprotein (LDL) levels and statin use. Methods Using linked administrative databases in Ontario, Canada, we conducted a population-based retrospective cohort study of patients aged ≥66 years, diagnosed with AF between 2009–2019. We used cause-specific hazard regression to determine the association of statin use with stroke rate. We developed a second cause-specific regression model for patients with at least one lipid profile measurement in the year before AF diagnosis to study the association of LDL levels with stroke rate, while adjusting for statin use. LDL levels were modeled using restricted cubic splines (RCS). Both models were adjusted for age, sex, heart failure, hypertension, diabetes, stroke or transient ischemic attack, and vascular disease at baseline, plus anticoagulation as a time-varying covariate. Results We studied 261,659 qualifying patients (median age 78 years, 49% female), of whom 3,954 (1.5%) developed a stroke during one-year follow-up. A total of 142,834 (54.6%) patients were treated with statins and 145,775 (55.7%) had lipid measurements before AF diagnosis. The adjusted RCS analyses (see Figure) indicated increasing hazard ratios (HRs) for stroke with increasing low-density lipoprotein (LDL) values above 1.5mmol/L. Statin use was associated with a lower stroke rate relative to non-users (hazard ratio 0.87, 95% confidence interval 0.81–0.93, p-value <0.0001). Conclusion LDL levels above 1.5mmol/L were independently associated with higher stroke rates in patients with AF, while statins were associated with lower stroke rates independent of anticoagulation. This suggests that LDL measurements may improve stroke risk stratification in AF, while statins may offer an underutilized pathway to lower stroke risk in AF. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): 1) Heart and stroke foundation of Canada HR for Stroke and LDL level
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".