Clinical Trials In Traumatic Brain Injury Research Articles

Predictive Models of Long-Term Outcome in Patients with Moderate to Severe Traumatic Brain Injury are Biased Toward Mortality Prediction.

The prognostication of long-term functional outcomes remains challenging in patients with traumatic brain injury (TBI). Our aim was to demonstrate that intensive care unit (ICU) variables are not efficient to predict 6-month functional outcome in survivors with moderate to severe TBI (msTBI) but are mostly associated with mortality, which leads to a mortality bias for models predicting a composite outcome of mortality and severe disability. We analyzed the data from the multicenter randomized controlled Continuous Hyperosmolar Therapy in Traumatic Brain-Injured Patients trial and developed predictive models using machine learning methods and baseline characteristics and predictors collected during ICU stay. We compared our models' predictions of 6-month binary Glasgow Outcome Scale extended (GOS-E) score in all patients with msTBI (unfavorable GOS-E 1-4 vs. favorable GOS-E 5-8) with mortality (GOS-E 1 vs. GOS-E 2-8) and binary functional outcome in survivors with msTBI (severe disability GOS-E 2-4 vs. moderate to no disability GOS-E 5-8). We investigated the link between ICU variables and long-term functional outcomes in survivors with msTBI using predictive modeling and factor analysis of mixed data and validated our hypotheses on theInternational Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model. Based on data from 370 patients with msTBI and classically used ICU variables, the prediction of the 6-month outcome in survivors was inefficient (mean area under the receiver operating characteristic 0.52). Using factor analysis of mixed data graph, we demonstrated that high-variance ICU variables were not associated with outcome in survivors with msTBI (p = 0.15 for dimension 1, p = 0.53 for dimension 2) but mostly with mortality (p < 0.001 for dimension 1), leading to a mortality bias for models predicting a composite outcome of mortality and severe disability. We finally identified this mortality bias in the IMPACT model. We demonstrated using machine learning-based predictive models that classically used ICU variables are strongly associated with mortality but not with 6-month outcome in survivors with msTBI, leading to a mortality bias when predicting a composite outcome of mortality and severe disability.

MRI and Clinical Variables for Prediction of Outcomes After Pediatric Severe Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of death and disability in children, and predicting functional outcome after TBI is challenging. Magnetic resonance imaging (MRI) is frequently conducted after severe TBI; however, the predictive value of MRI remains uncertain. To identify early MRI measures that predict long-term outcome after severe TBI in children and to assess the added predictive value of MRI measures over well-validated clinical predictors. This preplanned prognostic study used data from the Approaches and Decisions in Acute Pediatric TBI (ADAPT) prospective observational comparative effectiveness study. The ADAPT study enrolled 1000 consecutive children (aged <18 years) with severe TBI between February 1, 2014, and September 30, 2017. Participants had a Glasgow Coma Scale (GCS) score of 8 or less and received intracranial pressure monitoring. Magnetic resonance imaging scans performed as part of standard clinical care within 30 days of injury were collected at 24 participating sites in the US, UK, and Australia. Summary imaging measures were correlated with the Glasgow Outcome Scale-Extended for Pediatrics (GOSE-Peds), and the predictive value of MRI measures was compared with the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) core clinical predictors. Data collection, image analysis, and data analyses were completed in July 2023. Pediatric severe TBI with an MRI scan performed as part of clinical care. All measures were selected a priori. Magnetic resonance imaging measures included contusion, ischemia, diffuse axonal injury, intracerebral hemorrhage, and brainstem injury. Clinical predictors included the IMPACT core measures (GCS motor score and pupil reactivity). All models adjusted for age and sex. Outcome measures included the GOSE-Peds score obtained at 3, 6, and 12 months after injury. This study included 233 children with severe TBI who were enrolled at participating sites and had an MRI scan and preselected clinical predictors available. Their median age was 6.9 (IQR, 3.0-13.3) years, and more than half of participants (134 [57.5%]) were male. In a multivariable model including MRI measures and IMPACT core clinical variables, contusion volume (odds ratio [OR], 1.13; 95% CI, 1.02-1.26), brain ischemia (OR, 2.11; 95% CI, 1.58-2.81), brainstem lesions (OR, 5.40; 95% CI, 1.90-15.35), and pupil reactivity were each independently associated with GOSE-Peds score. Adding MRI measures to the IMPACT clinical predictors significantly improved model fit and discrimination between favorable and unfavorable outcomes compared with IMPACT predictors alone (area under the receiver operating characteristic curve, 0.77; 95% CI, 0.72-0.85 vs 0.67; 95% CI, 0.61-0.76 for GOSE-Peds score >3 at 6 months after injury). In this prognostic study of children with severe TBI, the addition of MRI measures significantly improved outcome prediction over well-established and validated clinical predictors. Magnetic resonance imaging should be considered in children with severe TBI to inform prognosis and may also promote stratification of patients in future clinical trials.

Automated Measurement of Cerebral Hemorrhagic Contusions and Outcomes After Traumatic Brain Injury in the TRACK-TBI Study

Because withdrawal of life-sustaining therapy based on perceived poor prognosis is the most common cause of death after moderate or severe traumatic brain injury (TBI), the accuracy of clinical prognoses is directly associated with mortality. Although the location of brain injury is known to be important for determining recovery potential after TBI, the best available prognostic models, such as the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) score, do not currently incorporate brain injury location. To test whether automated measurement of cerebral hemorrhagic contusion size and location is associated with improved prognostic performance of the IMPACT score. This prognostic cohort study was performed in 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018. Adult participants aged 17 years or older from the US-based Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study with moderate or severe TBI (Glasgow Coma Scale score 3-12) and contusions detected on brain computed tomography (CT) scans were included. The data analysis was performed between January 2023 and February 2024. Labeled contusions detected on CT scans using Brain Lesion Analysis and Segmentation Tool for Computed Tomography (BLAST-CT), a validated artificial intelligence algorithm. The primary outcome was a Glasgow Outcome Scale-Extended (GOSE) score of 4 or less at 6 months after injury. Whether frontal or temporal lobe contusion volumes improved the performance of the IMPACT score was tested using logistic regression and area under the receiver operating characteristic curve comparisons. Sparse canonical correlation analysis was used to generate a disability heat map to visualize the strongest brainwide associations with outcomes. The cohort included 291 patients with moderate or severe TBI and contusions (mean [SD] age, 42 [18] years; 221 [76%] male; median [IQR] emergency department arrival Glasgow Coma Scale score, 5 [3-10]). Only temporal contusion volumes improved the discrimination of the IMPACT score (area under the receiver operating characteristic curve, 0.86 vs 0.84; P = .03). The data-derived disability heat map of contusion locations showed that the strongest association with unfavorable outcomes was within the bilateral temporal and medial frontal lobes. These findings suggest that CT-based automated contusion measurement may be an immediately translatable strategy for improving TBI prognostic models.

Diversity trends in traumatic brain injury clinical trials in the United States

ABSTRACT Background This study aims to understand the demographic representation of patients in Traumatic Brain Injury (TBI) clinical trials by evaluating the proportions of patients from various demographic categories amongst completed TBI clinical trials in the United States. Methods ClinicalTrials.gov was queried for active TBI clinical trials. One hundred and eight completed trials in the United States were selected based on inclusion criteria, and information regarding intervention, setting, age, sex, race, and ethnicity was extracted. 2002–2006 TBI incidence data was obtained from the CDC. Chi-squared testing was applied to analyze the relationship between distributions of race and sex in the collected clinical trials and the national TBI data, and logistic regression was conducted to identify variables that may predict reporting of race or ethnicity. Results About 53.7% of selected clinical trials reported racial data and 34.3% reported ethnicity data. Logistic regression identified that clinical trials in defined phases were more likely to report racial data (p = 0.047 [1.015, 9.603]). Conclusion Current TBI trials do not consistently report race or ethnicity data. Future efforts to ensure equitable representation in clinical trials may involve reform of recruitment processes and accountability measures implemented within the grant application process to ensure proper racial and ethnicity data reporting.

Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis.

Recent evidence links the prognosis of traumatic brain injury (TBI) to various factors, including baseline clinical characteristics, TBI specifics, and neuroimaging outcomes. This study focuses on identifying risk factors for short-term survival in severe traumatic brain injury (sTBI) cases and developing a prognostic model. Analyzing 430 acute sTBI patients from January 2018 to December 2023 at the 904th Hospital's Neurosurgery Department, this retrospective case-control study separated patients into survival outcomes: 288 deceased and 142 survivors. It evaluated baseline, clinical, hematological, and radiological data to identify risk and protective factors through univariate and Lasso regression. A multivariate model was then formulated to pinpoint independent prognostic factors, assessing their relationships via Spearman's correlation. The model's accuracy was gauged using the Receiver Operating Characteristic (ROC) curve, with additional statistical analyses for quantitative factors and model effectiveness. Internal validation employed ROC, calibration curves, Decision Curve Analysis (DCA), and Clinical Impact Curves (CIC) to assess model discrimination, utility, and accuracy. The International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) and Corticosteroid Randomization After Significant Head injury (CRASH) models were also compared through multivariate regression. Factors like unilateral and bilateral pupillary non-reactivity at admission, the derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR), D-dimer to fibrinogen ratio (DFR), infratentorial hematoma, and Helsinki CT score were identified as independent risk factors (OR > 1), whereas serum albumin emerged as a protective factor (OR < 1). The model showed superior predictive performance with an AUC of 0.955 and surpassed both IMPACT and CRASH models in predictive accuracy. Internal validation confirmed the model's high discriminative capability, clinical relevance, and effectiveness. Short-term survival in sTBI is significantly influenced by factors such as pupillary response, dNLR, PLR, DFR, serum albumin levels, infratentorial hematoma occurrence, and Helsinki CT scores at admission. The developed nomogram accurately predicts sTBI outcomes, offering significant clinical utility.

ANTI-CD49D ANTIBODY TREATMENT IMPROVES SURVIVAL AND ATTENUATES NEUROCOGNITIVE DEFICITS AFTER TRAUMATIC BRAIN INJURY IN AGED MICE.

Patients 65 years and older account for an increasing proportion of traumatic brain injury (TBI) patients. Aged TBI patients experience increased morbidity and mortality compared with young TBI patients. We previously demonstrated a marked accumulation of CD8 + T-cells within the brains of aged TBI mice compared with young TBI mice. Therefore, we hypothesized that blocking peripheral T-cell infiltration into the injured brain would improve neurocognitive outcomes in aged mice after TBI. Young and aged male C57BL/6 mice underwent TBI via controlled cortical impact versus sham injury. Two hours after injuries, mice received an anti-CD49d antibody (aCD49d Ab) to block peripheral lymphocyte infiltration or its isotype control. Dosing was repeated every 2 weeks. Mortality was tracked. Neurocognitive testing for anxiety, associative learning, and memory was assessed. Motor function was evaluated. Plasma was collected for cytokine analysis. Flow cytometry was used to phenotype different immune cells within the brains. Consequently, aCD49d Ab treatment significantly improved post-TBI survival, anxiety level, associative learning, memory, and motor function in aged mice 2 months after TBI compared with isotype control treated mice. aCD49d Ab treatment augmented T H 2 response in the plasma of aged mice 2 months after TBI compared with isotype control-treated mice. Notably, aCD49d Ab treatment significantly reduced activated CD8 + cytotoxic T-cells within aged mouse brains after TBI. Contrastingly, no difference was detected in young mice after aCD49d Ab treatment. Collectively, aCD49 Ab treatment reduced T-cells in the injured brain, improved survival, and attenuated neurocognitive and gait deficits. Hence, aCD49d Ab may be a promising therapeutic intervention in aged TBI subjects-a population often excluded in TBI clinical trials.

Selection of a statistical analysis method for the Glasgow Outcome Scale-Extended endpoint for estimating the probability of favorable outcome in future severe TBI clinical trials.

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.

Glasgow coma scale pupil score (GCS-P) and the hospital mortality in severe traumatic brain injury: analysis of 1,066 Brazilian patients.

Pupil reactivity and the Glasgow Coma Scale (GCS) score are the most clinically relevant information to predict the survival of traumatic brain injury (TBI) patients. We evaluated the accuracy of the GCS-Pupil score (GCS-P) as a prognostic index to predict hospital mortality in Brazilian patients with severe TBI and compare it with a model combining GCS and pupil response with additional clinical and radiological prognostic factors. Data from 1,066 patients with severe TBI from 5 prospective studies were analyzed. We determined the association between hospital mortality and the combination of GCS, pupil reactivity, age, glucose levels, cranial computed tomography (CT), or the GCS-P score by multivariate binary logistic regression. Eighty-five percent (n = 908) of patients were men. The mean age was 35 years old, and the overall hospital mortality was 32.8%. The area under the receiver operating characteristic curve (AUROC) was 0.73 (0.70-0.77) for the model using the GCS-P score and 0.80 (0.77-0.83) for the model including clinical and radiological variables. The GCS-P score showed similar accuracy in predicting the mortality reported for the patients with severe TBI derived from the International Mission for Prognosis and Clinical Trials in TBI (IMPACT) and the Corticosteroid Randomization After Significant Head Injury (CRASH) studies. Our results support the external validation of the GCS-P to predict hospital mortality following a severe TBI. The predictive value of the GCS-P for long-term mortality, functional, and neuropsychiatric outcomes in Brazilian patients with mild, moderate, and severe TBI deserves further investigation.

PREDICT-TBI: Comparison of Physician Predictions with the IMPACT Model to Predict 6-Month Functional Outcome in Traumatic Brain Injury.

Predicting functional outcome in critically ill patients with traumatic brain injury (TBI) strongly influences end-of-life decisions and information for surrogate decision makers. Despite well-validated prognostic models, clinicians most often rely on their subjective perception of prognosis. In this study, we aimed to compare physicians' predictions with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic model for predicting an unfavorable functional outcome at 6months after moderate or severe TBI. PREDICT-TBI is a prospective study of patients with moderate to severe TBI. Patients were admitted to a neurocritical care unit and were excluded if they died or had withdrawal of life-sustaining treatments within the first 24h. In a paired study design, we compared the accuracy of physician prediction on day 1 with the prediction of the IMPACT model as two diagnostic tests in predicting unfavorable outcome 6months after TBI. Unfavorable outcome was assessed by the Glasgow Outcome Scale from 1 to 3 by using a structured telephone interview. The primary end point was the difference between the discrimination ability of the physician and theIMPACT model assessed by the area under the curve. Of the 93 patients with inclusion and exclusion criteria, 80 patients reached the primary end point. At 6months, 29 patients (36%) had unfavorable outcome. A total of 31 clinicians participated in the study. Physicians' predictions showed an area under the curve of 0.79 (95% confidence interval 0.68-0.89), against 0.80 (95% confidence interval 0.69-0.91) for the laboratory IMPACT model, with no statistical difference (p = 0.88). Both approaches were well calibrated. Agreement between physicians was moderate (κ = 0.56). Lack of experience was not associated with prediction accuracy (p = 0.58). Predictions made by physicians for functional outcome were overall moderately accurate, and no statistical difference was found with the IMPACT models, possibly due to a lack of power. The significant variability between physician assessments suggests prediction could be improved through peer reviewing, with the support of the IMPACT models, to provide a realistic expectation of outcome to families and guide discussions about end-of-life decisions.

Association of Posttraumatic Epilepsy With Long-term Functional Outcomes in Individuals With Severe Traumatic Brain Injury.

Nearly one-third of patients with severe traumatic brain injury (TBI) develop posttraumatic epilepsy (PTE). The relationship between PTE and long-term outcomes is unknown. We tested whether, after controlling for injury severity and age, PTE is associated with worse functional outcomes after severe TBI. We performed a retrospective analysis of a prospective database of patients with severe TBI treated from 2002 through 2018 at a single level 1 trauma center. Glasgow Outcome Scale (GOS) was collected at 3, 6, 12, and 24 months postinjury. We used repeated-measures logistic regression predicting GOS, dichotomized as favorable (GOS 4-5) and unfavorable (GOS 1-3), and a separate logistic model predicting mortality at 2 years. We used predictors as defined by the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) base model (i.e., age, pupil reactivity, and GCS motor score), PTE status, and time. Of 392 patients who survived to discharge, 98 (25%) developed PTE. The proportion of patients with favorable outcomes at 3 months did not differ between those with and without PTE (23% [95% Confidence Interval [CI]: 15%-34%] vs 32% [95% CI: 27%-39%]; p = 0.11) but was significantly lower at 6 (33% [95% CI: 23%-44%] vs 46%; [95% CI: 39%-52%] p = 0.03), 12 (41% [95% CI: 30%-52%] vs 54% [95% CI: 47%-61%]; p = 0.03), and 24 months (40% [95% CI: 47%-61%] vs 55% [95% CI: 47%-63%]; p = 0.04). This was driven by higher rates of GOS 2 (vegetative) and 3 (severe disability) outcomes in the PTE group. By 2 years, the incidence of GOS 2 or 3 was double in the PTE group (46% [95% CI: 34%-59%]) compared with that in the non-PTE group (21% [95% CI: 16%-28%]; p < 0.001), while mortality was similar (14% [95% CI: 7%-25%] vs 23% [95% CI: 17%-30%]; p = 0.28). In multivariate analysis, patients with PTE had lower odds of favorable outcome (odds radio [OR] 0.1; 95% CI: 0.1-0.4; p < 0.001), but not mortality (OR 0.9; 95% CI: 0.1-1.9; p = 0.46). Posttraumatic epilepsy is associated with impaired recovery from severe TBI and poor functional outcomes. Early screening and treatment of PTE may improve patient outcomes.

Abstract 194: The Effect Of Recombinant Erythropoietin On Very Long-term Outcome After Moderate To Severe Traumatic Brain Injury

Objectives: Recombinant erythropoietin (EPO) administered during intensive care is one possible treatment method for patients with traumatic brain injury that may increase short-term survival. Its effect on long-term outcome including functional outcome is unknown. Methods: We conducted a long-term follow up of patients included in the multi-center erythropoietin in traumatic brain injury (TBI) trial (Clinical Trials.gov NCT00987454) conducted between 2010 and 2014. We invited patients treated in all countries except France to participate in a follow-up and evaluated survival and functional outcome with the Glasgow outcome scale extended (GOSE) with categories 5 to 8 defined as good outcome. We categorized TBI severity with the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT-TBI) model and calculated adjusted odds ratios (OR) and 95% confidence intervals (CI). Results: Of 603 patients included in the original trial, 371 were included in the follow-up at a median of six years from injury (interquartile range 3-8 years). Out of 185 patients treated with EPO, 140 (76%) were alive and 110 (60%) had good outcome. The corresponding numbers for the 180 placebo patients were 136 (73%) and 100 (53%) (p=0.57, p=0.27). A multivariable model adjusting for the IMPACT severity provided comparable results the effect of EPO for survival (OR 1.3 95% CI 0.8-2.2, p=0.31)) and good outcome (OR 1.5 95% 0.9-2.4, p=0.09). Conclusions: EPO did not decrease mortality or improve outcome but the limited sample size does not rule out clinically meaningful effects suggesting the need for a larger study.

Consequences of inequity in theneurosurgical workforce: Lessons from traumatic braininjury.

Women and minorities leave or fail to advance in the neurosurgical workforce more frequently than white men at all levels from residency to academia. The consequences of this inequity are most profound in fields such as traumatic brain injury (TBI), which lacks objective measures. We evaluated published articles on TBI clinical research and found that TBI primary investigators or corresponding authors were 86·5% White and 59·5% male. First authors from the resulting publications were 92.6% white. Most study participants were male (68%). 64·4% of NIH-funded TBI clinical trials did not report or recruit any black subjects and this number was even higher for other races and the Hispanic ethnicity. We propose several measures for mitigation of the consequences of the inequitable workforce in traumatic brain injury that could potentially contribute to more equitable outcomes. The most immediately feasible of these is validation and establishment of objective measures for triage and prognostication that are less susceptible to bias than current protocols. We call for incorporation of gender and race neutral metrics for TBI evaluation to standardize classification of injury. We offer insights into how socioeconomic factors contribute to increased death rates from women and minority groups. We propose the need to study how these disparities are caused by unfair health insurance reimbursement practices. Surgical and clinical research inequities have dire consequences, and until those inequities can be corrected, mitigation of those consequences requires system wide change.

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).

Current Clinical Trials in Traumatic Brain Injury.

Traumatic brain injury (TBI) is one of the leading causes of morbidity, disability and mortality across all age groups globally. Currently, only palliative treatments exist, but these are suboptimal and do little to combat the progressive damage to the brain that occurs after a TBI. However, multiple experimental treatments are currently available that target the primary and secondary biochemical and cellular changes that occur after a TBI. Some of these drugs have progressed to clinical trials and are currently being evaluated for their therapeutic benefits in TBI patients. The aim of this study was to identify which drugs are currently being evaluated in clinical trials for TBI. A search of ClinicalTrials.gov was performed on 3 December 2021 and all clinical trials that mentioned “TBI” OR “traumatic brain injury” AND “drug” were searched, revealing 362 registered trials. Of the trials, 46 were excluded due to the drug not being mentioned, leaving 138 that were completed and 116 that were withdrawn. Although the studies included 267,298 TBI patients, the average number of patients per study was 865 with a range of 5–200,000. Of the completed studies, 125 different drugs were tested in TBI patients but only 7 drugs were used in more than three studies, including amantadine, botulinum toxin A and tranexamic acid (TXA). However, previous clinical studies using these seven drugs showed variable results. The current study concludes that clinical trials in TBI have to be carefully conducted so as to reduce variability across studies, since the severity of TBI and timing of therapeutic interventions were key aspects of trial success.

Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries.

Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches. To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings. We used the Duke University Medical Center National Trauma Data Bank and Mulago National Referral Hospital (MNRH) registry to predict in-hospital mortality for the HIC and LMIC settings, respectively. Six ML models were built on each data set, and the best model was chosen through nested cross-validation. The CRASH and IMPACT models were externally validated on the MNRH database. ML models built on National Trauma Data Bank (n = 5393, 84 predictors) demonstrated an area under the receiver operating curve (AUROC) of 0.91 (95% CI: 0.85-0.97) while models constructed on MNRH (n = 877, 31 predictors) demonstrated an AUROC of 0.89 (95% CI: 0.81-0.97). Direct comparison with CRASH and IMPACT models showed significant improvement of the proposed LMIC models regarding AUROC (P = .038). We developed high-performing well-calibrated ML models for predicting in-hospital mortality for both the HIC and LMIC settings that have the potential to influence clinical management and traumatic brain injury patient trajectories.

480 Deep Learning Analysis of Early MRI Scans After Severe Traumatic Brain Injury Predicts Long-term Outcomes

INTRODUCTION: Nearly three quarters of deaths in clinical trials of severe traumatic brain injury (TBI) occur due to withdrawal of life sustaining therapies, often due to perceived poor neurological prognosis. Most of these deaths occur within the first week when physicians historically lack enough accuracy to make these decisions. Prognostic models, such as the International Mission for Prognosis and Analysis of Clinical Trials in TBI, are designed to prognose outcomes upon admission and fail to utilize clinical information accumulated after admission. Quantitative prognostic models capturing this informaiton are needed to help guide care decisions. METHODS: We built our model using 266 patients from a prospective database of severe TBI patients from 2002 through 2018 with an MRI obtained 1 to 9 days post-trauma. We developed a customized, multi-channel, deep convolutional neural network model with an AlexNet backbone and pre-trained with ImageNet. The cohort was split into 80% training, 10% validation, and 10% independent testing set. We built separate models using T2 FLAIR and DWI sequences. To reduce the heterogeneity of imaging after a decompressive hemicraniectomy (DHC), we only used DHC patients for model training. RESULTS: No adverse clinical events occurred while obtaining early MRIs. The overall mortality was 25% (68) and 20% (34) in the 170 patients who did not have a DHC. In the independent test cohort, the T2 FLAIR model performed best, with an area under the curve (AUC) of 0.80 (95% Confidence Interval: 0.57-0.94) and accuracy of 83%. The DWI model had an AUC of 0.64 (0.50 – 0.79) with an accuracy of 78%. With a specificity of 100% (i.e., never recommending withdrawal of care in a patient who would otherwise survive), the sensitivity for mortality was 57%. For identifying patients with favorable recovery potential, the sensitivity was 83% with a specificity of 80%. CONCLUSION: Early MRIs are safe to obtain in the severe TBI patient population. Deep learning analysis of these MRIs can predict 6-month mortality to help guide neurological prognostication.

Predicting Clinical Outcomes 7-10 Years after Severe Traumatic Brain Injury: Exploring the Prognostic Utility of the IMPACT Lab Model and Cerebrospinal Fluid UCH-L1 and MAP-2.

Severe traumatic brain injury (TBI) is a major contributor to disability and mortality in the industrialized world. Outcomes of severe TBI are profoundly heterogeneous, complicating outcome prognostication. Several prognostic models have been validated for acute prediction of 6-month global outcomes following TBI (e.g., morbidity/mortality). In this preliminary observational prognostic study, we assess the utility of the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) Lab model in predicting longer term global and cognitive outcomes (7-10years post injury) and the extent to which cerebrospinal fluid (CSF) biomarkers enhance outcome prediction. Very long-term global outcome was assessed in a total of 59 participants (41 of whom did not survive their injuries) using the Glasgow Outcome Scale-Extended and Disability Rating Scale. More detailed outcome information regarding cognitive functioning in daily life was collected from 18 participants surviving to 7-10years post injury using the Cognitive Subscale of the Functional Independence Measure. A subset (n = 10) of these participants also completed performance-based cognitive testing (Digit Span Test) by telephone. The IMPACT lab model was applied to determine its prognostic value in relation to very long-term outcomes as well as the additive effects of acute CSF ubiquitin C-terminal hydrolase-L1 (UCH-L1) and microtubule associated protein 2 (MAP-2) concentrations. The IMPACT lab model discriminated favorable versus unfavorable 7- to 10-year outcome with an area under the receiver operating characteristic curve of 0.80. Higher IMPACT lab model risk scores predicted greater extent of very long-term morbidity (β = 0.488 p = 0.000) as well as reduced cognitive independence (β = - 0.515, p = 0.034). Acute elevations in UCH-L1 levels were also predictive of lesser independence in cognitive activities in daily life at very long-term follow-up (β = 0.286, p = 0.048). Addition of two CSF biomarkers significantly improved prediction of very long-term neuropsychological performance among survivors, with the overall model (including IMPACT lab score, UCH-L1, and MAP-2) explaining 89.6% of variance in cognitive performance 7-10years post injury (p = 0.008). Higher acute UCH-L1 concentrations were predictive of poorer cognitive performance (β = - 0.496, p = 0.029), whereas higher acute MAP-2 concentrations demonstrated a strong cognitive protective effect (β = 0.679, p = 0.010). Although preliminary, results suggest that existing prognostic models, including models with incorporation of CSF markers, may be applied to predict outcome of severe TBI years after injury. Continued research is needed examining early predictors of longer-term outcomes following TBI to identify potential targets for clinical trials that could impact long-ranging functional and cognitive outcomes.

103 Participants in Clinical Trials for Traumatic Brain Injury Are Not Representative of the Injured Population

INTRODUCTION: Traumatic brain injury (TBI) has higher mortality in minority populations, but these individuals are less likely to participate in TBI trials than non-minorities due to reasons ranging from historical abuse to socioeconomic status. Therefore, it is imperative barriers to minority inclusion in TBI trials are overcome. METHODS: 350 out of 592 TBI studies with published results were compared to the general population statistics on race/ethnicity and sex from the U.S. census and TBI statistics from the CDC. RESULTS: Trials with a completed status compared to withdrawn status were 128 times more likely to publish their findings. While sex was frequently reported (males- 68% of the study participants), the majority of studies did not report race/ethnicity. 9.9% of enrolled subjects are Black even though they represent 12.3% and 19% of TBI cases and mortalities, respectively. This same trend is seen in all other populations, except White people, who were found to be overrepresented in trials as 65.3% of participants are White even though they account for 63.1% and 52.0% of TBI cases and mortalities, respectively. Black patients were 1.9 more times likely to die from head injuries compared to White patients. The average number of days/sessions involved in TBI trials was 12 and the average trial duration was 6.3 years. 14 (29%) trials required phone calls for follow up visits and 14 trials specifically excluded non English speakers. CONCLUSION: The majority of TBI clinical trials reported sex, but not report race/ethnicity. Factors that may influence participation include trial duration and effort commitment. Our results demonstrate the need for demographic reporting to ensure the population being studied accurately reflects the population suffering from the burden of disease.

Traumatic axonal injury: is the prognostic information produced by conventional MRI and DTI complementary or supplementary?

A traumatic axonal injury (TAI) diagnosis has traditionally been based on conventional MRI, especially on those sequences with a higher sensitivity to edema and blood degradation products. A more recent technique, diffusion tensor imaging (DTI), can infer the microstructure of white matter (WM) due to the restricted diffusion of water in organized tissues. However, there is little information regarding the correlation of the findings obtained by both methods and their use for outcome prognosis. The main objectives of this study were threefold: 1) study the correlation between DTI metrics and conventional MRI findings; 2) evaluate whether the prognostic information provided by the two techniques is supplementary or complementary; and 3) determine the incremental value of the addition of these variables compared to a traditional prognostic model. The authors studied 185 patients with moderate to severe traumatic brain injury (TBI) who underwent MRI with DTI study during the subacute stage. The number and volume of lesions in hemispheric subcortical WM, corpus callosum (CC), basal ganglia, thalamus, and brainstem in at least four conventional MRI sequences (T1-weighted, T2-weighted, FLAIR, T2* gradient recalled echo, susceptibility-weighted imaging, and diffusion-weighted imaging) were determined. Fractional anisotropy (FA) was measured in 28 WM bundles using the region of interest method. Nonparametric tests were used to evaluate the colocalization of macroscopic lesions and FA. A multivariate logistic regression analysis was performed to assess the independent prognostic value of each neuroimaging modality after adjustment for relevant clinical covariates, and the internal validation of the model was evaluated in a contemporary cohort of 92 patients. Differences in the lesion load between patients according to their severity and outcome were found. Colocalization of macroscopic nonhemorrhagic TAI lesions (not microbleeds) and lower FA was limited to the internal and external capsule, corona radiata, inferior frontooccipital fasciculus, CC, and brainstem. However, a significant association between the FA value and the identification of macroscopic lesions in distant brain regions was also detected. Specifically, lower values of FA of some hemispheric WM bundles and the splenium of the CC were related to a higher number and volume of hyperintensities in the brainstem. The regression analysis revealed that age, motor score, hypoxia, FA of the genu of the CC, characterization of TAI lesions in the CC, and the presence of thalamic/basal ganglia lesions were independent prognostic factors. The performance of the proposed model was higher than that of the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in TBI) model in the validation cohort. Very limited colocalization of hyperintensities (none for microbleeds) with FA values was discovered. DTI and conventional MRI provide complementary prognostic information, and their combination can improve the performance of traditional prognostic models.

Traumatic Brain Injury: What Is a Favorable Outcome?

Traumatic brain injury (TBI) results in disparate outcomes ranging from persistent disorders of consciousness to symptom resolution. Despite the breadth and complexity of TBI recovery, most clinical trials dichotomize outcome by establishing an arbitrary cut-point, above and below which recovery is described as "favorable" and "unfavorable," respectively. For example, the widely used eight-level Glasgow Outcome Scale-Extended (GOSE) is typically collapsed into these two categories. Dichotomizing the GOSE into "favorable" and "unfavorable" outcome may limit detection of treatment effects in TBI clinical trials, contribute to imprecise prognostic counseling, and unduly influence decision-making with regard to withdrawal of life-sustaining therapy. We illustrate the lack of standardization in defining "unfavorable" and "favorable" TBI outcome on the GOSE by identifying the broad range of cut-points, from a score of 3 (part-time supervision in the home required) to 7 (presence of some residual of symptoms), that have been used to dichotomize the GOSE. We also highlight the ethical concerns related to characterizing TBI outcomes solely from the perspective of investigators and clinicians, rather than patients and caregivers. Finally, we suggest that a pragmatic, immediate solution to GOSE dichotomization is to report the likelihood of achieving each of the eight GOSE outcome levels and propose a study design for a new patient- and caregiver-centered TBI outcome metric.