FITBIR: Accelerating Synthesis of TBI Research Using Novel Methods (FAST RUN Methods) 2.0
Abstract
Rationale: Since 2000, more than 375,000 U.S. Military Service Members have sustained a traumatic brain injury (TBI). In 2014, about 2.87 million TBI-related emergency department visits, hospitalizations, and deaths occurred in the United States, including over 837,000 among children. Recent research has shown that TBI is often associated with problems with functioning, mental health, and quality of life. However, many important research gaps remain. For example, we don’t know enough about how symptoms change over time, how symptoms are impacted by TBI severity, and which treatments can help which groups of people (like Service Members/Veterans versus civilians). The FITBIR (Federal Interagency Traumatic Brain Injury Research) database is a compilation of many TBI research datasets. It is the largest repository of TBI research, containing over 4.9 million records from over 80,000 participants. These data can help improve our understanding of TBI prevention, diagnosis, prognosis, and treatment. Our project is based on our current work, which merged 12 of the FITBIR datasets to look at trends in TBI data across large groups. Since we created that large meta-dataset, more studies have been added to FITBIR, which we will merge with our existing dataset to create an even larger and more robust TBI dataset. We have a team of national content experts who will lead papers on how TBI is related to: (1) Cognitive Functioning and Neurologic Outcomes, (2) PTSD (posttraumatic stress disorder), (3) Depression and Suicide, (4) Substance Use, (5) Sleep, and (6) Employment, Functioning, and Quality of Life. Each paper will examine (1) time since injury, (2) TBI severity, (3) Service Member/Veteran vs. civilian status, and (4) demographics. Objectives: With our project, FITBIR: Accelerating Synthesis of TBI Research Using Novel Methods 2.0, (FAST RUN Methods 2.0), we will use cutting-edge data combination methods to rapidly create large, user-friendly datasets from all studies in the FITBIR database. Our approach will help uncover new TBI research findings by combining data across a large group of studies and creating one large database so that subgroups can be compared. This will identify specific symptom presentations and prognoses for subgroups of individuals, which will help improve the outcomes of civilians, Service Members, and Veterans with TBI. To do this, we use advanced software and analysis techniques, which we have already tested on a small group of FITBIR studies, and this project will update our meta-database to include all newly available FITBIR data. We will achieve our objective by using publicly available, open-source software and cutting-edge information technology methods to create data visualizations that help people analyze and understand results from the large, combined database. We plan to make our methods publicly available on the FITBIR platform so future researchers, policymakers, and other stakeholders can access up-to-date, merged results for core FITBIR variables as our datasets are updated and expanded. We will create interactive data visualization products designed to work with the FITBIR website so that FITBIR users can build on our datasets and methods for their own TBI research. Study Design, Clinical Implications, Risks, and Benefits: Overall, we will use meta-analysis and advanced data combination methods improve the understanding of key TBI-related psychological health and functioning outcomes. There are few risks associated with this research because FITBIR data are all de-identified, so no publicly available data contain private or identifiable information about participants. There are many benefits to our proposed study. We will harness the power of merged, large-scale data from FITBIR to examine symptom trajectories, which will aide in treatment planning. We will examine what factors impact what works for whom so that patients, providers, and policymakers can make more informed treatme
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2210939
Entities
People
- Maya O Neil
Organizations
- United States Army
- Veterans Affairs Medical Center (Oregon)