作者地址
Lanzhou Univ, Hlth Technol Assessment Ctr, Evidence Based Social Sci Res Ctr, Sch Publ Hlth, Lanzhou, Peoples R China
摘要
Objective: A network meta-analysis (NMA) was conducted to investigate the effect of varenicline (VAR), bupropion (BUP), and nicotine replacement therapy (NRT) on smoking cessation.
Methods: Eight databases were searched in May 2021, and only randomized controlled trials (RCTs) using varenicline, bupropion, or NRT (single or combined) for smoking cessation were included. The risk of bias in the included RCTs was assessed using the Cochrane Handbook tool. Stata 15.1 software was used to perform NMA, and the quality of the evidence was evaluated using Confidence in Network Meta-analysis (CINeMA).
Findings: Twenty RCTs involving 16,702 smokers were included. The risk of bias results showed that 10 RCTs were rated as high, three were low, and seven were unclear. A total of 21 pairs were compared based on seven interventions. The NMA showed that, compared to the placebo (PLA), the other six interventions had significant efficacy in smoking cessation, where VAR + BUP showed the best effect of all treatments (odds ratio (OR) = 6.08, 95% confidence interval (CI) [3.47, 10.66]). Moreover, VAR + BUP was superior to VAR + NRT (OR = 1.66, 95% CI [1.07, 2.59]) and the three monotherapies (VAR, BUP, and NRT). In the monotherapies, the results of pairwise comparisons of VAR, BUP, and NRT did not show significant differences. Finally, the surface under the cumulative ranking curve (SUCRA) value indicated that VAR + BUP had the greatest probability of becoming the best intervention.
Conclusions: The efficacy of VAR, BUP, and NRT alone increased the odds of smoking abstinence better than the placebo, combined interventions were superior to monotherapy, and VAR combined with other interventions had a better smoking cessation effect.
资助信息
National Natural Science Foundation of China (Grant: 72074103);the Fundamental Research Funds for the Central Universities (lzujbky-2021-ct06, lzujbky-2021-kb22) ; the Gansu Special Project of Soft Science (20CX9ZA109)
