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: 10409839
• 负责人: Rene S. Kahn
• 金额: $ 468.47万
• 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/10409839
• 关键词: Address; Adolescent; Affect; Algorithms; Anxiety Disorders; Artificial Intelligence; Attenuated; Behavior; Big Data; Biological Markers; Child; Clinical; Clinical Trials; 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; Recovery; Research; Research Personnel; Risk; Safety; Sampling; Schizophrenia; Scientist; Secure; Site; Social Functioning; Standardization; Substance Use Disorder; Suggestion; Symptoms; Technology; Thinking; Time; Training; Transact; United States; Validation; Visualization software; adverse outcome; analytical tool; attenuated psychosis syndrome; base; bioinformatics infrastructure; biomarker identification; candidate marker; clinical heterogeneity; clinical high risk for psychosis; 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; predictive modeling; prevent; prospective; psychosis risk; 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”（clinical high risk，缩写为CRH）是一个前期，其特征是： 精神病症状的特点是思维，动机， 影响行为和功能下降对这一弱势群体的早期干预对于预防 精神病发作以及其他不良后果。然而，症状的表现和随后的 过程是高度可变的，并且缺乏生物标志物来指导治疗开发。因此，为了改善 预测模型是临床相关的，需要解决几个问题：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 使用创新算法来克服研究之间的差异性，具有广泛的变量。解决 这些挑战，提出了“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，在大规模研究的各个方面都有成熟的能力， 研究，以及计算专业知识。最终目标是确定新的生物标志物， 这些亚型将增强未来的临床试验。为此，PREDICT-DPACC将1）聚合现有的数据， 2）提供协作管理、指导、数据处理和 协调新的U 01多站点网络;以及3）开发和应用高级算法， 生物标志物，预测结果，并分层成亚型的基础上，结果轨迹，首先从 现有的数据，然后提炼和应用到新的数据。预测DPACC团队拥有广泛的， 全面而强大的基础设施，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EU 01网络的需求。精心挑选的现存数据将被 快速获取、处理并上传到NIMH数据档案（NDA）。建议的分析方法有 强大而健壮，利用计算机科学家开发人员的专业知识和经验， 临床研究者U 01网络将由一个在管理大型 研究，熟悉此类研究的需求，灵活，并了解研究的各个方面， 包括测量、结果、生物标志物和群组。在实现这一U24的目标，并支持 根据U 01网络，PREDICT-DPACC的预期结果将是新的预测性生物标志物， 结果，临床上有用的新定义，以及新的策划和全面的治疗 数据集（现有的和新的）以及与研究共享的处理工具和预测算法 通过NIMH数据档案。