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

• 批准号: 10437438
• 负责人: Rachel Lynn Tomko
• 金额: $ 7.54万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2024-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10437438
• 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; Event; 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; base; burden of illness; capsule; cigarette smoking; clinical decision support; flexibility; implementation facilitation; improved; individualized medicine; machine learning model; medication compliance; medication nonadherence; mobile application; nicotine replacement; novel; novel strategies; outcome prediction; 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; 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; Event; 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; base; burden of illness; capsule; cigarette smoking; clinical decision support; flexibility; implementation facilitation; improved; individualized medicine; machine learning model; medication compliance; medication nonadherence; mobile application; nicotine replacement; novel; novel strategies; outcome prediction; 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

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 吸烟会导致三分之一的癌症死亡。美国约有14％的成年人 目前的吸烟者。虽然几个食品药品监督管理局（FDA）批准了戒烟 存在药物疗法[ 保持低位，大部分吸烟者对现有治疗没有反应。个性化治疗 建议在其中为吸烟者提供戒烟药物治疗的建议 个体特征可以改善FDA批准的戒烟药物治疗的两种利用 并退出吸烟者的成功。我们的目标是根据人口统计和临床数据开发算法 在治疗前进行评估，以估算单个吸烟者对FDA批准的药物治疗的反应 用于戒烟，包括Varenicline，安非他酮和尼古丁替代疗法（NRT）。模型将 解释药物和不遵守的不利影响的可能性。个人估计 响应将通过现有的复杂分析建模（例如机器学习技术）获得 来自单个大规模随机对照试验的数据（辉瑞公司进行的Eagles试验 葛兰素史克林，美国样本，n = 4207）。老鹰试验提供了一个丰富的数据集，比较了三个 在大型且具有临床代表性的样本中，由FDA批准的药物正对头。在老鹰审判中， 参与者被随机分配接受Varenicline（每天两次1毫克），安非他酮（每天两次150毫克）， NRT贴片（每天21 mg用锥度）或安慰剂药胶囊/斑块12周。戒烟 测量了第9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）优化一个模型，以预测个人的特定吸烟可能性 响应每种治疗的成功。与老鹰试验中的主要分析一致，我们将定义 在第9至12周，一氧化碳确认的连续禁欲作为治疗成功。 其次，我们还将在第9至24周期间检查连续的禁欲。我们将发展一个患者 以及面向提供商的移动应用程序原型，可实现最合适的算法并前瞻性预测 新患者使用各种药物进行戒烟的可能性。移动应用将允许新的 患者根据认为在最终模型中相关的预测因素来完成一组评估集。 应用程序原型的开发将使我们在以后的研究中完成用户测试和完善。 最后，我们将开发一个r包，以促进与与之合作的统计学家实施类似模型 其他疾病数据。