Development of a Tool to Measure Consumer Preferences in MDD Treatment

开发衡量消费者重度抑郁症治疗偏好的工具

基本信息

批准号：
7794986
负责人：
PAUL F CRITS-CHRISTOPH
金额：
$ 23.63万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-04-01 至 2012-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7794986
关键词：
Adverse event Advisory Committees Algorithms American Psychological Association Attention Behavior Benefits and Risks Characteristics Clinical Clinical Treatment Communities Community Mental Health Centers Computer software Consumer Preferences Consumer Satisfaction Data Data Aggregation Databases Decision Making Decision Modeling Development Diagnosis Dimensions Discipline Double-Blind Method Drug Approval Evidence Based Medicine Evidence based practice Evidence based treatment Face Foundations Funding Future Generations Goals Habits Health Professional Incidence Individual Institute of Medicine (U.S.)Intervention Intuition Length Literature Major Depressive Disorder Measures Medical Medicine Mental Health Mental disorders Meta-Analysis Methodology Methods Occupational Outcome Patients Performance Pharmaceutical Preparations Pharmacotherapy Physicians Psychology Psychotherapy Publishing Randomized Clinical Trials Randomized Controlled Trials Reaction Reading Recommendation Research Rest Safety Services Source Specific qualifier value Surveys Time Translating Visual Weight Work analog base clinical decision-making clinical practice comparative disability efficacy testing evidence base experience improved instrument meetings nondrug therapy preference public health relevance response social success systematic review therapy design tool user-friendly working group

项目摘要

DESCRIPTION (provided by applicant): Evidence-based practice is defined as the integration of best research evidence with clinical expertise and consumer preferences. However, little attention has been devoted to how to integrate consumer preferences into evidence-based practice in the treatment of major depressive disorder. No practical clinical methodology is available that provides real-time, consumer-weighting of preferences that would permit empirical findings to be used to individualize treatment choice for each mental health consumer with major depressive disorder. The overall goal of this R34 application is to develop and pilot a multi-attribute decision modeling approach in which clinical treatment decisions for people seeking treatment for major depressive disorder in a community mental health setting are guided by evidence-based practice data that has been customized to the preferences of individual consumers. We will apply multi-attribute decision modeling to match up consumers' ratings of their preferences regarding specific treatment attributes (i.e., efficacy, safety, tolerability) to the performance of available treatments as measured by meta-analytic data on each of the attributes (e.g., response rate; incidence of adverse events). Three development steps are proposed here: (1) compile information from existing meta-analyses, or conduct meta-analyses as needed, on the performance of existing evidence-based pharmacotherapies and psychotherapies for major depressive disorder in regard to a list of salient treatment attributes (efficacy; adverse events; tolerability; time commitment), (2) conduct a survey of 80 consumers and 40 clinicians from a community mental health center to evaluate the importance of various specified treatment attributes, and solicit additional treatment attributes deemed to be important, in the treatment of major depressive disorder, and (3) conduct a study examining the feasibility, ease of use, and predictive validity of 3 measures for assessing consumer preferences in regard to a final list of treatment attributes. This final study will be conducted using 72 consumers seeking treatment for major depressive disorder in a community mental health center, with preferences being used to predict duration of time each consumer stays on the treatment recommended to them at the agency. Results of these studies will be used (in future work) to develop a software product that provides real-time assessment of consumer preferences together with a matching of the preferences to attributes of evidence-based treatments for major depressive disorder so that an individualized treatment recommendation is produced to guide the clinician in decision making. Public Health Relevance: Major depressive disorder is one of the most common psychiatric disorders and is associated with considerable social and occupational disability. Incorporating consumer preferences into treatment will facilitate the tailoring of evidence-based practice to the individual and potentially increase consumer satisfaction and improve outcomes.

描述（由申请人提供）：循证实践被定义为最佳研究证据与临床专业知识和消费者偏好的整合。然而，很少有人关注如何将消费者偏好融入重度抑郁症治疗的循证实践中。目前还没有实用的临床方法可以提供实时的消费者偏好权重，从而可以利用经验发现为每个患有重度抑郁症的心理健康消费者提供个性化的治疗选择。该 R34 应用程序的总体目标是开发和试点一种多属性决策建模方法，在该方法中，在社区心理健康环境中寻求重度抑郁症治疗的人们的临床治疗决策以基于证据的定制实践数据为指导符合个人消费者的喜好。我们将应用多属性决策模型，将消费者对特定治疗属性（即功效、安全性、耐受性）的偏好评级与通过每个属性（例如，疗效、安全性、耐受性）的荟萃分析数据衡量的可用治疗的性能相匹配。、反应率；不良事件的发生率）。这里提出了三个发展步骤：（1）根据现有的荟萃分析汇编信息，或根据需要对现有循证药物疗法和心理疗法在重度抑郁症中的表现进行荟萃分析，并列出一系列显着的治疗属性（功效；不良事件；耐受性；时间投入），(2) 对社区精神卫生中心的 80 名消费者和 40 名临床医生进行调查，以评估各种指定治疗属性的重要性，并征求认为额外的治疗属性在重度抑郁症的治疗中很重要，(3) 进行一项研究，检验 3 种措施的可行性、易用性和预测有效性，以评估消费者对最终治疗属性列表的偏好。这项最终研究将使用 72 名在社区心理健康中心寻求重度抑郁症治疗的消费者进行，并使用偏好来预测每位消费者接受该机构推荐的治疗的持续时间。这些研究的结果将用于（在未来的工作中）开发一种软件产品，该产品提供消费者偏好的实时评估，并将偏好与重度抑郁症循证治疗的属性相匹配，以便提供个性化的治疗建议旨在指导临床医生做出决策。公共卫生相关性：重度抑郁症是最常见的精神疾病之一，与相当大的社会和职业残疾有关。将消费者偏好纳入治疗将有助于针对个人定制循证实践，并有可能提高消费者满意度并改善治疗结果。