Integrative Data Analysis to Predict Alcohol Clinical Course and Inform Practice

综合数据分析预测酒精临床过程并为实践提供信息

• 批准号: 8561848
• 负责人: Katie A Witkiewitz
• 金额: $ 31.38万
• 依托单位: UNIVERSITY OF NEW MEXICO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-05 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8561848
• 关键词: Abstinence; Achievement; Address; Administrator; Adoption; Aftercare; Age; Alcohol consumption; Alcohols; Behavior; Behavior Therapy; Behavioral; Characteristics; Client; Clinical; Data; Data Analyses; Data Set; Decision Making; Development; Drug usage; Employment; Enrollment; Environmental Risk Factor; Event; Feedback; Funding; Gender; Guidelines; Health Personnel; Heavy Drinking; Individual; Intervention; Measures; Modeling; Motivation; Occupations; Outcome; Outcomes Research; Pattern; Pharmacological Treatment; Policy Maker; Predisposition; Probability; Procedures; Provider; Recommendation; Relapse; Research; Risk; Risk Factors; Sample Size; Self Efficacy; Support Groups; Support System; System; Testing; Theoretical model; Time; Treatment Factor; Treatment outcome; United Kingdom; alcohol abuse therapy; alcohol relapse; alcohol use disorder; base; clinical decision-making; clinical practice; drinking; evidence base; experience; improved; interest; knowledge base; programs; public health relevance; success; theories; therapy development; treatment planning; treatment program; treatment trial

DESCRIPTION (provided by applicant): In spite of the adoption of many evidence-based behavioral and pharmacological interventions by alcohol treatment providers it is still common for drinking to occur after treatment, frequently at problematic levels. Very little is known, however, about who is most at risk for heavy drinking relapses, under what conditions non- problematic drinking escalates to pre-treatment levels of drinking or, conversely, heavy drinking is reduced after an initial lapse, and when non-problematic drinking resolves without the need for additional intervention efforts. In general, the mechanisms of change following alcohol treatment are not well understood. To address these gaps in understanding, this study will build an empirical knowledge base of predictors of successful alcohol treatment outcomes among individuals who receive alcohol treatment. The achievement of study aims will provide clinicians with both normative-based guidelines to identify "at risk" individuals during treatment as well as criteria to evaluate the need for additional treatment when post treatment drinking occurs. To develop these guidelines and criteria, integrative data analysis of factors influencing alcohol clinical course among more than 4,000 individuals (n = 4,415) who participated in four publicly funded alcohol treatment studies will be conducted. In order to characterize individual drinking norms this study will consider the complexities of alcohol clinical course by drawing on a dynamic theoretical model of relapse that emphasizes the interaction between predispositions and risk factors in the prediction of alcohol clinical course. The first aim of this research is to examine individual patterns of drinking and the probabilities of lapsing at various time points during and following treatment, as well as the probabilities of transitioning from a lapse to relapse. Using these probabilities of drinking as the basis for defining unsuccessful and successful treatment outcomes, this research aims to examine the degree to which individual characteristics and treatment factors interact with individual behaviors and environmental factors in the prediction of alcohol treatment outcomes. The results from this study will be directly applicable to clinical practice by providing guidance on the individual characteristics, precipitating events, and treatment modifiable factors that are associated with changes in drinking patterns during treatment and one year following treatment. These data will allow providers to predict client outcomes during and after treatment and will offer practical strategies regarding steps that could reduce the likelihood of a return to heavy drinking. The results will also help clinicians identify those individual characteristics and environmental factors that predict a higher probability of maintaining a low-risk drinking trajectory. Moreover, the probabilities derived from the research can be used for the development of an empirically-based clinical decision making support system. Such a system, unprecedented in the alcohol treatment field, could be widely disseminated to clinicians, program evaluators, and policy makers in order to help improve alcohol treatment decision-making and improve treatment outcomes.

描述（由申请人提供）：尽管酒精治疗提供者采用了许多基于证据的行为和药理学干预措施，但治疗后饮酒仍然很常见，而且经常处于有问题的水平。然而，关于谁最有可能重度酗酒复发、在什么条件下无问题饮酒会升级到治疗前的饮酒水平，或者相反，在初次戒酒后重度饮酒会减少，以及何时无问题饮酒，人们知之甚少。饮酒问题无需额外的干预措施即可解决。一般来说，酒精治疗后的变化机制尚不清楚。为了解决这些理解上的差距，本研究将建立一个经验知识库，以预测接受酒精治疗的个体的成功酒精治疗结果。研究目标的实现将为临床医生提供基于规范的指南，以识别治疗期间的“高危”个体，以及评估治疗后饮酒发生时是否需要额外治疗的标准。为了制定这些指南和标准，将对参与四项公共资助的酒精治疗研究的 4,000 多名个体 (n = 4,415) 中影响酒精临床过程的因素进行综合数据分析。为了描述个人饮酒规范的特征，本研究将通过利用复发的动态理论模型来考虑酒精临床病程的复杂性，该模型强调在预测酒精临床病程中倾向和危险因素之间的相互作用。这项研究的首要目的是 检查个体的饮酒模式和治疗期间和治疗后不同时间点的戒酒概率，以及从戒酒过渡到复发的概率。本研究利用这些饮酒概率作为定义不成功和成功治疗结果的基础，旨在研究在预测酒精治疗结果时个体特征和治疗因素与个体行为和环境因素相互作用的程度。这项研究的结果将直接应用于临床实践，为与治疗期间和治疗后一年饮酒模式变化相关的个体特征、诱发事件和治疗可改变因素提供指导。这些数据将使提供者能够预测治疗期间和治疗后客户的结果，并提供实用的策略 关于可以减少重新酗酒可能性的措施。研究结果还将帮助临床医生识别那些预测维持低风险饮酒轨迹的可能性更高的个人特征和环境因素。此外，从研究中得出的概率可用于开发基于经验的临床决策支持系统。这样的系统在酒精治疗领域是前所未有的，可以广泛传播给临床医生、项目评估者和政策制定者，以帮助改善酒精治疗决策并改善治疗结果。