Abstract 208: No Independent Association Between Extended-Release Niacin (ERN) Therapy and Ischemic Stroke in Patients with Established Cardiovascular Disease: A Detailed Analysis from the AIM HIGH Trial
Bibliographic record
Abstract
Background: Statins reduce cardiovascular (CV) events and stroke by 20-25% in both patients with established cardiovascular disease (CVD) and in asymptomatic patients at high risk for CVD. Meta-analyses also show that niacin reduces major CV events by 25% and stroke by 27%. In AIM HIGH, which evaluated the addition of extended-release niacin (ERN) to simvastatin in patients with established CV disease and low baseline levels of HDL-C, we observed an unexpected increased rate of ischemic stroke in those randomized to ERN. The present analysis explores possible reasons for the observed numerical excess of ERN-associated stroke. Methods and Results: Among 3,414 patients (85% male; mean age: 64±9 years) randomized to simvastatin + ERN (1,500-2,000mg/day or matching placebo to achieve an on-treatment LDL-C target of 40-80mg/dL), there was no difference in the trial’s composite primary endpoint at a mean 36-month follow-up. This included 50 fatal or non-fatal ischemic strokes, of which 18 (1.85%) occurred in the placebo arm and 32 (2.3%) in the niacin arm (age-adjusted HR 1.78; 95% CI 1.00-3.17, p=0.050). Multivariate step-wise analyses showed strong associations between ischemic stroke risk and age > 65 (HR 3.60 95% CI 1.83-7.09, p=0.0002) and history of stroke/TIA/carotid disease (HR 2.19 95% CI 1.23-3.90, p=0.0075), a moderate association with Lp(a) (HR 2.80, 95%CI 1.25 - 6.27, p=.012 comparing the middle to the lowest tertile), but an insignificant association between ischemic stroke and ERN (HR 1.74 95% CI .97-3.11, p=0.063). Conclusion: Although there was a numerical excess in ischemic strokes associated with the addition of ERN to simvastatin, the number of events was small and multivariable analysis accounting for known risk factors revealed no significant association between niacin treatment and subsequent ischemic stroke risk.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".