Predicting individual responses to treatment for alcohol use disorder.

预测个体对酒精使用障碍治疗的反应。

• 批准号: 10659811
• 负责人: M LEE VAN HORN
• 金额: $ 61.1万
• 依托单位: UNIVERSITY OF NEW MEXICO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-20 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659811
• 关键词: Alcohol consumption; Algorithms; Area; Behavior Therapy; Behavioral; Biological Markers; Caring; Characteristics; Client; Clinical; Clinical Data; Clinical Trials; Clinical Trials Cooperative Group; Communication; Complex; Data; Disease; Ensure; Goals; Heavy Drinking; Heterogeneity; Individual; Individual Differences; Intervention; Literature; Methods; Naltrexone; Outcome; Patients; Pharmaceutical Preparations; Pharmacological Treatment; Prediction of Response to Therapy; Prevention program; Provider; Psychosocial Factor; Public Health; Publishing; Randomized, Controlled Trials; Reaction; Relapse; Research; Research Personnel; Sampling; Selection for Treatments; Specific qualifier value; Symptoms; Telephone; Testing; Treatment Effectiveness; Treatment Efficacy; Treatment outcome; Validation; Work; acamprosate; alcohol abuse therapy; alcohol intervention; alcohol response; alcohol use disorder; behavior test; clinical decision-making; clinical practice; design; experience; improved; improved outcome; individual response; individualized medicine; machine learning algorithm; mindfulness intervention; novel strategies; personalized approach; personalized medicine; pharmacologic; predicting response; prediction algorithm; predictive modeling; randomized trial; randomized, clinical trials; relapse prevention; response; simulation; theories; topiramate; treatment effect; treatment response; usability

Project summary: Treatment of alcohol use disorder (AUD) is characterized by common relapse, heterogeneity in findings, and many diverse interventions which show modest efficacy but fail to out perform each other. Research aiming to explain the existing heterogeneity has found many significant moderators of treatment effects but few of these have effect sizes large enough to indicate that they should be used in clinical practice for targeting treatments. New personalized medicine methods which use machine learning algorithms to create predictions of responses to AUD treatment which take into account multiple predictors show early promise. This research This research uses data from 11 randomized clinical trials, 6 of behavioral relapse prevention programs and 5 of pharmacological interventions to reduce heavy drinking, to develop and cross validate individual predictions of treatment effects on heavy drinking. We will also test the significance of individual differences for each intervention and provide predictive intervals for individuals describing their expected response to different interventions. The study also aims to test new approaches for combining data across multiple trials and for improving precision of predictions in order to make the use of the predicted individual treatment effects (PITEs) framework more useful in clinical practice. At the end of this study there will be published algorithms for comparing predictions of treatment effects for new individuals across multiple treatments, predictive intervals for those effects, and an assessment of internal and, where possible, external validation of those predictions. The work emphasizes replicability of results through cross-validation (which will itself be tested with simulations), a priori specification of predictive methods and covariates, and use of an expert panel to make theory and literature informed decisions. This research is designed to make personalized medicine for treatment of AUD usable in clinical practice through its integration of theory, clinical experience brought by the clinical advisory board, and clear communication of results to a clinical audience.

项目摘要： 酒精使用障碍（AUD）的治疗的特征是常见复发，发现中的异质性和 许多不同的干预措施表现出适度的功效，但无法相互表现。旨在的研究 解释现有的异质性发现了许多重要的治疗效果主持人，但其中很少 具有足够大的效应大小，以表明应将其用于临床实践中用于靶向治疗。 使用机器学习算法来创建预测的新的个性化医学方法 考虑到多个预测因子的AUD治疗的反应显示了早期的希望。这项研究 该研究使用来自11项随机临床试验，6个行为复发预防计划的数据和5个 减少大量饮酒的药理干预措施，以发展和跨越验证个人预测 治疗对大量饮酒的影响。我们还将测试每个人的个体差异的重要性 干预并为个人描述其对不同的反应的个人提供预测间隔 干预措施。该研究还旨在测试新方法，以合并多个试验和 提高预测精度，以便使用预测的个人治疗效果（pites） 框架在临床实践中更有用。 在这项研究结束时，将发表算法来比较治疗效果的预测 跨多种疗法的新人，这些影响的预测间隔以及内部评估 并且，在可能的情况下，对这些预测的外部验证。工作强调结果的可复制性 通过交叉验证（本身将通过模拟测试），预测方法的先验规范 和协变量，并使用专家小组来做出理论和文学知情的决定。这项研究是 旨在制作个性化医学，以通过其整合在临床实践中的AUD可用 从理论上讲，临床咨询委员会带来的临床经验，以及结果与 临床观众。