Interpretation Bias as a Mechanism of Treatment Response in OCD - Administrative Supplement

解释偏差作为强迫症治疗反应的机制 - 行政补充

• 批准号: 10581322
• 负责人: Martha J Falkenstein
• 金额: $ 5.4万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10581322
• 关键词: Address; Adherence; Administrative Supplement; Anxiety Disorders; Area; Behavioral; Characteristics; Clinical; Clinical Treatment; Cognitive; Development; Disease; Ecological momentary assessment; Exposure to; Future; Goals; Individual; Intervention; Investigational Therapies; Machine Learning; Maintenance; Maps; Mentored Patient-Oriented Research Career Development Award; Mentors; Mentorship; Methodology; Methods; Modeling; Modification; Obsessive-Compulsive Disorder; Outcome; Patients; Population; Prevention; Probability; Refractory; Relapse; Research Personnel; Sampling; Sampling Studies; Stimulus; Symptoms; Techniques; Technology; Testing; Training; analog; career development; computerized; effectiveness trial; feasibility testing; improved; innovation; multimodality; novel; patient oriented research; personalized predictions; programs; reduce symptoms; response; skills; technology development; treatment response

Project Summary Although exposure and response prevention (ERP) is well-established as the first line of treatment for obsessive- compulsive disorder (OCD), 40-50% of patients who receive this intervention do not recover. Novel augmentations are needed to more efficiently target the mechanisms that maintain OCD symptoms in this population. One candidate mechanism is interpretation bias, the misinterpretation of ambiguous stimuli as threatening, contributing to the development and maintenance of OCD. In the absence of interventions that more efficiently and effectively target interpretation bias, it will likely remain difficult to increase rates of treatment response and decrease relapse. The objective of this proposal is to test the feasibility, acceptability, adherence, target engagement, and clinical outcomes of an intervention targeting interpretation bias, with the ultimate goal of contributing to the development of novel, scalable, technology-driven augmentations to ERP. Improvements in interpretation bias have been associated with OC symptom reduction. Therefore, an accessible intervention which directly targets interpretation bias may be an ideal augmentation to improve clinical outcomes during ERP. Cognitive Bias Modification for Interpretation Bias (CBM-I), a computerized intervention, has shown reliable effects for engagement of interpretation. Studies of CBM-I in OCD have been largely conducted in analogue samples, and studies with clinical samples have been limited. This proposal will address the critical need of developing interventions to augment ERP by utilizing CBM-I, with primary aims to: 1) test whether CBM-I induces changes in interpretation bias in OCD and to determine if these changes are associated with clinical outcomes across multi-modal assessments, and 2) leverage advances in machine learning to develop personalized predictions of which individuals with OCD are best-suited for CBM-I. We hypothesize that a multivariate model incorporating pre-treatment clinical, behavioral and demographic characteristics will predict patient-specific probability of responding to CBM-I. These aims map onto the candidate’s training goals, with critical new training and mentorship provided in the areas of: 1) effectiveness trials methodology with an experimental therapeutics approach (Co-Primary Mentor Dr. Courtney Beard; Co-Mentor Dr. Sabine Wilhelm); 2) ecological momentary assessment (Co-Primary Mentors Dr. Christian Webb and Dr. Beard; Collaborator Dr. Justin Baker); 3) machine learning techniques (Dr. Webb and Collaborator Dr. Boyu Ren); and 4) career development (Co-Mentor Dr. Kerry Ressler). This K23 Award will support an innovative program of patient-oriented research and provide the candidate with the skills necessary to become an independent investigator focused on advancing the understanding, prediction, and treatment of non-response and relapse, to optimize outcomes of exposure therapy for refractory patients with OC-related disorders.

项目摘要 虽然暴露和反应预防（ERP）是公认的治疗强迫症的第一线， 强迫症（OCD），40-50%接受这种干预的患者没有康复。小说 需要增强以更有效地靶向维持强迫症症状的机制， 人口一种可能的机制是解释偏差，即把模棱两可的刺激误解为 威胁，有助于发展和维持强迫症。在没有干预的情况下， 有效地针对解释偏倚，可能仍然难以提高治疗率 反应并减少复发。本提案的目的是测试可行性、可接受性、遵守性， 目标参与，以及针对解释偏倚的干预措施的临床结局，最终目标 有助于开发新颖的，可扩展的，技术驱动的ERP增强功能。改进 解释偏差与OC症状减轻有关。因此， 直接针对解释偏倚的方法可能是改善ERP期间临床结果的理想增强方法。 认知偏差修正解释偏差（CBM-I），一个计算机化的干预，已被证明是可靠的， 对口译工作的影响。CBM-I在强迫症中的研究主要是在模拟 样本和临床样本的研究有限。该提案将满足以下迫切需要： 通过利用CBM-I开发干预措施以增强ERP，主要目的是：1）测试CBM-I是否诱导 强迫症解释偏倚的变化，并确定这些变化是否与临床结局相关 跨多模态评估，2）利用机器学习的进步来开发个性化的 预测哪些强迫症患者最适合CBM-I。我们假设一个多变量模型 结合治疗前的临床，行为和人口统计学特征将预测患者特异性 对第一次建立信任措施作出反应的可能性。这些目标映射到候选人的培训目标， 在以下领域提供指导：1）使用实验疗法的有效性试验方法 方法（共同主要导师考特尼比尔德博士;共同导师萨宾威廉博士）; 2）生态瞬间 评估（共同主要导师Christian Webb博士和Beard博士;合作者Justin Baker博士）; 3）机器 学习技巧（Webb博士和合作者Boyu Ren博士）;以及4）职业发展（共同导师Kerry博士 Ressler）。该K23奖将支持以患者为导向的研究创新计划，并提供 候选人具备成为独立调查员所需的技能， 了解、预测和治疗无反应和复发，以优化暴露结果 难治性OC相关疾病患者的治疗。