Cardiovascular and Neuropsychiatric Events after Varenicline Use for Smoking Cessation
Bibliographic record
Abstract
RATIONALE: Varenicline aids in smoking cessation but has also been associated with serious adverse events. OBJECTIVES: The aim of this study was to determine the risks of cardiovascular and neuropsychiatric events after varenicline receipt in a real-world setting. METHODS: A population-based, self-controlled risk interval study using linked universal health administrative data from the diverse, multicultural population of Ontario, Canada, was conducted. In two separate analyses, new varenicline users between September 1, 2011 and February 15, 2014 were observed from 1 year before to 1 year after varenicline receipt. The relative incidences of cardiovascular and neuropsychiatric hospitalizations and emergency department visits in the 12 weeks after varenicline receipt (the risk interval) compared with the remaining observation period (the control interval) were estimated in two separate fixed-effect conditional Poisson regressions. Sensitivity analyses tested the robustness of the results. MEASUREMENTS AND MAIN RESULTS: Among 56,851 new users of varenicline, 6,317 cardiovascular and 10,041 neuropsychiatric hospitalizations and emergency department visits occurred from 1 year before to 1 year after receipt. The incidence of cardiovascular events was 34% higher in the risk compared with the control interval (relative incidence, 1.34; 95% confidence interval, 1.25-1.44). Findings were consistent in sensitivity analyses, most notably in those without any history of previous cardiovascular disease. The relative incidence of neuropsychiatric events was marginally significant in the primary (relative incidence, 1.06; 95% confidence interval, 1.00-1.13) but not all sensitivity analyses. CONCLUSIONS: Varenicline appears to be associated with an increased risk of cardiovascular but not neuropsychiatric events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".