Enhancing Clinical Decision-Making in Modular Youth Psychotherapy

项目摘要。接受精神保健的青少年往往有多种疾病，独特的治疗- 相关的个人和家庭特征，以及治疗期间可能发生的问题。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选择，以建立个性化的治疗方案。 治疗，可能是特别合适的。一个研究得很好的模块化青年心理治疗是模块化 焦虑，抑郁，创伤或行为问题儿童治疗方法（MATCH）。在一些 在对元素选择进行密集的一对一专家支持的试验中，MATCH的表现明显优于常规试验 护理和循证标准化的心理治疗;但在试验中， 可行的）支持，匹配没有超过通常的护理。这一差异凸显了一个关键挑战， MATCH和其他模块化心理治疗：治疗元素的选择。因为MATCH允许任何 以任何顺序实施的疗程的数量，治疗元件序列的实际上无限的阵列可以 被选中。这种灵活性支持个性化，但也可能影响MATCH的有效性， 其他模块化心理疗法，因为它可以不清楚哪些模块应该使用时。像在其他 模块化治疗，要求使用MATCH的临床医生使用临床判断，并提供决策- 根据过去的文献和客户每周的评估进行指导。但研究表明， 使用档案数据的统计决策模型通常优于临床医生的判断。当前没有任何 模块化青年心理治疗采用数据驱动的统计模型，为决策提供信息。这是 可以理解：目前没有这样的模型。拟议的研究将是填补空缺的第一步。 这一差距;它将使用档案处理数据的统计模型，以模块化方式提供决策指导。 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 该项目将通过“重新分析. 试验”使用“计算方法.以促进临床决策”。具体而言，该项目将 从6个MATCH试验（N=602，6-15岁）中汇总数据，以： 使用每个治疗元素和随后的症状变化之间的个人关联 治疗阶段（目标1）;使用统计学学习来确定这些关联的调节因素， 元素和随后的症状（目标2）;并且，在一个坚持的样本中，测试 模型建议和青少年的治疗过程预测长期治疗结果（目标3）。到 告知临床决策指导工具的未来发展（F31之后），将对临床医生进行采访， 如何在临床实践中使用基于模型的结果（探索性目标4）。最终，与 NIMH的优先事项，这项工作可能会通知临床决策，在青年心理治疗在几个数据驱动的 方法，最终提高模块化青年心理治疗的效率和有效性的目标。

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.