Cardiovascular Events Associated With Smoking Cessation Pharmacotherapies
Bibliographic record
Abstract
BACKGROUND: Stopping smoking is associated with many important improvements in health and quality of life. The use of cessation medications is recommended to increase the likelihood of quitting. However, there is historical and renewed concern that smoking cessation therapies may increase the risk of cardiovascular disease events associated within the quitting period. We aimed to examine whether the 3 licensed smoking cessation therapies-nicotine replacement therapy, bupropion, and varenicline-were associated with an increased risk of cardiovascular disease events using a network meta-analysis. METHODS AND RESULTS: We searched 10 electronic databases, were in communication with authors of published randomized, clinical trials (RCTs), and accessed internal US Food and Drug Administration reports. We included any RCT of the 3 treatments that reported cardiovascular disease outcomes. Among 63 eligible RCTs involving 21 nicotine replacement therapy RCTs, 28 bupropion RCTs, and 18 varenicline RCTs, we found no increase in the risk of all cardiovascular disease events with bupropion (relative risk [RR], 0.98; 95% confidence interval [CI], 0.54-1.73) or varenicline (RR, 1.30; 95% CI, 0.79-2.23). There was an elevated risk associated with nicotine replacement therapy that was driven predominantly by less serious events (RR, 2.29; 95% CI, 1.39-3.82). When we examined major adverse cardiovascular events, we found a protective effect with bupropion (RR, 0.45; 95% CI, 0.21-0.85) and no clear evidence of harm with varenicline (RR, 1.34; 95% CI, 0.66-2.66) or nicotine replacement therapy (RR, 1.95; 95% CI, 0.26-4.30). CONCLUSION: Smoking cessation therapies do not appear to raise the risk of serious cardiovascular disease events.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".