Lactic acid, neuron-specific enolase, and blood-brain barrier index after a severe traumatic brain injury: a prospective study

OBJECTIVE

To explore the clinical significance of dynamic monitoring of cerebrospinal fluid (CSF) and serum Lactic acid（Lac), neuron-specific enolase (NSE), and the blood-brain barrier (BBB) index in evaluating the condition and prognosis after a severe traumatic brain injury (TBI).

METHODS

A total of 52 severe TBI patients admitted to the Department of Neurosurgery within 24 hours after injury were dynamically monitored. CSF and serum samples were collected on the 1st, 3rd, and 7th day after a severe TBI to monitor the changes in Lac, NSE, and the BBB index. Intracranial pressure (ICP), Glasgow coma scale (GCS), and 6-month Glasgow outcome scale-extended (GOS-E) were tested. According to the results of GOS-E, the patients were divided into two groups (i.e. the poor prognosis group and good prognosis group). Statistical analysis was conducted to investigate the clinical significance of dynamic monitoring of CSF and serum Lac, NSE, and BBB index after a severe TBI.

RESULTS

After a severe TBI, the levels of Lac, NSE, and BBB in CSF and serum were significantly higher than those in the normal range. Lac, NSE, and the BBB index did not correlate with ICP (except serum Lac) but had correlations with GCS and post-injury 6 months post-injury (except serum Lac). Moreover, the correlations between Lac, NSE, and BBB index were statistically significant (p < 0.05): CSF Lac and CSF NSE; CSF Lac and serum NSE; Lac and BBB index of CSF; Lac and BBB index of CSF; NSE and CSE of serum; CSF NSE and BBB index; and serum NSE and BBB index. Additionally, serum NSE is correlated with NSE in CSF (p < 0.05).

CONCLUSION

After a severe TBI, dynamic monitoring of CSF and serum Lac, NSE, and BBB index has the potential to assess the condition, predict the prognosis, and have clinical significance.

Isotonic balanced fluid versus 0.9% saline in patients with moderate to severe traumatic brain injury: A double-blinded randomised controlled trial

BACKGROUND AND IMPORTANCE

Traumatic brain injury (TBI) is a global health concern with significant economic impact. Optimal fluid therapy aims to restore intravascular volume, maintain cerebral perfusion pressure and blood flow, thus preventing secondary brain injury. While 0.9% saline (NS) is commonly used, concerns about acid-base and electrolyte imbalance and development of acute kidney injury (AKI) lead to consideration of balanced fluids as an alternative.

OBJECTIVES

This study aimed to compare the outcomes of patients with moderate to severe TBI treated with Sterofundin (SF) versus NS.

DESIGN, SETTINGS AND PARTICIPANTS

A double-blinded randomised controlled trial of patients aged 18 to 65 years with TBI was conducted at the University Malaya Medical Centre from February 2017 to November 2019.

INTERVENTION OR EXPOSURE

Patients were randomly assigned to receive either NS or SF. The study fluids were administered for 72 h as continuous infusions or boluses. Participants, investigators, and staff were blinded to the fluid type.

OUTCOMES MEASURE AND ANALYSIS

The primary outcome was in-hospital mortality. Relative risk (RR) with 95% confidence interval (CI) was calculated.

MAIN RESULTS

A total of 70 patients were included in the analysis, with 38 in the NS group and 32 in the SF group. The in-hospital mortality rate were 3 (7.9%) in the NS group vs. 4 (12.5%) in the SF group, RR = 1.29 (95% CI, 0.64 to 2.59; p = 0.695). No patients developed AKI and required renal replacement therapy. ICP on day 3 was significantly higher in the SF group (18.60 ± 9.26) compared to 12.77 ± 3.63 in the NS group, (95% CI, -11.46 to 0.20; p = 0.037). There were no significant differences in 3-day biochemical parameters and cerebral perfusion pressure, ventilator-free days, length of ICU stay, or Glasgow Outcome Scale-Extended (GOS-E) score at 6 months.

CONCLUSIONS

In patients with moderate to severe TBI, the use of SF was not associated with reduced in-hospital mortality, development of AKI, or improved 6-month GOS-E when compared to NS.

Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation

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

Long-term Benefits for Younger Patients with Aggressive Immediate Intervention following Severe Traumatic Brain Injury: A Longitudinal Cohort Analysis of 175 Patients from a Prospective Registry

BACKGROUND

The prevalence of traumatic brain injury (TBI) continues to rise, in part as a reflection of a growing elderly population. Concomitantly, nihilism may exist following substantial neurotrauma from a myriad of commonplace mechanisms, such as traffic incidents, assaults, or falls.

OBJECTIVE

This study assesses long-term outcomes following aggressive surgical intervention with invasive neuromonitoring to guard against nihilism, especially for patients with advantageous characteristics such as younger age.

METHODS

A consecutive series of patients with severe TBI treated between 2008 and 2018 and enrolled into the Brain Trauma Research Center (BTRC) database, an Institutional Review Board (IRB 19030228) approved prospective, longitudinal cohort study, were extracted. Demographic and clinical data were analyzed. Long-term functional outcome was recorded with the eight-point Glasgow Outcome Scale-Extended (GOS-E) score at 3-, 6-, 12-, and 24-months by trained, qualified neuropsychology technicians. Chi-squared and analysis of variance tests were used to evaluate the relationship of age groups between different variables.

RESULTS

For this analysis, 175 patients with severe TBI who were enrolled in the BTRC database and required decompressive hemicraniectomy during the study period were included. Over one-third of the patients with a severe TBI, who were aged 35 years and younger, had a favorable outcome.

CONCLUSIONS

Despite enduring a severe TBI, a substantial percentage of younger patients achieved favorable outcomes following aggressive treatment. As such, establishing a prognosis should be deferred to allow for recovery via individualized rehabilitation, multidisciplinary support, and community reintegration programs to cope with various long-term psychological, cognitive, and functional disabilities.

Improving the Precision of the Glasgow Outcome Scale-Extended Using Item Response Theory: A TRACK-TBI Study

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

How Do Scores on the Functional Status Examination (FSE) Correspond to Scores on the Glasgow Outcome Scale-Extended (GOSE)?

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

A Manual for the Glasgow Outcome Scale-Extended Interview

The Glasgow Outcome Scale-Extended (GOSE) has become one of the most widely used outcome instruments to assess global disability and recovery after traumatic brain injury. Achieving consistency in the application of the assessment remains a challenge, particularly in multi-center studies involving many assessors. We present a manual for the GOSE interview that is designed to support both single- and multi-center studies and promote inter-rater agreement. Many patients fall clearly into a particular category; however, patients may have outcomes that are on the borderline between adjacent categories, and cases can present other challenges for assessment. The Manual includes the general principles of assessment, advice on administering each section of the GOSE interview, and guidance on "borderline" and "difficult" cases. Finally, we discuss the properties of the GOSE, including strengths and limitations, and outline recommendations for assessor training, accreditation, and monitoring.

Sliding Scoring of the Glasgow Outcome Scale-Extended as Primary Outcome in Traumatic Brain Injury Trials

The Glasgow Outcome Scale-Extended (GOS-E), an ordinal scale measuring global outcome, is used commonly as the primary outcome measure in clinical trials of traumatic brain injury. Analysis is often based on a dichotomization and thus has inherent statistical limitations, including loss of information related to the collapse of adjacent categories. A fixed dichotomization defines favorable outcome consistently for all subjects, whereas a sliding dichotomy tailors the definition of favorable outcome according to baseline prognosis/severity. Literature indicates that the sliding dichotomy is more statistically efficient than the fixed dichotomy; however, the sliding dichotomy still collapses categories and therefore discards information. We propose an alternative, a sliding scoring system for the GOS-E, intended to address the limitations of the sliding dichotomy. The score is assigned based on the number of levels between the achieved score and the favorable cut-point. The proposed scoring system reflects the magnitude of change, where change is defined according to each subject's baseline prognosis. Because the score is approximately continuous, statistical methods can rely on the normal distribution, both for analysis and study design. Two examples show the corresponding potential for improved power. A sliding score approach allows for quantification of the magnitude of change while still accounting for prognosis. Scientific advantages include increased power and an intuitive interpretation.

Favorable Functional Recovery in Severe Traumatic Brain Injury Survivors beyond Six Months

Favorable long-term functional outcomes after severe traumatic brain injury (TBI) may be underestimated. We analyzed 24-month functional outcomes from a consecutive series of severe TBI survivors. A prospective, observational database of severe TBI survivors from a single institution was analyzed. Glasgow Outcome Scale-Extended (GOS-E) scores were obtained at 3, 6, 12, and 24 months post-injury. GOS-E scores were dichotomized into unfavorable and favorable outcomes, and the proportion of survivors changing from unfavorable to favorable outcomes was calculated using Wilcoxon signed-rank tests. Surviving adults (N = 304; mean age ± standard deviation = 35.06 ± 15.11; 80.92% male; mode of initial GCS = 7) were analyzed. A statistically significant mean increase in GOS-E was noted from 3 to 6, 6 to 12, 12 to 24, and 6 to 24 months after injury (0.65 [p < 0.0001], 0.42 [p < 0.0001], 0.23 [p = 0.020], and 0.61 [p < 0.0001], respectively). Moreover, 43% of survivors from 3 to 6 months, 36% from 6 to 12 months, 38% from 12 to 24 months, and 54% from 6 to 24 months progressed from an unfavorable to a favorable outcome. Two thirds of survivors in the unfavorable category at 3 months had favorable outcomes at 2 years. Overall, 74% of surviving adults with a documented GOS-E at 2 years after injury had a favorable outcome. Severe TBI survivors demonstrated significant improvement in functional outcomes from 3 to 24 months after injury. At 2 years, three fourths of survivors had a favorable outcome. Long-term prognosis in severe TBI is better than broadly appreciated.

Longitudinal description of the glasgow outcome scale-extended for individuals in the traumatic brain injury model systems national database: a National Institute on Disability and Rehabilitation Research traumatic brain injury model systems study

OBJECTIVE

To comprehensively describe the temporal patterns of global outcome after traumatic brain injury (TBI) in the Traumatic Brain Injury Model Systems National Database (TBIMS NDB).

DESIGN

Longitudinal prospective cohort study.

SETTING

TBI Model Systems centers.

PARTICIPANTS

Patients (N=3870) ≥16 years of age with moderate or severe TBI enrolled in the TBIMS NDB.

INTERVENTIONS

None.

MAIN OUTCOME MEASURE

Glasgow Outcome Scale-Extended (GOS-E).

RESULTS

The trajectory of the GOS-E scores is best described with a model of quadratic change, in which scores initially increase and peak approximately 10 years after the first GOS-E assessment, and then decrease. Change occurs most rapidly in the initial and final years of the timeline. There was significant variability in each growth parameter (P<.05). A reduced multilevel model was built, including all covariates (age at first GOS-E assessment, FIM, race, sex, rehabilitation length of stay) that related significantly to the growth parameters. An interactive tool was created to generate individual level trajectories based on various combinations of covariate values. Results provide an individual level account of the chronological progression of TBI outcomes, as measured by the GOS-E.

CONCLUSIONS

Individual growth curve analysis is a statistically rigorous approach to describe temporal change with respect to the GOS-E at the individual level for participants within the TBIMS NDB. Results indicated that, for individuals in the TBIMS NDB as a group, functional status as measured by the GOS-E initially improves, plateaus, and then begins to decline. Factors such as age at first GOS-E assessment, race, FIM score at rehabilitation admission, and rehabilitation length of stay were found to influence baseline GOS-E scores, as well as the rate and extent of both improvement and decline over time. Additional research may be required to determine the generalizability of these findings and the usefulness of this tool for clinical applications.