Predicting Internet-Based Treatment Response for Major Depressive Disorder

预测重度抑郁症基于互联网的治疗反应

• 批准号: 9624631
• 负责人: RANDY PATRICK AUERBACH
• 金额: $ 18.08万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-08 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9624631
• 关键词: Predicting; Internet; Based; Treatment; Response

As many as 53% of students report experiencing depression during college, and these depressive episodes are associated with a higher frequency of academic problems, comorbidity, and suicide. Although there are effective options for treatment, the majority of individuals (>70%) do not pursue services, and even for those who do, response rates remain modest (~40-50%). As a means of increasing accessibility to treatment, internet-based interventions for depression have been developed and tested. Despite increased availability, response to internet-based interventions continues to vary substantially, and failed treatment often contributes to persistence and worsening of symptoms. Therefore, identifying individuals with a high likelihood of responding to internet-based treatment would represent a major advance and address a critical unmet need. In recent years, promising approaches for testing the heterogeneity of the treatment effects – delineating which individuals are likely to respond to a given treatment – have been developed. However, their use for identifying predictors of treatment response in depression remains unclear. To address this unmet need, the proposed study will test a new, cost-effective, and feasibly-scaled method of predicting differential treatment response following internet-based cognitive behavioral therapy (iCBT) for depression in a large, representative college sample (Boston Consortium of Colleges and Universities which includes 7 schools). Members of the consortium have committed to screening all incoming freshmen (N = ~14,000) through a rigorous online assessment and to offer iCBT to students with elevated levels of depressive symptoms (i.e., minor or major depression). The following steps will be pursued. First, in the initial phase of the study, freshmen students from the Boston Consortium will be screened for depression through an online survey, and they also will complete web-based neurocognitive tasks probing key mechanisms underpinning depression. Depressed students will be invited to enroll in iCBT, and a predictive algorithm will be developed based on assessments across different units of analysis (i.e., clinical characteristics, neurocognitive indices) to identify iCBT responders. Second, after the development phase, an independent sample of college students will be recruited to validate the predictive algorithm. This validation phase will use clinical indicators and neurocognitive data. Additionally, functional magnetic resonance imaging (fMRI) data targeting key mechanisms within the Research Domain Criteria (RDoC) will be acquired from a subset of participants. Neural data will be integrated to determine whether they improve the predictive algorithm. Third, data across independent samples will be combined, which will increase power to refine our predictive model for both acute and sustained response. In summary, there are alarming rates of depression among college students, and only a minority of students utilize mental health services. The proposed research will personalize our approach to depression treatment, which, ultimately, will improve effectiveness and better inform mental health care across college campuses.

多达 53% 的学生表示在大学期间经历过抑郁症，而这些抑郁症患者 发作与较高频率的学业问题、合并症和自杀有关。 有有效的治疗选择，大多数人（>70%）不寻求服务，甚至 对于那些这样做的人，回复率仍然较低（约 40-50%）。 尽管治疗方法有所增加，但基于互联网的抑郁症干预措施已经被开发出来并进行了测试。 可用性、对基于互联网的干预措施的反应仍然存在很大差异，并且治疗经常失败 导致症状持续存在和恶化，因此，识别具有高可能性的个体。 对基于互联网的治疗做出反应将是一项重大进步，并解决未满足的关键需求。 近年来，测试治疗效果异质性的有前景的方法——描绘 哪些人可能会对特定的治疗产生反应——但它们的用途已经被开发出来。 确定抑郁症治疗反应的预测因素仍不清楚。 拟议的研究将测试一种新的、具有成本效益且规模可行的预测差异化治疗的方法 基于互联网的认知行为疗法（iCBT）治疗抑郁症后的反应 大学样本（波士顿学院和大学联盟，其中包括 7 所学校的成员）。 联盟承诺通过严格的在线筛选所有新生（N = ~14,000） 评估并向患有抑郁症状（即轻微或严重抑郁症状）的学生提供 iCBT 首先，在研究的初始阶段，将采取以下步骤。 波士顿联盟将通过在线调查筛查抑郁症，他们还将完成 基于网络的神经认知任务探究抑郁症学生的关键机制。 受邀参加 iCBT，并将根据跨领域的评估开发预测算法 不同的分析单位（即临床特征、神经认知指数）来识别 iCBT 反应者。 其次，开发阶段结束后，将招募独立的大学生样本进行验证 预测算法。此验证阶段将使用临床指标和神经认知数据。 针对研究领域内关键机制的功能磁共振成像 (fMRI) 数据 标准 (RDoC) 将从参与者子集获取，神经数据将被整合以确定。 他们是否改进了预测算法第三，独立样本的数据将被合并， 这将增强我们针对急性和持续反应的预测模型的完善能力。 大学生抑郁症发病率惊人，只有少数学生利用心理 拟议的研究将使我们的抑郁症治疗方法个性化，其中， 最终，将提高效率并更好地为整个大学校园的心理健康保健提供信息。

项目成果

ADHD Comorbidity Structure and Impairment: Results of the WHO World Mental Health Surveys International College Student Project (WMH-ICS).

ADHD 合并症结构和损害：世界卫生组织世界心理健康调查国际大学生项目 (WMH-ICS) 的结果。

• 发表时间: 2022-06
• 作者: Mak, Arthur D P;Lee, Sue;Sampson, Nancy A;Albor, Yesica;Alonso, Jordi;Auerbach, Randy P;Baumeister, Harald;Benjet, Corina;Bruffaerts, Ronny;Cuijpers, Pim;Ebert, David D;Gutierrez;Hasking, Penelope;Lapsley, Coral;Lochner, Chris
• 通讯作者: Lochner, Chris