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。

基本信息

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
项目类别：
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

项目摘要

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、结果的异质性和许多不同的干预措施显示出一定的效果，但未能相互超越。研究旨在解释现有的异质性已经发现了许多治疗效果的显着调节因素，但其中很少效应大小足够大，表明它们应该在临床实践中用于靶向治疗。新的个性化医疗方法使用机器学习算法来创建预测考虑到多个预测因素，对 AUD 治疗的反应显示出了早期的希望。这项研究这项研究使用了 11 项随机临床试验、6 项行为复发预防计划和 5 项行为复发预防计划的数据。减少酗酒、开发和交叉验证个体预测的药物干预措施对酗酒的治疗效果。我们还将测试每个个体差异的显着性干预并为个人提供预测区间，描述他们对不同情况的预期反应干预措施。该研究还旨在测试合并多个试验数据的新方法以及提高预测精度，以便利用预测的个体治疗效果 (PITE) 框架在临床实践中更有用。在本研究结束时，将发布用于比较治疗效果预测的算法跨多种治疗的新个体、这些效果的预测间隔以及内部评估并在可能的情况下对这些预测进行外部验证。这项工作强调结果的可复制性通过交叉验证（其本身将通过模拟进行测试），预测方法的先验规范和协变量，并利用专家小组做出基于理论和文献的决策。这项研究是旨在通过整合使治疗 AUD 的个性化医疗可用于临床实践理论、临床顾问委员会带来的临床经验以及向临床专家清晰传达结果临床观众。