Classification Of Traumatic Brain Injury Research Articles

Characteristics of traumatic brain injury models: from macroscopic blood flow changes to microscopic mitochondrial changes.

Controlled cortical impingement is a widely accepted method to induce traumatic brain injury to establish a traumatic brain injury animal model. A strike depth of 1 mm at a certain speed is recommended for a moderate brain injury and a depth of > 2 mm is used to induce severe brain injury. However, the different effects and underlying mechanisms of these two model types have not been proven. This study investigated the changes in cerebral blood flow, differences in the degree of cortical damage, and differences in motor function under different injury parameters of 1 and 2 mm at injury speeds of 3, 4, and 5 m/s. We also explored the functional changes and mitochondrial damage between the 1 and 2 mm groups in the acute (7 days) and chronic phases (30 days). The results showed that the cerebral blood flow in the injured area of the 1 mm group was significantly increased, and swelling and bulging of brain tissue, increased vascular permeability, and large-scale exudation occurred. In the 2 mm group, the main pathological changes were decreased cerebral blood flow, brain tissue loss, and cerebral vasospasm occlusion in the injured area. Substantial motor and cognitive impairments were found on day 7 after injury in the 2 mm group; at 30 days after injury, the motor function of the 2 mm group mice recovered significantly while cognitive impairment persisted. Transcriptome sequencing showed that compared with the 1 mm group, the 2 mm group expressed more ferroptosis-related genes. Morphological changes of mitochondria in the two groups on days 7 and 30 using transmission electron microscopy revealed that on day 7, the mitochondria in both groups shrank and the vacuoles became larger; on day 30, the mitochondria in the 1 mm group became larger, and the vacuoles in the 2 mm group remained enlarged. By analyzing the proportion of mitochondrial subgroups in different groups, we found that the model mice had different patterns of mitochondrial composition at different time periods, suggesting that the difference in the degree of damage among traumatic brain injury groups may reflect the mitochondrial changes. Taken together, differences in mitochondrial morphology and function between the 1 and 2 mm groups provide a new direction for the accurate classification of traumatic brain injury. Our results provide reliable data support and evaluation methods for promoting the establishment of standard mouse controlled cortical impingement model guidelines.

Association of the Presenting Glasgow Coma Scale in patients who requires ICU admission or operative intervention following traumatic brain injury with the Marshall computed tomography (CT) classification of traumatic brain injury

BACKGROUND Glasgow coma scale (GCS) and Marshall computed tomography classification of traumatic brain injury can predict the severity of the brain injury in patients following trauma. This study aims to analyse the association between two, in patients who required ICU admission or neurosurgical intervention following trauma.
 METHODOLOGY Retrospective study of 64 patients who underwent ICU admission or neurosurgical intervention following traumatic brain injury from September 2017 to December 2020 in Nepal Mediciti Hospital. Majority of the mild head injury where CT scan was not performed, discharged from the emergency or did not need ICU admission or admitted in ward for observation, severe polytrauma were excluded from the study. Glasgow coma Scale was categorized into mild (13-15), moderate (9-12) and severe (<8). The Marshall CT scan Grade was dichotomized into (1-3) and (4-6).
 RESULTS Out 64 patients, majority were male 48 (84.4%), mean age 42.33 (SD±16.16). In admitted patients, 48.4 %( GCS< 8), 39.1% (GCS 9-12), 12.5% (GCS 13-15).The higher marshal grade (4-6) was present in 93.54 %( <8), 48% (9-12), 25% (13-15). There was significant association of the GCS with the Marshall CT scan grade (p=0.00).
 CONCLUSION There is significant association between the presenting GCS and Marshall CT Scan grade following TBI. The more severe patients with decreasing GCS have higher Marshall CT Scan grade in CT scan of the brain.  

Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study

BackgroundWhile the Glasgow coma scale (GCS) is one of the strongest outcome predictors, the current classification of traumatic brain injury (TBI) as ‘mild’, ‘moderate’ or ‘severe’ based on this fails to capture enormous heterogeneity in pathophysiology and treatment response. We hypothesized that data-driven characterization of TBI could identify distinct endotypes and give mechanistic insights.MethodsWe developed an unsupervised statistical clustering model based on a mixture of probabilistic graphs for presentation (< 24 h) demographic, clinical, physiological, laboratory and imaging data to identify subgroups of TBI patients admitted to the intensive care unit in the CENTER-TBI dataset (N = 1,728). A cluster similarity index was used for robust determination of optimal cluster number. Mutual information was used to quantify feature importance and for cluster interpretation.ResultsSix stable endotypes were identified with distinct GCS and composite systemic metabolic stress profiles, distinguished by GCS, blood lactate, oxygen saturation, serum creatinine, glucose, base excess, pH, arterial partial pressure of carbon dioxide, and body temperature. Notably, a cluster with ‘moderate’ TBI (by traditional classification) and deranged metabolic profile, had a worse outcome than a cluster with ‘severe’ GCS and a normal metabolic profile. Addition of cluster labels significantly improved the prognostic precision of the IMPACT (International Mission for Prognosis and Analysis of Clinical trials in TBI) extended model, for prediction of both unfavourable outcome and mortality (both p < 0.001).ConclusionsSix stable and clinically distinct TBI endotypes were identified by probabilistic unsupervised clustering. In addition to presenting neurology, a profile of biochemical derangement was found to be an important distinguishing feature that was both biologically plausible and associated with outcome. Our work motivates refining current TBI classifications with factors describing metabolic stress. Such data-driven clusters suggest TBI endotypes that merit investigation to identify bespoke treatment strategies to improve care.Trial registrationThe core study was registered with ClinicalTrials.gov, number NCT02210221, registered on August 06, 2014, with Resource Identification Portal (RRID: SCR_015582).

Label Alignment Improves EEG-based Machine Learning-based Classification of Traumatic Brain Injury.

Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of various biological conditions. However, one major hindrance to leveraging the potential of machine learning models is the requirement of huge datasets. In the biomedical domain, this becomes extremely difficult due to uncertainties in collecting high-quality data as well as, in the case of human subjects data, privacy. Further, when it comes to biomedical data, inter-subject variability has been a long-entrenched issue. The data obtained from different individuals will differ to a considerable extent that it becomes difficult to find population differences in small datasets. In this work, we investigate the use of label alignment techniques on an EEG-based Traumatic Brain Injury (TBI) classification task to overcome inter-subject variability, thereby increasing the classification accuracy. We show an increase in accuracy of around 6% in some cases as compared to our previous results. In the end, we also propose a methodology to incorporate TBI data from a different species (e.g., mice) after domain adaptation, which might further improve the performance by increasing the amount of training datasets available for the classification model.

183 Predicting Vasospasm and Mortality in Early Severe TBI: A Model Using Serum Cytokines and Clinical Data

INTRODUCTION: It is difficult to predict outcomes in patients with severe traumatic brain injury (sTBI) using current clinical tools. METHODS: Adult civilian patients were prospectively enrolled in the sTBI arm of the Surgical Critical Care Initiative (SC2i). Patient clinical and serum inflammatory and neuronal protein data were combined and evaluated using the machine learning methods of LASSO and CART to construct parsimonious models for predicting development of post-traumatic vasospasm and mortality. Cross validations were performed to assess the robustness of conclusions. Missing data were imputed with random forest techniques; only variables with less than 10% missingness were included. RESULTS: There were 53 patients, of whom 36 (67.9%) developed vasospasm and 10 (18.9%) died. The mean age was 39.2; 22.6% were female. There were an equal number of white (25) and black (25) patients. For vasospasm, LASSO identified Eotaxin and Marshall classification of traumatic brain injury as predictors (XV AUC = 0.77). CART identified S100B as a predictor (full data AUC = 0.75). For mortality, LASSO identified S100B and sRAGE (XV AUC = 0.97), while CART identified S100B (full data AUC = 0.912). CONCLUSION: Inflammatory and glial-specific protein levels following sTBI may have predictive value that exceeds conventional clinical variables for certain outcomes. Eotaxin, S100B and radiographic findings highly predict development of post-traumatic vasospasm. S100B and sRAGE highly predict mortality. These results warrant validation in a prospective cohort.

Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram.

Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may develop following-up persistent disability. The pathophysiologic causes for those with persistent postconcussive symptoms are most likely multifactorial and the underlying mechanism is not well understood, although it is clear that sleep disturbances feature prominently in those with persistent disability. The sleep electroencephalogram (EEG) provides a direct window into neuronal activity during an otherwise highly stereotyped behavioral state, and represents a promising quantitative measure for TBI diagnosis and prognosis. With the ever-evolving domain of machine learning, deep convolutional neural networks, and the development of better architectures, these approaches hold promise to solve some of the long entrenched challenges of personalized medicine for uses in recommendation systems and/or in health monitoring systems. In particular, advanced EEG analysis to identify putative EEG biomarkers of neurological disease could be highly relevant in the prognostication of mild TBI, an otherwise heterogeneous disorder with a wide range of affected phenotypes and disability levels. In this work, we investigate the use of various machine learning techniques and deep neural network architectures on a cohort of human subjects with sleep EEG recordings from overnight, in-lab, diagnostic polysomnography (PSG). An optimal scheme is explored for the classification of TBI versus non-TBI control subjects. The results were promising with an accuracy of ∼95% in random sampling arrangement and ∼70% in independent validation arrangement when appropriate parameters were used using a small number of subjects (10 mTBI subjects and 9 age- and sex-matched controls). We are thus confident that, with additional data and further studies, we would be able to build a generalized model to detect TBI accurately, not only via attended, in-lab PSG recordings, but also in practical scenarios such as EEG data obtained from simple wearables in daily life.

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.

Objectives:Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (TBI) history from those with stroke history and/or normal EEG.Methods:Support vector machine (SVM) and K-nearest neighbors (KNN) models were generated with a diverse feature set from Temple EEG Corpus for both two-class classification of patients with TBI history from normal subjects and three-class classification of TBI, stroke and normal subjects.Results:For two-class classification, an accuracy of 0.94 was achieved in 10-fold cross validation (CV), and 0.76 in independent validation (IV). For three-class classification, 0.85 and 0.71 accuracy were reached in CV and IV respectively. Overall, linear discriminant analysis (LDA) feature selection and SVM models consistently performed well in both CV and IV and for both two-class and three-class classification. Compared to normal control, both TBI and stroke patients showed an overall reduction in coherence and relative PSD in delta frequency, and an increase in higher frequency (alpha, mu, beta and gamma) power. But stroke patients showed a greater degree of change and had additional global decrease in theta power.Conclusions:Our study suggests that EEG data-driven machine learning can be a useful tool for TBI classification.Significance:Our study provides preliminary evidence that EEG ML algorithm can potentially provide specificity to separate different neurological conditions.

A-116 Neuropsychological Support for Use of Russell Posttraumatic Amnesia Classification of Traumatic Brain Injury

Abstract Objective To compare neurocognitive outcomes between 3 traumatic brain injury (TBI) groups (mild, moderate, severe) across 3 TBI classification systems: Glasgow Coma Scale (GCS) and Russell (mild &amp;lt;1 hour, moderate &amp;lt;24 hours and severe &amp;gt;24 hours) and the modified (mild &amp;lt;24 hours, moderate &amp;lt; one week and severe &amp;gt; one week) posttraumatic amnesia (PTA) systems. Method Private practice archival data were reviewed for ambulance/hospital documentation of lowest GCS and PTA duration. Exclusion criteria included ESL and failed tests of engagement. Tests included WAIS, WMS, WRAT, Halstead Reitan etc. Results There were 91 patients (16 mild, 30 moderate and 45 severe); 45 were male. Mean age and education was 30.9 and 12.6. Russell PTA classification yielded significant differences (t-tests) between mild and moderate TBI on 8 of 46 tests/measures and 13 differences in moderate versus severe and 24 differences in mild versus severe TBI. Differences were always severe &amp;gt; moderate &amp;gt; mild impairment, with most in psychomotor speed, memory, working memory and executive/frontal functions consistent with TBI. The modified PTA classification yielded 2 significant differences between mild and moderate, 6 differences between moderate and severe and 22 differences between mild and severe TBI. GCS yielded 0 differences between mild and moderate, 7 differences between moderate and severe and 14 differences between mild and severe TBI. The modified PTA and GCS reduced moderate TBI numbers and some differences were opposite expectations. Conclusions Russell PTA was superior to the modified PTA system and GCS in separation/discrimination (without reversals) and maintenance of moderate TBI as a substantive category.

Classification of Electroencephalogram in a Mouse Model of Traumatic Brain Injury Using Machine Learning Approaches.

Traumatic Brain Injury (TBI) is highly prevalent, affecting ~1% of the U.S. population, with lifetime economic costs estimated to be over $75 billion. In the U.S., there are about 50,000 deaths annually related to TBI, and many others are permanently disabled. However, it is currently unknown which individuals will develop persistent disability following TBI and what brain mechanisms underlie these distinct populations. The pathophysiologic causes for those are most likely multifactorial. Electroencephalogram (EEG) has been used as a promising quantitative measure for TBI diagnosis and prognosis. The recent rise of advanced data science approaches such as machine learning and deep learning holds promise to further analyze EEG data, looking for EEG biomarkers of neurological disease, including TBI. In this work, we investigated various machine learning approaches on our unique 24-hour recording dataset of a mouse TBI model, in order to look for an optimal scheme in classification of TBI and control subjects. The epoch lengths were 1 and 2 minutes. The results were promising with accuracy of ~80-90% when appropriate features and parameters were used using a small number of subjects (5 shams and 4 TBIs). We are thus confident that, with more data and studies, we would be able to detect TBI accurately, not only via long-term recordings but also in practical scenarios, with EEG data obtained from simple wearables in the daily life.

Apoptosis in cerebrospinal fluid as outcome predictors in severe traumatic brain injury: An observational study.

Traumatic brain injury (TBI), due to its high mortality and morbidity, is an important research topic. Apoptosis plays a pathogenic role in a series of neurological disorders, from neurodegenerative diseases to acute neurological lesions.In this study, we analyzed the association between apoptosis and the Glasgow Outcome Scale (GOS), to examine the potential of apoptosis as a biomarker for a TBI outcome. Patients with severe TBI were recruited at the Department of Neurosurgery, Wujin Hospital Affiliated with Jiangsu University, between January 2018 and December 2019. As a control group, healthy subjects were recruited. The concentrations of caspase-3, cytochrome c, sFas, and caspase-9 in the cerebrospinal fluid (CSF) were analyzed by enzyme-linked immunosorbent assay (ELISA). The association between the GOS and the clinical variables age, sex, initial Glasgow Coma Scale (GCS) score, intracranial pressure (ICP), cerebral perfusion pressure (CPP), initial computed tomography (CT) findings, and apoptotic factors was determined using logistic regression. The area under the receiver operator characteristic (ROC) curve (AUC), and thus the sensitivity and specificity of each risk factor, were obtained.The levels of caspase-3, cytochrome c, sFas, and caspase-9 in the TBI group were significantly higher than those in the control group (P < .05). The logistic regression results showed that ICP and caspase-3 were significant predictors of outcome at 6 months post-TBI (P < .05). The AUC was 0.925 and 0.888 for ICP and caspase-3, respectively. However, the AUC for their combined prediction was 0.978, with a specificity and sensitivity of 96.0% and 95.2%, respectively, showing that the combined prediction was more reliable than that of the 2 separate factors.We demonstrated that caspase-3, cytochrome C, sFas, and caspase-9 were significantly increased in the CSF of patients following severe TBI. Furthermore, we found that ICP and caspase-3 were more reliable for outcome prediction in combination, rather than separately.

Toward a New Multi-Dimensional Classification of Traumatic Brain Injury: A Collaborative European NeuroTrauma Effectiveness Research for Traumatic Brain Injury Study.

Traumatic brain injury (TBI) is currently classified as mild, moderate, or severe TBI by trichotomizing the Glasgow Coma Scale (GCS). We aimed to explore directions for a more refined multidimensional classification system. For that purpose, we performed a hypothesis-free cluster analysis in the Collaborative European NeuroTrauma Effectiveness Research for TBI (CENTER-TBI) database: a European all-severity TBI cohort (n = 4509). The first building block consisted of key imaging characteristics, summarized using principal component analysis from 12 imaging characteristics. The other building blocks were demographics, clinical severity, secondary insults, and cause of injury. With these building blocks, the patients were clustered into four groups. We applied bootstrap resampling with replacement to study the stability of cluster allocation. The characteristics that predominantly defined the clusters were injury cause, major extracranial injury, and GCS. The clusters consisted of 1451, 1534, 1006, and 518 patients, respectively. The clustering method was quite stable: the proportion of patients staying in one cluster after resampling and reclustering was 97.4% (95% confidence interval [CI]: 85.6-99.9%). These clusters characterized groups of patients with different functional outcomes: from mild to severe, 12%, 19%, 36%, and 58% of patients had unfavorable 6 month outcome. Compared with the mild and the upper intermediate cluster, the lower intermediate and the severe cluster received more key interventions. To conclude, four types of TBI patients may be defined by injury mechanism, presence of major extracranial injury and GCS. Describing patients according to these three characteristics could potentially capture differences in etiology and care pathways better than with GCS only.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

BackgroundBetter outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.MethodsContinuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1–2) or good (GOSE 3–8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.ResultsFifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.ConclusionsOur study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

Automated classification of traumatic brain injury using machine learning with multiple indices of Diffusion Tensor Imaging

Automated classification of traumatic brain injury using machine learning with multiple indices of Diffusion Tensor Imaging

Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes.

BackgroundThe 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.MethodsData 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.ResultsAmongst 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.ConclusionsSub-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.

Classification of traumatic brain injury severity using retrospective data

Objective: Accurate classification of traumatic brain injury (TBI) severity is essential to brain injury research. TBI heterogeneity complicates classification of the injury; is a significant barrier in the design of therapeutic interventions; and results in retrospective data which is difficult to translate. The objective of this study is to describe the differences in two current tools used in the classification of TBI severity, the Glasgow Coma Scale (GCS) and the head Abbreviated Injury Score (AIS), using retrospective data to compare their performance.Methods: Using correlational and descriptive statistics, this study examined two TBI severity classification methods across a large sample of TBI patients (N = 56,131), who were treated at level I and level II trauma centers in the United States and were included in the 2010 National Sample Program (NSP) of the National Trauma Data Bank (NTDB®).Results: The study population was 67% male, 67% non-Hispanic white, treated most often in trauma centers in the South (38%), with blunt trauma (93%) and from non-motor vehicle collisions (MVC’s) (56%). Observation of the AIS classification system demonstrated that it tends to over-score TBI severity compared to the GCS classification. The methods (GCS &amp; AIS) had a weak, inverse relationship with a correlation coefficient (Pearson’s r) of -0.3980, which was significant at p &lt; .001.Conclusions: The current study addressed the difficulties associated with categorizing TBI severity when analyzing retrospective data.  Although AIS is commonly used to classify severity in retrospective data when GCS is unavailable, the relationship between the two scales is relatively unknown. Results show that AIS and GCS are more closely related for severely brain injured patients but in cases of mild and moderate injury, AIS is less predictive of GCS. Since they are often used in conjunction in identifying brain injured severity in retrospective data, researchers cannot be certain that the tools are similarly classifying mild, moderate, and severe injuries. This study reinforces the need for additional TBI severity classification methods, such as neuroimaging techniques and biomarkers.

Progesterone for acute traumatic brain injury.

Traumatic brain injury (TBI) is a leading cause of death and disability, and the identification of effective, inexpensive and widely practicable treatments for brain injury is of great public health importance worldwide. Progesterone is a naturally produced hormone that has well-defined pharmacokinetics, is widely available, inexpensive, and has steroidal, neuroactive and neurosteroidal actions in the central nervous system. It is, therefore, a potential candidate for treating TBI patients. However, uncertainty exists regarding the efficacy of this treatment. This is an update of our previous review of the same title, published in 2012. To assess the effects of progesterone on neurologic outcome, mortality and disability in patients with acute TBI. To assess the safety of progesterone in patients with acute TBI. We updated our searches of the following databases: the Cochrane Injuries Group's Specialised Register (30 September 2016), the Cochrane Central Register of Controlled Trials (CENTRAL; Issue 9, 2016), MEDLINE (Ovid; 1950 to 30 September 2016), Embase (Ovid; 1980 to 30 September 2016), Web of Science Core Collection: Conference Proceedings Citation Index-Science (CPCI-S; 1990 to 30 September 2016); and trials registries: Clinicaltrials.gov (30 September 2016) and the World Health Organization (WHO) International Clinical Trials Registry Platform (30 September 2016). We included randomised controlled trials (RCTs) of progesterone versus no progesterone (or placebo) for the treatment of people with acute TBI. Two review authors screened search results independently to identify potentially relevant studies for inclusion. Independently, two review authors selected trials that met the inclusion criteria from the results of the screened searches, with no disagreement. We included five RCTs in the review, with a total of 2392 participants. We assessed one trial to be at low risk of bias; two at unclear risk of bias (in one multicentred trial the possibility of centre effects was unclear, whilst the other trial was stopped early), and two at high risk of bias, due to issues with blinding and selective reporting of outcome data.All included studies reported the effects of progesterone on mortality and disability. Low quality evidence revealed no evidence of a difference in overall mortality between the progesterone group and placebo group (RR 0.91, 95% CI 0.65 to 1.28, I² = 62%; 5 studies, 2392 participants, 2376 pooled for analysis). Using the GRADE criteria, we assessed the quality of the evidence as low, due to the substantial inconsistency across studies.There was also no evidence of a difference in disability (unfavourable outcomes as assessed by the Glasgow Outcome Score) between the progesterone group and placebo group (RR 0.98, 95% CI 0.89 to 1.06, I² = 37%; 4 studies; 2336 participants, 2260 pooled for analysis). We assessed the quality of this evidence to be moderate, due to inconsistency across studies.Data were not available for meta-analysis for the outcomes of mean intracranial pressure, blood pressure, body temperature or adverse events. However, data from three studies showed no difference in mean intracranial pressure between the groups. Data from another study showed no evidence of a difference in blood pressure or body temperature between the progesterone and placebo groups, although there was evidence that intravenous progesterone infusion increased the frequency of phlebitis (882 participants). There was no evidence of a difference in the rate of other adverse events between progesterone treatment and placebo in the other three studies that reported on adverse events. This updated review did not find evidence that progesterone could reduce mortality or disability in patients with TBI. However, concerns regarding inconsistency (heterogeneity among participants and the intervention used) across included studies reduce our confidence in these results.There is no evidence from the available data that progesterone therapy results in more adverse events than placebo, aside from evidence from a single study of an increase in phlebitis (in the case of intravascular progesterone).There were not enough data on the effects of progesterone therapy for our other outcomes of interest (intracranial pressure, blood pressure, body temperature) for us to be able to draw firm conclusions.Future trials would benefit from a more precise classification of TBI and attempts to optimise progesterone dosage and scheduling.

Through the looking glass: early non-invasive imaging in TBI predicts the need for interventions.

BackgroundEarly diagnosis and treatment of traumatic brain injury (TBI) lead to better outcomes. It is difficult to predict which patients benefit from specialised centres, leading to over triage or delay in definitive care. We propose that a non-invasive test comprising optic nerve sheath ultrasound, transcranial Doppler and quantitative papillary reactivity is feasible, correlates with CT findings and may allow for accurate early identification of TBI.MethodsA 1-year, prospective observation study evaluated a low-risk, non-invasive method of assessing brain injury. Patients underwent a non-invasive neurological examination for trauma, including the above assessments. Data from the three examinations were collected within 6 hours of injury and at 24 hours, and were analysed. Demographics, haemodynamic data, imaging results and short-term outcomes/interventions were recorded.ResultsTrauma patients over the age of 18 years, with a Glascow coma scale (GCS) of <12 or CT evidence of TBI, and intubated were included (N=100). These were divided into +CT (n=49) and −CT groups (n=51) according to the Marshall CT classification of TBI. The +CT group was older, with worse GCS and higher lactate (p=0.008, p=0.001 and p=0.01) but were otherwise well matched. The +CT group included all TBI types, with 96% of the patients having more than one type of TBI. Pulsatility index and neurologic pupillary index were predictive of a +CT (p=0.04, p=0.02). Area under the receiver-operating curve for the logistic regression model for the prediction of positive radiographic findings was r=0.718. Finally, we suggest a preliminary scoring heuristic for predicting a positive radiological finding in a patient with TBI.ConclusionsThe proposed examination is a feasible, non-invasive tool that may have clinical utility in the early prediction of TBI. If validated, it may improve trauma triage for the brain-injured patient. Further studies are warranted to validate this model.

Diffusion Tensor Imaging of TBI: Potentials and Challenges.

Neuroimaging plays a critical role in the setting in traumatic brain injury (TBI). Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging technique that is capable of providing rich information on the brain's neuroanatomic connectome. The purpose of this article is to systematically review the role of DTI and advanced diffusion techniques in the setting of TBI, including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging, diffusion spectrum imaging, and q-ball imaging. We discuss clinical applications of DTI and review the DTI literature as it pertains to TBI. Despite the continued advancements in DTI and related diffusion techniques over the past 20 years, DTI techniques are sensitive for TBI at the group level only and there is insufficient evidence that DTI plays a role at the individual level. We conclude by discussing future directions in DTI research in TBI including the role of machine learning in the pattern classification of TBI.

Chapter 2 - Classification of traumatic brain injury: past, present, and future

Disease classification is central to the practice of medicine; it systematizes clinical knowledge and experience. Classification is essential for diagnosis and effective treatment of human disease. Progress in classifying traumatic brain injury (TBI) for targeted treatment has lagged behind other diseases such as cancer, and has contributed to a lack of progress in the field. Today TBI is most frequently classified as mild, moderate, or severe using the Glasgow Coma Scale (GCS). However, the GCS is symptoms-based and does not allow for targeting of specific pathology. Here we review general schemas for disease classification and how they have evolved over time. We discuss the characteristics of an ideal classification system and the unique challenges inherent to achieving such a system for TBI. Current means of classifying TBI are reviewed, as are the strengths and limitations of these approaches. Generating the data required to modernize TBI classification and to perhaps facilitate a targeted, precision medicine approach to its management will require a highly collaborative international effort. Fortunately these efforts are underway and will benefit from the lessons and tools that have come from other areas of medicine that have already found success with this approach.

Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): a prospective longitudinal observational study.

Current classification of traumatic brain injury (TBI) is suboptimal, and management is based on weak evidence, with little attempt to personalize treatment. A need exists for new precision medicine and stratified management approaches that incorporate emerging technologies. To improve characterization and classification of TBI and to identify best clinical care, using comparative effectiveness research approaches. This multicenter, longitudinal, prospective, observational study in 22 countries across Europe and Israel will collect detailed data from 5400 consenting patients, presenting within 24 hours of injury, with a clinical diagnosis of TBI and an indication for computed tomography. Broader registry-level data collection in approximately 20,000 patients will assess generalizability. Cross sectional comprehensive outcome assessments, including quality of life and neuropsychological testing, will be performed at 6 months. Longitudinal assessments will continue up to 24 months post TBI in patient subsets. Advanced neuroimaging and genomic and biomarker data will be used to improve characterization, and analyses will include neuroinformatics approaches to address variations in process and clinical care. Results will be integrated with living systematic reviews in a process of knowledge transfer. The study initiation was from October to December 2014, and the recruitment period was for 18 to 24 months. Collaborative European NeuroTrauma Effectiveness Research in TBI should provide novel multidimensional approaches to TBI characterization and classification, evidence to support treatment recommendations, and benchmarks for quality of care. Data and sample repositories will ensure opportunities for legacy research. Comparative effectiveness research provides an alternative to reductionistic clinical trials in restricted patient populations by exploiting differences in biology, care, and outcome to support optimal personalized patient management.