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

Dysregulated brain-gut axis in the setting of traumatic brain injury: review of mechanisms and anti-inflammatory pharmacotherapies

Traumatic brain injury (TBI) is a chronic and debilitating disease, associated with a high risk of psychiatric and neurodegenerative diseases. Despite significant advancements in improving outcomes, the lack of effective treatments underscore the urgent need for innovative therapeutic strategies. The brain-gut axis has emerged as a crucial bidirectional pathway connecting the brain and the gastrointestinal (GI) system through an intricate network of neuronal, hormonal, and immunological pathways. Four main pathways are primarily implicated in this crosstalk, including the systemic immune system, autonomic and enteric nervous systems, neuroendocrine system, and microbiome. TBI induces profound changes in the gut, initiating an unrestrained vicious cycle that exacerbates brain injury through the brain-gut axis. Alterations in the gut include mucosal damage associated with the malabsorption of nutrients/electrolytes, disintegration of the intestinal barrier, increased infiltration of systemic immune cells, dysmotility, dysbiosis, enteroendocrine cell (EEC) dysfunction and disruption in the enteric nervous system (ENS) and autonomic nervous system (ANS). Collectively, these changes further contribute to brain neuroinflammation and neurodegeneration via the gut-brain axis. In this review article, we elucidate the roles of various anti-inflammatory pharmacotherapies capable of attenuating the dysregulated inflammatory response along the brain-gut axis in TBI. These agents include hormones such as serotonin, ghrelin, and progesterone, ANS regulators such as beta-blockers, lipid-lowering drugs like statins, and intestinal flora modulators such as probiotics and antibiotics. They attenuate neuroinflammation by targeting distinct inflammatory pathways in both the brain and the gut post-TBI. These therapeutic agents exhibit promising potential in mitigating inflammation along the brain-gut axis and enhancing neurocognitive outcomes for TBI patients.

Early Shared Decision-Making for Older Adults with Traumatic Brain Injury: Using Time-Limited Trials and Understanding Their Limitations

Older adults account for a disproportionate share of the morbidity and mortality after traumatic brain injury (TBI). Predicting functional and cognitive outcomes for individual older adults after TBI is challenging in the acute phase of injury. Given that neurologic recovery is possible and uncertain, life-sustaining therapy may be pursued initially, even if for some, there is a risk of survival to an undesired level of disability or dependence. Experts recommend early conversations about goals of care after TBI, but evidence-based guidelines for these discussions or for the optimal method for communicating prognosis are limited. The time-limited trial (TLT) model may be an effective strategy for managing prognostic uncertainty after TBI. TLTs can provide a framework for early management: specific treatments or procedures are used for a defined period of time while monitoring for an agreed-upon outcome. Outcome measures, including signs of worsening and improvement, are defined at the outset of the trial. In this Viewpoint article, we discuss the use of TLTs for older adults with TBI, their potential benefits, and current challenges to their application. Three main barriers limit the implementation of TLTs in these scenarios: inadequate models for prognostication; cognitive biases faced by clinicians and surrogate decision-makers, which may contribute to prognostic discordance; and ambiguity regarding appropriate endpoints for the TLT. Further study is needed to understand clinician behaviors and surrogate preferences for prognostic communication and how to optimally integrate TLTs into the care of older adults with TBI.

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.

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.

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans

Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict outcomes in comatose patients. Purpose To develop and evaluate a prognostic model combining deep learning of head CT scans and clinical information to predict long-term outcomes after sTBI. Materials and Methods This was a retrospective analysis of two prospectively collected databases. The model-building set included 537 patients (mean age, 40 years ± 17 [SD]; 422 men) from one institution from November 2002 to December 2018. Transfer learning and curriculum learning were applied to a convolutional neural network using admission head CT to predict mortality and unfavorable outcomes (Glasgow Outcomes Scale scores 1-3) at 6 months. This was combined with clinical input for a holistic fusion model. The models were evaluated using an independent internal test set and an external cohort of 220 patients with sTBI (mean age, 39 years ± 17; 166 men) from 18 institutions in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study from February 2014 to April 2018. The models were compared with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) model and the predictions of three neurosurgeons. Area under the receiver operating characteristic curve (AUC) was used as the main model performance metric. Results The fusion model had higher AUCs than did the IMPACT model in the prediction of mortality (AUC, 0.92 [95% CI: 0.86, 0.97] vs 0.80 [95% CI: 0.71, 0.88]; P < .001) and unfavorable outcomes (AUC, 0.88 [95% CI: 0.82, 0.94] vs 0.82 [95% CI: 0.75, 0.90]; P = .04) on the internal data set. For external TRACK-TBI testing, there was no evidence of a significant difference in the performance of any models compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.90) in the prediction of mortality. The Imaging model (AUC, 0.73; 95% CI: 0.66-0.81; P = .02) and the fusion model (AUC, 0.68; 95% CI: 0.60, 0.76; P = .02) underperformed as compared with the IMPACT model (AUC, 0.83; 95% CI: 0.77, 0.89) in the prediction of unfavorable outcomes. The fusion model outperformed the predictions of the neurosurgeons. Conclusion A deep learning model of head CT and clinical information can be used to predict 6-month outcomes after severe traumatic brain injury. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Haller in this issue.

Development and Verification of Prognostic Prediction Models for Patients with Brain Trauma Based on Coagulation Function Indexes

Objective

To assess the effect of adding coagulation indices to the currently existing prognostic prediction models of traumatic brain injury (TBI) in the prediction of outcome.

Methods

A total of 210 TBI patients from 2017 to 2019 and 131 TBI patients in 2020 were selected for development and internal verification of the new model. The primary outcomes include death at 14 days and Glasgow Outcome Score (GOS) at 6 months. The performance of each model is evaluated by means of discrimination (area under the curve (AUC)), calibration (Hosmer-Lemeshow (H-L) goodness-of-fit test), and precision (Brier score).

Results

The IMPACT Core model showed better prediction ability than the CRASH Basic model. Adding one coagulation index at a time to the IMPACT Core model, the new combined models IMPACT Core+FIB and IMPACT Core+APTT are optimal for the 6-month unfavorable outcome and 6-month mortality, respectively (AUC, 0.830 and 0.878). The new models were built based on the regression coefficients of the models. Internal verification indicated that for the prediction of 6-month unfavorable outcome and 6-month mortality, both the IMPACT Core+FIB model and the IMPACT Core+APTT model show better discrimination (AUC, 0.823 vs. 0.818 and 0.853 vs. 0.837), better calibration (HL, p = 0.114 and p = 0.317) and higher precision (Brier score, 0.148 vs. 0.141 and 0.147 vs. 0.164), respectively, than the original models.

Conclusion

Our research shows that the combination of the traumatic brain injury prognostic models and coagulation indices can improve the 6-month outcome prediction of patients with TBI.

Cognitive Reserve, Early Cognitive Screening, and Relationship to Long-Term Outcome after Severe Traumatic Brain Injury

The objective was to investigate the relationship between early global cognitive functioning using the Barrow Neurological Institute Screen for Higher Cerebral Functions (BNIS) and cognitive flexibility (Trail Making Test (TMT), TMT B-A), with long-term outcome assessed by the Mayo-Portland Adaptability Index (MPAI-4) in severe traumatic brain injury (sTBI) controlling for the influence of cognitive reserve, age, and injury severity. Of 114 patients aged 18–65 with acute Glasgow Coma Scale 3–8, 41 patients were able to complete (BNIS) at 3 months after injury and MPAI-4 5–8 years after injury. Of these, 33 patients also completed TMT at 3 months. Global cognition and cognitive flexibility correlated significantly with long-term outcome measured with MPAI-4 total score (r_BNIS = 0.315; r_TMT = 0.355). Global cognition correlated significantly with the participation subscale (r = 0.388), while cognitive flexibility correlated with the adjustment (r = 0.364) and ability (r = 0.364) subscales. Adjusting for cognitive reserve and acute injury severity did not alter these relationships. The effect size for education on BNIS and TMT scores was large (d ≈ 0.85). Early screenings with BNIS and TMT are related to long-term outcome after sTBI and seem to measure complementary aspects of outcome. As early as 3 months after sTBI, educational level influences the scores on neuropsychological screening instruments.

Examining the accuracy of the AM-PAC "6-clicks" at predicting discharge disposition in traumatic brain injury

OBJECTIVE

To assess the accuracy of the AM-PAC "6-Clicks" in predicting discharge dispositions among severely injured patients with an acute traumatic brain injury (TBI).

METHODS

We performed a retrospective review of patients with a TBI who presented to our trauma center from 2016 through 2018 and received a "6-Clicks" assessment. Outcomes were hospital length of stay (LOS) and discharge disposition: home, inpatient rehabilitation facility (IRF), subacute location (SL), or death/hospice. Subgroup analyses evaluated patients with concomitant mobility-limiting injuries (CM-LI).

RESULTS

There were 432 patients with a TBI; 42.6% (n = 184) had CM-LI. CM-LI patients had lower "6-Clicks" scores compared to patients with an isolated TBI (9 vs 14, p < .0001) and a longer hospital LOS (16.5 d vs 9 d, p < .0001). Increasing "6-Clicks" scores were associated with a home discharge (OR 1.21, 95% CI 1.15-1.28, p < .0001) while decreasing scores were predictive of an IRF or SL discharge or death/hospice. Increasing scores correlated with decreasing hospital LOS for the cohort (β - 8.93, 95% CI -10.24 - -7.62, p < .0001).

CONCLUSION

Among patients with an acute TBI, increasing "6 Clicks" scores were associated with a shorter hospital LOS and greater likelihood of home discharge. Decreasing mobility scores correlated with discharge to an IRF, SL, and death/hospice.

Defining New Research Questions and Protocols in the Field of Traumatic Brain Injury through Public Engagement: Preliminary Results and Review of the Literature

Traumatic brain injury (TBI) is the most common cause of death and disability in the age group below 40 years. The financial cost of loss of earnings and medical care presents a massive burden to family, society, social care, and healthcare, the cost of which is estimated at £1 billion per annum (about brain injury (online)). At present, we still lack a full understanding on the pathophysiology of TBI, and biomarkers represent the next frontier of breakthrough discoveries. Unfortunately, many tenets limit their widespread adoption. Brain tissue sampling is the mainstay of diagnosis in neuro-oncology; following on this path, we hypothesise that information gleaned from neural tissue samples obtained in TBI patients upon hospital admission may correlate with outcome data in TBI patients, enabling an early, accurate, and more comprehensive pathological classification, with the intent of guiding treatment and future research. We proposed various methods of tissue sampling at opportunistic times: two methods rely on a dedicated sample being taken; the remainder relies on tissue that would otherwise be discarded. To gauge acceptance of this, and as per the guidelines set out by the National Research Ethics Service, we conducted a survey of TBI and non-TBI patients admitted to our Trauma ward and their families. 100 responses were collected between December 2017 and July 2018, incorporating two redesigns in response to patient feedback. 75.0% of respondents said that they would consent to a brain biopsy performed at the time of insertion of an intracranial pressure (ICP) bolt. 7.0% replied negatively and 18.0% did not know. 70.0% would consent to insertion of a jugular bulb catheter to obtain paired intracranial venous samples and peripheral samples for analysis of biomarkers. Over 94.0% would consent to neural tissue from ICP probes, external ventricular drains (EVD), and lumbar drains (LD) to be salvaged, and 95.0% would consent to intraoperative samples for further analysis.

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.

Predicting Long-Term Global Outcome after Traumatic Brain Injury: Development of a Practical Prognostic Tool Using the Traumatic Brain Injury Model Systems National Database

For patients surviving serious traumatic brain injury (TBI), families and other stakeholders often desire information on long-term functional prognosis, but accurate and easy-to-use clinical tools are lacking. We aimed to build utilitarian decision trees from commonly collected clinical variables to predict Glasgow Outcome Scale (GOS) functional levels at 1, 2, and 5 years after moderate-to-severe closed TBI. Flexible classification tree statistical modeling was used on prospectively collected data from the TBI-Model Systems (TBIMS) inception cohort study. Enrollments occurred at 17 designated, or previously designated, TBIMS inpatient rehabilitation facilities. Analysis included all participants with nonpenetrating TBI injured between January 1997 and January 2017. Sample sizes were 10,125 (year-1), 8,821 (year-2), and 6,165 (year-5) after cross-sectional exclusions (death, vegetative state, insufficient post-injury time, and unavailable outcome). In our final models, post-traumatic amnesia (PTA) duration consistently dominated branching hierarchy and was the lone injury characteristic significantly contributing to GOS predictability. Lower-order variables that added predictability were age, pre-morbid education, productivity, and occupational category. Generally, patient outcomes improved with shorter PTA, younger age, greater pre-morbid productivity, and higher pre-morbid vocational or educational achievement. Across all prognostic groups, the best and worst good recovery rates were 65.7% and 10.9%, respectively, and the best and worst severe disability rates were 3.9% and 64.1%. Predictability in test data sets ranged from C-statistic of 0.691 (year-1; confidence interval [CI], 0.675, 0.711) to 0.731 (year-2; CI, 0.724, 0.738). In conclusion, we developed a clinically useful tool to provide prognostic information on long-term functional outcomes for adult survivors of moderate and severe closed TBI. Predictive accuracy for GOS level was demonstrated in an independent test sample. Length of PTA, a clinical marker of injury severity, was by far the most critical outcome determinant.

Proving cortical death after vascular coma: Evoked potentials, EEG and neuroimaging

OBJECTIVES

Several studies have shown that bilateral abolition of somatosensory evoked potentials after a nontraumatic coma has 100% specificity for nonawakening with ethical consequences for active care withdrawal. We propose to evaluate the prognostic value of bilateral abolished cortical components of SEPs in severe vascular coma.

METHODS

A total of 144 comatose patients after subarachnoid haemorrhage were evaluated by multimodal evoked potentials (EPs); 7 patients presented a bilateral abolition of somatosensory and auditory EPs. Their prognosis value was interpreted with respect to brainstem auditory EPs, EEG, and structural imaging.

RESULTS

One patient emerged from vegetative state during follow-up; 6 patients did not return to consciousness. The main neurophysiological difference was a cortical reactivity to pain preserved in the patient who returned to consciousness. This patient had focal sub-cortical lesions, which could explain the abolition of primary cortical components by a bilateral deafferentation of somatosensory and auditory pathways.

CONCLUSIONS

This is the first report of a favourable outcome after a multimodal abolition of primary cortex EPs in vascular coma. For the 3 cases of vascular coma with preserved brainstem function, EEG reactivity and cortical EPs were abolished by a diffuse ischaemia close to cerebral anoxia.

SIGNIFICANCE

The complementarity of EPs, EEG, and imaging must be emphasised if therapeutic limitations are considered to avoid over-interpretation of the prognosis value of EPs.

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.

Referral Patterns as a Contextual Variable in Pediatric Brain Injury: A Retrospective Analysis

PURPOSE

Access to speech-language pathology (SLP) services is a critical variable in the rehabilitation of pediatric brain injury. In this study, we examined patterns of SLP referral and factors affecting referral during the acute period following brain injury in 2 large pediatric specialty hospitals.

METHOD

In a retrospective, cohort chart review study, data collection focused on referrals made during the acute period using International Classification of Diseases, Ninth Revision, Clinical Modification codes for primary diagnoses of brain injury between 2007 and 2014 (Centers for Disease Control and Prevention [CDC], 2014). A total of 200 charts were reviewed. Data extraction included demographic and injury-related variables, referral for rehabilitation across disciplines, and plans of care following assessment.

RESULTS

Samples for both facilities were similar except for primary mechanism of traumatic brain injuries and severity. SLP referral rate at Hospital 1 was 36% and only 2% at Hospital 2. Regression revealed that individuals were less likely to receive an SLP referral if injury severity was classified as unknown or mild or if they were younger in age.

CONCLUSION

SLP referral rates in the early acute period for children with brain injury were poor, creating a barrier to rehabilitation. This not only limits access to SLP services, but also may have broader and long-term impact.