Predicting 14-day mortality after severe traumatic brain injury: application of the IMPACT models in the brain trauma foundation TBI-trac® New York State database

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

Importance

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.

Objectives

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.

Design, Setting, and Participants

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.

Exposures

Pediatric severe TBI with an MRI scan performed as part of clinical care.

Main Outcomes and Measures

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.

Results

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).

Conclusions and Relevance

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.

Refining outcome prediction after traumatic brain injury with machine learning algorithms

Outcome after traumatic brain injury (TBI) is typically assessed using the Glasgow outcome scale extended (GOSE) with levels from 1 (death) to 8 (upper good recovery). Outcome prediction has classically been dichotomized into either dead/alive or favorable/unfavorable outcome. Binary outcome prediction models limit the possibility of detecting subtle yet significant improvements. We set out to explore different machine learning methods with the purpose of mapping their predictions to the full 8 grade scale GOSE following TBI. The models were set up using the variables: age, GCS-motor score, pupillary reaction, and Marshall CT score. For model setup and internal validation, a total of 866 patients could be included. For external validation, a cohort of 369 patients were included from Leuven, Belgium, and a cohort of 573 patients from the US multi-center ProTECT III study. Our findings indicate that proportional odds logistic regression (POLR), random forest regression, and a neural network model achieved accuracy values of 0.3-0.35 when applied to internal data, compared to the random baseline which is 0.125 for eight categories. The models demonstrated satisfactory performance during external validation in the data from Leuven, however, their performance were not satisfactory when applied to the ProTECT III dataset.

Identifying Age-Specific Risk Factors for Poor Outcomes After Trauma With Machine Learning

INTRODUCTION

Risk stratification for poor outcomes is not currently age-specific. Risk stratification of older patients based on observational cohorts primarily composed of young patients may result in suboptimal clinical care and inaccurate quality benchmarking. We assessed two hypotheses. First, we hypothesized that risk factors for poor outcomes after trauma are age-dependent and, second, that the relative importance of various risk factors are also age-dependent.

METHODS

A cohort study of severely injured adult trauma patients admitted to the intensive care unit 2014-2018 was performed using trauma registry data. Random forest algorithms predicting poor outcomes (death or complication) were built and validated using three cohorts: (1) patients of all ages, (2) younger patients, and (3) older patients. Older patients were defined as aged 55 y or more to maintain consistency with prior trauma literature. Complications assessed included acute renal failure, acute respiratory distress syndrome, cardiac arrest, unplanned intubation, unplanned intensive care unit admission, and unplanned return to the operating room, as defined by the trauma quality improvement program. Mean decrease in model accuracy (MDA), if each variable was removed and scaled to a Z-score, was calculated. MDA change ≥4 standard deviations between age cohorts was considered significant.

RESULTS

Of 5489 patients, 25% were older. Poor outcomes occurred in 12% of younger and 33% of older patients. Head injury was the most important predictor of poor outcome in all cohorts. In the full cohort, age was the most important predictor of poor outcomes after head injury. Within age cohorts, the most important predictors of poor outcomes, after head injury, were surgery requirement in younger patients and arrival Glasgow Coma Scale in older patients. Compared to younger patients, head injury and arrival Glasgow Coma Scale had the greatest increase in importance for older patients, while systolic blood pressure had the greatest decrease in importance.

CONCLUSIONS

Supervised machine learning identified differences in risk factors and their relative associations with poor outcomes based on age. Age-specific models may improve hospital benchmarking and identify quality improvement targets for older trauma patients.

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

BACKGROUND

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 6 months after moderate or severe TBI.

METHODS

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 24 h. 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 6 months 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 the IMPACT model assessed by the area under the curve.

RESULTS

Of the 93 patients with inclusion and exclusion criteria, 80 patients reached the primary end point. At 6 months, 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).

CONCLUSIONS

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.

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.

Diagnostic Value of the Optic Nerve Sheath in the Diagnosis of Increased Intracranial Pressure in Traumatic Brain Patients

Background

Increased intracranial pressure (ICP) is a modifiable secondary injury that is associated with poor outcomes in patients with traumatic brain injuries (TBIs). Therefore, the present study was conducted with the aim of determining the ICP of TBI patients by measuring the thickness of the optic nerve sheath diameter (ONSD).

Materials and Methods

The present cross-sectional study was conducted on 220 patients with severe TBI that referred to Khatam-al-Anbya Hospital in Zahedan in 2021. The measurement of ONSD was performed by ultrasonography.

Results

The results of this study revealed that 22.7% of TBI patients had high ICP. The mean of right and left ONSD in patients with normal ICP was 3.85 ± 0.83 and 3.85 ± 0.82 mm, respectively, and was significantly lower than that of patients with abnormal ICP (high ICP) with the mean of 3.85 ± 0.82 and 6.12 ± 0.84 mm, respectively (P value <.001). In addition, the right ONSD with the cutoff point of 5.13 mm, the sensitivity of 84%, and the specificity of 95.29% and the left ONSD with the cutoff point of 5.24 mm, the sensitivity of 90%, and the specificity of 95.88% had a significant diagnostic value in the diagnosis of high ICP (P value <.05).

Conclusion

The findings of the present study indicated that the measurement of ONSD is a cost-effective and minimally invasive procedure with a higher accuracy in diagnosing high ICP in TBI patients.

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).

Mortality prediction in severe traumatic brain injury using traditional and machine learning algorithms

Prognostic prediction of traumatic brain injury (TBI) in patients is crucial in clinical decision and health care policy making. This study aimed to develop and validate prediction models for in-hospital mortality after severe traumatic brain injury (sTBI). We developed and validated logistic regression (LR), LASSO regression, and machine learning (ML) algorithms including support vector machines (SVM) and XGBoost models. Fifty-four candidate predictors were included. Model performance was expressed in terms of discrimination (C-statistic) and calibration (intercept and slope). For model development, 2804 patients with sTBI in the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China Registry study were included. External validation was performed in 1113 patients with sTBI in the CENTER-TBI European Registry study. XGBoost achieved high discrimination in mortality prediction, and it outperformed logistic and LASSO regression. The XGBoost model established in this study also outperformed prediction models currently available, including the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) core and International Mission for Prognosis and Analysis of Clinical Trials (CRASH) basic models. When including 54 variables, XGBoost and SVM reached C-statistics of 0.87 (95% confidence interval [CI]: 0.81-0.92) and 0.85 (95% CI: 0.79-0.90) at internal validation, and 0.88 (95% CI: 0.87-0.88) and 0.86 (95% CI: 0.85-0.87) at external validation, respectively. A simplified version of XGBoost and SVM using 26 variables selected by recursive feature elimination (RFE) reached C-statistics of 0.87 (95% CI: 0.82-0.92) and 0.86 (95% CI: 0.80-0.91) at internal validation, and 0.87 (95% CI: 0.87-0.88) and 0.87 (95% CI: 0.86-0.87) at external validation, respectively. However, when the number of variables included decreased, the difference between ML and LR diminished. All the prediction models can be accessed via a web-based calculator. Glasgow Coma Scale (GCS) score, age, pupillary light reflex, Injury Severity Score (ISS) for brain region, and the presence of acute subdural hematoma were the five strongest predictors for mortality prediction. The study showed that ML techniques such as XGBoost may capture information hidden in demographic and clinical predictors of patients with sTBI and yield more precise predictions compared with LR approaches.

Effect of Fever on the Clinical Outcomes of Traumatic Brain Injury by Age

Background and objective: Fever is a common symptom in patients with traumatic brain injury (TBI). However, the effect of fever on the clinical outcomes of patients with TBI is not well characterized. Our study aims to determine the impact of fever on the clinical outcomes of patients with TBI and test the interaction effect of fever on study outcomes according to age group. Materials and methods: Our retrospective study included adult patients with TBI who were transported to a level 1 trauma center by the emergency medical services (EMS) team. The main exposure is fever, defined as a body temperature of 38 °C or above, in the emergency department (ED). The primary outcome was mortality at hospital discharge. We conducted a multivariable logistic regression analysis to estimate the effect sizes of fever on study outcomes. We also conducted an interaction analysis between fever and age group on study outcomes. Results: In multivariable logistic regression analysis, patients with TBI who had fever showed no significant difference in mortality at hospital discharge (aOR, 95% CIs: 1.24 (0.57−3.02)). Fever significantly increased the mortality of elderly patients (>65 years) with TBI (1.39 (1.13−1.50)), whereas there was no significant effect on mortality in younger patients (18−64 years) (0.85 (0.51−1.54)). Conclusions: Fever was associated with mortality only in elderly patients with TBI.

External validation of the Glasgow coma scale-pupils in low- to middle-income country patients with traumatic brain injury: Could “motor score-pupil” have higher prognostic value?

Background:

The objective of this study is to validate the admission Glasgow coma scale (GCS) associated with pupil response (GCS-P) to predict traumatic brain injury (TBI) patient’s outcomes in a low- to middle-income country and to compare its performance with that of a simplified model combining the better motor response of the GCS and the pupilar response (MS-P).

Methods:

This is a prospective cohort of patients with TBI in a tertiary trauma reference center in Brazil. Predictive values of the GCS, GCS-P, and MS-P were evaluated and compared for 14 day and in-hospital mortality outcomes and length of hospital stay (LHS).

Results:

The study enrolled 447 patients. MS-P demonstrated better discriminative ability than GCS to predict mortality (AUC 0.736 × 0.658; P < 0.001) and higher AUC than GCS-P (0.736 × 0.704, respectively; P = 0.073). For hospital mortality, MS-P demonstrated better discrimination than GCS (AUC, 0.750 × 0.682; P < 0.001) and higher AUC than GCS-P (0.750 × 0.714; P = 0.027). Both scores were good predictors of LHS (r2 = 0.084 [GCS-P] × 0.079 [GCS] × 0.072 [MS-P]).

Conclusion:

The predictive value of the GCS, GCS-P, and MS-P scales was demonstrated, thus contributing to its external validation in low- to middle-income country.

Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation

Survivors of traumatic brain injury (TBI) have an unpredictable clinical course. This unpredictability makes clinical resource allocation for clinicians and anticipatory guidance for patients difficult. Historically, experienced clinicians and traditional statistical models have insufficiently considered all available clinical information to predict functional outcomes for a TBI patient. Here, we harness artificial intelligence and apply machine learning and statistical models to predict the Functional Independence Measure (FIM) scores after rehabilitation for traumatic brain injury (TBI) patients. Tree-based algorithmic analysis of 629 TBI patients admitted to a large acute rehabilitation facility showed statistically significant improvement in motor and cognitive FIM scores at discharge.

Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis

This review aimed to analyze the results of investigations that performed external validation or that compared prognostic models to identify the models and their variations that showed the best performance in predicting mortality, survival, and unfavorable outcome after severe traumatic brain injury. Pubmed, Embase, Scopus, Web of Science, Cumulative Index to Nursing and Allied Health Literature, Google Scholar, TROVE, and Open Grey databases were searched. A total of 1616 studies were identified and screened, and 15 studies were subsequently included for analysis after applying the selection criteria. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) models were the most externally validated among studies of severe traumatic brain injury. The results of the review showed that most publications encountered an area under the curve ≥ 0.70. The area under the curve meta-analysis showed similarity between the CRASH and IMPACT models and their variations for predicting mortality and unfavorable outcomes. Calibration results showed that the variations of CRASH and IMPACT models demonstrated adequate calibration in most studies for both outcomes, but without a clear indication of uncertainties in the evaluations of these models. Based on the results of this meta-analysis, the choice of prognostic models for clinical application may depend on the availability of predictors, characteristics of the population, and trauma care services.

Rubiano A, G. Kolias A, Silva Paiva W. Evaluation of Computed Tomography Scoring Systems in the Prediction of Short-Term Mortality in Traumatic Brain Injury Patients from a Low- to Middle-Income Country

The present study aims to evaluate the accuracy of the prognostic discrimination and prediction of the short-term mortality of the Marshall computed tomography (CT) classification and Rotterdam and Helsinki CT scores in a cohort of TBI patients from a low- to middle-income country. This is a post hoc analysis of a previously conducted prospective cohort study conducted in a university-associated, tertiary-level hospital that serves a population of >12 million in Brazil. Marshall CT class, Rotterdam and Helsinki scores, and their components were evaluated in the prediction of 14-day and in-hospital mortality using Nagelkerk's pseudo-R² and area under the receiver operating characteristic curve. Multi-variate regression was performed using known outcome predictors (age, Glasgow Coma Scale, pupil response, hypoxia, hypotension, and hemoglobin values) to evaluate the increase in variance explained when adding each of the CT classification systems. Four hundred forty-seven patients were included. Mean age of the patient cohort was 40 (standard deviation, 17.83) years, and 85.5% were male. Marshall CT class was the least accurate model, showing pseudo-R² values equal to 0.122 for 14-day mortality and 0.057 for in-hospital mortality, whereas Rotterdam CT scores were 0.245 and 0.194 and Helsinki CT scores were 0.264 and 0.229. The AUC confirms the best prediction of the Rotterdam and Helsinki CT scores regarding the Marshall CT class, which presented greater discriminative ability. When associated with known outcome predictors, Marshall CT class and Rotterdam and Helsinki CT scores showed an increase in the explained variance of 2%, 13.4%, and 21.6%, respectively. In this study, Rotterdam and Helsinki scores were more accurate models in predicting short-term mortality. The study denotes a contribution to the process of external validation of the scores and may collaborate with the best risk stratification for patients with this important pathology.

Long-term outcome after severe traumatic brain injury: a systematic literature review

BACKGROUND

Expectation of long-term outcome is an important factor in treatment decision-making after severe traumatic brain injury (sTBI). Conclusive long-term outcome data substantiating these decisions is nowadays lacking. This systematic review aimed to provide an overview of the scientific literature on long-term outcome after sTBI.

METHODS

A systematic search was conducted using PubMed from 2008 to 2020. Studies were included when reporting long-term outcome ≥ 2 years after sTBI (GCS 3-8 or AIS head score ≥ 4), using standardized outcome measures. Study quality and risk of bias were assessed using the QUIPS tool.

RESULTS

Twenty observational studies were included. Studies showed substantial variation in study objectives and study methodology. GOS-E (n = 12) and GOS (n = 8) were the most frequently used outcome measures. Mortality was reported in 46% of patients (range 18-75%). Unfavourable outcome rates ranged from 29 to 100% and full recovery was seen in 21-27% of patients. Most surviving patients reported SF-36 scores lower than the general population.

CONCLUSION

Literature on long-term outcome after sTBI was limited and heterogeneous. Mortality and unfavourable outcome rates were high and persisting sequelae on multiple domains common. Nonetheless, a considerable proportion of survivors achieved favourable outcome. Future studies should incorporate standardized multidimensional and temporal long-term outcome measures to strengthen the evidence-base for acute and subacute decision-making.

HIGHLIGHTS

1. Expectation of long-term outcome is an important factor in treatment decision-making for patients with severe traumatic brain injury (sTBI). 2. Favourable outcome and full recovery after sTBI are possible, but mortality and unfavourable outcome rates are high. 3. sTBI survivors are likely to suffer from a wide range of long-term consequences, underscoring the need for long-term and multi-modality outcome assessment in future studies. 4. The quality of the scientific literature on long-term outcome after sTBI can and should be improved to advance treatment decision-making.

Prognosis in moderate-severe traumatic brain injury in a Swedish cohort and external validation of the IMPACT models

Background

A major challenge in management of traumatic brain injury (TBI) is to assess the heterogeneity of TBI pathology and outcome prediction. A reliable outcome prediction would have both great value for the healthcare provider, but also for the patients and their relatives. A well-known prediction model is the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic calculator. The aim of this study was to externally validate all three modules of the IMPACT calculator on TBI patients admitted to Uppsala University hospital (UUH).

Method

TBI patients admitted to UUH are continuously enrolled into the Uppsala neurointensive care unit (NICU) TBI Uppsala Clinical Research (UCR) quality register. The register contains both clinical and demographic data, radiological evaluations, and outcome assessments based on the extended Glasgow outcome scale extended (GOSE) performed at 6 months to 1 year. In this study, we included 635 patients with severe TBI admitted during 2008–2020. We used IMPACT core parameters: age, motor score, and pupillary reaction.

Results

The patients had a median age of 56 (range 18–93), 142 female and 478 male. Using the IMPACT Core model to predict outcome resulted in an AUC of 0.85 for mortality and 0.79 for unfavorable outcome. The CT module did not increase AUC for mortality and slightly decreased AUC for unfavorable outcome to 0.78. However, the lab module increased AUC for mortality to 0.89 but slightly decreased for unfavorable outcome to 0.76. Comparing the predicted risk to actual outcomes, we found that all three models correctly predicted low risk of mortality in the surviving group of GOSE 2–8. However, it produced a greater variance of predicted risk in the GOSE 1 group, denoting general underprediction of risk. Regarding unfavorable outcome, all models once again underestimated the risk in the GOSE 3–4 groups, but correctly predicts low risk in GOSE 5–8.

Conclusions

The results of our study are in line with previous findings from centers with modern TBI care using the IMPACT model, in that the model provides adequate prediction for mortality and unfavorable outcome. However, it should be noted that the prediction is limited to 6 months outcome and not longer time interval.

Hospital Volume-Outcome Relationship in Severe Traumatic Brain Injury: A Nationwide Observational Study in Japan

OBJECTIVE

The hospital volume-outcome relationship in patients with severe traumatic brain injury (TBI) remains unclear. This study investigated the association between the volume of patients with severe TBI and in-hospital mortality.

METHODS

This observational study identified patients with severe TBI (Glasgow Coma Scale score <9 and Abbreviated Injury Scale head score ≥3) from the Japan Trauma Databank (2010-2018). Hospitals were grouped on the basis of annual patient volume as follows: low-volume (4-19 patients/year); middle-volume (20-35 patients/year); and high-volume (36-51 patients/year) groups. The association between hospital volume categories and in-hospital mortality was examined using a multivariate mixed-effect logistic regression analysis. A subgroup analysis was performed based on the presence of severe extracranial injuries.

RESULTS

A total of 11,344 patients from 64 hospitals were included. The median age of the patients was 57 years (interquartile range, 40-77), and 7933 (70.0%) patients were men. A total of 4879 (43.1%) patients died in the hospital. The medium-volume (adjusted odds ratio [OR], 0.76; 95% confidence interval [CI], 0.62-0.93) and high-volume (adjusted OR, 0.69; 95% CI, 0.52-0.94) groups were significantly associated with lower in-hospital mortality. The subgroup analysis revealed that the medium-volume (adjusted OR, 0.70; 95% CI, 0.54-0.92) and high-volume (adjusted OR, 0.64; 95% CI, 0.42-0.96) groups were significantly associated with lower in-hospital mortality for isolated TBI patients.

CONCLUSIONS

Higher hospital volumes were significantly associated with lower in-hospital mortality after severe TBI. Regionalization and referral to higher-volume hospitals are beneficial for severe TBI patients.

Machine Learning Model to Predict Ventilator Associated Pneumonia in patients with Traumatic Brain Injury: The C.5 Decision Tree Approach

BACKGROUND

There is paucity in the literature to predict the occurrence of Ventilator Associated Pneumonia (VAP) in patients with Traumatic Brain Injury (TBI). We aimed to build a C.5. Decision Tree (C.5 DT) machine learning model to predict VAP in patients with moderate to severe TBI.

METHODS

This was a retrospective study including all adult patients who were hospitalized with TBI plus head abbreviated injury scale (AIS) ≥ 3 and were mechanically ventilated in a level 1 trauma center between 2014 and 2019.

RESULTS

A total of 772 eligible patients were enrolled, of them 169 had VAP (22%). The C.5 DT model achieved moderate performance with 83.5% accuracy, 80.5% area under the curve, 71% precision, 86% negative predictive value, 43% sensitivity, 95% specificity and 54% F-score. Out of 24 predictors, C.5 DT identified 5 variables predicting occurrence of VAP post-moderate to severe TBI (Time from injury to emergency department arrival, blood transfusion during resuscitation, comorbidities, Injury Severity Score and pneumothorax).

CONCLUSIONS

This study could serve as baseline for the quest of predicting VAP in patients with TBI through the utilization of C.5. DT machine learning approach. This model helps provide timely decision support to caregivers to improve patient's outcomes.

Mismatch between midline shift and hematoma thickness as a prognostic factor of mortality in patients sustaining acute subdural hematoma

Background

Acute subdural hematoma (ASDH) is a traumatic lesion commonly found secondary to traumatic brain injury. Radiological findings on CT, such as hematoma thickness (HT) and structures midline shift (MLS), have an important prognostic role in this disease. The relationship between HT and MLS has been rarely studied in the literature. Thus, this study aimed to assess the prognostic accuracy of the difference between MLS and HT for acute outcomes in patients with ASDH in a low-income to middle-income country.

Methods

This was a post-hoc analysis of a prospective cohort study conducted in a university-associated tertiary-level hospital in Brazil. The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis) statement guidelines were followed. The difference values between MLS and HT (Zumkeller index, ZI) were divided into three categories (<0.00, 0.01–3, and >3). Logistic regression analyses were performed to reveal the OR of categorized ZI in predicting primary outcome measures. A Cox regression was also performed and the results were presented through HR. The discriminative ability of three multivariate models including clinical and radiological variables (ZI, Rotterdam score, and Helsinki score) was demonstrated.

Results

A total of 114 patients were included. Logistic regression demonstrated an OR value equal to 8.12 for the ZI >3 category (OR 8.12, 95% CI 1.16 to 40.01; p=0.01), which proved to be an independent predictor of mortality in the adjusted model for surgical intervention, age, and Glasgow Coma Scale (GCS) score. Cox regression analysis demonstrated that this category was associated with 14-day survival (HR 2.92, 95% CI 1.38 to 6.16; p=0.005). A multivariate analysis performed for three models including age and GCS with categorized ZI or Helsinki or Rotterdam score demonstrated area under the receiver operating characteristic curve values of 0.745, 0.767, and 0.808, respectively.

Conclusions

The present study highlights the potential usefulness of the difference between MLS and HT as a prognostic variable in patients with ASDH.

Level of evidence

Level III, epidemiological study.

Development of a Mortality Prediction Tool in Pediatric Severe Traumatic Brain Injury

Severe traumatic brain injury (sTBI) is a leading cause of pediatric death, yet outcomes remain difficult to predict. The goal of this study was to develop a predictive mortality tool in pediatric sTBI. We retrospectively analyzed 196 patients with sTBI (pre-sedation Glasgow Coma Scale [GCS] score <8 and head Maximum Abbreviated Injury Scale (MAIS) score >4) admitted to a pediatric intensive care unit (PICU). Overall, 56 patients with sTBI (29%) died during PICU stay. Of the survivors, 88 (63%) were discharged home, and 52 (37%) went to an acute care or rehabilitation facility. Receiver operating characteristic (ROC) curve analyses of admission variables showed that pre-sedation GCS score, Rotterdam computed tomography (CT) score, and partial thromboplastin time (PTT) were fair predictors of PICU mortality (area under the curve [AUC] = 0.79, 0.76, and 0.75, respectively; p < 0.001). Cutoff values best associated with PICU mortality were pre-sedation GCS score <5 (sensitivity = 0.91, specificity = 0.54), Rotterdam CT score >3 (sensitivity = 0.84, specificity = 0.53), and PTT >34.5 sec (sensitivity = 0.69 specificity = 0.67). Combining pre-sedation GCS score, Rotterdam CT score, and PTT in ROC curve analysis yielded an excellent predictor of PICU mortality (AUC = 0.91). In summary, pre-sedation GCS score (<5), Rotterdam CT score (>3), and PTT (>34.5 sec) obtained on hospital admission were fair predictors of PICU mortality, ranked highest to lowest. Combining these three admission variables resulted in an excellent pediatric sTBI mortality prediction tool for further prospective validation.

Predicting in-hospital mortality after traumatic brain injury: External validation of CRASH-basic and IMPACT-core in the national trauma data bank

BACKGROUND

Traumatic brain injury (TBI) prognostic prediction models offer value to individualized treatment planning, systematic outcome assessments and clinical research design but require continuous external validation to ensure generalizability to different settings. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis on Clinical Trials in TBI (IMPACT) models are widely available but lack robust assessments of performance in a current national sample of patients. The purpose of this study is to assess the performance of the CRASH-Basic and IMPACT-Core models in predicting in-hospital mortality using a nationwide retrospective cohort from the National Trauma Data Bank (NTDB).

METHODS

The 2016 NTDB was used to analyze an adult cohort with moderate-severe TBI (Glasgow Coma Scale [GCS] ≤ 12, head Abbreviated Injury Scale of 2-6). Observed in-hospital mortality or discharge to hospice was compared to the CRASH-Basic and IMPACT-Core models' predicted probability of 14-day or 6-month mortality, respectively. Performance measures included discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (calibration plots and Brier scores). Further sensitivity analysis included patients with GCS ≤ 14 and considered patients discharged to hospice to be alive at 14-days.

RESULTS

A total of 26,228 patients were included in this study. Both models demonstrated good ability in differentiating between patients who died and those who survived, with IMPACT demonstrating a marginally greater AUC (0.863; 95% CI: 0.858 - 0.867) than CRASH (0.858; 0.854 - 0.863); p < 0.001. On calibration, IMPACT overpredicted at lower scores and underpredicted at higher scores but had good calibration-in-the-large (indicating no systemic over/underprediction), while CRASH consistently underpredicted mortality. Brier scores were similar (0.152 for IMPACT, 0.162 for CRASH; p < 0.001). Both models showed slight improvement in performance when including patients with GCS ≤ 14.

CONCLUSION

Both CRASH-Basic and IMPACT-Core accurately predict in-hospital mortality following moderate-severe TBI, and IMPACT-Core performs well beyond its original GCS cut-off of 12, indicating potential utility for mild TBI (GCS 13-15). By demonstrating validity in the NTDB, these models appear generalizable to new data and offer value to current practice in diverse settings as well as to large-scale research design.

Severe traumatic brain injury management in Tanzania: analysis of a prospective cohort

OBJECTIVE

Given the high burden of neurotrauma in low- and middle-income countries (LMICs), in this observational study, the authors evaluated the treatment and outcomes of patients with severe traumatic brain injury (TBI) accessing care at the national neurosurgical institute in Tanzania.

METHODS

A neurotrauma registry was established at Muhimbili Orthopaedic Institute, Dar-es-Salaam, and patients with severe TBI admitted within 24 hours of injury were included. Detailed emergency department and subsequent medical and surgical management of patients was recorded. Two-week mortality was measured and compared with estimates of predicted mortality computed with admission clinical variables using the Corticoid Randomisation After Significant Head Injury (CRASH) core model.

RESULTS

In total, 462 patients (mean age 33.9 years) with severe TBI were enrolled over 4.5 years; 89% of patients were male. The mean time to arrival to the hospital after injury was 8 hours; 48.7% of patients had advanced airway management in the emergency department, 55% underwent cranial CT scanning, and 19.9% underwent surgical intervention. Tiered medical therapies for intracranial hypertension were used in less than 50% of patients. The observed 2-week mortality was 67%, which was 24% higher than expected based on the CRASH core model.

CONCLUSIONS

The 2-week mortality from severe TBI at a tertiary referral center in Tanzania was 67%, which was significantly higher than the predicted estimates. The higher mortality was related to gaps in the continuum of care of patients with severe TBI, including cardiorespiratory monitoring, resuscitation, neuroimaging, and surgical rates, along with lower rates of utilization of available medical therapies. In ongoing work, the authors are attempting to identify reasons associated with the gaps in care to implement programmatic improvements. Capacity building by twinning provides an avenue for acquiring data to accurately estimate local needs and direct programmatic education and interventions to reduce excess in-hospital mortality from TBI.

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.

Early Head Computed Tomography Abnormalities Associated with Elevated Intracranial Pressure in Severe Traumatic Brain Injury

BACKGROUND AND PURPOSE

Intracranial pressure (ICP) monitoring is recommended in severe traumatic brain injury (sTBI), yet invasive monitoring has risks, and many patients do not develop elevated ICP. Tools to identify patients at risk for ICP elevation are limited. We aimed to identify early radiologic biomarkers of ICP elevation.

METHODS

In this retrospective study, we analyzed a prospectively enrolled cohort of patients with a sTBI at an academic level 1 trauma center. Inclusion criteria were nonpenetrating TBI, age ≥16 years, Glasgow Coma Scale (GCS) score ≤8, and presence of an ICP monitor. Two independent reviewers manually evaluated 30 prespecified features on serial head computed tomography (CTs). Patient characteristics and radiologic features were correlated with elevated ICP. The primary outcome was clinically relevant ICP elevation, defined as ICP ≥ 20 mm Hg on at least 5 or more hourly recordings during postinjury days 0-7 with concurrent administration of an ICP-lowering treatment.

RESULTS

Among 111 sTBI patients, the median GCS was 6 (interquartile range 3-8), and 45% had elevated ICP. Features associated with elevated ICP were younger age (every 10-year decrease, odds ratio [OR] 1.4), modified Fisher scale (mFS) score at 0-4 hours postinjury (every 1 point, OR 1.8), and combined volume of contusional hemorrhage and peri-hematoma edema (10 ml, OR 1.2) at 4-18 hours postinjury.

CONCLUSIONS

Younger age, mFS score, and volume of contusion are associated with ICP elevation in patients with a sTBI. Imaging features may stratify patients by their risk of subsequent ICP elevation.

An evaluation of outcomes in patients with traumatic brain injury at a referral hospital in Tanzania: evidence from a survival analysis

OBJECTIVE

The purpose of this study was to determine if patients with traumatic brain injury (TBI) in low- and middle-income countries who receive surgery have better outcomes than patients with TBI who do not receive surgery, and whether this differs with severity of injury.

METHODS

The authors generated a series of Kaplan-Meier plots and performed multiple Cox proportional hazard models to assess the relationship between TBI surgery and TBI severity. The TBI severity was categorized using admission Glasgow Coma Scale scores: mild (14, 15), moderate (9-13), or severe (3-8). The authors investigated outcomes from admission to hospital day 14. The outcome considered was the Glasgow Outcome Scale-Extended, categorized as poor outcome (1-4) and good outcome (5-8). The authors used TBI registry data collected from 2013 to 2017 at a regional referral hospital in Tanzania.

RESULTS

Of the final 2502 patients, 609 (24%) received surgery and 1893 (76%) did not receive surgery. There were significantly fewer road traffic injuries and more violent causes of injury in those receiving surgery. Those receiving surgery were also more likely to receive care in the ICU, to have a poor outcome, to have a moderate or severe TBI, and to stay in the hospital longer. The hazard ratio for patients with TBI who underwent operation versus those who did not was 0.17 (95% CI 0.06-0.49; p < 0.001) in patients with moderate TBI; 0.2 (95% CI 0.06-0.64; p = 0.01) for those with mild TBI, and 0.47 (95% CI 0.24-0.89; p = 0.02) for those with severe TBI.

CONCLUSIONS

Those who received surgery for their TBI had a lower hazard for poor outcome than those who did not. Surgical intervention was associated with the greatest improvement in outcomes for moderate head injuries, followed by mild and severe injuries. The findings suggest a reprioritization of patients with moderate TBI-a drastic change to the traditional practice within low- and middle-income countries in which the most severely injured patients are prioritized for care.

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.

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.

Modeling the return to consciousness after severe traumatic brain injury at a large academic level 1 trauma center

OBJECTIVE

Severe traumatic brain injury (sTBI) carries significant morbidity and mortality. It remains difficult to counsel families on functional prognosis and plan research initiatives aimed at treating traumatic coma. In order to better address these problems, the authors set out to develop statistical models using retrospective data to identify admission characteristics that correlate with time until the return of consciousness, defined as the time to follow commands (TFC). These results were then used to create a TFC score, allowing for rapid identification of patients with predicted prolonged TFC.

METHODS

Data were reviewed and collected from medical records of sTBI patients with Glasgow Coma Scale (GCS) motor subscores ≤ 5 who were admitted to Stony Brook University Hospital from January 2011 to July 2018. Data were used to calculate descriptive statistics and build binary logistic regression models to identify admission characteristics that correlated with in-hospital mortality and in-hospital command-following. A Cox proportional hazards model was used to identify admission characteristics that correlated with the length of TFC. A TFC score was developed using the significant variables identified in the Cox regression model.

RESULTS

There were 402 adult patients who met the inclusion criteria for this study. The average age was 50.5 years, and 122 (30.3%) patients were women. In-hospital mortality was associated with older age, higher Injury Severity Score (ISS), higher Rotterdam score (head CT grading system), and the presence of bilateral fixed and dilated pupils (p < 0.01). In-hospital command-following was anticorrelated with age, ISS, Rotterdam score, and the presence of a single fixed and dilated pupil (p < 0.05). TFC was anticorrelated with age, ISS, Rotterdam score, and the presence of a single fixed and dilated pupil. Additionally, patients who sustained injuries from falls from standing height had a shorter average TFC. The 3 significant variables from the Cox regression model that explained the most variance were used to create a 4-point TFC score. The most significant of these characteristics were Rotterdam head CT scores, high impact traumas, and the presence of a single fixed and dilated pupil. Importantly, the presence of a single fixed and dilated pupil was correlated with longer TFC but no increase in likelihood of in-hospital mortality.

CONCLUSIONS

The creation of the 4-point TFC score will allow clinicians to quickly identify patients with predicted prolonged TFC and estimate the likelihood of command-following at different times after injury. Discussions with family members should take into account the likelihood that patients will return to consciousness and survive after TBI.

Testing a Multivariate Proteomic Panel for Traumatic Brain Injury Biomarker Discovery: A TRACK-TBI Pilot Study

The complex and heterogeneous nature of traumatic brain injury (TBI) has rendered the identification of diagnostic and prognostic biomarkers elusive. A single acute biomarker may not be sufficient to categorize injury severity and/or predict outcome. Using multivariate dimension reduction analyses, we tested the sensitivity and specificity of a multi-analyte panel of proteins as an ensemble biomarker for TBI. Serum was collected within 24 h of injury in a cohort of 130 patients enrolled in the multi-center prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study and run on an array that measured 72 proteins. Using unsupervised principal components analysis, we first identified the subset of protein changes accounting for the most variance across patients. This yielded a group of 21 proteins that reflected an inverse relationship between inflammatory cytokines and regulators of anti-inflammation, and generated an individual inflammatory profile score for each patient. We then tested the association between these scores and computed tomography (CT) findings at hospital admission, as well as their prognostic association with functional recovery at 3 and 6 months (Glasgow Outcome Scale-Extended), and cognitive recovery at 6 months (California Verbal Learning Test, Second Edition) after injury. Inflammatory signatures were significantly increased in patients with positive CT findings, as well as in those who showed poor or incomplete recovery. Inflammation biomarker scores also showed significant sensitivity and specificity as a discriminator of these outcome measures (all areas under the curve [AUCs] >0.62). This proof of concept for the feasibility of multivariate biomarker identification demonstrates the prognostic validity of using a proteomic panel as a potential biomarker for TBI.

Comparison of two simple models for prediction of short term mortality in patients after severe traumatic brain injury

INTRODUCTION

The subscale motor score of Glasgow Coma Scale (msGCS) and the Abbreviated Injury Score of head region (HAIS) are validated prognostic factors in traumatic brain injury (TBI). The aim was to compare the prognostic performance of a HAIS-based prediction model including HAIS, pupil reactivity and age, and the reference prediction model including msGCS in emergency department (ED), pupil reactivity and age.

METHODS

Secondary analysis of a prospective epidemiological study including patients after severe TBI (HAIS > 3) with follow-up from the time of accident until 14 days or earlier death was performed in Switzerland. Performance of prediction, based on accuracy of discrimination [area under the receiver-operating curve (AUROC)], calibration (Hosmer-Lemeshow test) and validity (bootstrapping with 2000 repetitions to correct) for optimism of the two prediction models were investigated. A non-inferiority approach was performed and an a priori threshold for important differences was established.

RESULTS

The cohort included 808 patients [median age 56 {inter-quartile range (IQR) 33-71}, median motor part of GCS in ED 1 (1-6), abnormal pupil reactivity 29.0%] with a death rate of 29.7% at 14 days. The accuracy of discrimination was similar (AUROC HAIS-based prediction model: 0.839; AUROC msGCS-based prediction model: 0.826, difference of the 2 AUROC 0.013 (-0.007 to 0.037). A similar calibration was observed (Hosmer-Lemeshow X² 11.64, p = 0.168 vs. Hosmer-Lemeshow X² 8.66, p = 0.372). Internal validity of HAIS-based prediction model was high (optimism corrected AUROC: 0.837).

CONCLUSIONS

Performance of prediction for short-term mortality after severe TBI with HAIS-based prediction model was non-inferior to reference prediction model using msGCS as predictor.

Comparison of two prehospital predictive models for mortality and impaired consciousness after severe traumatic brain injury

BACKGROUND

The primary aim was to investigate the performance of a National Advisory Committee for Aeronautics based predictive model (NACA-BM) for mortality at 14 days and a reference model using motor GCS (GCS-RM). The secondary aim was to compare the models for impaired consciousness of survivors at 14 days (IC-14; GCS ≤ 13).

METHODS

Patients ≥16 years having sustained TBI with an abbreviated injury scale score of head region (HAIS) of >3 were included. Multivariate logistic regression models were used to test models for death and IC-14. The discrimination was assessed using area under the receiver-operating curves (AUROCs); noninferiority margin was -5% between the AUROCs. Calibration was assessed using the Hosmer Lemeshow goodness-of-fit test.

RESULTS

Six hundred and seventy seven patients were included. The median age was 54 (IQR 32-71). The mortality rate was 31.6%; 99 of 438 surviving patients (22.6%) had an IC-14. Discrimination of mortality was 0.835 (95%CI 0.803-0.867) for the NACA-BM and 0.839 (0.807-0.872) for the GCS-RM; the difference of the discriminative ability was -0.4% (-2.3% to +1.7%). Calibration was appropriate for the NACA-BM (χ² 8.42; P = 0. 393) and for the GCS-RM (χ² 3.90; P = 0. 866). Discrimination of IC-14 was 0.757 (0.706-0.808) for the NACA-BM and 0.784 (0.734-0.835) for the GCS-RM; the difference of the discriminative ability was -2.5% (-7.8% to +2.6%). Calibration was appropriate for the NACA-BM (χ² 10.61; P = 0.225) and for the GCS-RM (χ² 6.26; P = 0.618).

CONCLUSIONS

Prehospital prediction of mortality after TBI was good with both models, and the NACA-BM was not inferior to the GCS-RM. Prediction of IC-14 was moderate in both models.

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.

The Application of the CRASH-CT Prognostic Model for Older Adults With Traumatic Brain Injury: A Population-Based Observational Cohort Study

OBJECTIVE

To examine the performance of the Corticosteroid Randomization After Significant Head injury (CRASH) trial prognostic model in older patients with traumatic brain injury.

SETTING

The National Study on Costs and Outcomes of Trauma cohort, established at 69 hospitals in the United States in 2001 and 2002.

PARTICIPANTS

Adults with traumatic brain injury and an initial Glasgow Coma Scale score of 14 or less.

DESIGN

The CRASH-CT model predicting death within 14 days was deployed in all patients. Model performance in older patients (aged 65-84 years) was compared with that in younger patients (aged 18-64 years).

MAIN MEASURES

Model discrimination (as defined by the c-statistic) and calibration (as defined by the Hosmer-Lemeshow P value).

RESULTS

CRASH-CT model discrimination was not significantly different between the older (n = 356; weighted n = 524) and younger patients (n = 981; weighted n = 2602) and was generally adequate (c-statistic 0.83 vs 0.87, respectively; P = .11). CRASH-CT model calibration was adequate for the older patients and inadequate for younger patients (Hosmer-Lemeshow P values .12 and .001, respectively), possibly reflecting differences in sample size. Calibration-in-the-large showed no systematic under- or overprediction in either stratum.

CONCLUSION

The CRASH-CT model may be valid for use in a geriatric population.

Temporal Trends in Functional Outcomes after Severe Traumatic Brain Injury: 2006-2015

Severe traumatic brain injury (TBI) is associated with poor outcomes; however, little is known about whether these outcomes are improving over time. This study examined temporal trends in functional outcomes of severe TBI at six months post-injury. We conducted a retrospective cohort study (January 1, 2006 to December 31, 2015) of hospitalized adult (≥16 years) patients with severe TBI using data from the population-based Victorian State Trauma Registry. The primary outcome was the Glasgow Outcome Scale-Extended (GOS-E) at six months post-injury, dichotomized as upper severe disability or worse (GOS-E ≤4, termed "unfavorable outcome") and lower moderate disability or better (GOS-E ≥5; termed "favorable outcome"). Multivariable logistic regression was used to investigate temporal trends in functional outcomes at six months post-injury. Of the 1966 patients with severe TBI who were followed up at six months post-injury (median age, 42 years (interquartile range [IQR]: 25-68); male, 73%), a majority of patients had an unfavorable outcome (GOS-E ≤4; n = 1372, 70%). After adjusting for confounders, there was no change in functional outcomes over time (adjusted odds ratio [AOR] = 1.02, 95% confidence interval [CI]: 0.98,1.06; p = 0.35). Similarly, there was no change in the adjusted odds of death (GOS-E = 1) at six months post-injury (AOR = 1.04, 95% CI: 1.00,1.08; p = 0.08). Using a population-wide, high quality, comprehensive registry, we demonstrated no change in death or functional outcomes after severe TBI between 2006 and 2015 in a mature trauma system. There is a clear need to identify targeted improvements in the treatment of these patients with the aim of reducing in-hospital death and improving long-term outcomes.

A systematic review of cerebral microdialysis and outcomes in TBI: relationships to patient functional outcome, neurophysiologic measures, and tissue outcome

OBJECTIVE

To perform a systematic review on commonly measured cerebral microdialysis (CMD) analytes and their association to: (A) patient functional outcome, (B) neurophysiologic measures, and (C) tissue outcome; after moderate/severe TBI. The aim was to provide a foundation for next-generation CMD studies and build on existing pragmatic expert guidelines for CMD.

METHODS

We searched MEDLINE, BIOSIS, EMBASE, Global Health, Scopus, Cochrane Library (inception to October 2016). Strength of evidence was adjudicated using GRADE.

RESULTS

(A) Functional Outcome: 55 articles were included, assessing outcome as mortality or Glasgow Outcome Scale (GOS) at 3-6 months post-injury. Overall, there is GRADE C evidence to support an association between CMD glucose, glutamate, glycerol, lactate, and LPR to patient outcome at 3-6 months. (B) Neurophysiologic Measures: 59 articles were included. Overall, there currently exists GRADE C level of evidence supporting an association between elevated CMD measured mean LPR, glutamate and glycerol with elevated ICP and/or decreased CPP. In addition, there currently exists GRADE C evidence to support an association between elevated mean lactate:pyruvate ratio (LPR) and low PbtO2. Remaining CMD measures and physiologic outcomes displayed GRADE D or no evidence to support a relationship. (C) Tissue Outcome: four studies were included. Given the conflicting literature, the only conclusion that can be drawn is acute/subacute phase elevation of CMD measured LPR is associated with frontal lobe atrophy at 6 months.

CONCLUSIONS

This systematic review replicates previously documented relationships between CMD and various outcome, which have driven clinical application of the technique. Evidence assessments do not address the application of CMD for exploring pathophysiology or titrating therapy in individual patients, and do not account for the modulatory effect of therapy on outcome, triggered at different CMD thresholds in individual centers. Our findings support clinical application of CMD and refinement of existing guidelines.

Temporal Profile of Microtubule-Associated Protein 2: A Novel Indicator of Diffuse Brain Injury Severity and Early Mortality after Brain Trauma

This study compared cerebrospinal fluid (CSF) levels of microtubule-associated protein 2 (MAP-2) from adult patients with severe traumatic brain injury (TBI) with uninjured controls over 10 days, and examined the relationship between MAP-2 concentrations and acute clinical and radiologic measures of injury severity along with mortality at 2 weeks and over 6 months. This prospective study, conducted at two Level 1 trauma centers, enrolled adults with severe TBI (Glasgow Coma Scale [GCS] score ≤8) requiring a ventriculostomy, as well as controls. Ventricular CSF was sampled from each patient at 6, 12, 24, 48, 72, 96, 120, 144, 168, 192, 216, and 240 h following TBI and analyzed via enzyme-linked immunosorbent assay for MAP-2 (ng/mL). Injury severity was assessed by the GCS score, Marshall Classification on computed tomography (CT), Rotterdam CT score, and mortality. There were 151 patients enrolled-130 TBI and 21 control patients. MAP-2 was detectable within 6 h of injury and was significantly elevated compared with controls (p < 0.001) at each time-point. MAP-2 was highest within 72 h of injury and decreased gradually over 10 days. The area under the receiver operating characteristic curve for deciphering TBI versus controls at the earliest time-point CSF was obtained was 0.96 (95% CI 0.93-0.99) and for the maximal 24-h level was 0.98 (95% CI 0.97-1.00). The area under the curve for initial MAP-2 levels predicting 2-week mortality was 0.80 at 6 h, 0.81 at 12 h, 0.75 at 18 h, 0.75 at 24 h, and 0.80 at 48 h. Those with Diffuse Injury III-IV had much higher initial (p = 0.033) and maximal (p = 0.003) MAP-2 levels than those with Diffuse Injury I-II. There was a graded increase in the overall levels and peaks of MAP-2 as the degree of diffuse injury increased within the first 120 h post-injury. These data suggest that early levels of MAP-2 reflect severity of diffuse brain injury and predict 2-week mortality in TBI patients. These findings have implications for counseling families and improving clinical decision making early after injury and guiding multidisciplinary care. Further studies are needed to validate these findings in a larger sample.

The aggressiveness of neurotrauma practitioners and the influence of the IMPACT prognostic calculator

Published guidelines have helped to standardize the care of patients with traumatic brain injury; however, there remains substantial variation in the decision to pursue or withhold aggressive care. The International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic calculator offers the opportunity to study and decrease variability in physician aggressiveness. The authors wish to understand how IMPACT’s prognostic calculations currently influence patient care and to better understand physician aggressiveness. The authors conducted an anonymous international, multidisciplinary survey of practitioners who provide care to patients with traumatic brain injury. Questions were designed to determine current use rates of the IMPACT prognostic calculator and thresholds of age and risk for death or poor outcome that might cause practitioners to consider withholding aggressive care. Correlations between physician aggressiveness, putative predictors of aggressiveness, and demographics were examined. One hundred fifty-four responses were received, half of which were from physicians who were familiar with the IMPACT calculator. The most frequent use of the calculator was to improve communication with patients and their families. On average, respondents indicated that in patients older than 76 years or those with a >85% chance of death or poor outcome it might be reasonable to pursue non-aggressive care. These thresholds were robust and were not influenced by provider or institutional characteristics. This study demonstrates the need to educate physicians about the IMPACT prognostic calculator. The consensus values for age and prognosis identified in our study may be explored in future studies aimed at reducing variability in physician aggressiveness and should not serve as a basis for withdrawing care.

Volumetric analysis of day of injury computed tomography is associated with rehabilitation outcomes after traumatic brain injury

BACKGROUND

Day-of-injury (DOI) brain lesion volumes in traumatic brain injury (TBI) patients are rarely used to predict long-term outcomes in the acute setting. The purpose of this study was to investigate the relationship between acute brain injury lesion volume and rehabilitation outcomes in patients with TBI at a level one trauma center.

METHODS

Patients with TBI who were admitted to our rehabilitation unit after the acute care trauma service from February 2009-July 2011 were eligible for the study. Demographic data and outcome variables including cognitive and motor Functional Independence Measure (FIM) scores, length of stay (LOS) in the rehabilitation unit, and ability to return to home were obtained. The DOI quantitative injury lesion volumes and degree of midline shift were obtained from DOI brain computed tomography scans. A multiple stepwise regression model including 13 independent variables was created. This model was used to predict postrehabilitation outcomes, including FIM scores and ability to return to home. A p value less than 0.05 was considered significant.

RESULTS

Ninety-six patients were enrolled in the study. Mean age was 43 ± 21 years, admission Glasgow Coma Score was 8.4 ± 4.8, Injury Severity Score was 24.7 ± 9.9, and head Abbreviated Injury Scale score was 3.73 ± 0.97. Acute hospital LOS was 12.3 ± 8.9 days, and rehabilitation LOS was 15.9 ± 9.3 days. Day-of-injury TBI lesion volumes were inversely associated with cognitive FIM scores at rehabilitation admission (p = 0.004) and discharge (p = 0.004) and inversely associated with ability to be discharged to home after rehabilitation (p = 0.006).

CONCLUSION

In a cohort of patients with moderate to severe TBI requiring a rehabilitation unit stay after the acute care hospital stay, DOI brain injury lesion volumes are associated with worse cognitive FIM scores at the time of rehabilitation admission and discharge. Smaller-injury volumes were associated with eventual discharge to home. Volumetric neuroimaging in the acute injury phase may improve surgeons' ultimate outcome predictions in TBI patients.

LEVEL OF EVIDENCE

Prognostic/epidemiologic study, level V.

DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans

Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n = 109; Age: M = 47.2, SD = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.

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.

Cerebrospinal Fluid Cortisol Mediates Brain-Derived Neurotrophic Factor Relationships to Mortality after Severe TBI: A Prospective Cohort Study

Distinct regulatory signaling mechanisms exist between cortisol and brain derived neurotrophic factor (BDNF) that may influence secondary injury cascades associated with traumatic brain injury (TBI) and predict outcome. We investigated concurrent CSF BDNF and cortisol relationships in 117 patients sampled days 0–6 after severe TBI while accounting for BDNF genetics and age. We also determined associations between CSF BDNF and cortisol with 6-month mortality. BDNF variants, rs6265 and rs7124442, were used to create a gene risk score (GRS) in reference to previously published hypothesized risk for mortality in “younger patients” (<48 years) and hypothesized BDNF production/secretion capacity with these variants. Group based trajectory analysis (TRAJ) was used to create two cortisol groups (high and low trajectories). A Bayesian estimation approach informed the mediation models. Results show CSF BDNF predicted patient cortisol TRAJ group (P = 0.001). Also, GRS moderated BDNF associations with cortisol TRAJ group. Additionally, cortisol TRAJ predicted 6-month mortality (P = 0.001). In a mediation analysis, BDNF predicted mortality, with cortisol acting as the mediator (P = 0.011), yielding a mediation percentage of 29.92%. Mediation effects increased to 45.45% among younger patients. A BDNF^*GRS interaction predicted mortality in younger patients (P = 0.004). Thus, we conclude 6-month mortality after severe TBI can be predicted through a mediation model with CSF cortisol and BDNF, suggesting a regulatory role for cortisol with BDNF's contribution to TBI pathophysiology and mortality, particularly among younger individuals with severe TBI. Based on the literature, cortisol modulated BDNF effects on mortality after TBI may be related to known hormone and neurotrophin relationships to neurological injury severity and autonomic nervous system imbalance.

Past, Present, and Future of Traumatic Brain Injury Research

Traumatic brain injury (TBI) is the greatest cause of death and severe disability in young adults; its incidence is increasing in the elderly and in the developing world. Outcome from severe TBI has improved dramatically as a result of advancements in trauma systems and supportive critical care, however we remain without a therapeutic which acts directly to attenuate brain injury. Recognition of secondary injury and its molecular mediators has raised hopes for such targeted treatments. Unfortunately, over 30 late-phase clinical trials investigating promising agents have failed to translate a therapeutic for clinical use. Numerous explanations for this failure have been postulated and are reviewed here. With this historical context we review ongoing research and anticipated future trends which are armed with lessons from past trials, new scientific advances, as well as improved research infrastructure and funding. There is great hope that these new efforts will finally lead to an effective therapeutic for TBI as well as better clinical management strategies.

Clinical Results and Outcome Improvement Over Time in Traumatic Brain Injury

Prognostic models for traumatic brain injury (TBI) are important tools both in clinical practice and research if properly validated, preferably by external validation. Prognostic models also offer the possibility of monitoring performance by comparing predicted outcomes with observed outcomes. In this study, we applied the prognostic models developed by the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) in an Italian multi-center database (Neurolink) with two aims: to compare observed with predicted outcomes and to check for a possible improvement of clinical outcome over the 11 years of patient inclusion in Neurolink. We applied the IMPACT models to patients included in Neurolink between 1997 and 2007. Performance of the models was assessed by determining calibration (with calibration plots) and discrimination (by the area under the receiver operating characteristic curve [AUC]). Logistic regression analysis was used to analyze a possible trend in outcomes over time, adjusted for predicted outcomes. A total of 1401 patients were studied. Patients had a median age of 40 years and 51% had a Glasgow Coma Scale motor score of 5 or 6. The models showed good discrimination, with AUCs of 0.86 (according to the Core Model) and 0.88 (Extended Model), and adequate calibration, with the overall observed risk of unfavorable outcome and mortality being less than predicted. Outcomes significantly improved over time. This study shows that the IMPACT models performed reasonably well in the Neurolink data and can be used for monitoring performance. After adjustment for predicted outcomes with the prognostic models, we observed a substantial improvement of patient outcomes over time in the three Neurolink centers.

Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury

BACKGROUND AND OBJECTIVE

Prediction of medical outcomes may potentially benefit from using modern statistical modeling techniques. We aimed to externally validate modeling strategies for prediction of 6-month mortality of patients suffering from traumatic brain injury (TBI) with predictor sets of increasing complexity.

METHODS

We analyzed individual patient data from 15 different studies including 11,026 TBI patients. We consecutively considered a core set of predictors (age, motor score, and pupillary reactivity), an extended set with computed tomography scan characteristics, and a further extension with two laboratory measurements (glucose and hemoglobin). With each of these sets, we predicted 6-month mortality using default settings with five statistical modeling techniques: logistic regression (LR), classification and regression trees, random forests (RFs), support vector machines (SVM) and neural nets. For external validation, a model developed on one of the 15 data sets was applied to each of the 14 remaining sets. This process was repeated 15 times for a total of 630 validations. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminative ability of the models.

RESULTS

For the most complex predictor set, the LR models performed best (median validated AUC value, 0.757), followed by RF and support vector machine models (median validated AUC value, 0.735 and 0.732, respectively). With each predictor set, the classification and regression trees models showed poor performance (median validated AUC value, <0.7). The variability in performance across the studies was smallest for the RF- and LR-based models (inter quartile range for validated AUC values from 0.07 to 0.10).

CONCLUSION

In the area of predicting mortality from TBI, nonlinear and nonadditive effects are not pronounced enough to make modern prediction methods beneficial.

A fast cranial drilling technique in treating severe intracranial hemorrhage

Background:

This study is a retrospective case analysis of 143 patients who suffered from severe intracranial hemorrhage and underwent a fast and simple procedure of cranial drilling followed with external ventricle drain treatment (referred as Fast-D here after) during 2003–2013 to evaluate the clinical effectiveness of the treatment.

Methods:

Fast-D procedure was conducted on 143 patients with severe acute craniocerebral diseases. Those patients were evaluated using activities of daily living (ADL) scales at hospital discharge and after 6-month of physical therapy, and were compared to 36 patients with similar craniocerebral diseases but received the traditional Dandy's surgical treatment.

Results:

At discharge, 11% (16 cases) was classified as ADL I (fully functional for physical and social activities); 26% (37 cases) had ADL II (fully functional for physical activities but partially impaired for social activities); 34% (49 cases) was ADL III (require assistance performing physical activities); 9% (13 cases) was ADL IV (being conscious, but completely lost ability of physical activities); 27% (10 cases) was ADL V (vegetative stage); and 13% (18 cased) was ADL VI (died) among the 143 patients. Six-month physical therapy improved ADL in 88% of the patients. Those outcomes are equal or better than the more complicated Dandy's procedure probably due to the time-saving factor.

Conclusion:

Fast-D procedure is much faster (6.7 min vs. 53.6 min of the Dandy's procedure) and can be performed outside operating rooms (computed tomography room or bedside). This technique could serve as a tool to rapidly release intracranial pressure and reduce subsequent morbidity and mortality of severe craniocerebral diseases when resource and condition are limited and more elaborate operating room procedures are not possible.

Interrelation between Neuroendocrine Disturbances and Medical Complications Encountered during Rehabilitation after TBI

Traumatic brain injury is not a discrete event but an unfolding sequence of damage to the central nervous system. Not only the acute phase but also the subacute and chronic period after injury, i.e., during inpatient rehabilitation, is characterized by multiple neurotransmitter alterations, cellular dysfunction, and medical complications causing additional secondary injury. Neuroendocrine disturbances also influence neurological outcome and are easily overlooked as they often present with diffuse symptoms such as fatigue, depression, poor concentration, or a decline in overall cognitive function; these are also typical sequelae of traumatic brain injury. Furthermore, neurological complications such as hydrocephalus, epilepsy, fatigue, disorders of consciousness, paroxysmal sympathetic hyperactivity, or psychiatric-behavioural symptoms may mask and/or complicate the diagnosis of neuroendocrine disturbances, delay appropriate treatment and impede neurorehabilitation. The present review seeks to examine the interrelation between neuroendocrine disturbances with neurological complications frequently encountered after moderate to severe TBI during rehabilitation. Common neuroendocrine disturbances and medical complications and their clinical implications are discussed.

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.

Association between optic nerve sheath diameter and mortality in patients with severe traumatic brain injury

PURPOSE

Increased intracranial pressure (ICP) is associated with worse outcomes following traumatic brain injury (TBI). Studies have confirmed that ICP is correlated with optic nerve sheath diameter (ONSD) on ultrasound. The aim of our study was to assess the independent relationship between ONSD measured using CT and mortality in a population of patients admitted with severe TBI.

METHODS

We conducted a retrospective cohort study of patients with a TBI requiring ICP monitoring admitted to the ICU between April 2006 and May 2012 to two neurotrauma centers. ONSD was independently measured by two physicians blinded to patient outcomes. Multivariable logistic regression modeling was used to assess an association between ONSD and hospital mortality.

RESULTS

A total of 220 patients were included in the analysis. Overall, the cohort had a mean age of 35 (SD 17) years and 171 of 220 (79 %) were male. The median admission GCS was 6 (IQR 3-8). Intra-class correlation coefficient between raters for ONSD measurements was 0.92 (95 % CI 0.90-0.94, P < 0.0001). On multivariable analysis, each 1 mm increase in ONSD was associated with a twofold increase in hospital mortality (OR 2.0, 95 % CI 1.2-3.2, P = 0.007). Using linear regression, ONSD was independently associated with increased ICP in the first 48 h after admission (β = 4.4, 95 % CI 2.5-6.3, P < 0.0001).

CONCLUSIONS

In patients with TBI, ONSD measured on CT scanning was independently associated with ICP and mortality.