Post-concussive symptoms occur frequently after mild traumatic brain injury (mTBI) and may be categorized as cognitive, somatic, or emotional. We aimed to: 1) assess whether patient demographics and clinical variables predict development of each of these three symptom categories, and 2) develop a prediction model for 6-month post-concussive symptoms. Patients with mTBI (Glasgow Coma Scale score 13-15) from the prospective multi-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot study (2010-2012) who completed the Rivermead Post Concussion Symptoms Questionnaire (RPQ) at 6 months post-injury were included. Linear regression was utilized to determine the predictive value of candidate predictors for cognitive, somatic, and emotional subscales individually, as well as the overall RPQ. The final prediction model was developed using least absolute shrinkage and selection operator shrinkage and bootstrap validation. We included 277 mTBI patients (70% male; median age 42 years). No major differences in the predictive value of our set of predictors existed for the cognitive, somatic, and emotional subscales, and therefore one prediction model for the RPQ total scale was developed. Years of education, pre-injury psychiatric disorders, and prior TBI were the strongest predictors of 6-month post-concussive symptoms. The total set of predictors explained 21% of the variance, which decreased to 14% after bootstrap validation. Demographic and clinical variables at baseline are predictive of 6-month post-concussive symptoms following mTBI; however, these variables explain less than one-fifth of the total variance in outcome. Model refinement with larger datasets, more granular variables, and objective biomarkers are needed before implementation in clinical practice.

Background

The current classification of traumatic brain injury (TBI) into "mild", "moderate", or "severe" does not adequately account for the patient heterogeneity that still exists within each of these categories. The objective of this study was to identify "sub-groups" of mild TBI (mTBI) patients based on data available at the time of the initial post-TBI patient evaluation and to determine if the sub-grouping correlates with patient outcomes at 90 and 180 days post-TBI.

Methods

Data from patients in the TRACK-TBI Pilot dataset who had a Glasgow Coma Scale (GCS) score of 13 to 15 at arrival to the Emergency Department and a closed head injury were included. Considering 53 clinical variables that are typically available during the initial evaluation of the patient with mild TBI, sparse heirarchial clustering with cluster quality assessment was used to identify the optimal number of patient sub-groups. Patient sub-groups were then compared for ten outcomes measured at 90 or 180 days post-TBI.

Results

Amongst the 485 patients with mTBI, optimal clustering was based on the inclusion of 12 clinical variables that divided the patients into 5 mild TBI sub-groups. Clinical variables driving the sub-clustering included: gender, employment status, marital status, TBI due to falling, brain CT scan result, systolic blood pressure, diastolic blood pressure, administration of IV fluids in the Emergency Department, alcohol use, tobacco use, history of neurologic disease, and history of psychiatric disease. These 5 mild TBI sub-groups differed in their 90 day and 180 day outcomes within several domains including global outcomes, persistence of TBI-related symptoms, and neuropsychological impairment.

Conclusions

Sub-groups of patients with mTBI can be identified according to clinical variables that are relatively easy to obtain at the time of initial patient evaluation. A patient's sub-group assignment is associated with multidimensional patient outcomes at 90 and 180 days. These findings support the notion that there are clinically meaningful subgroups of patients amongst those currently classified as having mTBI.

Plasma tau and glial fibrillary acidic protein (GFAP) are promising biomarkers for identifying traumatic brain injury (TBI) patients with intracranial trauma on computed tomography (CT). Accuracy in older adults with mild TBI (mTBI), the fastest growing TBI population, is unknown. Our aim was to assess for age-related differences in diagnostic accuracy of plasma tau and GFAP for identifying intracranial trauma on CT. Samples from 169 patients (age <40 years [n = 79], age 40-59 years [n = 60], age 60 years+ [n = 30]), a subset of patients from the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) Pilot study who presented with mTBI (Glasgow Coma Scale score of 13-15), received head CT, and consented to blood draw within 24 h of injury, were assayed for hyperphosphorylated-tau (P-tau), total-tau (T-tau; both via amplification-linked enhanced immunoassay using multi-arrayed fiberoptics), and GFAP (via sandwich enzyme-linked immunosorbent assay). P-tau, T-tau, P-tau:T-tau ratio, and GFAP concentration were significantly associated with CT findings. Overall, discriminative ability declined with increasing age for all assays, but this decline was only statistically significant for GFAP (area under the receiver operating characteristic curve [AUC]: old 0.73 [reference group; ref] vs. young 0.93 [p = 0.037] or middle-aged 0.92 [p = 0.0497]). P-tau concentration consistently showed the highest diagnostic accuracy across all age-groups (AUC: old 0.84 [ref] vs. young 0.95 [p = 0.274] or middle-aged 0.93 [p = 0.367]). Comparison of models including P-tau alone versus P-tau plus GFAP revealed significant added value of GFAP. In conclusion, the GFAP assay was less accurate for identifying intracranial trauma on CT among older versus younger mTBI patients. Mechanisms of this age-related difference, including role of assay methodology, specific TBI neuroanatomy, pre-existing conditions, and anti-thrombotic use, warrant further study.

Mild Traumatic Brain Injury (mTBI), or concussion, is a major public health concern. There is controversy in the literature regarding the true incidence of postconcussion syndrome (PCS), with the constellation of physical, cognitive, emotional, and sleep symptoms after mTBI. In the current study, we report on the incidence and evolution of PCS symptoms and patient outcomes after mTBI at 3, 6, and 12 months in a large, prospective cohort of mTBI patients. Participants were identified as part of the prospective, multi-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study. The study population was mTBI patients (Glasgow Coma Scale score of 13-15) presenting to the emergency department, including patients with a negative head computed tomography discharged to home without admission to hospital; 375 mTBI subjects were included in the analysis. At both 6 and 12 months after mTBI, 82% (n=250 of 305 and n=163 of 199, respectively) of patients reported at least one PCS symptom. Further, 44.5 and 40.3% of patients had significantly reduced Satisfaction With Life scores at 6 and 12 months, respectively. At 3 months after injury, 33% of the mTBI subjects were functionally impaired (Glasgow Outcome Scale-Extended score ≤6); 22.4% of the mTBI subjects available for follow-up were still below full functional status at 1 year after injury. The term "mild" continues to be a misnomer for this patient population and underscores the critical need for evolving classification strategies for TBI for targeted therapy.

The purpose of the present study was to evaluate the impact of sleep disturbances on subsequent depressive symptomatology among a representative sample of patients following traumatic brain injury (TBI). Within a retrospective cohort design, our sample included 305 individuals from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot; NINDS-OD09-004) database. At 3-months post-TBI, symptoms of insomnia were reported by 34% of patients, and symptoms of hypersomnia were reported by 39% of patients. For the vast majority of individuals, sleep complaints were likely to persist through 6-month follow-up. Symptoms of hypersomnia but not insomnia at three months were associated with worsened depressive symptomatology at six months. These results highlight the importance of sleep disturbances in recovery from TBI and suggest targeted sleep treatments as a pathway to improve outcomes and quality of life following TBI.

Traumatic brain injury (TBI) is among the leading causes of death and disability worldwide, with enormous negative social and economic impacts. The heterogeneity of TBI combined with the lack of precise outcome measures have been central to the discouraging results from clinical trials. Current approaches to the characterization of disease severity and outcome have not changed in more than three decades. This prospective multicenter observational pilot study aimed to validate the feasibility of implementing the TBI Common Data Elements (TBI-CDEs). A total of 650 subjects who underwent computed tomography (CT) scans in the emergency department within 24 h of injury were enrolled at three level I trauma centers and one rehabilitation center. The TBI-CDE components collected included: 1) demographic, social and clinical data; 2) biospecimens from blood drawn for genetic and proteomic biomarker analyses; 3) neuroimaging studies at 2 weeks using 3T magnetic resonance imaging (MRI); and 4) outcome assessments at 3 and 6 months. We describe how the infrastructure was established for building data repositories for clinical data, plasma biomarkers, genetics, neuroimaging, and multidimensional outcome measures to create a high quality and accessible information commons for TBI research. Risk factors for poor follow-up, TBI-CDE limitations, and implementation strategies are described. Having demonstrated the feasibility of implementing the TBI-CDEs through successful recruitment and multidimensional data collection, we aim to expand to additional study sites. Furthermore, interested researchers will be provided early access to the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) data set for collaborative opportunities to more precisely characterize TBI and improve the design of future clinical treatment trials. (ClinicalTrials.gov Identifier NCT01565551.).

Traumatic brain injury (TBI) results in heterogeneous pathology affecting multiple cells and tissue types in the brain. It is likely that assessment of such complexity will require simultaneous measurement of multiple molecular biomarkers in a single sample of biological fluid. We measured glial fibrillary acidic protein (GFAP), ubiquitin c-terminal hydrolase L1 (UCH-L1), neurofilament light chain (NF-L) and total tau in plasma samples obtained from 107 subjects enrolled in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) Study using the Quanterix Simoa 4-Plex assay. We also measured NF-L using the Simoa singleplex assay. We computed the correlation between the different biomarkers and calculated the discriminative value of each biomarker for distinguishing between subjects with abnormal versus normal head computed tomography (CT). We found a strong correlation between NF-L values derived from the multiplex and singleplex assays (correlation coefficient = 0.997). Among biomarker values derived from the multiplex assay, the strongest correlation was between the axonal and neuronal markers, NF-L and UCH-L1 (coefficient = 0.71). The weakest correlation was between the glial marker GFAP and the axonal marker tau (coefficient = 0.06). The areas under the curves for distinguishing between subjects with/without abnormal head CT for multiplex GFAP, UCH-L1, NF-L, and total tau were: 0.88 (95% confidence interval 0.81-0.95), 0.86 (0.79-0.93), 0.84 (0.77-0.92), and 0.77 0.67-0.86), respectively. We conclude that the multiplex assay provides simultaneous quantification of GFAP, UCH-L1, NF-L, and tau, and may be clinically useful in the diagnosis of TBI as well as identifying different types of cellular injury.

Objective

To determine the clinical relevance, if any, of traumatic intracranial findings on early head computed tomography (CT) and brain magnetic resonance imaging (MRI) to 3-month outcome in mild traumatic brain injury (MTBI).

Methods

One hundred thirty-five MTBI patients evaluated for acute head injury in emergency departments of 3 LEVEL I trauma centers were enrolled prospectively. In addition to admission head CT, early brain MRI was performed 12 ± 3.9 days after injury. Univariate and multivariate logistic regression were used to assess for demographic, clinical, socioeconomic, CT, and MRI features that were predictive of Extended Glasgow Outcome Scale (GOS-E) at 3 months postinjury.

Results

Twenty-seven percent of MTBI patients with normal admission head CT had abnormal early brain MRI. CT evidence of subarachnoid hemorrhage was associated with a multivariate odds ratio of 3.5 (p = 0.01) for poorer 3-month outcome, after adjusting for demographic, clinical, and socioeconomic factors. One or more brain contusions on MRI, and ≥4 foci of hemorrhagic axonal injury on MRI, were each independently associated with poorer 3-month outcome, with multivariate odds ratios of 4.5 (p = 0.01) and 3.2 (p = 0.03), respectively, after adjusting for head CT findings and demographic, clinical, and socioeconomic factors.

Interpretation

In this prospective multicenter observational study, the clinical relevance of abnormal findings on early brain imaging after MTBI is demonstrated. The addition of early CT and MRI markers to a prognostic model based on previously known demographic, clinical, and socioeconomic predictors resulted in a >2-fold increase in the explained variance in 3-month GOS-E.

Brain lesions are subtle or absent in most patients with mild traumatic brain injury (mTBI) and the standard clinical criteria are not reliable for predicting long-term outcome. This study investigates resting-state functional MRI (rsfMRI) to assess semiacute alterations in brain connectivity and its relationship with outcome measures assessed 6 months after injury. Seventy-five mTBI patients were recruited as part of the prospective multicenter Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) pilot study and compared with matched 47 healthy subjects. Patients were classified following radiological criteria: CT/MRI positive, evidence of lesions; CT/MRI negative, without evidence of brain lesions. rsfMRI data were acquired and then processed using probabilistic independent component analysis. We compared the functional connectivity of the resting-state networks (RSNs) between patients and controls, as well as group differences in the interactions between RSNs, and related both to cognitive and behavioral performance at 6 months post-injury. Alterations were found in the spatial maps of the RSNs between mTBI patients and healthy controls in networks involved in behavioral and cognition processes. These alterations were predictive of mTBI patients' outcomes at 6 months post-injury. Moreover, different patterns of reduced network interactions were found between the CT/MRI positive and CT/MRI negative patients and the control group. These rsfMRI results demonstrate that even mTBI patients not showing brain lesions on conventional CT/MRI scans can have alterations of functional connectivity at the semiacute stage that help explain their outcomes. These results suggest rsfMRI as a sensitive biomarker both for early diagnosis and for prediction of the cognitive and behavioral performance of these patients.

Glial fibrillary acidic protein and its breakdown products (GFAP-BDP) are brain-specific proteins released into serum as part of the pathophysiological response after traumatic brain injury (TBI). We performed a multi-center trial to validate and characterize the use of GFAP-BDP levels in the diagnosis of intracranial injury in a broad population of patients with a positive clinical screen for head injury. This multi-center, prospective, cohort study included patients 16-93 years of age presenting to three level 1 trauma centers with suspected TBI (loss of consciousness, post-trauma amnesia, and so on). Serum GFAP-BDP levels were drawn within 24 h and analyzed, in a blinded fashion, using sandwich enzyme-linked immunosorbent assay. The ability of GFAP-BDP to predict intracranial injury on admission computed tomography (CT) as well as delayed magnetic resonance imaging was analyzed by multiple regression and assessed by the area under the receiver operating characteristic curve (AUC). Utility of GFAP-BDP to predict injury and reduce unnecessary CT scans was assessed utilizing decision curve analysis. A total of 215 patients were included, of which 83% suffered mild TBI, 4% moderate, and 12% severe; mean age was 42.1±18 years. Evidence of intracranial injury was present in 51% of the sample (median Rotterdam Score, 2; interquartile range, 2). GFAP-BDP demonstrated very good predictive ability (AUC=0.87) and demonstrated significant discrimination of injury severity (odds ratio, 1.45; 95% confidence interval, 1.29-1.64). Use of GFAP-BDP yielded a net benefit above clinical screening alone and a net reduction in unnecessary scans by 12-30%. Used in conjunction with other clinical information, rapid measurement of GFAP-BDP is useful in establishing or excluding the diagnosis of radiographically apparent intracranial injury throughout the spectrum of TBI. As an adjunct to current screening practices, GFAP-BDP may help avoid unnecessary CT scans without sacrificing sensitivity (Registry: ClinicalTrials.gov Identifier: NCT01565551).