Enhancing Clinical Decision-Making in Modular Youth Psychotherapy

项目摘要。接受精神保健的年轻人往往有多种障碍，独特的治疗- 相关的个人和家庭特征，以及在治疗过程中可能转移的问题。对于这些年轻人来说， 模块化心理治疗，提供者从治疗元素菜单中进行选择，以建立个性化 治疗，可能特别合适。一种经过充分研究的模块化青少年心理疗法是模块化的 焦虑、抑郁、创伤或行为问题儿童的治疗方法(Match)。在一些 在元素选择方面有密集的一对一专家支持的试验，Match的表现明显优于往常 护理和循证标准化心理治疗；但在不那么密集(更实用)的试验中 可行)支持，Match没有超过平时的护理。这种差异突出了一个关键挑战 匹配和其他模块化心理疗法：治疗元素的选择。因为Match允许任何 以任何顺序实施的会话的数量，几乎无限的治疗元素序列阵列可以 被选中。这种灵活性支持个性化，但也可能影响匹配和 其他模块化心理治疗，因为可能不清楚哪些模块应该在什么时候使用。就像在其他 模块化疗法，使用Match的临床医生被要求使用临床判断，并被提供决策- 根据过去的文献和客户每周评估提供指导。但研究表明， 使用档案数据的统计决策模型往往优于临床医生的判断。目前，没有 模块化的青年心理治疗使用数据驱动的统计模型来为决策提供信息。这是 可以理解：目前还没有这样的型号。这项拟议的研究将是朝着填补 这一差距；它将使用档案处理数据的统计模型来提供模块化的决策指导 青年心理治疗，旨在提高其效率和效果。根据NIMH优先级3.2，建议的 该项目将通过“重新分析…”来指导“调整现有干预措施以优化结果”聚合临床 使用计算方法的试验…以促进临床决策“。具体来说，该项目将 汇总来自六个匹配试验(N=602，年龄6-15岁)的数据，以：建立人与人之间和人内的短期模型 每种治疗元素的使用与随后的不同症状变化之间的人的关联 治疗阶段(目标1)；使用统计学习来确定这些关联的调节因素 元素和后续症状(目标2)；以及，在坚持的样本中，测试 示范建议和青少年的疗程预测长期治疗结果(目标3)。至 告知临床决策指导工具的未来发展(在F31之后)，将就以下事项采访临床医生 如何将基于模型的研究结果用于临床实践(探索性目标4)。归根结底，与 NIMH的优先事项，这项工作可能会在几个数据驱动下为青少年心理治疗的临床决策提供信息 在这方面，我们将继续努力，以期最终提高单元青年心理治疗的效率和效果。

Project Summary. Youths receiving mental healthcare often have multiple disorders, distinctive treatment- relevant personal and family characteristics, and problems that may shift during treatment. For these youths, modular psychotherapies, in which providers select from a menu of therapeutic elements to build personalized treatments, may be especially appropriate. One well-studied modular youth psychotherapy is Modular Approach to Therapy for Children with Anxiety, Depression, Trauma, or Conduct Problems (MATCH). In some trials with intensive, one-on-one expert support for element selection, MATCH significantly outperformed usual care and evidence-based standardized psychotherapies; but in trials with less intensive (more practically feasible) support, MATCH did not outperform usual care. This discrepancy highlights a key challenge of MATCH and other modular psychotherapies: selection of treatment elements. Because MATCH allows any number of sessions implemented in any order, a virtually unlimited array of treatment element sequences could be selected. This flexibility supports personalization but may also impact the effectiveness of MATCH and other modular psychotherapies, because it can be unclear which modules should be used when. As in other modular therapies, clinicians using MATCH are asked to use clinical judgment and are provided with decision- making guidance based on past literature and client weekly assessments. But research has shown that statistical decision-making models using archival data very often outperform clinician judgment. Currently, no modular youth psychotherapies employ data-driven statistical models to inform decision-making. This is understandable: no such models are currently available. The proposed study will be an initial step toward filling this gap; it will use statistical models of archival treatment data to provide decision-making guidance in modular youth psychotherapy, aimed at enhancing its efficiency and effectiveness. Per NIMH Priority 3.2, the proposed project will inform “tailoring existing interventions to optimize outcomes” by “reanalyzing… aggregated clinical trials” using “computational approaches… to facilitate clinical decision-making”. Specifically, the project will aggregate data from six MATCH trials (N=602, ages 6-15), to: model short-term between-person and within- person associations between use of each treatment element and subsequent symptom change at different stages of treatment (Aim 1); use statistical learning to identify moderators of these associations between elements and subsequent symptoms (Aim 2); and, in a holdout sample, test whether agreement between the model recommendations and youths’ treatment course predicts long-term treatment outcomes (Aim 3). To inform future development of clinical decision guidance tools (after the F31), clinicians will be interviewed about how model-based findings might be used in clinical practice (Exploratory Aim 4). Ultimately, consistent with NIMH priorities, this work may inform clinical decision making in youth psychotherapy in several data-driven ways, with the goal of eventually enhancing the efficiency and effectiveness of modular youth psychotherapies.