A novel data-driven approach for personalizing smoking cessation pharmacotherapy

一种新的数据驱动的个性化戒烟药物治疗方法

基本信息

批准号：
10578721
负责人：
Rachel Lynn Tomko
金额：
$ 7.53万
依托单位：
MEDICAL UNIVERSITY OF SOUTH CAROLINA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-03-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10578721
关键词：
Abstinence Accounting Address Adherence Adult Adverse effects Adverse event Algorithms Archives Area Under Curve Bupropion Carbon Monoxide Cessation of life Characteristics Clinic Clinical Clinical Data Complex Computer software Data Data Set Development Disease Future Goals Head Heterogeneity High Prevalence Individual Machine Learning Malignant Neoplasms Measures Modeling Multi-Institutional Clinical Trial Outcome Participant Patients Performance Persons Pharmaceutical Preparations Pharmacotherapy Placebos Population Positioning Attribute Probability Provider Randomized Randomized, Controlled Trials Recommendation Reproducibility Sampling Serious Adverse Event Smoker Smoking Smoking Cessation Intervention Statistical Models Surveys Techniques Testing Time Translating Translations Treatment outcome Uncertainty United States United States Food and Drug Administration Work alcohol abstinence alcohol abuse therapy alcohol use disorder alternative treatment burden of illness capsule cigarette smoking clinical decision support flexibility implementation facilitation improved individualized medicine medication compliance mobile application nicotine replacement novel novel strategies personalized approach personalized medicine pill predictive modeling programs prospective prototype psychiatric comorbidity response sex side effect smoking cessation standard of care statistics success support tools tobacco smokers tool treatment response uptake varenicline

项目摘要

PROJECT ABSTRACT Cigarette smoking contributes to one-third of cancer deaths. Approximately 14% of adults in the United States are current tobacco smokers. Though several Food and Drug Administration (FDA)-approved smoking cessation pharmacotherapies exist [e.g., varenicline, bupropion, nicotine replacement therapy (NRT)], utilization rates remain low and a substantial portion of smokers do not respond to existing treatments. A personalized treatment recommendation in which smokers are provided with a smoking cessation pharmacotherapy based on their individual characteristics may improve both utilization of FDA-approved smoking cessation pharmacotherapies and quit success among smokers. Our goal is to develop an algorithm, based on demographic and clinical data assessed prior to treatment, to estimate individual smokers' likely response to FDA-approved pharmacotherapies for smoking cessation, including varenicline, bupropion, and nicotine replacement therapy (NRT). Models will account for the likelihood of adverse effects of medication and non-adherence. Individual estimates of treatment response will be obtained through sophisticated analytic modeling (e.g., machine learning techniques) of existing data from a single, large-scale randomized controlled trial (EAGLES trial conducted by Pfizer and GlaxoSmithKline, United States sample, N=4207). The EAGLES trial provides a rich dataset comparing three FDA-approved medications head-to-head in a large and clinically representative sample. In the EAGLES trial, participants were randomly assigned to receive varenicline (1 mg twice daily), bupropion (150 mg twice daily), NRT patch (21 mg per day with taper), or placebo pill capsules/patches for 12 weeks. Smoking cessation outcomes at weeks 9 through 12 were measured. We propose to use multiple statistical techniques (e.g., machine learning) to optimize a model for predicting an individual's likelihood of specific smoking cessation success in response to each treatment. Consistent with the primary analyses in the EAGLES trial, we will define treatment success as carbon monoxide-confirmed continuous abstinence during weeks 9 through 12. Secondarily, we will also examine continuous abstinence during weeks 9 through 24. We will develop a patient and provider-facing mobile app prototype that implements the best-fitting algorithm and prospectively predicts new patients' likelihood of smoking cessation with various pharmacotherapies. The mobile app will allow a new patient to complete a reduced set of assessments based on the predictors deemed relevant in the final model. The development of an app prototype will position us to complete user testing and refinement in a future study. Finally, we will develop a R package to facilitate implementation of similar models by statisticians working with other disease data.

项目摘要吸烟导致三分之一的癌症死亡。大约 14% 的美国成年人是目前的吸烟者。尽管一些食品和药物管理局 (FDA) 批准戒烟现有药物疗法 [例如伐尼克兰、安非他酮、尼古丁替代疗法 (NRT)]、利用率仍然很低，而且很大一部分吸烟者对现有治疗没有反应。个性化治疗建议根据吸烟者的情况提供戒烟药物治疗个体特征可能会提高 FDA 批准的戒烟药物疗法的利用率并在吸烟者中取得成功。我们的目标是开发一种基于人口统计和临床数据的算法在治疗前进行评估，以估计个体吸烟者对 FDA 批准的药物疗法的可能反应用于戒烟，包括伐尼克兰、安非他酮和尼古丁替代疗法 (NRT)。模型将考虑药物不良反应和不依从的可能性。治疗的个人估计响应将通过现有的复杂分析模型（例如机器学习技术）获得数据来自一项大规模随机对照试验（辉瑞和辉瑞公司进行的 EAGLES 试验） GlaxoSmithKline，美国样本，N=4207）。 EAGLES 试验提供了丰富的数据集，比较了三个 FDA 批准的药物在大量且具有临床代表性的样本中进行了正面对比。在 EAGLES 试验中，参与者被随机分配接受伐尼克兰（1 毫克，每天两次）、安非他酮（150 毫克，每天两次）、 NRT 贴剂（每天 21 毫克，逐渐递减）或安慰剂药丸胶囊/贴剂，持续 12 周。戒烟测量第 9 周至第 12 周的结果。我们建议使用多种统计技术（例如，机器学习）优化模型来预测个人特定戒烟的可能性对每次治疗的成功反应。与 EAGLES 试验中的主要分析一致，我们将定义治疗成功，即在第 9 周至第 12 周内持续戒除一氧化碳。其次，我们还将检查第 9 周至第 24 周期间的持续禁欲情况。我们将培养一名患者和面向提供商的移动应用程序原型，该原型实现了最佳拟合算法并进行前瞻性预测新患者通过各种药物疗法戒烟的可能性。该移动应用程序将允许新的患者根据最终模型中认为相关的预测因素完成一组简化的评估。应用程序原型的开发将使我们能够在未来的研究中完成用户测试和改进。最后，我们将开发一个 R 包，以方便统计学家与其他人合作实施类似的模型其他疾病数据。