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: 10621232
• 负责人: Rene S. Kahn
• 金额: $ 467.42万
• 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/10621232
• 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“临床高风险”（CHR）是一个前期，其特征是衰减 精神病符号以偏离思维，动力，动力，动机的正常发展为标志的精神病符号 影响，行为和功能下降。早期干预该CHR人群对于防止 精神病发作以及其他不良结果。但是，症状及其后续的表现 当然是高度可变的，并且存在生物标志物来指导治疗的发展。那是为了改善 在临床上相关的预测模型，需要解决一些问题：1）关注结果 超越精神病； 2）考虑样品和结果中的异质性； 3）集成数据集 使用创新算法来克服整个研究的可变性，多种变量。解决 这些挑战是拟议的“精神病风险评估数据整合和计算技术： 数据处理，分析和协调中心”（预测DPACC）汇集了多学科 由经验丰富的研究人员组成的团队在大规模研究的各个方面都具有良好的能力，CHR 研究以及计算专业知识。最终目标是识别新的CHR生物标志物和CHR 亚型将增强未来的临床试验。为此，预测DPACC将1）汇总额外的chr- 来自旧数据集的相关数据集； 2）提供协作管理，指导，数据处理和 新U01多站点网络的协调； 3）开发和应用高级算法以识别 预测结果并根据结果轨迹将CHR分类为亚型的生物标志物，首先是 数据范围，然后完善并应用于新数据。预测DPACC团队拥有广泛的 综合，强大的基础设施，足以容纳多个 数据类型并最佳地满足CHR U01网络的需求。精心选择的范围数据将是 迅速获得，处理和上传到NIMH数据存档（NDA）。提出的分析方法是 强大而健壮，利用计算机科学家开发人员的专业知识和经验，并经验丰富 临床研究人员。 U01网络将由一个在管理大型方面经验丰富的团队协调 研究，熟悉此类研究的需求，灵活，并且在CHR研究的各个方面都有知识， 包括措施，结果，生物标志物和同类。达到了U24的目标以及受支持的目标后 U01网络，预测DPACC的预期结果将是CHR的新预测生物标志物 结果，临床上有用的ChR子类型的新定义以及新的策划和全面的CHR 与研究共享的数据集（现有和新）以及处理工具和预测算法 通过NIMH数据存档进行社区。