risk stratification of "mild" traumatic brain injury by frederick korley

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