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

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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.