Reduction in Early Stroke Risk in Carotid Stenosis With Transient Ischemic Attack Associated With Statin Treatment
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Statins reduce stroke risk when initiated months after transient ischemic attack (TIA)/stroke and reduce early vascular events in acute coronary syndromes, possibly via pleiotropic plaque stabilization. Few data exist on acute statin use in TIA. We aimed to determine whether statin pretreatment at TIA onset modified early stroke risk in carotid stenosis. METHODS: We analyzed data from 2770 patients with TIA from 11 centers, 387 with ipsilateral carotid stenosis. ABCD2 score, abnormal diffusion weighted imaging, medication pretreatment, and early stroke were recorded. RESULTS: In patients with carotid stenosis, 7-day stroke risk was 8.3% (95% confidence interval [CI], 5.7-11.1) compared with 2.7% (CI, 2.0%-3.4%) without stenosis (P<0.0001; 90-day risks 17.8% and 5.7% [P<0.0001]). Among carotid stenosis patients, nonprocedural 7-day stroke risk was 3.8% (CI, 1.2%-9.7%) with statin treatment at TIA onset, compared with 13.2% (CI, 8.5%-19.8%) in those not statin pretreated (P=0.01; 90-day risks 8.9% versus 20.8% [P=0.01]). Statin pretreatment was associated with reduced stroke risk in patients with carotid stenosis (odds ratio for 90-day stroke, 0.37; CI, 0.17-0.82) but not nonstenosis patients (odds ratio, 1.3; CI, 0.8-2.24; P for interaction, 0.008). On multivariable logistic regression, the association remained after adjustment for ABCD2 score, smoking, antiplatelet treatment, recent TIA, and diffusion weighted imaging hyperintensity (adjusted P for interaction, 0.054). CONCLUSIONS: In acute symptomatic carotid stenosis, statin pretreatment was associated with reduced stroke risk, consistent with findings from randomized trials in acute coronary syndromes. These data support the hypothesis that statins started acutely after TIA symptom onset may also be beneficial to prevent early stroke. Randomized trials addressing this question are required.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".