The patient with severe traumatic brain injury: clinical decision-making: the first 60 min and beyond

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

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

The Australian Traumatic Brain Injury Initiative: Statement of Working Principles and Rapid Review of Methods to Define Data Dictionaries for Neurological Conditions

The Australian Traumatic Brain Injury Initiative (AUS-TBI) aims to develop a health informatics approach to collect data predictive of outcomes for persons with moderate-severe TBI across Australia. Central to this approach is a data dictionary; however, no systematic reviews of methods to define and develop data dictionaries exist to-date. This rapid systematic review aimed to identify and characterize methods for designing data dictionaries to collect outcomes or variables in persons with neurological conditions. Database searches were conducted from inception through October 2021. Records were screened in two stages against set criteria to identify methods to define data dictionaries for neurological conditions (International Classification of Diseases, 11th Revision: 08, 22, and 23). Standardized data were extracted. Processes were checked at each stage by independent review of a random 25% of records. Consensus was reached through discussion where necessary. Thirty-nine initiatives were identified across 29 neurological conditions. No single established or recommended method for defining a data dictionary was identified. Nine initiatives conducted systematic reviews to collate information before implementing a consensus process. Thirty-seven initiatives consulted with end-users. Methods of consultation were "roundtable" discussion (n = 30); with facilitation (n = 16); that was iterative (n = 27); and frequently conducted in-person (n = 27). Researcher stakeholders were involved in all initiatives and clinicians in 25. Importantly, only six initiatives involved persons with lived experience of TBI and four involved carers. Methods for defining data dictionaries were variable and reporting is sparse. Our findings are instructive for AUS-TBI and can be used to further development of methods for defining data dictionaries.

The Australian Traumatic Brain Injury Initiative: Single Data Dictionary to Predict Outcome for People With Moderate-Severe Traumatic Brain Injury

In this series of eight articles, the Australian Traumatic Brain Injury Initiative (AUS-TBI) consortium describes the Australian approach used to select the common data elements collected acutely that have been shown to predict outcome following moderate-severe traumatic brain injury (TBI) across the lifespan. This article presents the unified single data dictionary, together with additional measures chosen to facilitate comparative effectiveness research and data linkage. Consultations with the AUS-TBI Lived Experience Expert Group provided insights on the merits and considerations regarding data elements for some of the study areas, as well as more general principles to guide the collection of data and the selection of meaningful measures. These are presented as a series of guiding principles and themes. The AUS-TBI Aboriginal and Torres Strait Islander Advisory Group identified a number of key points and considerations for the project approach specific to Aboriginal and Torres Strait Islander peoples, including key issues of data sovereignty and community involvement. These are outlined in the form of principles to guide selection of appropriate methodologies, data management, and governance. Implementation of the AUS-TBI approach aims to maximize ongoing data collection and linkage, to facilitate personalization of care and improved outcomes for people who experience moderate-severe TBI.

Treatment-limiting decisions in patients with severe traumatic brain injury in the Netherlands

Introduction

Treatment-limiting decisions (TLDs) can be inevitable severe traumatic brain injury (s-TBI) patients, but data on their use remain scarce.

Research question

To investigate the prevalence, timing and considerations of TLDs in s-TBI patients.

Material and methods

s-TBI patients between 2008 and 2017 were analysed retrospecively. Patient data, timing, location, involvement of proxies, and reasons for TLDs were collected. Baseline characteristics and in-hospital outcomes were compared between s-TBI patients with and without TLDs.

Results

TLDs were reported in 117 of 270 s-TBI patients (43.3%) and 95.9% of deaths after s-TBI were preceded by a TLD. The majority of TLDs (68.4%) were categorized as withdrawal of therapy, of which withdrawal of organ-support in 64.1%. Neurosurgical intervention was withheld in 29.9%. The median time from admission to TLD was 2 days [IQR, 0-8] and 50.4% of TLDs were made within 3 days of admission. The main reason for a TLD was that the patients were perceived as unsalvageable (66.7%). Nearly all decisions were made multidisciplinary (99.1%) with proxies involvement (75.2%). The predicted mortality (CRASH-score) between patients with and without TLDs were 72.6 vs. 70.6%. The percentage of TLDs in s-TBI patients increased from 20.0% in 2008 to 42.9% in 2012 and 64.3% in 2017.

Discussion and conclusion

TLDs occurred in almost half of s-TBI patients and were instituted more frequently over time. Half of TLDs were made within 3 days of admission in spite of baseline prognosis between groups being similar. Future research should address whether prognostic nihilism contributes to self-fulfilling prophecies.

Epidemiological characteristics for patients with traumatic brain injury and the nomogram model for poor prognosis: an 18-year hospital-based study

Objective

Traumatic brain injury (TBI) is a global social, economic, and health challenge that is associated with premature death and long-term disability. In the context of rapid development of urbanization, the analysis of TBI rate and mortality trend could provide abundant diagnosis and treatment suggestions, which helps to form future reference on public health strategies.

Methods

In this study, as one of major neurosurgical centers in China, we focused on the regime shift of TBI based on 18-year consecutive clinical data and evaluated the epidemiological features. In our current study, a total of 11,068 TBI patients were reviewed.

Results

The major cause of TBI was road traffic injuries (44.%), while the main type of injury was cerebral contusion (n = 4,974 [44.94%]). Regarding to temporal changes, a decreasing trend in TBI incidence for patients under 44 years old was observed, while an increasing trend for those aged over 45 years was indicated. Incidences of RTI and assaults decreased, while ground level fall presented increasing incidences. The total number of deaths was 933 (8.43%), with a decreasing trend in overall mortality since 2011. Age, cause of injury, GCS at admission, Injury Severity Score, shock state at admission, trauma-related diagnoses and treatments were significantly associated with mortality. A predictive nomogram model for poor prognosis was developed based on patient's GOS scores at discharge.

Conclusions

The trends and characteristics of TBI patients changed with rapid development of urbanization in the past 18 years. Further larger studies are warranted to verify its clinical suggestions.

Traumatic Brain Injury Induces Microglial and Caspase3 Activation in the Retina

Traumatic brain injury (TBI) is among the main causes of sudden death after head trauma. These injuries can result in severe degeneration and neuronal cell death in the CNS, including the retina, which is a crucial part of the brain responsible for perceiving and transmitting visual information. The long-term effects of mild-repetitive TBI (rmTBI) are far less studied thus far, even though damage induced by repetitive injuries occurring in the brain is more common, especially amongst athletes. rmTBI can also have a detrimental effect on the retina and the pathophysiology of these injuries is likely to differ from severe TBI (sTBI) retinal injury. Here, we show how rmTBI and sTBI can differentially affect the retina. Our results indicate an increase in the number of activated microglial cells and Caspase3-positive cells in the retina in both traumatic models, suggesting a rise in the level of inflammation and cell death after TBI. The pattern of microglial activation appears distributed and widespread but differs amongst the various retinal layers. sTBI induced microglial activation in both the superficial and deep retinal layers. In contrast to sTBI, no significant change occurred following the repetitive mild injury in the superficial layer, only the deep layer (spanning from the inner nuclear layer to the outer plexiform layer) shows microglial activation. This difference suggests that alternate response mechanisms play a role in the case of the different TBI incidents. The Caspase3 activation pattern showed a uniform increase in both the superficial and deep layers of the retina. This suggests a different action in the course of the disease in sTBI and rmTBI models and points to the need for new diagnostic procedures. Our present results suggest that the retina might serve as such a model of head injuries since the retinal tissue reacts to both forms of TBI and is the most accessible part of the human brain.

AUS-TBI: The Australian Health Informatics Approach to Predict Outcomes and Monitor Intervention Efficacy after Moderate-to-Severe Traumatic Brain Injury

Predicting and optimizing outcomes after traumatic brain injury (TBI) remains a major challenge because of the breadth of injury characteristics and complexity of brain responses. AUS-TBI is a new Australian Government–funded initiative that aims to improve personalized care and treatment for children and adults who have sustained a TBI. The AUS-TBI team aims to address a number of key knowledge gaps, by designing an approach to bring together data describing psychosocial modulators, social determinants, clinical parameters, imaging data, biomarker profiles, and rehabilitation outcomes in order to assess the influence that they have on long-term outcome. Data management systems will be designed to track a broad range of suitable potential indicators and outcomes, which will be organized to facilitate secure data collection, linkage, storage, curation, management, and analysis. It is believed that these objectives are achievable because of our consortium of highly committed national and international leaders, expert committees, and partner organizations in TBI and health informatics. It is anticipated that the resulting large-scale data resource will facilitate personalization, prediction, and improvement of outcomes post-TBI.

Challenges Encountered in Surgical Traumatic Brain Injury Research: A Need for Methodological Improvement of Future Studies

BACKGROUND

Investigating neurosurgical interventions for traumatic brain injury (TBI) involves complex methodological and practical challenges. In the present report, we have provided an overview of the current state of neurosurgical TBI research and discussed the key challenges and possible solutions.

METHODS

The content of our report was based on an extensive literature review and personal knowledge and expert opinions of senior neurosurgeon researchers and epidemiologists.

RESULTS

Current best practice research strategies include randomized controlled trials (RCTs) and comparative effectiveness research. The performance of RCTs has been complicated by the heterogeneity of TBI patient populations with the associated sample size requirements, the traditional eminence-based neurosurgical culture, inadequate research budgets, and the often acutely life-threatening setting of severe TBI. Statistical corrections can mitigate the effects of heterogeneity, and increasing awareness of clinical equipoise and informed consent alternatives can improve trial efficiency. The substantial confounding by indication, which limits the interpretability of observational research, can be circumvented by using an instrumental variable analysis. Traditional TBI outcome measures remain relevant but do not adequately capture the subtleties of well-being, suggesting a need for multidimensional approaches to outcome assessments.

CONCLUSIONS

In settings in which traditional RCTs are difficult to conduct and substantial confounding by indication can be present, observational studies using an instrumental variable analysis and "pragmatic" RCTs are promising alternatives. Embedding TBI research into standard clinical practice should be more frequently considered but will require fundamental modifications to the current health care system. Finally, multimodality outcome assessment will be key to improving future surgical and nonsurgical TBI research.

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.

Factors Affecting Neurosurgeons' Decisions to Forgo Life-Sustaining Treatments After Traumatic Brain Injury

BACKGROUND

Traumatic brain injury (TBI) is a multifaceted condition that causes mortality and disability worldwide. Limited data are available on the factors associated with the decision for the withdrawal of life-sustaining treatment (WLST) for patients with TBI. In the present study, we aimed to determine the risk factors and attitudes affecting neurosurgeons when deciding on WLST for patients with TBI using a multicenter survey.

METHODS

An online questionnaire was applied worldwide and shared using social media platforms and electronic mail to ∼5000 neurosurgeons. The social media group "Neurosurgery Cocktail" was used to post a link to the questionnaire. In addition, randomly chosen neurosurgery clinics around the world were sent the survey via electronic mail.

RESULTS

Of the participants, 17.22% had decided on WLST after TBI for >26 patients. Neurosurgeons with more WLST decisions were older, had had more clinical experience and intensive care unit (ICU) training, and were better prepared to involve the family members of TBI patients in their decision-making compared with those with fewer WLST decisions. The respondents stated that the patient's family, ICU consultants, and themselves played the most influential role in the WLST decisions, with the hospital administration, social workers, spiritual caregivers, and nurses having lesser roles. The current and presenting Glasgow coma scale scores, pupillary response, advanced patient age, candidates for a vegetative state, and impaired neurological function were significant factors associated with the WLST decision.

CONCLUSIONS

To the best of our knowledge, the present study is the first to evaluate neurosurgeons concerning their opinions and behaviors regarding WLST decisions after TBI. Increased patient age, Glasgow coma scale score, pupillary response, the presence of comorbidities, candidacy for a vegetative state, and impaired neurological function were the main factors contributing to the decision for WLST. We also found that the family, ICU consultants, and the attending neurosurgeon had the most effective roles in the decisions regarding WLST.

Machine Learning Algorithms to Predict In-Hospital Mortality in Patients with Traumatic Brain Injury

Traumatic brain injury (TBI) can lead to severe adverse clinical outcomes, including death and disability. Early detection of in-hospital mortality in high-risk populations may enable early treatment and potentially reduce mortality using machine learning. However, there is limited information on in-hospital mortality prediction models for TBI patients admitted to emergency departments. The aim of this study was to create a model that successfully predicts, from clinical measures and demographics, in-hospital mortality in a sample of TBI patients admitted to the emergency department. Of the 4881 TBI patients who were screened at the emergency department at a high-level first aid duty hospital in northern Taiwan, 3331 were assigned in triage to Level I or Level II using the Taiwan Triage and Acuity Scale from January 2008 to June 2018. The most significant predictors of in-hospital mortality in TBI patients were the scores on the Glasgow coma scale, the injury severity scale, and systolic blood pressure in the emergency department admission. This study demonstrated the effective cutoff values for clinical measures when using machine learning to predict in-hospital mortality of patients with TBI. The prediction model has the potential to further accelerate the development of innovative care-delivery protocols for high-risk patients.