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: 10092398
• 负责人: Rene S. Kahn
• 金额: $ 393.52万
• 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 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10092398
• 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 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; Procedures; Process; Protocols documentation; Psychotic Disorders; Quality Control; Recovery; Research; Research Personnel; Risk; Risk stratification; Safety; Sampling; 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; candidate marker; clinical heterogeneity; 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 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; psychotic symptoms; quality assurance; recruit; research study; resilience; response; 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）是一个以减弱为特征的前期阶段。 精神病症状，其特征是思维、动机、 影响、行为和功能下降。对这一 CHR 人群的早期干预对于预防至关重要 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，并且缺乏指导治疗开发的生物标志物。因此，要改善 临床相关的预测模型需要解决几个问题：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 使用创新算法克服广泛的变量来克服研究之间的变异性。致地址 针对这些挑战，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具有经过验证的能力，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，以及 CHR 将增强未来临床试验的亚型。为此，PREDICT-DPACC 将 1) 汇总现有的 CHR- 来自遗留数据集的相关数据集； 2）提供协同管理、指导、数据处理和 协调新的 U01 多站点网络； 3）开发并应用先进的算法来识别 预测结果的生物标志物，并根据结果轨迹将 CHR 分层为亚型，首先来自 现有数据，然后提炼并应用于新数据。 PREDICT-DPACC 团队拥有广泛的、 全面、强大的基础设施，足够灵活，可以容纳多个 数据类型并以最佳方式满足 CHR U01 网络的需求。精心挑选的现有数据将 快速获取、处理并上传到 NIMH 数据档案 (NDA)。提出的分析方法是 强大而稳健，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 网络将由在管理大型网络方面经验丰富的团队进行协调 研究，熟悉此类研究的需求，灵活，并且了解 CHR 研究的各个方面， 包括测量、结果、生物标志物和队列。在实现本次 U24 的目标以及所支持的 U01 网络，PREDICT-DPACC 的预期结果将成为 CHR 的新预测生物标志物 结果、临床上有用的 CHR 亚型的新定义以及新策划和全面的 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 通过 NIMH 数据档案库社区。