Post-Traumatic Epilepsy: Epidemiology Personal Biology, Clinical Predictors, & Disability Burden Professor and Vice-Chair Faculty Development Endowed Chair, Translational Research Director Brain Injury Medicine Fellowship Dept. Physical Medicine and Rehabilitation Professor, Neuroscience Professor, Clinical & Translational Science Institute Associate Director Rehabilitation Research Safar Center for Resuscitation Research Training Faculty, Center for Neuroscience University of Pittsburgh UPMC Rehabilitation Institute
Transcript
Post-Traumatic Epilepsy: Epidemiology Personal Biology, Clinical Predictors, &
Disability BurdenProfessor and Vice-Chair Faculty Development
Endowed Chair, Translational ResearchDirector Brain Injury Medicine Fellowship
Dept. Physical Medicine and RehabilitationProfessor, Neuroscience
Professor, Clinical & Translational Science InstituteAssociate Director Rehabilitation Research
Safar Center for Resuscitation Research Training Faculty, Center for Neuroscience
University of Pittsburgh
UPMC Rehabilitation Institute
Presentation Overview
• PTE Introduction and Assessment in the TBI MS Cohort
• PTE Epidemiology
• PTE Risk Prognostication
• PTE Associated Mental Health Conditions
Post-traumatic Seizures: An Introduction
• TBI accounts for as much as 20% of all symptomatic cases of epilepsy in the general population.
• Those with TBI are 12X more likely than the a person in the general population to suffer seizures.
• The incidence of post traumatic seizures (PTS) ranges from 6-30%• Risk increases with greater injury severity
• PTS risk as high as 50% in military populations• VHIS: penetrating head injury
• Most (Up to 80% ) of initial PTS episodes occur within 24 months of injury
• High recurrence rate (86%) among those with initial seizure after TBI.
PTE: What is the Rehabilitation Relevance???• PTE can impact TBI rehabilitation and recovery in many ways
• PTE carries a high risk for sudden death and decreased life expectancy due both to seizure-related and external causes. • suicide and fatal accidents
• PTE development may contribute to other comorbidity and disability burden (e.g. comorbid mood and cognitive disorders)
• PTE treatment comes with side effects that can impact other domains such as mood and cognition.
• PTE treatment adds to pharmacological complexity to long-term management.
• PTE can impact vocational rehabilitation outcomes and other community reintegration roles.• Return to driving• Return to work• Roles with injury risk if developing a seizure.
Introducing: the TBI-MS National Database• The TBIMS-NDB is a multicenter, prospective, observational, life-time study investigating
recovery and outcomes following moderate-to-severe TBI and inpatient rehabilitation in a heterogeneous population across the United States.
• All participating sites (~21 sites) have an affiliated trauma center with acute neurosurgical capabilities and associated comprehensive inpatient rehabilitation.
• TBI-MS Eligibility criteria are: • moderate-to-severe TBI (posttraumatic amnesia [PTA] > 24 h, loss of consciousness
• [LOC] > 30 min, or emergency department Glasgow Coma Scale [GCS] score < 13, or positive neuroimaging findings),
• age ≥16 years, admitted to a participating hospital emergency department within 24 h of injury,
• receipt of both acute care and inpatient rehabilitation within a TBIMS-designated hospital system.
• All enrolled individuals, or legal proxy, provided written informed consent;
• >15,000 individuals have been enrolled in this study
PTE Epidemiology and Risk Prediction: What are the Issues?• Annegers 1998: Seminal population-based study in the US examined PTS in a
predominantly white population from 1935 to 1984 (n=4541)
• Among individuals (children/adults) with severe TBI, the cumulative probability of late PTS was 10.0% five years after TBI; early PTS occurred in 2.6% of individuals.
• Other studies report variable PTE prevalence numbers that were dependent
on variable study characteristics
• Some risk factors identified, but variability in who will develop PTS remains high
• Weiss (1986), Feeney (1972) : Prior attempts at prediction modeling has
not reliable or clinically useful today
• Completed decades ago
• No access to imaging markers
• Not reflective of current standards of care.
Contemporary SurveyTBI Epidemiology
Study specific inclusion criteria:
▪ Patients with moderate to severe TBI that completed a follow up interview 5 years post-injury.
▪ Individuals were then excluded if data regarding seizure activity during acute care hospitalization, or year 1 and year 2 postinjury, were not available.
▪ Individuals included were enrolled 1989-2000 across 13 enters, with follow-up assessments completed by 2006.
• Depressed Skull Fracture• Penetrating TBI• Co-occurring SCI• Seizure during acute care
• Procedures:• Craniectomy• Craniotomy
Table 4. Variables Included for Prognostic Model to Predict PTS at Year 1
Variable* Retained in Reduced Model
Adjusted Odds Ratio
P-value
Pre-injury Treatment for mental health condition/Psychiatric hospitalization (ref=neither)
Yes --- ---
Treatment, no hospitalization 1.25 0.30
Treatment and Hospitalization 1.63 0.103
Hospitalization 5.96 0.004
Pre-injury Incarceration Yes 2.78 <0.001
Duration PTA (days) Yes 1.01 0.011
Subdural Hematoma Yes 1.72 0.003
Contusion Load (ref=0) Yes
1 2.14 0.001
2 2.11 0.001
3 1.93 0.018
4 1.71 0.097
Seizure during Acute Hospitalization Yes 2.12 0.001
Craniotomy Yes 1.86 0.001
Craniectomy Yes 3.64 <0.001
*Variables included in saturated logistic regression model Unless noted, reference group for adjust odds ratio is variable not present TBI: Traumatic Brain Injury; PTA: Post-traumatic Amnesia
Table 5. Variables Included for Prognostic Model to Predict PTS at Year 2
Variable* Retained in Reduced Model
Adjusted Odds Ratio
P-value
Pre-injury Incarceration Yes 2.14 0.009
Subdural Hematoma Yes 2.29 <0.001
Retained Fragment Yes 2.23 0.010
Seizure during Acute Hospitalization
Yes 3.57 <0.001
Craniotomy Yes 1.64 0.036
Craniectomy Yes 2.71 <0.001
*Variables included in saturated logistic regression model Unless noted, reference group for adjust odds ratio is variable not present TBI: Traumatic Brain Injury; PTA: Post-traumatic Amnesia
Table 3. Final Prognostic Model and Fit Statistics for Prediction of PTS
Year 2 1000 0.758 0.789 0.716 PTS Year 2 = -3.23 + 0.76*PreInjury Incarceration + 0.83*Subdural hematoma + 0.80*Retained Fragment + 1.27*Seizure Acute Hospitalization + 0.49*Craniotomy + 1.00*Craniectomy
1 Bootstrapped model 2 Optimism corrected
Prognostic Model Key Points
• Prognostic models at Years 1 and 2 post-TBI performed well at discriminating between individuals who did and did not develop PTS
• Developed models reflect current trends in TBI severity, diagnosis, and treatment
• Neurosurgical procedures were among the strongest predictors of PTS in each model
• Pre-injury personal and medical history variables were included as significant predictors of PTS at each time-point.
• C-statistics show good prediction capacity, but room for improvement
• Personal Biology may improve PTE prediction models
• Techniques such as LASSO regression may better generalize to the larger population with TBI and provide some quantitative individualized assessment of PTE
• Candidate gene pathways for PTE:• Adenosine, GABA, Glutamate, Inflammation
• 2010 Genetic variabililty in A1 receptor associations with PTE• 2013 Genetic Variability with GAD1 gene associations with PTE• 2014 Genetic and proteomic associations with IL-1β• 2016/2018: Genetic Variability with neuronal/astrocyte glutamate transporter.
Goal: Generate a marker of cumulative genetic risk for PTE
Adenosine A1 receptor and PTE
• rs3766553 associated with DNA block that is ~ 9263 base-pairs,& corresponds to amino acids 113 through 326 of the receptor(Rosen, 2003).
• Codes the middle of the intracellular domain, the 4th-7th trans-membrane regions, & the cytoplasmic portion of the receptorthat interacts with the G-protein (Olah, 2000).
• Genetic variation within rs3766553 may be associated withligand binding & signal transduction (Ji , 1998) to affect PTE risk
N=206 Caucasian adults with severe TBI. Epilepsy Research 2010
• Genetic variants located in GABA, Adenosine and Glutamate Pathways• Patients with no risk variants have 10% chance of PTE over first 3 years post injury, while
those with 3 or more have an 80% chance of PTE.
Can PTE Risk Prediction be Individualized??
• LASSO (Least Absolute Shrinkage and Selection Operator) Logistic Regression
• LASSO models improve the prediction precision and interpretability of regression models by altering the model fitting process to select only a subset of covariates that:• Reduce variance• Reduce bias
• LASSO: Generalizable model equations that provide individualized & quantitative risk.
• Method that performs both variable selection and regularization in order to enhance prediction precision and interpretability of the statistical model it produces.
• LASSO models improve the prediction precision and interpretability of regression models by altering the model fitting process to select only a subset of covariates for use in the final model rather than using all of them.
• LASSO regression adds a penalty (shrinkage) equal to the absolute value of the magnitude of coefficients.
• A tuning parameter, ? controls the strength of the penalty. ? is basically the amount of shrinkage:• When ? = 0, no parameters are eliminated. The
estimate is equal to the one found with logistic regression.
• As ? increases, more and more coefficients are set to zero and eliminated (theoretically, when ? = 8 , all coefficients are eliminated).
LASSO Logistic
Clinical Factor: Craniectomy
Shrinkage(Variance)
Predicted Risk
True RiskSparsity(Bias)
TBI-MS Follow up study LASSO model generation/validation
CASE EXAMPLE 1: JOHN DOE
• Synopsis: 40 year old man from
Pittsburgh spent 5 days in acute care
following a TBI. He has a history of a
mental health disorder and alcohol
dependence. He experienced an acute
seizure, had two parenchymal contusions
and a SDH. He underwent a craniectomy.
• Risk Score 0.598: Patient at high risk
of seizure
0.45 0.50 0.55 0.60 0.65 0.70
05
10
15
20
25
Estimated Probability of PTE
Den
sity
Distribution of Estimated Risks
no PTE
PTE
Distribution of Probabilities associated with PTE and no PTE Group
CASE EXAMPLE 2: JOE DOE
• Synopsis: 40 year old man spent 10 days
in acute care following a TBI. Similarly, he
experienced two parenchymal contusions
and a SDH. He suffered from post-
traumatic amnesia and a spinal cord
injury.
• Risk Score 0.519: Patient at low risk
of having seizure
0.45 0.50 0.55 0.60 0.65 0.70
05
10
15
20
25
Estimated Probability of PTE
Den
sity
Distribution of Estimated Risks
no PTE
PTE
Mental Health Comorbidity and PTE
• The most common comorbid condition associated with epilepsy in the general population is depression, followed closely by anxiety.
• Health-related quality of life is often poor among individuals living with epilepsy in the general population due to comorbid disease burden
• 50% of individuals with TBI experienced depression at some point in the first year after injury, with point prevalence rates as high as 31%.• Symptoms persist for years post-injury
• There is a high degree (60%) of comorbid anxiety after TBI.
• No studies that assess the impact of PTE on depression and anxiety after moderate-to severe TBI.
n=1954 participants in TBI-MS NDB injured between July 2010 and November 2012
n=678 with Year 1 Follow-up Seizure data n=677 with Year 2 Follow-up Seizure data
n=1239 participants with Year 1 or Year 2 Seizure Data
n=715 missing seizure data*
n=453/452 with Year 1 Follow-up GAD7/PHQ9 data
n=434/433 with Year 2 Follow-up GAD7/PHQ9 data
n=20 participants with both
n=867 participants with Year 1 or Year 2 Mental Health data
n=372 missing mental health data±
n=116 participants with both
PTE & Depression/Anxiety• Study specific inclusion
criteria:• participants were
restricted to those who had a TBI between July 2010 and November 2012, based on having a 1 and/or 2 year follow-up during the time frame in which seizure, depression, and anxiety follow-up measures were concurrently collected.
Epilepsy and Behavior 2017 Aug;73:240-246. doi: 10.1016/j.yebeh.2017.06.001.
Table 2. Depression and Anxiety Differences by PTE Status
Table 4. Contribution of Seizures in Year 2 to Comorbid Depression and Anxiety (n=408)
Base Model Base + Follow-up Model
Base + Follow-up + Seizures Model
OR p OR p OR p
Age .128 .127 .155
31-60 1.53 .184 1.54 .177 1.56 .169
61+ 0.55 .298 0.56 .315 0.61 .401
Sex 0.77 .439 0.732 .373 0.69 .302
Race .073 .089 .130
Black 2.08 .053 1.98 .074 1.79 .134
Other 1.93 .081 1.95 .079 1.94 .082
Previous Mental Health Treatment
0.98 .959 0.94 .865 0.88 .748
Substance Abuse 1.25 .470 1.32 .376
FIM (discharge) 0.99 .385 0.99 .419
Seizures in Year 2 2.71 .049
Referents: Age=16-30 years old; Sex=Male; Race=White; Absence of previous mental health treatment, substance abuse, seizures
Key Points•The Year-2 cohort with PTE had higher frequencies of anxiety (42.2%) and depression (42.4%) and more symptoms than those without PTE
•There were no significant differences in anxiety and depression frequency in the Year-1 cohorts with and without PTE
Key Points•Those with PTE in Year-2 had 2.71 times the odds of having clinically significant depression and anxiety after accounting for other relevant predictors.
•Understanding biological and psychological causal factors contributing to/resulting from PTE and mental health is needed for effective treatment planning and management
Future Directions: Multidimensional Assessment of Impact of PTE on recovery after moderate to severe TBI.
Acknowledgements
• Wagner Group/PITT Collaborators: Anne Ritter, DPH, Shannon JuengstPh.D., Raj Kumar PhD., Seo Young Park PhD., Anthony Fabio PhD., Maria Brooks, Ph.D, Patricia Arenth, PhD., Kristen Breslin UPSOM MS3
• National Institute for Disability, Independent Living and Rehabilitation Research (NIDILRR): TBI-MS Network and National Database.
• NIDILRR TBI MS Writing Group: Jerzy Szaflarski MD., Ross Zafonte DO., Mary Jo Pugh PhD., William Walker, MD., Flora Hammond MD., Timothy Shea MD., Allen Brown MD., Tamara Bushnik PhD., Jason Krellman PhD.
