Effective treatment of traumatic brain injury (TBI) remains one of the greatest unmet needs in public health. Each year in the US, at least 1.7 million people suffer TBI; an estimated 3.2 to 5.3 million people live with the long-term physical, cognitive, and psychological health disabilities of TBI, with annual direct and indirect costs estimated at over $60 billion. The unique public-private partnership of investigators, philanthropy, and industry leaders brought together in the multicenter Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) proposal share a mission to accelerate clinical research in TBI. The goal is to create a large, high quality TBI database that integrates clinical, imaging, proteomic, genomic, and outcome biomarkers, and provides analytic tools and resources to establish more precise methods for TBI diagnosis and prognosis, improve outcome assessment, and compare the effectiveness and costs of tests, treatments, and services. The investigators hypothesize that this approach will deliver better characterization and stratification of patients, allowing meaningful comparisons of treatments and outcomes and thereby improving the next generation of clinical trials.
Specific Aim 1. To create a widely accessible, comprehensive TBI Information Commons that integrates clinical, imaging, proteomic, genomic, and outcome biomarkers from subjects across the age and injury spectra, and provides analytic tools and resources to support TBI research.
Specific Aim 2. To validate imaging, proteomic, and genetic biomarkers that will improve classification of TBI, permit appropriate selection and stratification of patients for clinical trils, and contribute to the development of a new taxonomy for TBI.
Specific Aim 3. To evaluate a flexible outcome assessment battery comprised of a broad range of TBI common data elements that enables assessment of multiple outcome domains across all phases of recovery and at all levels of TBI severity.
Specific Aim 4. To determine which tests, treatments, and services are effective and appropriate for which TBI patients, and use this evidence to recommend practices that offer the best value. The project will directly impact public health by creating an open-access Information Commons populated with robust Common Data Elements that will make international research collaboration a reality. Detailed clinical data on 3,000 subjects (11 sites) across the injury spectrum, along with CT/MRI imaging, blood biospecimens, and detailed outcomes, will be collected and analyzed, permitting the identification/validation of biomarkers, and identification of structural abnormalities that may be predictive of outcomes, making strides toward a new taxonomy for TBI. The infrastructure of integrated databases and imaging and biosample repositories will create a high quality, legacy database for current and future generations of international researchers.

Public Health Relevance

Traumatic Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) will directly impact public health by creating an open-access Information Commons of integrated clinical, imaging, proteomic, genomic, and outcome biomarkers, which will permit more precise TBI diagnosis, prognosis, and treatment.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01NS086090-03
Application #
8909220
Study Section
Special Emphasis Panel (ZNS1)
Program Officer
Vivalda, Joanna
Project Start
2013-09-30
Project End
2016-08-31
Budget Start
2015-09-01
Budget End
2016-08-31
Support Year
3
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94118
Wang, Kevin K; Yang, Zhihui; Zhu, Tian et al. (2018) An update on diagnostic and prognostic biomarkers for traumatic brain injury. Expert Rev Mol Diagn 18:165-180
Kim, Hosung; Caldairou, Benoit; Bernasconi, Andrea et al. (2018) Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling. Front Neuroinform 12:39
Ko, J; Hemphill, M; Yang, Z et al. (2018) Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles. Lab Chip 18:3617-3630
Bodien, Yelena G; McCrea, Michael; Dikmen, Sureyya et al. (2018) Optimizing Outcome Assessment in Multicenter TBI Trials: Perspectives From TRACK-TBI and the TBI Endpoints Development Initiative. J Head Trauma Rehabil 33:147-157
Pirracchio, Romain; Yue, John K; Manley, Geoffrey T et al. (2018) Collaborative targeted maximum likelihood estimation for variable importance measure: Illustration for functional outcome prediction in mild traumatic brain injuries. Stat Methods Med Res 27:286-297
Kreitzer, Natalie P; Hart, Kimberly; Lindsell, Christopher J et al. (2018) A Comparison of Satisfaction With Life and the Glasgow Outcome Scale-Extended After Traumatic Brain Injury: An Analysis of the TRACK-TBI Pilot Study. J Head Trauma Rehabil :
Tubi, Meral A; Lutkenhoff, Evan; Blanco, Manuel Buitrago et al. (2018) Early seizures and temporal lobe trauma predict post-traumatic epilepsy: A longitudinal study. Neurobiol Dis :
Gardner, Raquel C; Rubenstein, Richard; Wang, Kevin K W et al. (2018) Age-Related Differences in Diagnostic Accuracy of Plasma Glial Fibrillary Acidic Protein and Tau for Identifying Acute Intracranial Trauma on Computed Tomography: A TRACK-TBI Study. J Neurotrauma 35:2341-2350
Dikmen, Sureyya; Machamer, Joan; Temkin, Nancy (2017) Mild Traumatic Brain Injury: Longitudinal Study of Cognition, Functional Status, and Post-Traumatic Symptoms. J Neurotrauma 34:1524-1530
Nielson, Jessica L; Cooper, Shelly R; Yue, John K et al. (2017) Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis. PLoS One 12:e0169490

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