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.

项目概要： 酒精使用障碍（AUD）的治疗特点是常见的复发，结果的异质性， 许多不同的干预措施显示出适度的功效，但未能胜过其他干预措施。研究旨在 解释现有的异质性已经发现了许多治疗效果的重要调节因素，但其中很少 具有足够大的效应量，表明它们应该用于临床实践中的靶向治疗。 新的个性化医疗方法，使用机器学习算法来预测 考虑到多个预测因素的AUD治疗反应显示出早期的希望。本研究 这项研究使用的数据来自11个随机临床试验，6个行为复发预防计划和5个 药物干预，以减少大量饮酒，开发和交叉验证个人预测 对酗酒的治疗效果。我们还将测试每个个体差异的显著性 干预和提供预测区间的个人描述他们的预期反应不同 干预措施。该研究还旨在测试将多项试验的数据结合起来的新方法， 提高预测精度，以便利用预测的个体治疗效应（PITE） 在临床实践中更有用的框架。 在本研究结束时，将发表用于比较以下治疗效果预测的算法： 多个治疗的新个体，这些效应的预测区间，以及内部 并在可能的情况下，对这些预测进行外部验证。这项工作强调成果的可复制性 通过交叉验证（这本身将与模拟测试），预测方法的先验规范 和协变量，并使用专家小组作出理论和文献知情的决定。本研究是 旨在通过其整合，使治疗AUD的个性化药物可用于临床实践 的理论，临床咨询委员会带来的临床经验，并明确传达结果， 临床观众