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: 10457174
• 负责人: Rene S. Kahn
• 金额: $ 77.74万
• 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/10457174
• 关键词: Address; Affect; Algorithms; Anxiety Disorders; Attenuated; Behavior; Big Data; Biological Markers; Clinical; Clinical Trials; Collection; Common Data Element; Computer software; Computers; Custom; Data; Data Aggregation; Data Analyses; Data Set; Development; Disease; Early Intervention; Early identification; Enrollment; Ensure; Evaluation; FAIR principles; Funding; Future; Goals; Grant; Heterogeneity; Human Resources; Impaired cognition; Individual; Informatics; Infrastructure; Intervention; Lead; Machine Learning; Medicine; Mental disorders; Methods; Modification; Monitor; Moods; Motivation; National Institute of Mental Health; Outcome; Parents; Pharmacotherapy; Population; Population Heterogeneity; Privatization; Procedures; Process; Protocols documentation; Psychoses; Quality Control; Recovery; Research; Research Personnel; Risk; Safety; Sampling; Schizophrenia; Scientist; Secure; Site; Software Tools; Standardization; Substance Use Disorder; Symptoms; System; Technology; Testing; Thinking; Time; Training; Transact; Travel; Validation; Visualization; Visualization software; Work; adverse outcome; analytical tool; attenuated psychosis syndrome; barrier to care; base; bioinformatics infrastructure; candidate marker; clinical heterogeneity; clinical risk; clinically relevant; cloud based; computer science; computerized data processing; dashboard; data archive; data de-identification; data dictionary; data harmonization; data infrastructure; data integration; data tools; demographics; design; disability; drug development; effective intervention; effective therapy; experience; functional decline; functional disability; high risk; improved; individualized medicine; innovation; multidisciplinary; multimodal data; multimodality; multiple data types; novel marker; outcome prediction; predictive marker; predictive modeling; prevent; prospective; psychotic symptoms; quality assurance; recruit; research study; resilience; response; risk prediction model; risk stratification; software development; targeted treatment; therapy development; tool; web based interface; web site

The “clinical high risk” (CHR) for psychosis syndrome is an antecedent period characterized by attenuated psychotic symptoms marked by subtle deviations from normal development in thinking, motivation, affect, behavior, and a decline in functioning. Early intervention in this 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. To improve clinically relevant predictive models, several issues need to be addressed: 1) to focus on outcomes beyond psychosis; 2) to take into account heterogeneity in samples and outcomes; and 3) to integrate data sets with a broad array of variables using innovative algorithms. To address these challenges, the Accelerated Medicines Partnership Schizophrenia (AMP SCZ) study will collect diverse multi-modal data via two research networks (PRESCIENT and ProNET – 42 acquisition sites) in conjunction with the Psychosis Risk Evaluation Data Integration and Computational Technologies: Data Processing, Analysis, and Coordination Center (PREDICT-DPACC). The ultimate goal is to identify new CHR biomarkers, and CHR subtypes that will enhance future clinical trials and lead to effective new treatments. The PREDICT-DPACC is tasked with, 1) providing collaborative management, direction, data processing and coordination for the two research networks; and 2) developing and applying advanced algorithms to identify biomarkers that predict outcomes, in addition to stratifying CHR into subtypes based on outcome trajectories. The PREDICT-DPACC team will include multiple data types and will address the needs of the CHR research networks and the overall AMP SCZ goals. Data will be rapidly obtained, processed, and uploaded to the NIMH Data Archive (NDA). Planned analysis methods will be powerful and robust, leveraging the expertise and experience of computer scientist developers, and experienced clinical researchers. This supplement will allow the PREDICT-DPACC team to address unexpected personnel effort needs to meet the goals set forth in the original grant submission, including, but not limited to, 1) two networks with separate and independent data capture systems that need separate development of software tools to aggregate data, which involves twice the effort to install, test, and deploy tools on their infrastructure for each network; 2) coordination with both networks to ensure that the forms and data dictionaries match across networks and with the NIMH National Data Archive; 3) the study dashboard needs to be customized further to meet the visualization needs of both networks; 4) the inclusion of additional healthy controls, and co-enrollment requirements also deviate from what was expected and complicates the proposed analytic approaches; 5) there is also participation in additional unexpected organizational activities such as team workgroups, which will continue and are needed to harmonize ideas, methods, and approaches to meet AMP SCZ goals. The supplement will fill in gaps that could not have been anticipated at the start of this study and will allow us to meet the scientific goals which have not changed.

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： 以思维、动机、情感等方面与正常发展的细微偏差为特征的精神病症状， 行为和功能下降。对这一人群的早期干预对于预防精神病发作至关重要 以及其他不良后果。然而，症状的表现和随后的过程是高度 变量，并且缺乏指导治疗开发的生物标志物。改善临床相关性 预测模型，需要解决几个问题：1）关注精神病以外的结果; 2）采取 考虑到样本和结果的异质性;以及3）将数据集与广泛的变量进行整合 使用创新的算法。为了应对这些挑战，加速药物伙伴关系精神分裂症 (AMP SCZ）研究将通过两个研究网络（PRESCIENT和ProNET - 42个采集点）与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（预测DPACC）。最终目标是 确定新的生物标志物和新的亚型，这将加强未来的临床试验，并导致有效的新的 治疗。PREDICT-DPACC的任务是：1）提供协作管理、指导、数据 两个研究网络的处理和协调; 2）开发和应用先进的算法 除了根据结果将肿瘤分为亚型外， 轨迹PREDICT-DPACC小组将包括多种数据类型，并将满足环境署的需求。 研究网络和总体AMP SCZ目标。数据将被快速获取、处理并上传到 NIMH数据档案（NDA）。计划中的分析方法将是强大而稳健的， 计算机科学家开发人员和经验丰富的临床研究人员的经验。该补充将 允许PREDICT-DPACC团队解决意外的人员工作需求，以实现 原始拨款提交，包括但不限于：1）两个网络，具有单独和独立的数据 捕获需要单独开发软件工具来汇总数据的系统，这涉及两倍于 努力在每个网络的基础设施上安装、测试和部署工具; 2）与两个网络协调 确保表格和数据字典在网络上匹配，并与NIMH国家数据档案库相匹配; 3)研究仪表板需要进一步定制，以满足两个网络的可视化需求; 4） 纳入额外的健康对照和共同入组要求也偏离了预期 并使所提出的分析方法复杂化; 5）还参与了额外的意外 组织活动，如团队工作组，将继续进行，并需要协调想法， 方法和途径，以满足AMP SCZ的目标。补充将填补空白， 这是本研究开始时的预期，将使我们能够实现尚未改变的科学目标。