Psychosis Risk Evaluation, Data Integration and Computational Technologies (PREDICT): Data Processing, Analysis, and Coordination Center

精神病风险评估、数据集成和计算技术 (PREDICT)：数据处理、分析和协调中心

• 批准号: 10912925
• 负责人: Rene S. Kahn
• 金额: $ 235.34万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-09 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10912925
• 关键词: Address; Adolescent; Affect; Algorithms; Anxiety Disorders; Artificial Intelligence; Attenuated; Behavior; Big Data; Biological Markers; Child; Clinical; Clinical Trials; Clinical stratification; Collection; Common Data Element; Communities; Community Outreach; Computer Analysis; Computer software; Computers; Data; Data Aggregation; Data Analyses; Data Collection; Data Set; Databases; Development; Disease; Disease remission; Early Intervention; Early identification; Enrollment; Ensure; Ethics; Evaluation; FAIR principles; Follow-Up Studies; Funding; Future; Goals; Heterogeneity; Human Resources; Impaired cognition; Individual; Informatics; Infrastructure; Instruction; Intervention; Lead; Leadership; Longterm Follow-up; Machine Learning; Measures; Mental disorders; Meta-Analysis; Methods; Monitor; Moods; Motivation; National Institute of Mental Health; Online Systems; Outcome; Output; Perception; Persons; Procedures; Process; Protocols documentation; Psychoses; Quality Control; Recommendation; Recovery; Research; Research Personnel; Risk; Safety; Sampling; Schizophrenia; Scientist; Secure; Site; Social Functioning; Standardization; Substance Use Disorder; Symptoms; Technology; Thinking; Time; Training; United States; Validation; Visualization software; adverse outcome; analytical tool; attenuated psychosis syndrome; bioinformatics infrastructure; biomarker identification; biomarker validation; candidate identification; candidate marker; clinical heterogeneity; clinical high risk for psychosis; clinical predictive model; clinical risk; clinical subtypes; clinically relevant; cloud based; cohort; computerized data processing; data acquisition; data archive; data dictionary; data dissemination; data harmonization; data infrastructure; data integration; data repository; data tools; deep learning; demographics; design; disability; effective intervention; experience; flexibility; functional decline; functional disability; high risk; high risk population; improved; inclusion criteria; innovation; meetings; member; multidisciplinary; multimodal data; multimodality; multiple data types; outcome prediction; persistent symptom; prediction algorithm; predictive marker; prevent; prospective; psychosis risk; psychotic; psychotic symptoms; quality assurance; recruit; research study; resilience; response; risk prediction; risk stratification; schizophrenia risk; success; therapy development; tool; working group

The “clinical high risk” (CHR) for psychosis syndrome is an antecedent period characterized by attenuated psychotic symptoms that are marked by subtle deviations from normal development in thinking, motivation, affect, behavior, and a decline in functioning. Early intervention in this CHR population is critical to prevent psychosis onset as well as other adverse outcomes. However, the presentation of symptoms and subsequent course is highly variable, and there is a paucity of biomarkers to guide treatment development. Thus, to improve predictive models that are clinically relevant, several issues need to be addressed: 1) focusing on outcomes beyond psychosis; 2) taking into account heterogeneity in samples and outcomes; and 3) integrating data sets with a broad array of variables using innovative algorithms to overcome variability across studies. To address these challenges, the proposed “Psychosis Risk Evaluation Data Integration and Computational Technologies: Data Processing, Analysis, and Coordination Center” (PREDICT-DPACC) brings together a multidisciplinary team of highly experienced researchers with proven capabilities in all aspects of large-scale studies, CHR studies, as well as computational expertise. The ultimate goal is to identify new CHR biomarkers, and CHR subtypes that will enhance future clinical trials. To do so, the PREDICT-DPACC will 1) aggregate extant CHR- related data sets from legacy datasets; 2) provide collaborative management, direction, data processing and coordination for new U01 multisite network(s); and 3) develop and apply advanced algorithms to identify biomarkers that predict outcomes, and to stratify CHR into subtypes based on outcome trajectories, first from the extant data and then refined and applied to the new data. The PREDICT-DPACC team has the broad, comprehensive, and robust infrastructure that is sufficiently flexible to accommodate the inclusion of multiple data types and to optimally address the needs of the CHR U01 network(s). Carefully selected extant data will be rapidly obtained, processed, and uploaded to the NIMH Data Archive (NDA). Proposed analysis methods are powerful and robust, leveraging the expertise and experience of computer scientist developers, and experienced clinical researchers. The U01 network(s) will be coordinated by a team that is experienced in managing large studies, familiar with the needs of such studies, flexible, and is knowledgeable in all aspects of CHR studies, including measures, outcomes, biomarkers, and cohorts. Upon meeting the goals of this U24, and the supported U01 network(s), the expected outcomes of the PREDICT-DPACC will be new predictive biomarkers for CHR outcomes, new definitions of CHR subtypes that are clinically useful, and new curated and comprehensive CHR datasets (extant and new) as well as processing tools and prediction algorithms that are shared with the research community through the NIMH Data Archive.

精神病综合征的临床高危(Chr)是一个先证期，其特征是 精神错乱症状，表现为思维、动机、精神状态等方面与正常发展的微妙偏离， 情绪、行为和功能衰退。对这一慢性阻塞性肺病人群的早期干预是预防 精神病发作以及其他不良后果。然而，症状的出现和随后的 病程变化很大，缺乏指导治疗进展的生物标记物。因此，要提高 对于临床相关的预测模型，有几个问题需要解决：1)关注结果 超越精神病；2)考虑样本和结果的异质性；3)整合数据集 使用创新的算法来克服不同研究之间的变异性。致信地址 这些挑战，拟议的“精神病风险评估数据集成和计算技术： 数据处理、分析和协调中心“(Predict-DPACC)汇集了多学科 由经验丰富的研究人员组成的团队，他们在大规模研究的各个方面都具有被证明的能力 学习，以及计算专业知识。最终目标是识别新的CHR生物标记物，以及CHR 将加强未来临床试验的亚型。为此，预测-DPACC将1)聚合现有的CHR- 来自传统数据集的相关数据集；2)提供协作管理、指导、数据处理和 协调新的U01多站点网络(S)；以及3)开发和应用先进的算法来识别 预测结果的生物标记物，并根据结果轨迹将CHR分层为亚型，首先来自 现有的数据，然后提炼并应用于新的数据。预测-DPACC团队拥有广泛的， 全面、强大的基础设施，具有足够的灵活性，可容纳多个 数据类型，并以最佳方式满足CHR U01网络的需求(S)。精心挑选的现有数据将是 快速获取、处理并上传到NIMH数据档案(NDA)。建议的分析方法有 强大和健壮，利用计算机科学家开发人员的专业知识和经验，以及经验丰富的 临床研究人员。U01网络(S)将由一支经验丰富的管理大型 研究，熟悉这类研究的需要，灵活，并对CHR研究的各个方面都有了解， 包括测量、结果、生物标记物和队列。在达到本U24的目标后，并支持 U01网络(S)，预测-DPACC的预期结果将成为CHR的新的预测生物标志物 结果，对临床有用的CHR亚型的新定义，以及新的精选和全面的CHR 与研究共享的数据集(现有的和新的)以及处理工具和预测算法 社区通过NIMH数据档案。