risk stratification of "mild" traumatic brain injury by frederick korley
TRANSCRIPT
Risk Stratification of “Mild” Traumatic Brain Injury
Frederick Korley, M.D., Ph.D.
Statement of Problem
Korley FK, Pham JC, Kirsch TD: Use of advanced radiology during visits to US emergency departments
for injury-related conditions, 1998-2007. JAMA 304(13): 1465-71, 2010.
4.8 million persons evaluated in the ED for TBI
each year
2.5 million diagnosed with TBI
Korley FK, Kelen GD, Jones CM, Diaz-Arrastia R: Emergency Department Evaluation of Traumatic Brain Injury in the United States, 2009-2010. J Head Trauma Rehabil. 2015 Sep 10
Korley FK, Pham JC, Kirsch TD: Use of advanced radiology during visits to US emergency departments for injury-related conditions, 1998-2007. JAMA 304(13): 1465-71, 2010.
Hypothesis
A data-driven, multi-disciplinary approach utilizing novel methods (proteomics, genomics, metabolomics, advanced imaging) for characterizing patient and injury characteristics, and coupled with existing clinical data will improve TBI risk-stratification.
Head Injury Serum Markers for Assessing Response to Trauma (HeadSMART) Cohort
• Prospective observational cohort• Two demographically distinct academic EDs• Data: NINDS common data elements • Serum, plasma and mRNA sampling at 0, 4, 24 hours; 3
and 7 days; 1, 3 and 6 months. DNA at baseline• Outcome assessment
• Phone• Battery of cognitive and psychiatric assessments in
person
What is TBI? Who should be included in studies?
• American congress of Rehabilitation Medicine’s Definition• Traumatically induced physiological disruption of brain
function, as manifested by:• LOC• Memory loss• Altered mental status• Focal neurologic deficit
• What about head injury not meeting “TBI” criteria?• Head Injury BRain Injury Disputed (HIBRID)
Risk of prolonged recovery in HIBRID patients
• To determine the risk of prolonged recovery in HIBRID patients
• Method: • Population:
• HeadSMART TBI patients categorized as: HIBRID, ACRM+ CT-; ACRM+ CT+
• Control groups: Non-head injury trauma controls, healthy controls
• Outcomes:• Disability (Glasgow Outcome Scale Extended)• Post-concussive symptoms (Rivermead Post-
Concussive Questionnaire)• Depression (Patient Health Questionnaire 9)
Recovery at 1 month Post-Injury
Patients’ expectations
You were evaluated for a head injury during your visit. What is your understanding regarding how well you will heal from this head injury?
Accuracy based on functional disability
Accuracy based on post-concussive symptoms
Discussed with physicians, high risk (n=7) 57.1% 42.9%Discussed with physicians, low risk (n=38) 55.3% 60.5%Did not discuss, high risk (n=9) 100% 75.0%Did not discuss, low risk (n=38) 60.5% 57.9%Did not discuss, no idea (n=12) 58.3% (poor),
41.7% (good)50.0% (poor), 50% (good)
How good is clinician gestalt for identifying high risk?
Based on what you know now about this patient's presentation, do you think this patient will have a complete functional recovery i.e. they will be back to their pre-TBI functional state at 3 months after injury?
Accuracy based on functional disability
Accuracy based on having post-concussive symptoms
Yes 53.9% 59.4%No 40.0% 61.6%
Based on what you know now about this patient's presentation, do you think this patient will have 3 or more post-concussive symptoms (for example: headaches, fatigue, insomnia, loss of concentration, noise and light sensitivity, memory loss, dizziness) at 3 months after injury?
Accuracy based on functional disability
Accuracy based on having post-concussive symptoms
91 – 100% certain
37.3% 68.9%
71 – 90% certain
55.6% 52.2%
<70% certain 60.0% 59.5%
Day-of-injury serum BDNF can predict risk
Day-of-injury serum BDNF can predict risk
p = 0.005
Ongoing Work
• Examine the diagnostic and prognostic utility of the following biomarkers in TBI: GFAP, S100B, BDNF, Troponin, Total tau, phosphorylated Tau, ICAM 5, Neurogranin, beta synuclein, among others
• Evaluate the effect of catecholamine surge in TBI and its effect on cerebrovascular reactivity
• Examine the metabolomic profile of recovery from TBI• Develop prognostic models using machine learning tools
Acknowledgements
• Patients and Family Members• Subject Enrollment
– Hayley Falk M.Sc– AJ Hall– Freshta Akabari– Uju Ofoche – Olivia Lardo– Braden Anderson
• Neuropsychiatry– Alex Vassila B.S.– Vani Rao M.D.– Durga Roy M.D.– Matthew Peters M.D.– Kostas Lyketsos M.D., M.P.H.
• Neurocognitive/Rehab– Kathleen Bechtold Ph.D.
• Neurology– Ramon Diaz-Arrastia M.D., Ph.D
• Proteomics– Allen Everett, M.D.– Jenny Van Eyk, Ph.D.– David Lubman, Ph.D.
• Metabolomics– Charles Burant, Ph.D.
• Neuroradiology– Haris Sair M.D.
• Machine learning– Scott Levin Ph.D.– Kayvan Najarian, Ph.D.
• Funding– ImmunArray– Biodirection– Robert Wood Johnson Medical
Faculty Development Award– University of Michigan Injury
Center