High‐risk carotid plaques and incident ischemic stroke in patients with atrial fibrillation in the Cardiovascular Health Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Whether carotid artery disease could improve stroke risk stratification tools in patients with atrial fibrillation (AF) remains uncertain. This study was undertaken to investigate the risk of ischemic stroke associated with occlusive and nonocclusive carotid atherosclerotic disease in patients with AF in the prospective population-based Cardiovascular Health Study. METHODS: We included participants aged ≥65 years with AF. We used multivariable Cox regression analysis to explore the risk of ischemic stroke associated with the percentage of carotid stenosis, plaque irregularity, echogenicity, and vulnerability (markedly irregular, ulcerated, or hypoechoic plaques). RESULTS: A total of 1398 participants were included (55.2% female, 61.7% aged 65-74 years). The maximum carotid stenosis was <50%, 50%-99%, and 100% in 94.5%, 5%, and 0.5% of participants, respectively. High-risk plaques based on echogenicity and plaque irregularity were found in 25.6% and 8.9% of participants, respectively. After a median follow-up of 10.9 years (interquartile range = 7.5-15.6), 298 ischemic strokes were recorded. There was no difference in the incidence of ischemic stroke according to the degree of carotid artery stenosis (p = 0.44), plaque echogenicity (low vs. high risk, p = 0.68), plaque irregularity (low vs. high risk, p = 0.55), and plaque vulnerability (p = 0.86). The CHA₂DS₂-VASc score was associated with an increased risk of ischemic stroke (adjusted hazard ratio = 1.28, 95% confidence interval = 1.18-1.40, p < 0.001). Both maximum grade of stenosis and plaque vulnerability were not associated with incident ischemic stroke (all p > 0.05). CONCLUSIONS: Neither the degree of carotid stenosis nor the presence of vulnerable plaques was associated with incident ischemic stroke in this cohort of individuals with AF. This suggests that carotid disease was probably not a significant contributor to ischemic stroke in this population.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".