UPMC Rehabilitation Institute

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1 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

2 Presentation Overview PTE Introduction and Assessment in the TBI MS Cohort PTE Epidemiology PTE Risk Prognostication PTE Associated Mental Health Conditions

3 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.

4 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.

5 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

6 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.

7 Contemporary Survey TBI 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 across 13 enters, with follow-up assessments completed by Epilepsia Dec;57(12): doi: /epi

8 Acute Seizure Status Table 1: Demographic and Injury Characteristics at Baseline Visit N(%) Sample Size = 796 Age at Injury* 35.4 (15.7) Sex Male 595 (74.8) Female 201 (25.2) Race White 464 (58.3) Black 246 (30.9) Other 86 (10.8) Cause of Injury MVA 411 (51.6) Fall 109 (13.7) Any Violence 164 (20.6) Any Sport 9 (1.1) Other 103 (12.9) Injury Severity Moderate 79 (9.9) Severe 717 (90.1) PTA (days)* 31.6 (26.3) LOC (days)* 10.7 (18.9) Admission DRS* 13.3 (5.4) Length of Acute Stay (days)* 22.8 (19.4) *mean(sd); PTA Post-Traumatic Amnesia; LOC Loss of Consciousness; DRS Disability Rating Scale Table 2. Frequency Measures of PTS at Follow-up Time Points after TBI New Onset Seizure Incidence Incidence of Late PTS since last follow-up* Time Point N (%) N (%) N (%) Initial Population Immediate (<24hrs) Early (1<7 days) Late (>7 days) 71 (8.9) (1.9) (1.8) Cumulative Incidence Late PTS since injury Year 1 64 (9.2) 86 (10.8) 95 (11.9) Year 2 32 (5.0) 39 (5.5) 134 (16.8) Year 5 25 (4.2) 29 (4.3) 163 (20.5) *Year-1 represents late PTS incidence since discharge from rehabilitation

9 Table 3. Incidence of Late PTS 1 Stratified by Variables of Interest During Acute Care Hospitalization Acute Seizure Status Late PTS Year 1 Late PTS Year 5 N(%) P value 2 N(%) P value 2 No Seizure Seizure No Seizure Seizure 710 (89.2) 86 (10.8) 641 (90.3) 69 (9.7) None 634 (90.8) 64 (9.2) Ref. 577 (91.0) 57 (9.0) Ref. Immediate 54 (76.1) 17 (23.9) < (81.5) 10 (18.5) Early 13 (92.9) 1 (7.1) > (92.3) 1 (7.7) >0.999 Late 9 (69.2) 4 (30.8) (88.9) 1 (11.1) No seizures during acute hospitalization (including late) contribute to definition of late PTS 2 P value for Fisher exact test

10 Significant Relative Risk Variables Year 1 Variable A: Race other than black/white (RR=2.22) B: SAH (RR=2.06) B: Contusion Load (RR=2.17) C: Surgical Evacuation (RR=3.05) A: Age, race, sex, and injury severity were mutually adjusted. B: Adjusted for age, race, sex, and injury severity. C: Adjusted for age, race, sex, injury severity, EDH, SDH, & contusion load. Year 5 Variables A: Race: black vs. white individuals (RR=3.02) A: Age years (RR=2.43) years (RR=3.02) C: Surgical Evacuation (RR=2.72) *Immediate/Early PTS RR (2.04) was increased for those undergoing surgical evacuation procedures.

11 PTE Epidemiology Key Points In this prospective, longitudinal, observational study, PTS incidence was similar to previously published studies. During acute hospitalization, PTS incidence was highest immediately following TBI and associated with late PTS 1 and 5 years post-injury. Late PTS first emerging during acute care hospitalization was associated with subsequent PTS during first year post-tbi. Adjusted PTS RR was increased during acute care and years 1 and 5 for those with surgical evacuation. Race and age may be relevant to the development of PTE

12 PTE Prognostication: What Do We Know? Injury Based Risk Factors dural penetration with bone and metal fragments subdural hematoma significant midline shift skull fracture intracranial operations cortical contusions Prognostic models for PTE: What is the Value Proposition? Can estimate risk for developing an outcome of interest based on specific characteristics Accurate PTS risk prediction can help define high-risk populations e.g. clinical intervention trials. Predictive models may also inform clinical algorithms e.g. benefit from seizure prophylaxis or treatment protocols

13 Contemporary Prognostic PTE Models Study specific inclusion criteria: Participants injured between October 1, 2011 and August 31, Participants with 1 or 2 year follow up. Enrollment Data Collection: Demographic, social, and injury characteristics, International Classification of Disease revision 9 (ICD- 9) codes. Pre-injury personal and medical history. CT scan data were classified by trained raters based on a composite of findings on CT scan over the first 7 days post-injury. Prospective follow-up data: collected via a semi-structured telephone administered battery. proxy interviews were completed if an individual with TBI could not provide reliable responses. Epilepsia Sep;57(9): doi: /epi

14 Variables Tested in the Prognostic Model Demographics: Age, sex, race Personal and Medical History: Previous TBI Pre-injury EtOH and/or substance use. Preinjury condition resulting in cognitive and/or motor disability Pre-injury Mental health condition treatment/hospitalization Suicide attempt Incarceration Military service/combat Injury Characteristics: Injury Severity Acute ETOH Post-traumatic Amnesia/LOC CT Head: SDH, SAH, IVH, EDH (present/absent) contusion load Depressed Skull Fracture Penetrating TBI Co-occurring SCI Seizure during acute care Procedures: Craniectomy Craniotomy

15 Table 4. Variables Included for Prognostic Model to Predict PTS at Year 1 Variable* Pre-injury Treatment for mental health condition/psychiatric hospitalization (ref=neither) Retained in Adjusted P-value Reduced Model Odds Ratio Yes Treatment, no hospitalization Treatment and Hospitalization Hospitalization Pre-injury Incarceration Yes 2.78 <0.001 Duration PTA (days) Yes Subdural Hematoma Yes Contusion Load (ref=0) Yes Seizure during Acute Hospitalization Yes Craniotomy Yes 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 Adjusted Odds P-value Reduced Model Ratio Pre-injury Incarceration Yes Subdural Hematoma Yes 2.29 <0.001 Retained Fragment Yes Seizure during Acute Yes 3.57 <0.001 Hospitalization Craniotomy Yes 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 0.49*PreInjury Mental Health Treatment a Hospitalization *PreInjury Psychiatri 1.02*PreInjury Incarceration *Post- 0.54*Subdural Hematoma *Contusio 0.75*Contusion Load *Contusion L Load *Seizure Acute Hospitalizatio Hospitalization *Craniotomy * Table 3. Final Prognostic Model and Fit Statistics for Prediction of PTS Model Number C Statistics Final Prognostic Model f Bootstrap Samples Saturated Model Training Model 1 Final Model 2 Year PTS Year 1 = *PreInjury Mental 1.29*Craniectomy Year PTS Year 2 = *PreInjury Incarce hematoma *Retained Fragment Bootstrapped model 2 Optimism corrected

16 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

17 Can Personal Biology Improve PTE Prediction? Idiopathic seizure/epilepsy development & treatment response: BRD2: juvenile myoclonic epilepsy ABCB1: treatment resistant epilepsy Complement C3: temporal lobe epilepsy 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

18 Adenosine A1 receptor and PTE rs 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 4 th -7 th transmembrane regions, & the cytoplasmic portion of the receptor that interacts with the G-protein (Olah, 2000). Genetic variation within rs may be associated with ligand binding & signal transduction (Ji, 1998) to affect PTE risk N=206 Caucasian adults with severe TBI. Epilepsy Research 2010

19 Gene Risk Scores: Biosusceptibility Post-traumatic Epilepsy Prediction 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.

20 Can PTE Risk Prediction be Individualized?? LASSO (Least Absolute Shrinkage and Selection Operator) Logistic Operator) Logistic Regression Regression LASSO models improve the prediction model it produces. precision and interpretability of regression models by altering the model fitting process rather than to using select all of them. only a subset of covariates that: Reduce variance Reduce bias LASSO Regression Defined LASSO (Least Absolute Shrinkage and Selection Method that performs both variable selection and regularization in order to enhance prediction precision and interpretability of the statistical 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 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 LASSO: Generalizable model regression. equations that provide individualized 8,all coefficients are eliminated). & quantitative risk. As? increases, more and more coefficients are set to zero and eliminated (theoretically, when? = Shrinkage (Variance) Sparsity (Bias) Clinical Factor: Craniectomy LASSO Predicted Risk True Risk Logistic TBI-MS Follow up study LASSO model generation/validation

21 Density 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. Distribution of Probabilities associated with PTE and no PTE Group no PTE PTE Risk Score 0.598: Patient at high risk of seizure Estimated Probability of PTE

22 Density 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 posttraumatic amnesia and a spinal cord injury. no PTE PTE Risk Score 0.519: Patient at low risk of having seizure Estimated Probability of PTE

23 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.

24 PTE & Depression/Anxiety n=1954 participants in TBI-MS NDB injured between July 2010 and November 2012 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. n=1239 participants with Year 1 or Year 2 Seizure Data n=678 with Year 1 Follow-up Seizure data n=677 with Year 2 Follow-up Seizure data n=453/452 with Year 1 Follow-up GAD7/PHQ9 data n=116 participants with both n=867 participants with Year 1 or Year 2 Mental Health data n=20 participants with both n=434/433 with Year 2 Follow-up GAD7/PHQ9 data n=715 missing seizure data * n=372 missing mental health data ± Epilepsy and Behavior 2017 Aug;73: doi: /j.yebeh

25 Table 2. Depression and Anxiety Differences by PTE Status PTE No PTE Year 1 n=54 n=399 Z or χ 2 p-value Depressive Symptoms 6.8 (7.1) 5.4 (5.9) Depression Status + 14 (25.9%) 84 (21.1%) Anxiety Symptoms 4.8 (6.0) 3.9 (5.0) Anxiety Status + 14 (25.9%) 81 (20.3%) Comorbid Depression and Anxiety + 12 (22.2%) 58 (14.6%) Year 2 n=33 n=401 Z or χ 2 p-value Depressive Symptoms 9.1 (6.9) 5.4 (5.8) Depression Status + 14 (42.4%) 86 (21.5%) Anxiety Symptoms 7.6 (6.7) 4.1 (5.0) Anxiety Status + 14 (42.2%) 64 (16.0%) Comorbid Depression and Anxiety + 11 (33.3%) 50 (12.5%) Note. PTE=Post-traumatic Epilepsy; + Clinically significant symptoms present 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 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 Sex Race Black Other Previous Mental Health Treatment Substance Abuse FIM (discharge) Seizures in Year Referents: Age=16-30 years old; Sex=Male; Race=White; Absence of previous mental health treatment, substance abuse, seizures

26 Future Directions: Multidimensional Assessment of Impact of PTE on recovery after moderate to severe TBI.

27 Acknowledgements Wagner Group/PITT Collaborators: Anne Ritter, DPH, Shannon Juengst Ph.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.

28 Questions? UPMC Rehabilitation Institute