Brown, Allen W.

Headache is one of the most frequently reported symptoms following traumatic brain injury (TBI). Little is known about how these headaches change over time. We describe the natural history of headache in individuals with moderate to severe TBI over 5 years after injury. A total of 316 patients were prospectively enrolled and followed at 3, 6, 12, and 60 months after injury. Individuals were 72% male, 73% white, and 55% injured in motor vehicle crashes, with an average age of 42. Pre-injury headache was reported in 17% of individuals. New or worse headache prevalence remained consistent with at least 33% at all time points. Incidence was >17% at all time points with first report of new or worse headache in 20% of participants at 60 months. Disability related to headache was high, with average headache pain (on 0–10 scale) ranging from 5.5 at baseline to 5.7 at 60 months post-injury, and reports of substantial impact on daily life across all time points. More than half of classifiable headaches matched the profile of migraine or probable migraine. Headache is a substantial problem after TBI. Results suggest that ongoing assessment and treatment of headache after TBI is needed, as this symptom may be a problem up to 5 years post-injury.

SummaryObjectiveDetermine incidence of posttraumatic seizure (PTS) following traumatic brain injury (TBI) among individuals with moderate‐to‐severe TBI requiring rehabilitation and surviving at least 5 years.MethodsUsing the prospective TBI Model Systems National Database, we calculated PTS incidence during acute hospitalization, and at years 1, 2, and 5 postinjury in a continuously followed cohort enrolled from 1989 to 2000 (n = 795). Incidence rates were stratified by risk factors, and adjusted relative risk (RR) was calculated. Late PTS associations with immediate (<24 h), early (24 h–7 day), or late seizures (>7 day) versus no seizure prior to discharge from acute hospitalization was also examined.ResultsPTS incidence during acute hospitalization was highest immediately (<24 h) post‐TBI (8.9%). New onset PTS incidence was greatest between discharge from inpatient rehabilitation and year 1 (9.2%). Late PTS cumulative incidence from injury to year 1 was 11.9%, and reached 20.5% by year 5. Immediate/early PTS RR (2.04) was increased for those undergoing surgical evacuation procedures. Late PTS RR was significantly greater for individuals who self‐identified as a race other than black/white (year 1 RR = 2.22), and for black individuals (year 5 RR = 3.02) versus white individuals. Late PTS was greater for individuals with subarachnoid hemorrhage (year 1 RR = 2.06) and individuals age 23–32 (year 5 RR = 2.43) and 33–44 (year 5 RR = 3.02). Late PTS RR years 1 and 5 was significantly higher for those undergoing surgical evacuation procedures (RR: 3.05 and 2.72, respectively).SignificanceIn this prospective, longitudinal, observational study, PTS incidence was similar to that in studies published previously. Individuals with immediate/late seizures during acute hospitalization have increased late PTS risk. Race, intracranial pathologies, and neurosurgical procedures also influenced PTS RR. Further studies are needed to examine the impact of seizure prophylaxis in high‐risk subgroups and to delineate contributors to race/age associations on long‐term seizure outcomes.

SummaryObjectivePosttraumatic seizures (PTS) are well‐recognized acute and chronic complications of traumatic brain injury (TBI). Risk factors have been identified, but considerable variability in who develops PTS remains. Existing PTS prognostic models are not widely adopted for clinical use and do not reflect current trends in injury, diagnosis, or care. We aimed to develop and internally validate preliminary prognostic regression models to predict PTS during acute care hospitalization, and at year 1 and year 2 postinjury.MethodsPrognostic models predicting PTS during acute care hospitalization and year 1 and year 2 post‐injury were developed using a recent (2011–2014) cohort from the TBI Model Systems National Database. Potential PTS predictors were selected based on previous literature and biologic plausibility. Bivariable logistic regression identified variables with a p‐value < 0.20 that were used to fit initial prognostic models. Multivariable logistic regression modeling with backward‐stepwise elimination was used to determine reduced prognostic models and to internally validate using 1,000 bootstrap samples. Fit statistics were calculated, correcting for overfitting (optimism).ResultsThe prognostic models identified sex, craniotomy, contusion load, and pre‐injury limitation in learning/remembering/concentrating as significant PTS predictors during acute hospitalization. Significant predictors of PTS at year 1 were subdural hematoma (SDH), contusion load, craniotomy, craniectomy, seizure during acute hospitalization, duration of posttraumatic amnesia, preinjury mental health treatment/psychiatric hospitalization, and preinjury incarceration. Year 2 significant predictors were similar to those of year 1: SDH, intraparenchymal fragment, craniotomy, craniectomy, seizure during acute hospitalization, and preinjury incarceration. Corrected concordance (C) statistics were 0.599, 0.747, and 0.716 for acute hospitalization, year 1, and year 2 models, respectively.SignificanceThe prognostic model for PTS during acute hospitalization did not discriminate well. Year 1 and year 2 models showed fair to good predictive validity for PTS. Cranial surgery, although medically necessary, requires ongoing research regarding potential benefits of increased monitoring for signs of epileptogenesis, PTS prophylaxis, and/or rehabilitation/social support. Future studies should externally validate models and determine clinical utility.

Background and Purpose— Identifying clinical data acquired at inpatient rehabilitation admission for stroke that accurately predict key outcomes at discharge could inform the development of customized plans of care to achieve favorable outcomes. The purpose of this analysis was to use a large comprehensive national data set to consider a wide range of clinical elements known at admission to identify those that predict key outcomes at rehabilitation discharge. Methods— Sample data were obtained from the Uniform Data System for Medical Rehabilitation data set with the diagnosis of stroke for the years 2005 through 2007. This data set includes demographic, administrative, and medical variables collected at admission and discharge and uses the FIM (functional independence measure) instrument to assess functional independence. Primary outcomes of interest were functional independence measure gain, length of stay, and discharge to home. Results— The sample included 148 367 people (75% white; mean age, 70.6±13.1 years; 97% with ischemic stroke) admitted to inpatient rehabilitation a mean of 8.2±12 days after symptom onset. The total functional independence measure score, the functional independence measure motor subscore, and the case-mix group were equally the strongest predictors for any of the primary outcomes. The most clinically relevant 3-variable model used the functional independence measure motor subscore, age, and walking distance at admission ( r 2 =0.107). No important additional effect for any other variable was detected when added to this model. Conclusions— This analysis shows that a measure of functional independence in motor performance and age at rehabilitation hospital admission for stroke are predominant predictors of outcome at discharge in a uniquely large US national data set.

Risk factors contributing to institutionalization after inpatient rehabilitation for people with traumatic brain injury (TBI) have not been well studied and need to be better understood to guide clinicians during rehabilitation. We aimed to develop a prognostic model that could be used at admission to inpatient rehabilitation facilities to predict discharge disposition. The model could be used to provide the interdisciplinary team with information regarding aspects of patients' functioning and/or their living situation that need particular attention during inpatient rehabilitation if institutionalization is to be avoided. The study population included 7219 patients with moderate-severe TBI in the Traumatic Brain Injury Model Systems (TBIMS) National Database enrolled from 2002–2012 who had not been institutionalized prior to injury. Based on institutionalization predictors in other populations, we hypothesized that among people who had lived at a private residence prior to injury, greater dependence in locomotion, bed-chair-wheelchair transfers, bladder and bowel continence, feeding, and comprehension at admission to inpatient rehabilitation programs would predict institutionalization at discharge. Logistic regression was used, with adjustment for demographic factors, proxy measures for TBI severity, and acute-care length-of-stay. C-statistic and predictiveness curves validated a five-variable model. Higher levels of independence in bladder management (adjusted odds ratio [OR], 0.88; 95% CI 0.83, 0.93), bed-chair-wheelchair transfers (OR, 0.81 [95% CI, 0.83–0.93]), and comprehension (OR, 0.78 [95% CI, 0.68, 0.89]) at admission were associated with lower risks of institutionalization on discharge. For every 10-year increment in age was associated with a 1.38 times higher risk for institutionalization (95% CI, 1.29, 1.48) and living alone was associated with a 2.34 times higher risk (95% CI, 1.86, 2.94). The c-statistic was 0.780. We conclude that this simple model can predict risk of institutionalization after inpatient rehabilitation for patients with TBI.

Background: Headache (HA) following traumatic brain injury (TBI) is common, but predictors and time course are not well established, particularly after moderate to severe TBI. Methods: A prospective, longitudinal cohort study of HA severity post-TBI was conducted on 450 participants at seven participating rehabilitation centers. Generalized linear mixed-effects models (GLMMs) were used to model repeated measures (months 3, 6, and 12 post-TBI) of two outcomes: HA density (a composite of frequency, duration, and intensity) and HA disruptions to activities of daily living (ADL). Results: Although HA density and ADL disruptions were nominally highest during the first three months post-TBI, neither showed significant changes over time. At all time points, history of pre-injury migraine was by far the strongest predictor of both HA density and ADL disruptions (odds ratio (OR) = 8.0 and OR = 7.2, averaged across time points, respectively). Furthermore, pre-injury non-migraine HA (at three and six months post-TBI), penetrating-type TBI (at six months post-TBI), and female sex (at six and 12 months post-TBI) were each associated with an increase in the odds of a more severe HA density. Severity of TBI (post-traumatic amnesia (PTA) duration) was not associated with either outcome. Conclusion: Individuals with HA at three months after moderate-severe TBI do not improve over the ensuing nine months with respect to HA density or ADL disruptions. Those with pre-injury HA, particularly of migraine type, are at greatest risk for HA post-TBI. Other independent risk factors are penetrating-type TBI and, to a lesser degree and post-acutely only, female sex. Individuals with these risk factors should be monitored and considered for aggressive early intervention.