Wolf Bethany J, Gray Kevin M, Dahne Jennifer R, Hashemi Daniel, Tomko Rachel L
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA.
Nicotine Tob Res. 2025 Apr 22;27(5):839-848. doi: 10.1093/ntr/ntae290.
Concerns about potential side effects remain a barrier to uptake of Food and Drug Administration-approved smoking cessation pharmacotherapy (ie, varenicline, bupropion, nicotine replacement therapy [NRT]). However, use of pharmacotherapy can double the odds of successful quitting. Knowledge of an individual's likelihood of side effects while taking smoking cessation pharmacotherapy could influence treatment planning discussions and monitoring.
We conducted a secondary, post hoc analysis to predict an individual's likelihood of adverse events (AEs) using the Evaluating Adverse Events in a Global Smoking Cessation Study data from 4209 adults in the United States who smoked. Participants were randomized to receive 12 weeks of treatment with varenicline, bupropion, NRT patch, or placebo. Our models predicted the likelihood of moderate to severe psychiatric and nonpsychiatric AEs during treatment.
Using pretreatment demographic and clinical data, multivariable logistic regression models yielded acceptable areas under the receiver operating characteristic curve for an individual's likelihood of moderate to severe (1) psychiatric AEs for bupropion and NRT and (2) nonpsychiatric AEs for varenicline and bupropion. Once we adjusted for demographic and baseline characteristics, medication was not associated with psychiatric AEs. Varenicline differed from placebo with regards to nonpsychiatric AEs.
It is possible to predict person-specific likelihood of moderate to severe psychiatric and nonpsychiatric AEs during smoking cessation treatment, though the probability of psychiatric AEs did not differ by medication. Future work should consider factors related to implementation in clinical settings, including determining whether lower burden assessment protocols can be equally accurate for AE prediction.
Using data from a large dataset people who smoke in the United States, it is possible to predict an individual's likelihood of psychiatric and nonpsychiatric AEs during smoking cessation treatment prior to initiating treatment. These predictive models provide a starting point for future work addressing how best to modify and integrate such clinical decision support algorithms into treatment for smoking cessation.
对潜在副作用的担忧仍然是阻碍人们采用美国食品药品监督管理局批准的戒烟药物疗法（即伐尼克兰、安非他酮、尼古丁替代疗法 [NRT]）的一个障碍。然而，药物疗法的使用可使成功戒烟的几率翻倍。了解个体在服用戒烟药物疗法时出现副作用的可能性，可能会影响治疗方案的讨论和监测。
我们进行了一项事后二次分析，利用来自美国4209名吸烟成年人的全球戒烟研究中评估不良事件的数据，预测个体发生不良事件（AE）的可能性。参与者被随机分配接受12周的伐尼克兰、安非他酮、NRT贴片或安慰剂治疗。我们的模型预测了治疗期间出现中度至重度精神和非精神AE的可能性。
利用治疗前的人口统计学和临床数据，多变量逻辑回归模型得出的受试者工作特征曲线下面积可接受，用于预测个体出现中度至重度（1）安非他酮和NRT的精神AE以及（2）伐尼克兰和安非他酮的非精神AE的可能性。在我们对人口统计学和基线特征进行调整后，药物与精神AE无关。伐尼克兰在非精神AE方面与安慰剂不同。
在戒烟治疗期间，有可能预测个体出现中度至重度精神和非精神AE的可能性，尽管精神AE的发生率在不同药物之间没有差异。未来的工作应考虑与临床环境中实施相关的因素，包括确定较低负担的评估方案对AE预测是否同样准确。
利用来自美国大量吸烟人群数据集的数据，在开始治疗前就有可能预测个体在戒烟治疗期间出现精神和非精神AE的可能性。这些预测模型为未来的工作提供了一个起点，即探讨如何最好地修改并将此类临床决策支持算法整合到戒烟治疗中。
