Efficacy and Safety of Smoking Cessation Interventions in Patients With Cardiovascular Disease
Bibliographic record
Abstract
BACKGROUND: Although the efficacy and safety of smoking cessation interventions are well established, their efficacy and safety in patients with cardiovascular disease (CVD) remain unclear. The objective of this study was to evaluate the efficacy and safety of pharmacological and behavioral smoking cessation interventions in CVD patients via a meta-analysis of randomized controlled trials. METHODS AND RESULTS: EMBASE, PsycINFO, MEDLINE, PubMed, and the Cochrane Tobacco Addiction Specialized Register were searched for randomized controlled trials evaluating the efficacy of smoking cessation pharmacotherapies and behavioral therapies in CVD patients. Outcomes of interest were smoking abstinence at 6 and 12 months, defined using the most rigorous criteria reported. Data were pooled across studies for direct comparisons using random-effects models. Network meta-analysis using a graph-theoretical approach was used to generate the indirect comparisons. Seven pharmacotherapy randomized controlled trials (n=2809) and 17 behavioral intervention randomized controlled trials (n=4666) met our inclusion criteria. Our network meta-analysis revealed that varenicline (relative risk [RR]: 2.64; 95% confidence interval [CI], 1.34-5.21) and bupropion (RR: 1.42; 95% CI, 1.01-2.01) were associated with greater abstinence than placebo. The evidence about nicotine replacement therapies was inconclusive (RR: 1.22; 95% CI, 0.72-2.06). Telephone therapy (RR: 1.47; 95% CI: 1.15-1.88) and individual counseling (RR: 1.64, 95% CI: 1.17-2.28) were both more efficacious than usual care, whereas in-hospital behavioral interventions were not (RR: 1.05; 95% CI, 0.78-1.43). CONCLUSIONS: Our meta-analysis suggests varenicline and bupropion, as well as individual and telephone counseling, are efficacious for smoking cessation in CVD patients.
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 | 0.044 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".