Traumatic brain injury (TBI) is a global health concern with significant economic impact. Optimal fluid therapy aims to restore intravascular volume, maintain cerebral perfusion pressure and blood flow, thus preventing secondary brain injury. While 0.9% saline (NS) is commonly used, concerns about acid-base and electrolyte imbalance and development of acute kidney injury (AKI) lead to consideration of balanced fluids as an alternative.
This study aimed to compare the outcomes of patients with moderate to severe TBI treated with Sterofundin (SF) versus NS.
A double-blinded randomised controlled trial of patients aged 18 to 65 years with TBI was conducted at the University Malaya Medical Centre from February 2017 to November 2019.
Patients were randomly assigned to receive either NS or SF. The study fluids were administered for 72 h as continuous infusions or boluses. Participants, investigators, and staff were blinded to the fluid type.
The primary outcome was in-hospital mortality. Relative risk (RR) with 95% confidence interval (CI) was calculated.
A total of 70 patients were included in the analysis, with 38 in the NS group and 32 in the SF group. The in-hospital mortality rate were 3 (7.9%) in the NS group vs. 4 (12.5%) in the SF group, RR = 1.29 (95% CI, 0.64 to 2.59; p = 0.695). No patients developed AKI and required renal replacement therapy. ICP on day 3 was significantly higher in the SF group (18.60 ± 9.26) compared to 12.77 ± 3.63 in the NS group, (95% CI, -11.46 to 0.20; p = 0.037). There were no significant differences in 3-day biochemical parameters and cerebral perfusion pressure, ventilator-free days, length of ICU stay, or Glasgow Outcome Scale-Extended (GOS-E) score at 6 months.
In patients with moderate to severe TBI, the use of SF was not associated with reduced in-hospital mortality, development of AKI, or improved 6-month GOS-E when compared to NS.

The Glasgow outcome scale-extended (GOS-E), an ordinal scale measure, is often selected as the endpoint for clinical trials of traumatic brain injury (TBI). Traditionally, GOS-E is analyzed as a fixed dichotomy with favorable outcome defined as GOS-E ≥ 5 and unfavorable outcome as GOS-E < 5. More recent studies have defined favorable vs unfavorable outcome utilizing a sliding dichotomy of the GOS-E that defines a favorable outcome as better than a subject\'s predicted prognosis at baseline. Both dichotomous approaches result in loss of statistical and clinical information. To improve on power, Yeatts et al proposed a sliding scoring of the GOS-E as the distance from the cutoff for favorable/unfavorable outcomes, and therefore used more information found in the original GOS-E to estimate the probability of favorable outcome. We used data from a published TBI trial to explore the ramifications to trial operating characteristics by analyzing the sliding scoring of the GOS-E as either dichotomous, continuous, or ordinal. We illustrated a connection between the ordinal data and time-to-event (TTE) data to allow use of Bayesian software that utilizes TTE-based modeling. The simulation results showed that the continuous method with continuity correction offers higher power and lower mean squared error for estimating the probability of favorable outcome compared to the dichotomous method, and similar power but higher precision compared to the ordinal method. Therefore, we recommended that future severe TBI clinical trials consider analyzing the sliding scoring of the GOS-E endpoint as continuous with continuity correction.

To develop the Glasgow Outcome Scale-Extended (GOSE) mobile application and examine the validity of the application against GOSE scoring based on traditional interview method.
Concurrent validity was determined by comparing two independent raters\' scoring for GOSE of 102 patients with traumatic brain injury, who had attended outpatient department of a tertiary neuro hospital. Agreement was assessed between the traditional interview-based pen and paper scoring and algorithm based mobile application scoring of GOSE.
Agreement was tested using Cohen\'s kappa, and the analysis revealed near perfect agreement between two raters (0.89) (p < 0.01).
The GOSE mobile application can measure GOSE Score similar to the traditional interview method. This application may help fasten the process of assessing outcome in TBI patients in clinical practice and in research.

The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight computed tomography findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Further, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.

The elderly population are at high risk for developing chronic subdural hematoma (cSDH). Surgical evacuation of cSDH is one of the most common procedures performed in neurosurgery. The present study aims to identify potential inflammatory biomarkers associated with its development and recurrence. Patients (>65 years of age) who presented with symptomatic cSDH (≥1 cm thickness or ≥5 mm midline shift [MLS]), requiring surgical intervention, were prospectively enrolled. The collected cSDH fluid was analyzed for inflammatory markers. Computed tomography (CT) scan data included pre-operative cSDH thickness and MLS. Outcome data included Glasgow Outcome Scale-Extended (GOS-E) score at 3, 6, and 12 months post-surgery, as well as cSDH recurrence. A decision tree model was used to determine the predictive power of extracted analytes for MLS, cSDH thickness, and recurrence. This pilot study includes 20 enrolled patients (mean age 77.9 ± 7.4 years and 85% falls). Rate of cSDH recurrence was 42%, with 21% requiring reoperation. Chemokine (C-X-C motif) ligand 9 (CXCL9) concentrations correlated with cSDH thickness (r = 0.975, p = 0.040). Interleukin (IL)-6 and vascular endothelial growth factor (VEGF)-A concentrations correlated with MLS (r = 0.974, p = 0.005; r = 0.472, p = 0.036, respectively). IL-5 concentrations correlated with more favorable GOS-E scores at 3, 6, and 12 months (r = 0.639, p = 0.006; r = 0.727, p = 0.003; r = 0.693, p = 0.026, respectively). Regulated on activation, normal T-cell expressed and secreted (RANTES) concentrations correlated with complete cSDH resolution (r = 0.514, p = 0.021). The decision tree model identified that higher concentrations of CXCL9 were predictive of MLS (risk ratio [RR] = 12.0), higher concentrations of IL-5 were predictive of cSDH thickness (RR = 4.5), and lower concentrations of RANTES were predictive of cSDH recurrence (RR = 2.2). CXCL9, IL-6, VEGF, IL-5, and RANTES are associated with recurrence after surgery and may be potential biomarkers for predicting cSDH recurrence and neurological outcomes.

The prevalence of traumatic brain injury (TBI) continues to rise, in part as a reflection of a growing elderly population. Concomitantly, nihilism may exist following substantial neurotrauma from a myriad of commonplace mechanisms, such as traffic incidents, assaults, or falls.
This study assesses long-term outcomes following aggressive surgical intervention with invasive neuromonitoring to guard against nihilism, especially for patients with advantageous characteristics such as younger age.
A consecutive series of patients with severe TBI treated between 2008 and 2018 and enrolled into the Brain Trauma Research Center (BTRC) database, an Institutional Review Board (IRB 19030228) approved prospective, longitudinal cohort study, were extracted. Demographic and clinical data were analyzed. Long-term functional outcome was recorded with the eight-point Glasgow Outcome Scale-Extended (GOS-E) score at 3-, 6-, 12-, and 24-months by trained, qualified neuropsychology technicians. Chi-squared and analysis of variance tests were used to evaluate the relationship of age groups between different variables.
For this analysis, 175 patients with severe TBI who were enrolled in the BTRC database and required decompressive hemicraniectomy during the study period were included. Over one-third of the patients with a severe TBI, who were aged 35 years and younger, had a favorable outcome.
Despite enduring a severe TBI, a substantial percentage of younger patients achieved favorable outcomes following aggressive treatment. As such, establishing a prognosis should be deferred to allow for recovery via individualized rehabilitation, multidisciplinary support, and community reintegration programs to cope with various long-term psychological, cognitive, and functional disabilities.

This study was designed to determine how raw scores correspond between two alternative measures of functional recovery from traumatic brain injury (TBI), the Functional Status Examination (FSE) and the Glasgow Outcome Scale-Extended (GOSE). Using data from 357 persons with moderate-severe TBI who participated in a large clinical trial, we performed item response theory analysis to characterize the relationship between functional ability measured by the FSE and GOSE at 6 months post-injury. Results revealed that raw scores for the FSE and GOSE can be linked, and a table is provided to translate scores from one instrument to the other. For example, a FSE score of 7 (on its 0-21 scale, where higher scores reflect more impairment) is equivalent to a GOSE score of 6 (where GOSE is scaled on an 8-point scale, with higher scores reflecting less impairment). These results allow clinicians or researchers who have a score for a person on one instrument to cross-reference it to a score on the other instrument. Importantly, this enables researchers to combine data sets where some persons only completed the GOSE and some only the FSE. In addition, an investigator could save participant time by eliminating one instrument from a battery of tests, yet still retain a score on that instrument for each participant. More broadly, the findings help anchor scores from these two instruments to the broader continuum of injury-related functional limitations.

Traumatic brain injury (TBI) is an extremely complex disease and current systems classifying TBI as mild, moderate, and severe often fail to capture this complexity. Neuroimaging cannot resolve the cellular and molecular changes due to lack of resolution, and post-mortem tissue examination may not adequately represent acute disease. Therefore, we examined the cellular and molecular sequelae of TBI in fresh brain samples and related these to clinical outcomes. Brain biopsies, obtained shortly after injury from 25 living adult patients suffering severe TBI, underwent immunohistochemical analysis. There were no adverse events. Immunostaining revealed various qualitative cellular and biomolecular changes relating to neuronal injury, dendritic injury, neurovascular injury, and neuroinflammation, which we classified into 4 subgroups for each injury type using the newly devised Yip, Hasan and Uff (YHU) grading system. Based on the Glasgow Outcome Scale-Extended, a total YHU grade of ≤8 or ≥11 had a favourable and unfavourable outcome, respectively. Biomolecular changes observed in fresh brain samples enabled classification of this heterogeneous patient population into various injury severity categories based on the cellular and molecular pathophysiology according to the YHU grading system, which correlated with outcome. This is the first study investigating the acute biomolecular response to TBI.

The Glasgow Outcome Scale-Extended (GOSE) is a functional outcome measure intended to place individuals with traumatic brain injury (TBI) into one of eight broad levels of injury-related disability. This simplicity is not always optimal, particularly when more granular assessment of individuals\' injury recovery is desired. The GOSE, however, is customarily assessed using a multi-question interview that contains richer information than is reflected in the GOSE score. Using data from the multi-center Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study (N = 1544), we used item response theory (IRT) to evaluate whether rescoring the GOSE using IRT, which posits that a continuous latent variable (disability) underlies responses, can yield a more precise index of injury-related functional limitations. We fit IRT models to GOSE interview responses collected at three months post-injury. Each participant\'s level of functional limitation was estimated from the model (GOSE-IRT) and comparisons were made between IRT-based and standard (GOSE-Ordinal) scores. The IRT scoring resulted in 141 possible scores (vs. 7 GOSE-Ordinal scores in this sample of individuals with GOSE scores ranging between 2 and 8). Moreover, GOSE-IRT scores were significantly more strongly associated with measures of TBI-related symptoms, psychological symptoms, and quality of life. Our findings demonstrate that rescoring the GOSE interview using IRT yields more granular, meaningful measurement of injury-related functional limitations, while adding no additional respondent or examiner burden. This technique may have utility for many applications, such as clinical trials aiming to detect small treatment effects, and small-scale studies that need to maximize statistical efficiency.

BACKGROUND: The present study intended to analyze the outcome of patients with severe brain injury one-year after discharge from early rehabilitation.
METHODS: Early neurological rehabilitation patients admitted to intensive or intermediate care units and discharged between June 2018 and May 2020 were screened for eligibility. The level of consciousness was evaluated using the Coma Recovery Scale-Revised (CRS-R) upon admission and at discharge. At one-year follow-up, the outcome was assessed with the Glasgow Outcome Scale-extended (GOSE). Demographical and clinical data collected during inpatient rehabilitation were used to predict the outcome 1 year after discharge.
RESULTS: Two hundred sixty-four patients (174 males, 90 females) with a median age of 62 years (IQR = 51-75) and a median duration of their disease of 18 days (IQR = 12-28) were included in the study. At follow-up, the mortality rate was 27% (n = 71). Age and discharge CRS-R total score were independent predictors in a Cox proportional hazards model with death (yes/no) as the dependent variable. According to the GOSE interviews, most patients were either dead (n = 71; 27%), in a vegetative state (n = 28; 11%) or had a severe disability (n = 124; 47%), whereas only a few patients showed a moderate disability (n = 18; 7%) or a good recovery (n = 23; 9%) 1 year after discharge. Age, non-traumatic etiology, discharge CRS-R total score and length of stay independently predicted whether the outcome was good or poor at follow-up.
CONCLUSIONS: Age was an important predictor for outcome at one-year follow-up, which might be due to altered brain plasticity and more comorbidities in elderly subjects. In addition, the present study demonstrated that the CRS-R total score at discharge might be more important for the prediction of one-year outcome than the initial assessment upon admission.