Outcome Prediction in Moderate and Severe Traumatic Brain Injury: A Focus on Computed Tomography Variables

The Australian Traumatic Brain Injury Initiative: Systematic Review of Clinical Factors Associated with Outcomes in People with Moderate-Severe Traumatic Brain Injury

The aim of the Australian Traumatic Brain Injury Initiative (AUS-TBI) is to design a data dictionary to inform data collection and facilitate prediction of outcomes for moderate-severe traumatic brain injury (TBI) across Australia. The process has engaged diverse stakeholders across six areas: social, health, clinical, biological, acute interventions, and long-term outcomes. Here, we report the results of the clinical review. Standardized searches were implemented across databases to April 2022. English-language reports of studies evaluating an association between a clinical factor and any clinical outcome in at least 100 patients with moderate-severe TBI were included. Abstracts, and full-text records, were independently screened by at least two reviewers in Covidence. The findings were assessed through a consensus process to determine inclusion in the AUS-TBI data resource. The searches retrieved 22,441 records, of which 1137 were screened at full text and 313 papers were included. The clinical outcomes identified were predominantly measures of survival and disability. The clinical predictors most frequently associated with these outcomes were the Glasgow Coma Scale, pupil reactivity, and blood pressure measures. Following discussion with an expert consensus group, 15 were recommended for inclusion in the data dictionary. This review identified numerous studies evaluating associations between clinical factors and outcomes in patients with moderate-severe TBI. A small number of factors were reported consistently, however, how and when these factors were assessed varied. The findings of this review and the subsequent consensus process have informed the development of an evidence-informed data dictionary for moderate-severe TBI in Australia.

Post-discharge 6-Month Functional Recovery of Traumatic Brain Injury Survivors with Unfavorable Functional Status at Discharge: A Registry-Based Cohort Study

BACKGROUND

Traumatic brain injury (TBI) is a major cause of physical disabilities worldwide. Herein, we aimed to investigate the factors contributing to post-discharge recovery in patients who were discharged with an unfavorable outcome.

METHODS

We collected data on the characteristics of patients, with a focus on those who survived TBI but had an unfavorable outcome at discharge as measured by Glasgow Outcome Scale Extended (GOSE) categories 2, 3, and 4. Post-discharge recovery was defined as achieving a favorable functional status at 6 months (GOSE of 5 or more) with a minimum 2-point increase in GOSE.

RESULTS

Of 4011 TBI patients in our registry, 797 had an unfavorable discharge functional status. In severe TBI, 51% achieved recovery, while in mild to moderate TBI, 57% achieved recovery after 6 months. Older patients and those with shorter intensive care unit length of stay were more likely to experience post-discharge recovery in both mild to moderate and severe TBI groups. The presence of skull base fracture was also associated with post-discharge recovery in severe TBI patients. Lastly, we show that, after adjustment for potential confounders, GOSE at discharge is associated with post-discharge recovery in both mild to moderate and severe TBI patients.

CONCLUSIONS

This study found that the majority of patients discharged with an unfavorable functional status were able to achieve a favorable outcome within 6 months. The novel post-discharge recovery in TBI patients might be a useful tool for illuminating the factors associated with a significant improvement after discharge.

Using metabolomics to predict severe traumatic brain injury outcome (GOSE) at 3 and 12 months

BACKGROUND

Prognostication is very important to clinicians and families during the early management of severe traumatic brain injury (sTBI), however, there are no gold standard biomarkers to determine prognosis in sTBI. As has been demonstrated in several diseases, early measurement of serum metabolomic profiles can be used as sensitive and specific biomarkers to predict outcomes.

METHODS

We prospectively enrolled 59 adults with sTBI (Glasgow coma scale, GCS ≤ 8) in a multicenter Canadian TBI (CanTBI) study. Serum samples were drawn for metabolomic profiling on the 1st and 4th days following injury. The Glasgow outcome scale extended (GOSE) was collected at 3- and 12-months post-injury. Targeted direct infusion liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS) and untargeted proton nuclear magnetic resonance spectroscopy (¹H-NMR) were used to profile serum metabolites. Multivariate analysis was used to determine the association between serum metabolomics and GOSE, dichotomized into favorable (GOSE 5-8) and unfavorable (GOSE 1-4), outcomes.

RESULTS

Serum metabolic profiles on days 1 and 4 post-injury were highly predictive (Q² > 0.4-0.5) and highly accurate (AUC > 0.99) to predict GOSE outcome at 3- and 12-months post-injury and mortality at 3 months. The metabolic profiles on day 4 were more predictive (Q² > 0.55) than those measured on day 1 post-injury. Unfavorable outcomes were associated with considerable metabolite changes from day 1 to day 4 compared to favorable outcomes. Increased lysophosphatidylcholines, acylcarnitines, energy-related metabolites (glucose, lactate), aromatic amino acids, and glutamate were associated with poor outcomes and mortality.

DISCUSSION

Metabolomic profiles were strongly associated with the prognosis of GOSE outcome at 3 and 12 months and mortality following sTBI in adults. The metabolic phenotypes on day 4 post-injury were more predictive and significant for predicting the sTBI outcome compared to the day 1 sample. This may reflect the larger contribution of secondary brain injury (day 4) to sTBI outcome. Patients with unfavorable outcomes demonstrated more metabolite changes from day 1 to day 4 post-injury. These findings highlighted increased concentration of neurobiomarkers such as N-acetylaspartate (NAA) and tyrosine, decreased concentrations of ketone bodies, and decreased urea cycle metabolites on day 4 presenting potential metabolites to predict the outcome. The current findings strongly support the use of serum metabolomics, that are shown to be better than clinical data, in determining prognosis in adults with sTBI in the early days post-injury. Our findings, however, require validation in a larger cohort of adults with sTBI to be used for clinical practice.

An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study

Background

Severe traumatic brain injury (sTBI) is extremely disabling and associated with high mortality. Early detection of patients at risk of short-term (≤14 days after injury) death and provision of timely treatment is critical. This study aimed to establish and independently validate a nomogram to estimate individualised short-term mortality for sTBI based on large-scale data from China.

Methods

The data were from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry (between Dec 22, 2014, and Aug 1, 2017; registered at ClinicalTrials.gov, NCT02210221). This analysis included information of eligible patients with diagnosed sTBI from 52 centres (2631 cases). 1808 cases from 36 centres were enrolled in the training group (used to construct the nomogram) and 823 cases from 16 centres were enrolled in the validation group. Multivariate logistic regression was used to identify independent predictors of short-term mortality and establish the nomogram. The discrimination of the nomogram was evaluated using area under the receiver operating characteristic curves (AUC) and concordance indexes (C-index), the calibration was evaluated using calibration curves and Hosmer-Lemeshow tests (H-L tests). Decision curve analysis (DCA) was used to evaluate the net benefit of the model for patients.

Findings

In the training group, multivariate logistic regression demonstrated that age (odds ratio [OR] 1.013, 95% confidence interval [CI] 1.003-1.022), Glasgow Coma Scale score (OR 33.997, 95% CI 14.657-78.856), Injury Severity Score (OR 1.020, 95% CI 1.009-1.032), abnormal pupil status (OR 1.738, 95% CI 1.178-2.565), midline shift (OR 2.266, 95% CI 1.378-3.727), and pre-hospital intubation (OR 2.059, 95% CI 1.472-2.879) were independent predictors for short-term death in patients with sTBI. A nomogram was built using the logistic regression prediction model. The AUC and C-index were 0.859 (95% CI 0.837-0.880). The calibration curve of the nomogram was close to the ideal reference line, and the H-L test p value was 0.504. DCA curve demonstrated significantly better net benefit with the model. Application of the nomogram in external validation group still showed good discrimination (AUC and C-index were 0.856, 95% CI 0.827-0.886), calibration, and clinical usefulness.

Interpretation

A nomogram was developed for predicting the occurrence of short-term (≤14 days after injury) death in patients with sTBI. This can provide clinicians with an effective and accurate tool for the early prediction and timely management of sTBI, as well as support clinical decision-making around the withdrawal of life-sustaining therapy. This nomogram is based on Chinese large-scale data and is especially relevant to low- and middle-income countries.

Funding

Shanghai Academic Research Leader (21XD1422400), Shanghai Medical and Health Development Foundation (20224Z0012).

Prediction of Mortality among Patients with Isolated Traumatic Brain Injury Using Machine Learning Models in Asian Countries: An International Multi-Center Cohort Study

Traumatic brain injury (TBI) is a significant healthcare concern in several countries, accounting for a major burden of morbidity, mortality, disability, and socioeconomic losses. Although conventional prognostic models for patients with TBI have been validated, their performance has been limited. Therefore, we aimed to construct machine learning (ML) models to predict the clinical outcomes in adult patients with isolated TBI in Asian countries. The Pan-Asian Trauma Outcome Study registry was used in this study, and the data were prospectively collected from January 1, 2015, to December 31, 2020. Among a total of 6540 patients (≥ 15 years) with isolated moderate and severe TBI, 3276 (50.1%) patients were randomly included with stratification by outcomes and subgrouping variables for model evaluation, and 3264 (49.9%) patients were included for model training and validation. Logistic regression was considered as a baseline, and ML models were constructed and evaluated using the area under the precision-recall curve (AUPRC) as the primary outcome metric, area under the receiver operating characteristic curve (AUROC), and precision at fixed levels of recall. The contribution of the variables to the model prediction was measured using the SHapley Additive exPlanations (SHAP) method. The ML models outperformed logistic regression in predicting the in-hospital mortality. Among the tested models, the gradient-boosted decision tree showed the best performance (AUPRC, 0.746 [0.700-0.789]; AUROC, 0.940 [0.929-0.952]). The most powerful contributors to model prediction were the Glasgow Coma Scale, O₂ saturation, transfusion, systolic and diastolic blood pressure, body temperature, and age. Our study suggests that ML techniques might perform better than conventional multi-variate models in predicting the outcomes among adult patients with isolated moderate and severe TBI.

A Clinical-Radiomics Based Nomogram to Predict Progressive Intraparenchymal Hemorrhage in Mild to Moderate Traumatic Injury Patients

PURPOSE

To develop a non-contrast computed tomography(NCCT)based radiomics model for predicting intraparenchymal hemorrhage progression in patients with mild to moderate traumatic brain injury(TBI).

METHODS

We retrospectively analyzed 166 mild to moderate TBI patients with intraparenchymal hemorrhage from January 2018 to December 2021. The enrolled patients were divided into training cohort and test cohort with a ratio of 6:4. Uni- and multivariable logistic regression analyses were implemented to screen clinical-radiological factors and to establish a clinical-radiological model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), the calibration curve, the decision curve analysis, sensitivity, and specificity.

RESULTS

Eleven radiomics features, presence with SDH, and D-dimer > 5 mg/l were selected to construct the combined clinical-radiomic model for the prediction of TICH in mild to moderate TBI patients. The AUC of the combined model was 0.81(95% confidence interval (CI), 0.72 to 0.90) in the training cohort and 0.88 (95% CI 0.79 to 0.96) in the test cohort, which were superior to the clinical model alone (AUC_training = 0.72, AUC_test = 0.74). The calibration curve demonstrated that the radiomics nomogram had a good agreement between prediction and observation. Decision curve analysis confirmed clinically useful.

CONCLUSIONS

The combined clinical-radiomic model that incorporates the radiomics score and clinical risk factors can serve as a reliable and powerful tool for Predicting intraparenchymal hemorrhage progression for patients with mild to moderate TBI.

Clinical Value of TCCD for Evaluating the Prognosis of Patients with Severe Traumatic Brain Injury After Large Decompressive Craniectomy: A Retrospective Study

INTRODUCTION

It is challenging to assess the prognosis of patients with severe traumatic brain injury (sTBI) after large decompressive craniectomy (DC). The aim of this study was to evaluate the clinical value of transcranial color-coded duplex sonography (TCCD) for assessing the prognosis of sTBI patients 6 months after large DC.

METHODS

This was a retrospective observational study that consecutively enrolled 84 patients with sTBI who were followed up for prognosis until 6 months after large DC. The primary endpoint was the Glasgow Outcome Score (GOS). According to the GOS, patients were divided into an unfavorable prognosis group (GOS 1-3, n = 47) and a favorable prognosis group (GOS 4-5, n = 37).

RESULTS

Significant between-group differences were found in age and hemodynamic parameters (systolic peak blood flow velocity, end-diastolic blood flow velocity, mean blood flow velocity, pulsatility index and resistance index) of the middle cerebral artery detected by TCCD (P < 0.05 for all). Subsequently, ridge regression was used to build a prognostic model for patients with large DC. Based on the cerebral hemodynamic parameters measured by TCCD and age, the mean (± standard deviation) area under the curve of the prognostic model in patients with sTBI after large DC was 0.76 ± 0.22. The sensitivity and specificity were 82.08% and 74.17%, respectively.

CONCLUSIONS

The cerebral hemodynamic parameters detected by TCCD, combined with age, may be used to predict the outcomes of patients with sTBI at 6 months after large DC. As a noninvasive method, TCCD has the potential to assess the prognosis of these patients.

TRIAL REGISTRATION

ChiCTR: ChiCTR1800019758. Registered 27 November 2018-retrospectively registered ( http://www.chictr.org.cn/index.aspx ).

Does poor methodological quality of prediction modeling studies translate to poor model performance? An illustration in traumatic brain injury

BACKGROUND

Prediction modeling studies often have methodological limitations, which may compromise model performance in new patients and settings. We aimed to examine the relation between methodological quality of model development studies and their performance at external validation.

METHODS

We systematically searched for externally validated multivariable prediction models that predict functional outcome following moderate or severe traumatic brain injury. Risk of bias and applicability of development studies was assessed with the Prediction model Risk Of Bias Assessment Tool (PROBAST). Each model was rated for its presentation with sufficient detail to be used in practice. Model performance was described in terms of discrimination (AUC), and calibration. Delta AUC (dAUC) was calculated to quantify the percentage change in discrimination between development and validation for all models. Generalized estimation equations (GEE) were used to examine the relation between methodological quality and dAUC while controlling for clustering.

RESULTS

We included 54 publications, presenting ten development studies of 18 prediction models, and 52 external validation studies, including 245 unique validations. Two development studies (four models) were found to have low risk of bias (RoB). The other eight publications (14 models) showed high or unclear RoB. The median dAUC was positive in low RoB models (dAUC 8%, [IQR - 4% to 21%]) and negative in high RoB models (dAUC - 18%, [IQR - 43% to 2%]). The GEE showed a larger average negative change in discrimination for high RoB models (- 32% (95% CI: - 48 to - 15) and unclear RoB models (- 13% (95% CI: - 16 to - 10)) compared to that seen in low RoB models.

CONCLUSION

Lower methodological quality at model development associates with poorer model performance at external validation. Our findings emphasize the importance of adherence to methodological principles and reporting guidelines in prediction modeling studies.

Early death prediction in children with traumatic brain injury using computed tomography scoring systems

PURPOSE

Marshall and Rotterdam are the most commonly used CT scoring systems to predict the outcome following traumatic brain injury (TBI). Although several studies have compared the performance of the two scoring systems in adult patients, none of these studies has evaluated the performance of the two scoring systems in pediatric patients. This study aimed to determine the predictive value of the Marshall and Rotterdam scoring systems in pediatric patients with TBI.

METHODS

This retrospective study included 105 children with admission GCS < 12, with a mean age of 6.2 (±3.5) years. Their initial CT and status at hospital discharge (dead or alive) were reviewed, and both the Marshall and Rotterdam scores were calculated. We examined whether each score was related to the early death of pediatric patients.

RESULTS

The pediatric patients with higher Marshall and Rotterdam scores had a higher mortality rate. There was a good correlation between the Marshall and Rotterdam scoring systems (Spearman's rho = 0.618, significant at the 0.05 level). Both systems demonstrated a high degree of discrimination when predicting early mortality. The Marshall scoring system had reasonable discrimination (AUC 0.782), and the Rotterdam scoring system had good discrimination (AUC 0.729). Comparing the two CT scoring systems, the Marshall scoring system provided a better positive predictive value (90%) for early mortality than the Rotterdam scoring system (78%).

CONCLUSIONS

Both the Marshall and Rotterdam scoring systems have good predictability for assessing mortality in pediatric patients with TBI. The performance of the Marshall scoring system was equal to or slightly better than that of the Rotterdam scoring system.

Prognostication of Mortality and Long-Term Functional Outcomes Following Traumatic Brain Injury: Can We Do Better?

Accurate prognostication of outcomes following traumatic brain injury (TBI) affects not only the aggressiveness of intervention and therapeutic decision-making but also clinicians' ability to provide reliable expectations. To investigate the relative ability of clinicians to accurately predict a patient's outcomes, compared with point-of-care prognostic models, we surveyed clinical providers of 86 patients with moderate-severe TBI at admission, Day 3, and Day 7 post-injury for a patient's predicted mortality and functional outcome at 6 months. The predicted mortality and functional outcomes were compared with actual occurrence of 14-day mortality and functional outcomes at six months. A prognostic score was then calculated utilizing the Corticoid Randomization After Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) models and categorized as high, intermediate, and low likelihood of mortality or poor functional outcome, and compared with clinical predictions. Overall, clinicians of varying backgrounds showed an accurate prediction of survival (87.2-97.4%) but struggled in prognosticating poor functional outcomes (24.3-36.6%). These values did not statistically improve over 7 days. Stratified CRASH (87.2%) and IMPACT (84.9%) accuracy rates were statistically better than clinical judgment alone in predicting functional outcomes (p < 0.0001). Prognostic models calculated at admission showed to be potentially useful, in conjunction with clinical judgment, in accurately predicting both survival and 6-month functional outcomes.

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach

Background

The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI).

Methods

A retrospective analysis was conducted for all adult patients who sustained TBI and were hospitalized at the trauma center from January 2014 to February 2019 with an abbreviated injury severity score for head region (HAIS) ≥ 3. We used the demographic characteristics, injuries and CT findings as predictors. Logistic regression (LR) and Artificial neural networks (ANN) were used to predict the in-hospital mortality. Accuracy, area under the receiver operating characteristics curve (AUROC), precision, negative predictive value (NPV), sensitivity, specificity and F-score were used to compare the models` performance.

Results

Across the study duration; 785 patients met the inclusion criteria (581 survived and 204 deceased). The two models (LR and ANN) achieved good performance with an accuracy over 80% and AUROC over 87%. However, when taking the other performance measures into account, LR achieved higher overall performance than the ANN with an accuracy and AUROC of 87% and 90.5%, respectively compared to 80.9% and 87.5%, respectively. Venous thromboembolism prophylaxis, severity of TBI as measured by abbreviated injury score, TBI diagnosis, the need for blood transfusion, heart rate upon admission to the emergency room and patient age were found to be the significant predictors of in-hospital mortality for TBI patients on MV.

Conclusions

Machine learning based LR achieved good predictive performance for the prognosis in mechanically ventilated TBI patients. This study presents an opportunity to integrate machine learning methods in the trauma registry to provide instant clinical decision-making support.

Predictors of Mortality in Traumatic Intracranial Hemorrhage: A National Trauma Data Bank Study

Background/Objective: Traumatic intracranial hemorrhage (tICH) accounts for significant trauma morbidity and mortality. Several studies have developed prognostic models for tICH outcomes, but previous models face limitations, including poor generalizability and limited accuracy. The objective was to develop a prognostic model and determine predictors of mortality using the largest trauma database in the U.S., applying rigorous analytical methodology with true hold-out-set model validation.

Methods: We identified 248,536 patients in the National Trauma Data Bank (NTDB) from 2012 to 2016 with a diagnosis code associated with tICH. For each admission, we collected demographic information, systolic blood pressure, blood alcohol level (BAL), Glasgow Coma Score (GCS), Injury Severity Score (ISS), presence of epidural/subdural/subarachnoid/intraparenchymal hemorrhage, comorbidities, complications, trauma center level, and trauma center region. Our final study population was 212,666 patients following exclusion of records with missing data. The dependent variable was patient death. Linear support vector machine (SVM) classification was carried out with recursive feature selection. Model performance was assessed using holdout 10-fold cross-validation.

Results: Cross-validation demonstrated a mean accuracy of 0.792 (95% CI 0.783–0.799). Accuracy, precision, recall, and AUC were 0.827, 0.309, 0.750, and 0.791, respectively. In the final model, high ISS, advanced age, subdural hemorrhage, and subarachnoid hemorrhage were associated with increased mortality, while high GCS verbal and motor subscores, current smoker, BAL beyond the legal limit, and level 1 trauma center were associated with decreased mortality.

Conclusions: A linear SVM model was developed for tICH, with nine features selected as predictors of mortality. These findings are applicable to multiple hemorrhage subtypes and may benefit the triage of high risk patients upon admission. While many studies have attempted to create models to predict mortality in TBI, we sought to confirm those predictors using modern modeling approaches, machine learning, and true hold-out test sets, using the largest available TBI database in the U.S. We find that while the predictors we identify are consistent with prior reports, overall prediction accuracy is somewhat lower than prior reports when assessed more rigorously.

Factors with the strongest prognostic value associated with in-hospital mortality rate among patients operated for acute subdural and epidural hematoma

INTRODUCTION

Traumatic brain injury (TBI) still remains a serious health problem and is called a "silent epidemic". Each year in Europe 262 per 100,000 individuals suffer from TBI. The most common consequence of severe head injuries include acute subdural (SDH) and epidural hematomas (EDH), which usually require immediate surgically treatment. The aim of our study is to identify factors which have the strongest prognostic value in relation to in-hospital mortality rate among of patients undergoing surgery for EDH and SDH.

PATIENTS AND METHODS

Cohort included 128 patients with isolated craniocerebral injuries who underwent surgery for EDH (28 patients) and SDH (100 patients) in a single, tertiary care Department of Neurosurgery. The data were collected on admission of patients to the Emergency Department and retrospectively analyzed. The following factors were analyzed: demographic data, physiological parameters, laboratory variables, computed tomography scan characteristics and the time between trauma and surgery. Likewise, we have investigated the in-hospital mortality of patients at the time of discharge.

RESULTS

We found that the factors with the strongest prognostic values were: the initial GCS score, respiratory rate, glycaemia, blood saturation, systolic blood pressure, midline shift and type of hematoma. Additionally, we proved that a drop by one point in the GCS score almost doubles the risk of in-hospital death while the presence of coagulopathy increases the risk of in-hospital death almost six times.

CONCLUSION

Most of the factors with the strongest prognostic value are factors that the emergency team can treat prior to the hospital admission. Coagulopathy, however that has the strongest influence on in-hospital death rate can only be efficiently treated in a hospital setting.

Prediction of in-hospital mortality in patients with post traumatic brain injury using National Trauma Registry and Machine Learning Approach

BACKGROUND

The use of machine learning techniques to predict diseases outcomes has grown significantly in the last decade. Several studies prove that the machine learning predictive techniques outperform the classical multivariate techniques. We aimed to build a machine learning predictive model to predict the in-hospital mortality for patients who sustained Traumatic Brain Injury (TBI).

METHODS

Adult patients with TBI who were hospitalized in the level 1 trauma center in the period from January 2014 to February 2019 were included in this study. Patients' demographics, injury characteristics and CT findings were used as predictors. The predictive performance of Artificial Neural Networks (ANN) and Support Vector Machines (SVM) was evaluated in terms of accuracy, Area Under the Curve (AUC), sensitivity, precision, Negative Predictive Value (NPV), specificity and F-score.

RESULTS

A total of 1620 eligible patients were included in the study (1417 survival and 203 non-survivals). Both models achieved accuracy over 91% and AUC over 93%. SVM achieved the optimal performance with accuracy 95.6% and AUC 96%.

CONCLUSIONS

for prediction of mortality in patients with TBI, SVM outperformed the well-known classical models that utilized the conventional multivariate analytical techniques.

Predicting mortality in traumatic intracranial hemorrhage

OBJECTIVE

Traumatic intracranial hemorrhage (tICH) is a significant source of morbidity and mortality in trauma patients. While prognostic models for tICH outcomes may assist in alerting clinicians to high-risk patients, previously developed models face limitations, including low accuracy, poor generalizability, and the use of more prognostic variables than is practical. This study aimed to construct a simpler and more accurate method of risk stratification for all tICH patients.

METHODS

The authors retrospectively identified a consecutive series of 4110 patients admitted to their institution's level 1 trauma center between 2003 and 2013. For each admission, they collected the patient's sex, age, systolic blood pressure, blood alcohol concentration, antiplatelet/anticoagulant use, Glasgow Coma Scale (GCS) score, Injury Severity Score, presence of epidural hemorrhage, presence of subdural hemorrhage, presence of subarachnoid hemorrhage, and presence of intraparenchymal hemorrhage. The final study population comprised 3564 patients following exclusion of records with missing data. The dependent variable under study was patient death. A k-fold cross-validation was carried out with the best models selected via the Akaike Information Criterion. These models risk stratified the study partitions into grade I (< 1% predicted mortality), grade II (1%-10% predicted mortality), grade III (10%-40% predicted mortality), or grade IV (> 40% predicted mortality) tICH. Predicted mortalities were compared with actual mortalities within grades to assess calibration. Concordance was also evaluated. A final model was constructed using the entire data set. Subgroup analysis was conducted for each hemorrhage type.

RESULTS

Cross-validation demonstrated good calibration (p < 0.001 for all grades) with a mean concordance of 0.881 (95% CI 0.865-0.898). In the authors' final model, older age, lower blood alcohol concentration, antiplatelet/anticoagulant use, lower GCS score, and higher Injury Severity Score were all associated with greater mortality. Subgroup analysis showed successful stratification for subarachnoid, intraparenchymal, grade II-IV subdural, and grade I epidural hemorrhages.

CONCLUSIONS

The authors developed a risk stratification model for tICH of any GCS score with concordance comparable to prior models and excellent calibration. These findings are applicable to multiple hemorrhage subtypes and can assist in identifying low-risk patients for more efficient resource allocation, facilitate family conversations regarding goals of care, and stratify patients for research purposes. Future work will include testing of more variables, validation of this model across institutions, as well as creation of a simplified model whose outputs can be calculated mentally.

Baseline Predictors of Survival, Neurological Recovery, Cognitive Function, Neuropsychiatric Outcomes, and Return to Work in Patients after a Severe Traumatic Brain Injury: an Updated Review

Introduction

Severe traumatic brain injury (sTBI) is a common cause of death and disability worldwide, with long-term squeal among survivors that include cognitive deficits, psychosocial and neuropsychiatric dysfunction, failure to return to pre-injury levels of work, school and inter-personal relationships, and overall reduced quality of and satisfaction with life.

Aim

The aim of this work is to review the current literature on baseline predictors of outcomes in adults post sTBI.

Method

Most of available literature on baseline predictors of outcomes in adults post sTBI were reviewed and summarized in this work.

Results

Currently, a sizeable number of composite predictors of mortality and overall function exists; however, these instruments tend to over-estimate poor outcomes and fail to address issues like cognition, psychosocial/ neuropsychiatric dysfunction, and return to work or school.

Conclusion

This article reviews currently-identified predictors of all these outcomes.

Prognosis in Moderate and Severe Traumatic Brain Injury: A Systematic Review of Contemporary Models and Validation Studies

Outcome prognostication in traumatic brain injury (TBI) is important but challenging due to heterogeneity of the disease. The aim of this systematic review is to present the current state-of-the-art on prognostic models for outcome after moderate and severe TBI and evidence on their validity. We searched for studies reporting on the development, validation or extension of prognostic models for functional outcome after TBI with Glasgow Coma Scale (GCS) ≤12 published between 2006-2018. Studies with patients age ≥14 years and evaluating a multi-variable prognostic model based on admission characteristics were included. Model discrimination was expressed with the area under the receiver operating characteristic curve (AUC), and model calibration with calibration slope and intercept. We included 58 studies describing 67 different prognostic models, comprising the development of 42 models, 149 external validations of 31 models, and 12 model extensions. The most common predictors were GCS (motor) score (n = 55), age (n = 54), and pupillary reactivity (n = 48). Model discrimination varied substantially between studies. The International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) models were developed on the largest cohorts (8509 and 10,008 patients, respectively) and were most often externally validated (n = 91), yielding AUCs ranging between 0.65-0.90 and 0.66-1.00, respectively. Model calibration was reported with a calibration intercept and slope for seven models in 53 validations, and was highly variable. In conclusion, the discriminatory validity of the IMPACT and CRASH prognostic models is supported across a range of settings. The variation in calibration, reflecting heterogeneity in reliability of predictions, motivates continuous validation and updating if clinical implementation is pursued.

Effect of integrin AV and B8 gene polymorphisms in patients with traumatic brain injury

Background: Α few genetic variants are associated with the outcome after traumatic brain injury (TBI). Integrins are glycoprotein receptors that play an important role in the integrity of microvasculature of the brain. Objective: To examine the role of integrin-AV (ITGAV) and integrin-B8 (ITGB8) tag single nucleotide polymorphisms (SNPs) on the outcome of patients with TBI. Methods: 363 participants were included and genotyped for 11 SNPs for ITGAV and 11 for ITGB8 gene. SNPs were tested for associations with the 6-month outcome after TBI, the presence of a hemorrhagic event after TBI, and the initial TBI severity after adjustment for TBI's main predictors. Results: The ITGAV rs3911239 CC and rs7596996 GG genotypes were associated with an unfavorable outcome after TBI, compared to the TT and AA genotypes, respectively. The ITGB8 rs10239099 CC and rs3757727 CC genotypes were associated with increased risk of any cerebral hemorrhagic event after TBI compared to GG and TT respectively. The ITGAV rs7589470 and rs7565633 were associated with the TBI's initial severity. Conclusions: ITGAV gene SNPs may be implicated in the outcome after TBI, as well as in the initial TBI severity, and also of ITGB8 gene SNPs in the risk of hemorrhagic event after a TBI.

Trauma demography and clinical epidemiology of motorcycle crash-related head injury in a neurosurgery practice in an African developing country

OBJECTIVE

Though motor vehicle crashes (MVCs) were the main cause of head trauma from road traffic injuries (RTIs), motorcycle crashes (MCCs) are now a major cause of RTI-related head injury (HI) in many developing countries.

METHODS

Using a prospective database of HIs from a neurosurgical practice in a sub-Saharan African developing country, a cross-sectional survey was conducted for the trauma demography and clinical epidemiology of this MCC-related HI.

RESULTS

Motorcycle crashes accounted for 57% (473/833) of all RTI-related HIs in this registry. The victims, with a mean age of 33.1 years (SD = 18.3), consisted mainly of males (83.1%), those of low socioeconomic status (>90%), and those aged between 20 and 40 years old (56%). MCCs involved only riders in 114 cases (114/473, 32.1%), of which 69% were motorcycle-motorcycle crashes. The HI was moderate-severe in 50.8%; clinical symptomatology of significant HI included loss of consciousness (92%), anisocoria (35%), Abbreviated Injury Scale head (AIS-head) score > 3 (28%), and CT-Rotterdam score > 3 (30%). Extracranial systemic injury involved the limbs most frequently, with an Injury Severity Score (ISS) >25 in 49%. The fatality rate was 24%. MCC-related HI among pedestrian victims involved more vulnerable age groups (the young and elderly) but have lower mean ISS compared to motorcycle passengers (mean ISS = 23.5 [11.6] vs. 27.4 [13.0]; 95% confidence interval [CI], 1.27-6.49; P = .004). In addition, compared to a contemporary cohort of MVC-related HIs in our registry, MCC victims were older (mean age 34.8 years [18.0] vs. 30.8 [18.4]; P = .002); had higher proportions of certain extracranial trauma like long bone fractures (71 vs. 29%; P = .02); and suffered fewer surgical brain lesions (25.5 vs. 17.2%; P = .004).

CONCLUSIONS

Motorcycle crashes are now a significant threat to the heads, limbs, and lives of vulnerable road users in developing countries.

Use of magnetic resonance imaging in severe pediatric traumatic brain injury: assessment of current practice

OBJECTIVE

There is no consensus on the optimal timing and specific brain MRI sequences in the evaluation and management of severe pediatric traumatic brain injury (TBI), and information on current practices is lacking. The authors performed a survey of MRI practices among sites participating in a multicenter study of severe pediatric TBI to provide information for designing future clinical trials using MRI to assess brain injury after severe pediatric TBI.

METHODS

Information on current imaging practices and resources was collected from 27 institutions participating in the Approaches and Decisions after Pediatric TBI Trial. Multiple-choice questions addressed the percentage of patients with TBI who have MRI studies, timing of MRI, MRI sequences used to investigate TBI, as well as the magnetic field strength of MR scanners used at the participating institutions and use of standardized MRI protocols for imaging after severe pediatric TBI.

RESULTS

Overall, the reported use of MRI in pediatric patients with severe TBI at participating sites was high, with 40% of sites indicating that they obtain MRI studies in > 95% of this patient population. Differences were observed in the frequency of MRI use between US and international sites, with the US sites obtaining MRI in a higher proportion of their pediatric patients with severe TBI (94% of US vs 44% of international sites reported MRI in at least 70% of patients with severe TBI). The reported timing and composition of MRI studies was highly variable across sites. Sixty percent of sites reported typically obtaining an MRI study within the first 7 days postinjury, with the remainder of responses distributed throughout the first 30-day postinjury period. Responses indicated that MRI sequences sensitive for diffuse axonal injury and ischemia are frequently obtained in patients with TBI, whereas perfusion imaging and spectroscopy techniques are less common.

CONCLUSIONS

Results from this survey suggest that despite the lack of consensus or guidelines, MRI is commonly obtained during the acute clinical setting after severe pediatric TBI. The variation in MRI practices highlights the need for additional studies to determine the utility, optimal timing, and composition of clinical MRI studies after TBI. The information in this survey describes current clinical MRI practices in children with severe TBI and identifies important challenges and objectives that should be considered when designing future studies.

Functional Outcomes in Moderate-to-Severe Traumatic Brain Injury Survivors

Introduction:

We aimed to analyze the functional outcomes based on the admission characteristics in individuals with moderate-to-severe traumatic brain injury (TBI) over a 5-year period.

Methods:

A retrospective cohort study was conducted to assess the cognitive, physical, and functional outcomes based on traditional and novel metrics used in potential outcome prediction.

Results:

A total of 201 participants were enrolled with a mean age of 31.9 ± 11.9 years. Glasgow Coma Score (GCS) at emergency department did not correlate with the functional independence measure (FIM) score or Ranchos Los Amigos (RLA) scores at discharge. The absolute functional gain was significantly higher in individuals who sustained TBI with RLA 4–5 (34.7 ± 18.8 vs. 26.5 ± 15.9, P = 0.006). Participants with RLA 4–5 on admission to rehabilitation showed good correlation with the absolute FIM gain. On multivariate regression analysis, only age (odds ratio 0.96; 95% confidence interval: 0.93–0.98; P = 0.005) was found to be the independent predictor of good functional outcome.

Conclusions:

Initial GCS is not a predictor of functional outcome in individuals who sustained TBI. Consideration of age and development of novel functional measures might be promising to predict the outcomes in individuals with moderate-to-severe TBI.

Moderate Traumatic Brain Injury: Clinical Characteristics and a Prognostic Model of 12-Month Outcome

BACKGROUND

Patients with moderate traumatic brain injury (TBI) often are studied together with patients with severe TBI, even though the expected outcome of the former is better. Therefore, we aimed to describe patient characteristics and 12-month outcomes, and to develop a prognostic model based on admission data, specifically for patients with moderate TBI.

METHODS

Patients with Glasgow Coma Scale scores of 9-13 and age ≥16 years were prospectively enrolled in 2 level I trauma centers in Europe. Glasgow Outcome Scale Extended (GOSE) score was assessed at 12 months. A prognostic model predicting moderate disability or worse (GOSE score ≤6), as opposed to a good recovery, was fitted by penalized regression. Model performance was evaluated by area under the curve of the receiver operating characteristics curves.

RESULTS

Of the 395 enrolled patients, 81% had intracranial lesions on head computed tomography, and 71% were admitted to an intensive care unit. At 12 months, 44% were moderately disabled or worse (GOSE score ≤6), whereas 8% were severely disabled and 6% died (GOSE score ≤4). Older age, lower Glasgow Coma Scale score, no day-of-injury alcohol intoxication, presence of a subdural hematoma, occurrence of hypoxia and/or hypotension, and preinjury disability were significant predictors of GOSE score ≤6 (area under the curve = 0.80).

CONCLUSIONS

Patients with moderate TBI exhibit characteristics of significant brain injury. Although few patients died or experienced severe disability, 44% did not experience good recovery, indicating that follow-up is needed. The model is a first step in development of prognostic models for moderate TBI that are valid across centers.

Efficiency and Limitations of Decompressive Craniectomy in Patients after Traumatic Brain Injury – Preliminary Results

Introduction: Decompressive craniectomy (DC) has been recently proven effective tier II therapeutic procedure in the treatment of refractory posttraumatic intracranial hypertension. However, its full potential and effectivity is yet to be described and this surgery remains controversial. The goals of our study include analysis of efficiency of DC and description of risk factors associated with unfavourable outcome.

Methods: 24 patients who underwent DC at the Clinic of Neurosurgery, JFM CU in Martin, during years 2015–2016 were prospectively observed. Selected demographic, clinical, and radiographic factors were analysed and compared with patient’s GOS (Glasgow Outcome Scale) at the time of their first ambulatory control (after 3.5 months in average).

Results: We observed mortality of 29.17 %. Good outcome (GOS 4–5) was achieved by 29.17 % of the patients as well. Preoperative GCS ≤ 5 (p = 0.049), intraventricular bleeding (p = 0.0268), midline shift ≥ 15 mm (p = 0.0067), and the volume of intracranial lesion (R = −0.41, p = 0.046), especially its extracerebral component (R = −0.46, p = 0.02), were identified as statistically significant negative prognostic factors.

Conclusion: DC is effective in the management of patients with traumatic brain injury. Good outcome is achieved by 29.17 % of the patients. Described negative prognostic factors (preoperative GCS ≤ 5, intraventricular bleeding, midline shift ≥ 15 mm, and increasing the volume of traumatic mass lesion) could help in targeting this surgery only to patients who are expected to benefit from it.

Clinical Epidemiology of Head Injury from Road-Traffic Trauma in a Developing Country in the Current Era

Objectives

Africa and other Asian low middle-income countries account for the greatest burden of the global road-traffic injury (RTI)-related head injury (HI). This study set out to describe the incidence, causation, and severity of RTI-related HI and associated injuries in a Nigerian academic neurosurgical practice.

Methods

This is a retrospective cross-sectional analysis of RTI-related HI from a prospective HI registry in an academic neurosurgery practice in Nigeria.

Results

All-terrain RTI accounted for 80.6% (833/1,034) of HI over a 7-year study period. All age groups were involved, mean 33.06 years (SD 18.30), mode 21–30, 231/833 (27.7%). The male:female ratio was 631:202, ≈3:1. The road trauma occurred exclusively from motorcycle-and motor-vehicle crash (MCC/MVC), MCC caused 56.8% (473/833) of these; the victims were vulnerable road users (VRU) in 74%, and >90% belong in the low socioeconomic class. Using the Glasgow Coma Scale grading, the HI was moderate/severe in 52%; loss of consciousness occurred in 93%, the Abbreviated Injury Severity-head > 3 in 74%, and computed tomography (CT) Rotterdam score > 3 in 52%. Significant extracranial injuries occurred in many organ systems, 421/833 (50.5%) having Injury Severity Score (ISS) > 25. Surgical lesions included extensive brain contusions in 157 (18.8%); acute extradural hematoma in 34 (4.1%); acute subdural hematoma in 32 (3.8%); and traumatic intracerebral hemorrhage in 27 (3.2%), but only 97 (11.6%) received operative care for various logistic reasons. The in-hospital outcome was good in 71.3% and poor in 28.7%; the statistically significant (p < 0.001) determinants of this outcome profile were the severity of the HI, the CT Rotterdam score, and the ISS.

Conclusion

In this study from Nigeria, RTI-related HI emanates from significant trauma to vulnerable road users and are caused exclusively by motorcycles and motor vehicles.

Cognitive Event-Related Potentials during the Sub-Acute Phase of Severe Traumatic Brain Injury and Their Relationship to Outcome

Predicting outcome in the early phase after severe traumatic brain injury (sTBI) is a major clinical challenge, particularly identifying patients with potential for good cognitive outcome. The current single-center prospective study aimed to explore presence and normalization of electroencephalography (EEG)-based event-related potentials (ERPs) in the early phase followings TBI, and their relationship to functional and cognitive outcome 6 months post-injury. Fourteen adult patients (eight males) with sTBI were recruited from the neurointensive care unit (mean age = 38.2 years [standard deviation (SD) = 14.7]; mean lowest Glasgow Coma Scale (GCS) score within first 24 h = 5.4, SD = 1.87). EEG recordings were conducted biweekly at three time-points applying an ERP paradigm encompassing a passive condition involving hearing their own name randomly interspersed between an unfamiliar name (UN), and an active condition with instruction to count their own name. Functional and cognitive outcome 6 months post-injury was measured with Glasgow Outcome Scale-Extended (GOSE) and neuropsychological tests of attention and memory. Ten patients demonstrated a significantly enhanced cognitive P3 in the active counting task compared with passive listening across recordings, and six presented with normalization of P3 in the counting task. Moreover, P3 amplitude to the counting task at the third time-point was positively correlated with both functional outcome (GOSE) and cognition (verbal learning, attentional set-shifting, and switching) 6 months post-injury. ERP can index cognitive capacities in the early phase following sTBI, and the cognitive P3 component in an active design is associated with functional and cognitive outcome, demonstrating that the cognitive P3 may yield valuable information of residual cognition and provide supplementary prognostic information.

Integrated Health Care Management of Moderate to Severe TBI in Older Patients-A Narrative Review

PURPOSE OF REVIEW

Traumatic brain injuries are common, especially within the elderly population, which is typically defined as age 65 and older. This narrative review aims at summarizing and critically evaluating important aspects of their health care management in covering the entire pathway from prehospital care to rehabilitation and beyond.

RECENT FINDINGS

The number of older patients with traumatic brain injury (TBI) is increasing, and there seem to be differences in all aspects of care along their pathway when compared to younger patients. Despite a higher mortality and a generally less favorable outcome, the current literature shows that older TBI patients have the potential to make significant improvements over time. More research is needed to evaluate the most efficient and integrated clinical pathway from prehospital interventions to rehabilitation as well as the optimal treatment of older TBI patients. Most importantly, they should not be denied access to specific treatments and therapies only based on age.

Formulation of a Three-Tier Cisternal Grade as a Predictor of In-Hospital Outcome from a Prospective Study of Patients with Traumatic Intracranial Hematoma

OBJECTIVE

Outcome prediction is of paramount importance in traumatic brain injury. Our objective of conducting this prospective study was to identify the predictors needed to formulate a prognostic score.

METHODS

Clinical and radiologic characteristics of 100 patients with traumatic intracranial hematoma were analyzed. Key measurements were taken in the midbrain and pontine regions and the status of each of the 9 basal cisterns was noted, by giving a score of 1 if they were visible and 0 if not. All the predictors were analyzed for outcome.

RESULTS

Total cisternal score was found to be an independent predictor of outcome. A grade was formulated by dividing the score into 3 levels.

CONCLUSIONS

The model based on cisternal status described in the study is technically simple and conveys the information regarding the outcome to the treating neurosurgeon. Because the score obtained seems to have low interobserver variation, we believe that it can be a useful tool not only in recording data in case files and interphysician communication but also in research into traumatic brain injury.

Prediction of behavioural and cognitive deficits in patients with traumatic brain injury at an acute rehabilitation setting

OBJECTIVE

The goal of this study was to identify factors that would predict short-term neuropsychological outcome in patients with traumatic brain injury (TBI) hospitalized in an acute rehabilitation setting.

METHODS

Data was collected in the context of an acute early rehabilitation setting of a trauma centre. A brief neuropsychological assessment was carried out for 348 patients within a month following their trauma.

RESULTS

Length of post-traumatic amnesia (PTA) was the best predictor of behavioural, memory and executive function variables within a month post TBI. The odds of being agitated, labile, irritable and disinhibited at one month post trauma were almost six times higher for those with PTA that lasted more than 7 days compared to those with a PTA of less than 24 hours. Also, the odds of having a higher mental manipulation score (less significant executive function impairment) were almost two times lower for those with frontal lesions, and three to six times lower for those with PTA of more than 24 hours. In addition, TBI severity, education and age were considered good predictors of some aspects of neuropsychological outcome.

CONCLUSION

This model may help clinicians and administrators recognize the probable post-traumatic deficits as quickly as possible and to plan interventions as well as post-acute discharge orientation accordingly and early on.

Comparison of Two Predictive Models for Short-Term Mortality in Patients after Severe Traumatic Brain Injury

The Glasgow Coma Scale (GCS) and the Abbreviated Injury Score of the head region (HAIS) are validated prognostic factors in traumatic brain injury (TBI). The aim of this study was to compare the prognostic performance of an alternative predictive model including motor GCS, pupillary reactivity, age, HAIS, and presence of multi-trauma for short-term mortality with a reference predictive model including motor GCS, pupil reaction, and age (IMPACT core model). A secondary analysis of a prospective epidemiological cohort study in Switzerland including patients after severe TBI (HAIS >3) with the outcome death at 14 days was performed. Performance of prediction, accuracy of discrimination (area under the receiver operating characteristic curve [AUROC]), calibration, and validity of the two predictive models were investigated. The cohort included 808 patients (median age, 56; interquartile range, 33-71), median GCS at hospital admission 3 (3-14), abnormal pupil reaction 29%, with a death rate of 29.7% at 14 days. The alternative predictive model had a higher accuracy of discrimination to predict death at 14 days than the reference predictive model (AUROC 0.852, 95% confidence interval [CI] 0.824-0.880 vs. AUROC 0.826, 95% CI 0.795-0.857; p < 0.0001). The alternative predictive model had an equivalent calibration, compared with the reference predictive model Hosmer-Lemeshow p values (Chi2 8.52, Hosmer-Lemeshow p = 0.345 vs. Chi2 8.66, Hosmer-Lemeshow p = 0.372). The optimism-corrected value of AUROC for the alternative predictive model was 0.845. After severe TBI, a higher performance of prediction for short-term mortality was observed with the alternative predictive model, compared with the reference predictive model.

Comparing health-related quality of life of Dutch and Chinese patients with traumatic brain injury: do cultural differences play a role?

BACKGROUND

There is growing interest in health related quality of life (HRQoL) as an outcome measure in international trials. However, there might be differences in the conceptualization of HRQoL across different socio-cultural groups. The objectives of current study were: (I) to compare HRQoL, measured with the short form (SF)-36 of Dutch and Chinese traumatic brain injury (TBI) patients 1 year after injury and; (II) to assess whether differences in SF-36 profiles could be explained by cultural differences in HRQoL conceptualization. TBI patients are of particular interest because this is an important cause of diverse impairments and disabilities in functional, physical, emotional, cognitive, and social domains that may drastically reduce HRQoL.

METHODS

A prospective cohort study on adult TBI patients in the Netherlands (RUBICS) and a retrospective cohort study in China were used to compare HRQoL 1 year post-injury. Differences on subscales were assessed with the Mann-Whitney U-test. The internal consistency, interscale correlations, item-internal consistency and item-discriminate validity of Dutch and Chinese SF-36 profiles were examined. Confirmatory factor analysis was performed to assess whether Dutch and Chinese data fitted the SF-36 two factor-model (physical and mental construct).

RESULTS

Four hundred forty seven Dutch and 173 Chinese TBI patients were included. Dutch patients obtained significantly higher scores on role limitations due to emotional problems (p < .001) and general health (p < .001), while Chinese patients obtained significantly higher scores on physical functioning (p < .001) and bodily pain (p = .001). Scores on these subscales were not explained by cultural differences in conceptualization, since item- and scale statistics were all sufficient. However, differences among Dutch and Chinese patients were found in the conceptualization of the domains vitality, mental health and social functioning.

CONCLUSIONS

One year after TBI, Dutch and Chinese patients reported a different pattern of HRQoL. Further, there might be cultural differences in the conceptualization of some of the SF-36 subscales, which has implications for outcome evaluation in multi-national trials.

Predictors of mortality in patients with isolated severe traumatic brain injury

BACKGROUND

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Many prognostic models predicting mortality in patients with TBI were developed, which also include patients with mild or moderate TBI and patients who suffered major extracranial injuries.

METHODS

From a prospective database, we conducted a retrospective medical chart review covering the period between January 2000 and December 2012 of patients with isolated severe TBI (Abbreviated Injury Score for head, AIS_H ≥ 3) without extracranial injuries, who were intubated in the field using the rapid sequence intubation method and were of age 16 or more. Prehospital vital signs, Injury Severity Score (ISS) and laboratory tests were compared in two study groups: survivors (n = 25) and non-survivors (n = 27). Selected variables identified during univariate analysis (p < 0.1) were then subjected to multivariate analysis logistic regression model.

RESULTS

Univariate analysis showed that in-hospital mortality was statistically significantly associated with male sex (p = 0.040), ISS (p = 0.005) and mydriasis (p = 0.012). For predicting mortality, area under the curve (AUC) was calculated: for ISS 0.76 (95 % confidence interval, CI; 0.63-0.90; p < 0.001) and for initial Glasgow Coma Scale (GCS) 0.64 (95 % CI, 0.49-0.80, p = 0.079). In the multivariate analysis, ISS (odds ratio, OR; 1.19, 95 % CI, 1.06-1.35; p = 0.004) and mydriasis (OR, 5.73; 95 % CI, 1.06-30.88; p = 0.042) were identified as independent risk factors for in-hospital mortality. The AUC for the regression model was 0.83 (95 % CI, 0.71-0.94; p < 0.001).

CONCLUSIONS

In prehospital intubated patients with isolated severe TBI only ISS and mydriasis were found to be independent predictors of in-hospital mortality.

Glial fibrillary acidic protein as a biomarker in severe traumatic brain injury patients: a prospective cohort study

Introduction

Glial fibrillary acidic protein (GFAP) may serve as a serum marker of traumatic brain injury (TBI) that can be used to monitor biochemical changes in patients and gauge the response to treatment. However, the temporal profile of serum GFAP in the acute period of brain injury and the associated utility for outcome prediction has not been elucidated.

Methods

We conducted a prospective longitudinal cohort study of consecutive severe TBI patients in a local tertiary neurotrauma center in Shanghai, China, between March 2011 and September 2014. All patients were monitored and managed with a standardized protocol with inclusion of hypothermia and other intensive care treatments. Serum specimens were collected on admission and then daily for the first 5 days. GFAP levels were measured using enzyme-linked immunosorbent assay techniques. Patient outcome was assessed at 6 months post injury with the Glasgow Outcome Scale and further grouped into death versus survival and unfavorable versus favorable.

Results

A total of 67 patients were enrolled in the study. The mean time from injury to admission was 2.6 hours, and the median admission Glasgow Coma Scale score was 6. Compared with healthy subjects, patients with severe TBI had increased GFAP levels on admission and over the subsequent 5 days post injury. Serum GFAP levels showed a gradual reduction from admission to day 3, and then rebounded on day 4 when hypothermia was discontinued with slow rewarming. GFAP levels were significantly higher in patients who died or had an unfavorable outcome across all time points than in those who were alive or had a favorable outcome. Results of receiver operating characteristic curve analysis indicated that serum GFAP at each time point could predict neurological outcome at 6 months. The areas under the curve for GFAP on admission were 0.761 for death and 0.823 for unfavorable outcome, which were higher than those for clinical variables such as age, Glasgow Coma Scale score, and pupil reactions.

Conclusions

Serum GFAP levels on admission and during the first 5 days of injury were increased in patients with severe TBI and were predictive of neurological outcome at 6 months.

Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score)

BACKGROUND

Early computerized tomography (CT) abnormalities are important predictors of outcome after traumatic brain injury (TBI).

OBJECTIVE

To develop a novel CT scoring system (Helsinki CT score) and to compare it with the Marshall CT classification and the Rotterdam CT score in predicting long-term outcome of patients with TBI.

METHODS

Eight hundred sixty-nine consecutive TBI patients were included in this open-cohort, retrospective, single-center study. Logistic regression was used to develop the Helsinki CT score. The scores from the Marshall, Rotterdam, and Helsinki CT scoring methods were added to a clinical model based on age, motor score, and pupils to evaluate their value in predicting outcome. Internal validity was assessed by a bootstrap technique and expressed as area under the curve (AUC). Outcome was 6-month unfavorable neurological outcome and mortality.

RESULTS

Variables included in the Helsinki CT score were bleeding type and size, intraventricular hemorrhage, and suprasellar cisterns. In the present data set, the performance of the Helsinki CT score was superior to that of the Marshall CT and Rotterdam CT scores (AUC, 0.74-0.75 vs 0.63-0.70; P < .001). Addition of the Helsinki CT score modestly increased prognostic performance of the clinical model (AUC neurological outcome +0.02 [P = .002]; AUC mortality, +0.01 [P = .21]). In contrast, the Marshall and Rotterdam CT scores were of no additional predictive value to the clinical model (P > .05).

CONCLUSION

Use of the novel Helsinki CT score improved outcome prediction accuracy, and the Helsinki CT score is a feasible alternative to the Rotterdam and Marshall CT systems. External validation of the Helsinki CT score is advocated to show generalizability.

What's new in the management of traumatic brain injury on neuro ICU?

PURPOSE OF REVIEW

In recent years, we have begun to better understand how to monitor the injured brain, look for less common complications and importantly, reduce unnecessary and potentially harmful intervention. However, the lack of consensus regarding triggers for intervention, best neuromonitoring techniques and standardization of therapeutic approach is in need of more careful study. This review covers the most recent evidence within this exciting and dynamic field.

RECENT FINDINGS

The role of intracranial pressure monitoring has been challenged; however, it still remains a cornerstone in the management of the severely brain-injured patient and should be used to compliment other techniques, such as clinical examination and serial imaging.The use of multimodal monitoring continues to be refined and it may be possible to use them to guide novel brain resuscitation techniques, such as the use of exogenous lactate supplementation in the future.

SUMMARY

Neurocritical care management of traumatic brain injury continues to evolve. However, it is important not to use a 'one-treatment-fits-all' approach, and perhaps look to use targeted therapies to individualize treatment.

Performance of IMPACT, CRASH and Nijmegen models in predicting six month outcome of patients with severe or moderate TBI: an external validation study

Background

External validation on different TBI populations is important in order to assess the generalizability of prognostic models to different settings. We aimed to externally validate recently developed models for prediction of six month unfavourable outcome and six month mortality.

Methods

The International Neurotrauma Research Organization – Prehospital dataset (INRO-PH) was collected within an observational study between 2009-2012 in Austria and includes 778 patients with TBI of GCS < = 12. Three sets of prognostic models were externally validated: the IMPACT core and extended models, CRASH basic models and the Nijmegen models developed by Jacobs et al – all for prediction of six month unfavourable outcome and six month mortality. The external validity of the models was assessed by discrimination (Area Under the receiver operating characteristic Curve, AUC) and calibration (calibration statistics and plots).

Results

Median age in the validation cohort was 50 years and 44% had an admission GSC motor score of 1-3. Six-month mortality was 27%. Mortality could better be predicted (AUCs around 0.85) than unfavourable outcome (AUCs around 0.80). Calibration plots showed that the observed outcomes were systematically better than was predicted for all models considered. The best performance was noted for the original Nijmegen model, but refitting led to similar performance for the IMPACT Extended, CRASH Basic, and Nijmegen models.

Conclusions

In conclusion, all the prognostic models we validated in this study possess good discriminative ability for prediction of six month outcome in patients with moderate or severe TBI but outcomes were systemically better than predicted. After adjustment for this under prediction in locally adapted models, these may well be used for recent TBI patients.