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The TRACK TBI study: Common Data Elements and Comparative Effectiveness in the Era of “Big Data”

The TRACK TBI study: Common Data Elements and Comparative Effectiveness in the Era of “Big Data”. AC Duhaime, M.D. Director, Pediatric Neurosurgery, Massachusetts General Hospital Professor, Neurosurgery, Harvard Medical School. Important Requirement for New Clinical Studies: Acronyms.

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The TRACK TBI study: Common Data Elements and Comparative Effectiveness in the Era of “Big Data”

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  1. The TRACK TBI study: Common Data ElementsandComparative Effectivenessin the Era of “Big Data” AC Duhaime, M.D. Director, Pediatric Neurosurgery, Massachusetts General Hospital Professor, Neurosurgery, Harvard Medical School

  2. Important Requirement for New Clinical Studies:Acronyms • TRACK TBI – Transforming Research and Clinical Knowledge in Traumatic Brain Injury • ADAPT - Approaches and Decisions for Acute Pediatric TBI Disclosure: Salary support from NINDS for this study

  3. What this talk will cover • What’s behind the trend toward studies of this design in head trauma? • Is it likely that anything will come of this type of study? • Will these studies affect my life? • Are there PM&R network and research opportunities in this study paradigm?

  4. Conceptual semantics • Head injury; head trauma • Traumatic brain injury (TBI) • The concussion “spectrum” TBI umbrella Extra-axial hemorrhage Skull injury Parenchymal brain injury Transient, “non-visible” or “non-structural” dysfunction

  5. Terminology and Classification of Head Injuries By type (radiology) “Where and what” – “epidural hematoma” Pathophysiologic descriptors – e.g. “brain swelling” By severity Glasgow Coma Scale (GCS) “Mild/minor, moderate, severe” Moving away from this By mechanism Forces involved – impact, inertial, blast

  6. Neuroprotectants tested in human TBI trials • Steroids – negative or worse • “Lazaroids” (21 aminosteroids) – negative • Ca+ channel blockers – negative x 3 • Glutamate antagonists (selfotel, aptiganel, eliprodil, licostinelgavestinel) – negative • Hypothermia – negative or worse (adults and children[stopped for futility] ) • Progesterone – stopped for futility (ProTECT)

  7. Limitations in past clinical HI trials Most had inclusion based on initial GCS Usually “GCS <=8” Mixed together many types of injuries and patients Outcomes general, dichotomized ALL trials failed

  8. Limitations in past clinical HI trials Most had inclusion based on “severity” Usually “GCS <=8” Mixed together many types of injuries and patients Outcomes general, dichotomized ALL trials failed

  9. Limitations in past clinical HI trials Most had inclusion based on “severity” Usually “GCS <=8” Mixed together many types of injuries and patients Outcomes general, dichotomized ALL trials failed Halo effect – control AND treatment groups showed improved outcomes due to standardization of care

  10. “Guidelines” and “linear algorithms” Many adapted from clinical trials protocols

  11. NIH 2008 – match treatment to pathophysiology Geoff Manley MD

  12. Trends in head injury management:from “severity-based”  to“pathoanatomic-based” treatments

  13. MGH initial triage/management protocol Categorization

  14. **Unoperated epidural, significant contusion, others at discretion of treatingphysician

  15. So how DO you figure out what works best, for which patients, at which time? • And how do you test NEW treatments?

  16. Three simultaneous trends have driven current TBI research model evolution • NINDS Common Data Elements • Comparative Effectiveness Research • Advances in informatics

  17. Multidimensional data for better patient/injury characterization • Clinical data • Imaging data • Treatment info • Acute • Rehab • Biomarker data • Comprehensive outcome data

  18. ALS Muscular dystrophy Epilepsy Friedrich’s ataxia Headache Huntington’s disease Multiple sclerosis Myesthenia gravis Neuromuscular diseases Parkinson’s disease Spinal muscular atrophy Stroke Traumatic brain injury Common Data Elements (CDE’s) “The purpose of the CDE Project is to standardize the collection of investigational data in order to facilitate comparison of results across studies and more effectively aggregate information into significant metadata results. The goal of the National Institute of Neurological Disorders and Stroke (NINDS) CDE Project specifically is to develop data standards for clinical research within the neurological community. Central to this project is the creation of common definitions and data sets so that information (data) is consistently captured and recorded across studies.”

  19. Design better outcome measures • Dichotomized Glasgow Outcome Scale too crude • Insensitive to small effects • Need “ecologic validity” • Should reflect how function is affected in the real world Lots of data for cognitive, psychological outcomes, less for specific motor function

  20. “Observational” study • You can treat patients however you want • Have to collect data, biospecimens, imaging, and outcomes per strict protocol • Data sharing agreement • Premise: by pooling data, may be able to interrogate data to answer many questions

  21. CDE process • Version 1.0 • Work groups • Manuscripts (2010) • Version 2.0 • Wider input; “core, basic, supplementary” • Website • commondataelements.ninds.nih.gov

  22. How do you describe and quantify rehab interventions?

  23. So, you’ve got a “list” of “CDE’s” – now what?

  24. Informatics }answers TranSMART

  25. CDE beta testing • TRACK TBI pilot (2010-11) • 4 sites, 650 patients, 11 months • Clinical data, blood specimens, MRI’s • Detailed outcomes • Federal Interagency TBI Research (FITBIR) data repository • First test of comparative effectiveness strategy

  26. TRACK TBI Pilot sample findings • In pts with normal CT, 30% had MRI abnormalities which predicted worse outcome at 3 months • Glialproteomic marker correlated with CT findings • PTSD vs. cognitive dysfunction separable by appropriate outcomes tools • Pts with milder injuries had similar outcomes at 6 mo whetherobserved in ED or admitted to ICU or floor No rehab questions yet!

  27. Where we are now: TRACK TBI • 11 centers; multi-site UO1 • Two also pediatric • 3000 patients over 4 years (~1500 to date) • Clinical data, biomarkers (n=89), genomics, MRI’s • FITBIR and central repositories • F/u 3, 6, 12 mo • Inclusion: • Present with TBI and enrolled within 24 hours • Have imaging

  28. Some specific CER aims • Effect of platelet drugs and transfusions on outcome • Predictors of low risk of deterioration allowing lower level of observation • Economic analysis • Many others proposed and possible • Role of early MRI in TBI in kids • Who needs what rehab?

  29. Conclusions • Large-scale, data-base oriented TBI studies are the current trend • Infrastructure now developed • Promising but time will tell • Common Data Elements in research • Will become standard • Many opportunities for rehab-based specialties Thank you

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