Antiepileptic drugs and the risk of ischaemic stroke and myocardial infarction: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: Hepatic enzyme-inducing antiepileptic drugs (AEDs) increase serum lipid levels and other atherogenic markers via the induction of cytochrome P450 and may therefore increase the risk of vascular events. We sought to assess the risk of ischaemic stroke and myocardial infarction (MI) according to AED enzymatic properties. DESIGN: Population-based cohort study with nested case-control analysis. SETTING: 650 general practices in the UK contributing to the Clinical Practice Research Datalink. PARTICIPANTS: A cohort of 252,407 incident AED users aged 18 or older between January 1990 and April 2013. For each case of ischaemic stroke or MI, up to 10 controls were randomly selected among the cohort members in the risk sets defined by the case and matched on age, sex, indication for AED, calendar time and duration of follow-up. INTERVENTIONS: Current use of enzyme-inducing and enzyme-inhibiting AEDs compared with non-inducing AEDs. PRIMARY OUTCOME MEASURES: Incidence rate ratios (RRs) of ischaemic stroke and MI. RESULTS: 5069 strokes and 3636 MIs were identified during follow-up. Inducing AEDs use was associated with a small increased risk of ischaemic stroke (RR=1.16, 95% CI 1.02 to 1.33) relative to non-inducing AEDs, most likely due to residual confounding. However, current use of inducing AEDs for ≥ 24 months was associated with a 46% increased risk of MI (RR=1.46, 95% CI 1.15 to 1.85) compared with the same duration of non-inducing AED, corresponding to a risk difference of 1.39/1000 (95% CI 0.33 to 2.45) persons per year. Current use of inhibiting AED was associated with a decreased risk of MI (RR=0.81, 95% CI 0.66 to 1.00). CONCLUSIONS: The use of enzyme-inducing AEDs was not associated with an increased risk of ischaemic stroke; a small increase of MI with prolonged use was observed. In contrast, use of inhibiting AEDs was associated with a decreased risk of MI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".