Sub-classifying patients with mild traumatic brain injury: A clustering approach based on baseline clinical characteristics and 90-day and 180-day outcomes

Can Long-Term Outcomes of Posttraumatic Headache be Predicted?

PURPOSE OF REVIEW

Headache is one of the most common symptoms of traumatic brain injury, and it is more common in patients with mild, rather than moderate or severe, traumatic brain injury. Posttraumatic headache can be the most persistent symptom of traumatic brain injury. In this article, we review the current understanding of posttraumatic headache, summarize the current knowledge of its pathophysiology and treatment, and review the research regarding predictors of long-term outcomes.

RECENT FINDINGS

To date, posttraumatic headache has been treated based on the semiology of the primary headache disorder that it most resembles, but the pathophysiology is likely to be different, and the long-term prognosis differs as well. No models exist to predict long-term outcomes, and few studies have highlighted risk factors for the development of acute and persistent posttraumatic headaches. Further research is needed to elucidate the pathophysiology and identify specific treatments for posttraumatic headache to be able to predict long-term outcomes. In addition, the effect of managing comorbid traumatic brain injury symptoms on posttraumatic headache management should be further studied. Posttraumatic headache can be a persistent symptom of traumatic brain injury, especially mild traumatic brain injury. It has traditionally been treated based on the semiology of the primary headache disorder it most closely resembles, but further research is needed to elucidate the pathophysiology of posttraumatic headache and determine risk factors to better predict long-term outcomes.

Traumatic Brain Injury in the Long-COVID Era

Major determinants of the biological background or reserve, such as age, biological sex, comorbidities (diabetes, hypertension, obesity, etc.), and medications (e.g., anticoagulants), are known to affect outcome after traumatic brain injury (TBI). With the unparalleled data richness of coronavirus disease 2019 (COVID-19; ∼375,000 and counting!) as well as the chronic form, long-COVID, also called post-acute sequelae SARS-CoV-2 infection (PASC), publications (∼30,000 and counting) covering virtually every aspect of the diseases, pathomechanisms, biomarkers, disease phases, symptomatology, etc., have provided a unique opportunity to better understand and appreciate the holistic nature of diseases, interconnectivity between organ systems, and importance of biological background in modifying disease trajectories and affecting outcomes. Such a holistic approach is badly needed to better understand TBI-induced conditions in their totality. Here, I briefly review what is known about long-COVID/PASC, its underlying-suspected-pathologies, the pathobiological changes induced by TBI, in other words, the TBI endophenotypes, discuss the intersection of long-COVID/PASC and TBI-induced pathobiologies, and how by considering some of the known factors affecting the person's biological background and the inclusion of mechanistic molecular biomarkers can help to improve the clinical management of TBI patients.

Discovery of Generalizable TBI Phenotypes Using Multivariate Time-Series Clustering

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, β, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype β signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

Neuroimaging to enhance understanding of cardiovascular autonomic changes associated with mild traumatic brain injury: a scoping review

BACKGROUND

Cardiovascular changes, such as altered heart rate and blood pressure, have been identified in some individuals following mild traumatic brain injury (mTBI) and may be related to disturbances of the autonomic nervous system and cerebral blood flow.

METHODS

We conducted a scoping review according to PRISMA-ScR guidelines across six databases (Medline, CINAHL, Web of Science, PsychInfo, SportDiscus and Google Scholar) to explore literature examining both cardiovascular parameters and neuroimaging modalities following mTBI, with the aim of better understanding the pathophysiological basis of cardiovascular autonomic changes associated with mTBI.

RESULTS

Twenty-nine studies were included and two main research approaches emerged from data synthesis. Firstly, more than half the studies used transcranial Doppler ultrasound and found evidence of cerebral blood flow impairments that persisted beyond symptom resolution. Secondly, studies utilizing advanced MRI identified microstructural injury within brain regions responsible for cardiac autonomic function, providing preliminary evidence that cardiovascular autonomic changes are a consequence of injury to these areas.

CONCLUSION

Neuroimaging modalities hold considerable potential to aid understanding of the complex relationship between cardiovascular changes and brain pathophysiology associated with mTBI. However, it is difficult to draw definitive conclusions from the available data due to variability in study methodology and terminology.

A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Clinical data associated with TBI patients are often measured over time and represented as time-series variables characterized by missing values and irregular time intervals. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.

Prevalence of comorbidities post mild traumatic brain injuries: a traumatic brain injury model systems study

Traumatic brain injury (TBI) is associated with an increased risk of long-lasting health-related complications. Survivors of brain trauma often experience comorbidities which could further dampen functional recovery and severely interfere with their day-to-day functioning after injury. Of the three TBI severity types, mild TBI constitutes a significant proportion of total TBI cases, yet a comprehensive study on medical and psychiatric complications experienced by mild TBI subjects at a particular time point is missing in the field. In this study, we aim to quantify the prevalence of psychiatric and medical comorbidities post mild TBI and understand how these comorbidities are influenced by demographic factors (age, and sex) through secondary analysis of patient data from the TBI Model Systems (TBIMS) national database. Utilizing self-reported information from National Health and Nutrition Examination Survey (NHANES), we have performed this analysis on subjects who received inpatient rehabilitation at 5 years post mild TBI. Our analysis revealed that psychiatric comorbidities (anxiety, depression, and post-traumatic stress disorder (PTSD)), chronic pain, and cardiovascular comorbidities were common among survivors with mild TBI. Furthermore, depression exhibits an increased prevalence in the younger compared to an older cohort of subjects whereas the prevalence of rheumatologic, ophthalmological, and cardiovascular comorbidities was higher in the older cohort. Lastly, female survivors of mild TBI demonstrated increased odds of developing PTSD compared to male subjects. The findings of this study would motivate additional analysis and research in the field and could have broader implications for the management of comorbidities after mild TBI.

Development of an Animal Model for Traumatic Brain Injury Augmentation of Heterotopic Ossification in Response to Local Injury

Heterotopic ossification (HO) is the abnormal growth of bone in soft connective tissues that occurs as a frequent complication in individuals with traumatic brain injury (TBI) and in rare genetic disorders. Therefore, understanding the mechanisms behind ectopic bone formation in response to TBI is likely to have a significant impact on identification of novel therapeutic targets for HO treatment. In this study, we induced repetitive mild TBI (mTBI) using a weight drop model in mice and then stimulated HO formation via a local injury to the Achilles tendon or fibula. The amount of ectopic bone, as evaluated by micro-CT analyses, was increased by four-fold in the injured leg of mTBI mice compared to control mice. However, there was no evidence of HO formation in the uninjured leg of mTBI mice. Since tissue injury leads to the activation of hypoxia signaling, which is known to promote endochondral ossification, we evaluated the effect of IOX2, a chemical inhibitor of PHD2 and a known inducer of hypoxia signaling on HO development in response to fibular injury. IOX2 treatment increased HO volume by five-fold compared to vehicle. Since pericytes located in the endothelium of microvascular capillaries are known to function as multipotent tissue-resident progenitors, we determined if activation of hypoxia signaling promotes pericyte recruitment at the injury site. We found that markers of pericytes, NG2 and PDGFRβ, were abundantly expressed at the site of injury in IOX2 treated mice. Treatment of pericytes with IOX2 for 72 h stimulated expression of targets of hypoxia signaling (Vegf and Epo), as well as markers of chondrocyte differentiation (Col2α1 and Col10α1). Furthermore, serum collected from TBI mice was more effective in promoting the proliferation and differentiation of pericytes than control mouse serum. In conclusion, our data show that the hypoxic state at the injury site in soft tissues of TBI mice provides an environment leading to increased accumulation and activation of pericytes to form endochondral bone.

Functional Connectivity in the Mouse Brainstem Represents Signs of Recovery from Concussion

Mild traumatic brain injury (mTBI) is one of the most frequent neurological disorders. Diagnostic criteria for mTBI are based on cognitive or neurological symptoms without fully understanding the neuropathological basis for explaining behaviors. From the neuropathological perspective of mTBI, recent neuroimaging studies have focused on structural or functional differences in motor-related cortical regions but did not compare topological network properties between the post-concussion days in the brainstem. We investigated temporal changes in functional connectivity and evaluated network properties of functional networks in the mouse brainstem. We observed a significantly decreased functional connectivity and global and local network properties on post-concussion day 7, which normalized on post-concussion day 14. Functional connectivity and local network properties on post-concussion day 2 were also significantly decreased compared with those on post-concussion day 14, but there were no significant group differences in global network properties between days 2 and 14. We also observed that the local efficiency and clustering coefficient of the brainstem network were significantly correlated with anxiety-like behaviors on post-concussion days 7 and 14. This study suggests that functional connectivity in the mouse brainstem provides vital recovery signs from concussion through functional reorganization.

Post-Concussion Symptoms and Disability in Adults with Mild Traumatic Brain Injury: A Systematic Review and Meta-Analysis

Studies investigating long-term symptoms and disability after mild traumatic brain injury (mTBI) have yielded mixed results. This systematic review and meta-analysis aimed to determine the prevalence of self-reported post-concussion symptoms (PCS) and disability following mTBI. We systematically searched MEDLINE, Embase, CINAHL, CENTRAL, and PsycInfo to identify inception cohort studies of adults with mTBI. Paired reviewers independently extracted data and assessed risk of bias with the Scottish Intercollegiate Guidelines Network criteria. We identified 43 eligible studies for the systematic review; 41 were rated as high risk of bias, primarily due to high attrition (> 20%). Twenty-one studies (49%) were included in the meta-analyses (five studies were narratively synthesized; 17 studies were duplicate reports). At 3-6 months post-injury, the estimated prevalence of PCS from random-effects meta-analyses was 31.3% (95% confidence interval [CI] = 25.4-38.4) using a lenient definition of PCS (2-4 mild severity PCS) and 18.3% (95% CI = 13.6-24.0) using a more stringent definition. The estimated prevalence of disability was 54.0% (95% CI = 49.4-58.6) and 29.6% (95% CI = 27.8-31.5) when defined as Glasgow Outcome Scale-Extended <8 and <7, respectively. The prevalence of symptoms similar to PCS was higher in adults with mTBI versus orthopedic injury (prevalence ratio = 1.57, 95% CI = 1.22-2.02). In a meta-regression, attrition rate was the only study-related factor significantly associated with higher estimated prevalence of PCS. Setting attrition to 0%, the estimated prevalence of PCS (lenient definition) was 16.1%. We conclude that nearly one in three adults who present to an emergency department or trauma center with mTBI report at least mild severity PCS 3-6 months later, but controlling for attrition bias, the true prevalence may be one in six. Studies with representative samples and high retention rates are needed.

Traumatic brain injury: progress and challenges in prevention, clinical care, and research

Traumatic brain injury (TBI) has the highest incidence of all common neurological disorders, and poses a substantial public health burden. TBI is increasingly documented not only as an acute condition but also as a chronic disease with long-term consequences, including an increased risk of late-onset neurodegeneration. The first Lancet Neurology Commission on TBI, published in 2017, called for a concerted effort to tackle the global health problem posed by TBI. Since then, funding agencies have supported research both in high-income countries (HICs) and in low-income and middle-income countries (LMICs). In November 2020, the World Health Assembly, the decision-making body of WHO, passed resolution WHA73.10 for global actions on epilepsy and other neurological disorders, and WHO launched the Decade for Action on Road Safety plan in 2021. New knowledge has been generated by large observational studies, including those conducted under the umbrella of the International Traumatic Brain Injury Research (InTBIR) initiative, established as a collaboration of funding agencies in 2011. InTBIR has also provided a huge stimulus to collaborative research in TBI and has facilitated participation of global partners. The return on investment has been high, but many needs of patients with TBI remain unaddressed. This update to the 2017 Commission presents advances and discusses persisting and new challenges in prevention, clinical care, and research.

In LMICs, the occurrence of TBI is driven by road traffic incidents, often involving vulnerable road users such as motorcyclists and pedestrians. In HICs, most TBI is caused by falls, particularly in older people (aged ≥65 years), who often have comorbidities. Risk factors such as frailty and alcohol misuse provide opportunities for targeted prevention actions. Little evidence exists to inform treatment of older patients, who have been commonly excluded from past clinical trials—consequently, appropriate evidence is urgently required. Although increasing age is associated with worse outcomes from TBI, age should not dictate limitations in therapy. However, patients injured by low-energy falls (who are mostly older people) are about 50% less likely to receive critical care or emergency interventions, compared with those injured by high-energy mechanisms, such as road traffic incidents.

Mild TBI, defined as a Glasgow Coma sum score of 13–15, comprises most of the TBI cases (over 90%) presenting to hospital. Around 50% of adult patients with mild TBI presenting to hospital do not recover to pre-TBI levels of health by 6 months after their injury. Fewer than 10% of patients discharged after presenting to an emergency department for TBI in Europe currently receive follow-up. Structured follow-up after mild TBI should be considered good practice, and urgent research is needed to identify which patients with mild TBI are at risk for incomplete recovery.

The selection of patients for CT is an important triage decision in mild TBI since it allows early identification of lesions that can trigger hospital admission or life-saving surgery. Current decision making for deciding on CT is inefficient, with 90–95% of scanned patients showing no intracranial injury but being subjected to radiation risks. InTBIR studies have shown that measurement of blood-based biomarkers adds value to previously proposed clinical decision rules, holding the potential to improve efficiency while reducing radiation exposure. Increased concentrations of biomarkers in the blood of patients with a normal presentation CT scan suggest structural brain damage, which is seen on MR scanning in up to 30% of patients with mild TBI. Advanced MRI, including diffusion tensor imaging and volumetric analyses, can identify additional injuries not detectable by visual inspection of standard clinical MR images. Thus, the absence of CT abnormalities does not exclude structural damage—an observation relevant to litigation procedures, to management of mild TBI, and when CT scans are insufficient to explain the severity of the clinical condition.

Although blood-based protein biomarkers have been shown to have important roles in the evaluation of TBI, most available assays are for research use only. To date, there is only one vendor of such assays with regulatory clearance in Europe and the USA with an indication to rule out the need for CT imaging for patients with suspected TBI. Regulatory clearance is provided for a combination of biomarkers, although evidence is accumulating that a single biomarker can perform as well as a combination. Additional biomarkers and more clinical-use platforms are on the horizon, but cross-platform harmonisation of results is needed. Health-care efficiency would benefit from diversity in providers.

In the intensive care setting, automated analysis of blood pressure and intracranial pressure with calculation of derived parameters can help individualise management of TBI. Interest in the identification of subgroups of patients who might benefit more from some specific therapeutic approaches than others represents a welcome shift towards precision medicine. Comparative-effectiveness research to identify best practice has delivered on expectations for providing evidence in support of best practices, both in adult and paediatric patients with TBI.

Progress has also been made in improving outcome assessment after TBI. Key instruments have been translated into up to 20 languages and linguistically validated, and are now internationally available for clinical and research use. TBI affects multiple domains of functioning, and outcomes are affected by personal characteristics and life-course events, consistent with a multifactorial bio-psycho-socio-ecological model of TBI, as presented in the US National Academies of Sciences, Engineering, and Medicine (NASEM) 2022 report. Multidimensional assessment is desirable and might be best based on measurement of global functional impairment. More work is required to develop and implement recommendations for multidimensional assessment. Prediction of outcome is relevant to patients and their families, and can facilitate the benchmarking of quality of care. InTBIR studies have identified new building blocks (eg, blood biomarkers and quantitative CT analysis) to refine existing prognostic models. Further improvement in prognostication could come from MRI, genetics, and the integration of dynamic changes in patient status after presentation.

Neurotrauma researchers traditionally seek translation of their research findings through publications, clinical guidelines, and industry collaborations. However, to effectively impact clinical care and outcome, interactions are also needed with research funders, regulators, and policy makers, and partnership with patient organisations. Such interactions are increasingly taking place, with exemplars including interactions with the All Party Parliamentary Group on Acquired Brain Injury in the UK, the production of the NASEM report in the USA, and interactions with the US Food and Drug Administration. More interactions should be encouraged, and future discussions with regulators should include debates around consent from patients with acute mental incapacity and data sharing. Data sharing is strongly advocated by funding agencies. From January 2023, the US National Institutes of Health will require upload of research data into public repositories, but the EU requires data controllers to safeguard data security and privacy regulation. The tension between open data-sharing and adherence to privacy regulation could be resolved by cross-dataset analyses on federated platforms, with the data remaining at their original safe location. Tools already exist for conventional statistical analyses on federated platforms, however federated machine learning requires further development. Support for further development of federated platforms, and neuroinformatics more generally, should be a priority.

This update to the 2017 Commission presents new insights and challenges across a range of topics around TBI: epidemiology and prevention (section 1 ); system of care (section 2 ); clinical management (section 3 ); characterisation of TBI (section 4 ); outcome assessment (section 5 ); prognosis (Section 6 ); and new directions for acquiring and implementing evidence (section 7 ). Table 1 summarises key messages from this Commission and proposes recommendations for the way forward to advance research and clinical management of TBI.

Multi-tract multi-symptom relationships in pediatric concussion

Background

The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships.

Methods

Using cross-sectional data from 306 previously concussed children aged 9-10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures.

Results

Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results.

Conclusions

Using a double-multivariate approach, we identified clinically-informative, cross-demographic multi-tract multi-symptom relationships. These results suggest that rather than clear one-to-one symptom-connectivity disturbances, concussions may be characterized by subtypes of symptom/connectivity relationships. The symptom/connectivity relationships identified in multi-tract multi-symptom pairs were not apparent in single-tract/single-symptom analyses. Future studies aiming to better understand connectivity/symptom relationships should take into account multi-tract multi-symptom heterogeneity.

Funding

Financial support for this work came from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (G.I.G.), an Ontario Graduate Scholarship (S.S.), a Restracomp Research Fellowship provided by the Hospital for Sick Children (S.S.), an Institutional Research Chair in Neuroinformatics (M.D.), as well as a Natural Sciences and Engineering Research Council CREATE grant (M.D.).

Modern Learning from Big Data in Critical Care: Primum Non Nocere

Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. In other research areas, such as clustering and prediction studies, there is more discussion regarding the benefit and efficiency of ML techniques compared with statistical learning. In this viewpoint, we aim to explain commonly used statistical learning and ML techniques and provide guidance for responsible use in the case of clustering and prediction questions in critical care. Clustering studies have been increasingly popular in critical care research, aiming to inform how patients can be characterized, classified, or treated differently. An important challenge for clustering studies is to ensure and assess generalizability. This limits the application of findings in these studies toward individual patients. In the case of predictive questions, there is much discussion as to what algorithm should be used to most accurately predict outcome. Aspects that determine usefulness of ML, compared with statistical techniques, include the volume of the data, the dimensionality of the preferred model, and the extent of missing data. There are areas in which modern ML methods may be preferred. However, efforts should be made to implement statistical frameworks (e.g., for dealing with missing data or measurement error, both omnipresent in clinical data) in ML methods. To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm.

A Child Presenting with a Glasgow Coma Scale Score of 13: Mild or Moderate Traumatic Brain Injury? A Narrative Review

OBJECTIVE

The objective of this article was to compare children with traumatic brain injury (TBI) and Glasgow Coma Scale score (GCS) 13 with children presenting with GCS 14 and 15 and GCS 9 to 12.

DATA SOURCE

We searched PubMed for clinical studies of children of 0 to 18 years of age with mild TBI (mTBI) and moderate TBI, published in English language in the period of 2000 to 2020.

STUDY SELECTION

We selected studies sub-classifying children with GCS 13 in comparison with GCS 14 and 15 and 9 to 12. We excluded reviews, meta-analyses, non-U.S./European population studies, studies of abusive head trauma, and severe TBI.

DATA SYNTHESIS

Most children (>85%) with an mTBI present at the emergency department with an initial GCS 15. A minority of only 5% present with GCS 13, 40% of which sustain a high-energy trauma. Compared with GCS 15, they present with a longer duration of unconsciousness and of post-traumatic amnesia. More often head computerized tomography scans show abnormalities (in 9-16%), leading to neurosurgical intervention in 3 to 8%. Also, higher rates of severe extracranial injury are reported. Admission is indicated in more than 90%, with a median length of hospitalization of more than 4 days and 28% requiring intensive care unit level care. These data are more consistent with children with GCS 9 to 12. In children with GCS 15, all these numbers are much lower.

CONCLUSION

We advocate classifying children with GCS 13 as moderate TBI and treat them accordingly.

The impact of cigarette smoking and nicotine on traumatic brain injury: a review

INTRODUCTION

Traumatic Brain Injury (TBI) and tobacco smoking are both serious public health problems. Many people with TBI also smoke. Nicotine, a component of tobacco smoke, has been identified as a premorbid neuroprotectant in other neurological disorders. This study aims to provide better understanding of relationships between tobacco smoking and nicotine use and effect on outcome/recovery from TBI.

METHODS

PubMed database, SCOPUS, and PTSDpub were searched for relevant English-language papers.

RESULTS

Twenty-nine human clinical studies and nine animal studies were included. No nicotine-replacement product use in human TBI clinical studies were identified. While smoking tobacco prior to injury can be harmful primarily due to systemic effects that can compromise brain function, animal studies suggest that nicotine as a pharmacological agent may augment recovery of cognitive deficits caused by TBI.

CONCLUSIONS

While tobacco smoking before or after TBI has been associated with potential harms, many clinical studies downplay correlations for most expected domains. On the other hand, nicotine could provide potential treatment for cognitive deficits following TBI by reversing impaired signaling pathways in the brain including those involving nAChRs, TH, and dopamine. Future studies regarding the impact of cigarette smoking and vaping on patients with TBI are needed .

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1–2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.

Prospective observational cohort study on epidemiology, treatment and outcome of patients with traumatic brain injury (TBI) in German BG hospitals

OBJECTIVES

Since 2000/2001, no large-scale prospective studies addressing traumatic brain injury (TBI) epidemiology in Germany have been published. Our aim was to look for a possible shift in TBI epidemiology described in other European countries, to look for possible changes in TBI management and to identify predictors of 1-year outcome especially in patients with mild TBI.

DESIGN

Observational cohort study.

SETTING

All patients suffering from a TBI of any degree between 1 October 2014 and 30 September 2015, and who arrived in one of the seven participating BG hospitals within 24 hours after trauma, were included.

PARTICIPANTS

In total, 3514 patients were included.

OUTCOME MEASURES

Initial care, acute hospital care and rehabilitation were documented using standardised documentation forms. A standardised telephone interview was conducted 3 and 12 months after TBI in order to obtain information on outcome.

RESULTS

Peaks were identified in males in the early 20s and mid-50s, and in both sexes in the late 70s, with 25% of all patients aged 75 or older. A fall was the most frequent cause of TBI, followed by traffic accidents (especially bicyclists). The number of head CT scans increased, and the number of conventional X-rays of the skull decreased compared with 2000/2001. Besides, more patients were offered rehabilitation than before. Though most TBI were classified as mild, one-third of the patients participating in the telephone interview after 12 months still reported troubles attributed to TBI. Negative predictors in mild TBI were female gender, intracranial bleeding and Glasgow Coma Scale (GCS) 13/14.

CONCLUSION

The observed epidemiologic shift in TBI (ie, elderly patients, more falls, more bicyclists) calls for targeted preventive measures. The heterogeneity behind the diagnosis 'mild TBI' emphasises the need for defining subgroups not only based on GCS.

Mental health after paediatric concussion: a systematic review and meta-analysis

OBJECTIVE

This systematic review and meta-analysis sought to rigorously examine mental health outcomes following paediatric concussion. To date, heterogeneous findings and methodologies have limited clinicians' and researchers' ability to meaningfully synthesise existing literature. In this context, there is a need to clarify mental health outcomes in a homogeneous sample, controlling for key methodological differences and applying a consistent definition of concussion across studies.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

We searched Medline, Embase, PsycINFO, CINAHL, SportDiscus, Scopus and PubMed.

ELIGIBILITY

Peer-reviewed studies published between 1980 and June 2020 that prospectively examined mental health outcomes after paediatric concussion, defined as per the Berlin Consensus Statement on Concussion in Sport.

RESULTS

Sixty-nine articles characterising 60 unique samples met inclusion criteria, representing 89 114 children with concussion. Forty articles (33 studies) contributed to a random effects meta-analysis of internalising (withdrawal, anxiety, depression, post-traumatic stress), externalising (conduct problems, aggression, attention, hyperactivity) and total mental health difficulties across three time points post-injury (acute, persisting and chronic). Overall, children with concussion (n=6819) experienced significantly higher levels of internalising (g=0.41-0.46), externalising (g=0.25-0.46) and overall mental health difficulties compared with controls (g=0.18-0.49; n=56 271), with effects decreasing over time.

SUMMARY/CONCLUSIONS

Our review highlights that mental health is central to concussion recovery. Assessment, prevention and intervention of mental health status should be integrated into standard follow-up procedures. Further research is needed to clarify the mechanisms underlying observed relationships between mental health, post-concussion symptoms and other psychosocial factors. Results suggest that concussion may both precipitate and exacerbate mental health difficulties, thus impacting delayed recovery and psychosocial outcomes.

Clinical Predictors of 3- and 6-Month Outcome for Mild Traumatic Brain Injury Patients with a Negative Head CT Scan in the Emergency Department: A TRACK-TBI Pilot Study

A considerable subset of mild traumatic brain injury (mTBI) patients fail to return to baseline functional status at or beyond 3 months postinjury. Identifying at-risk patients for poor outcome in the emergency department (ED) may improve surveillance strategies and referral to care. Subjects with mTBI (Glasgow Coma Scale 13–15) and negative ED initial head CT < 24 h of injury, completing 3- or 6-month functional outcome (Glasgow Outcome Scale-Extended; GOSE), were extracted from the prospective, multicenter Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot study. Outcomes were dichotomized to full recovery (GOSE = 8) vs. functional deficits (GOSE < 8). Univariate predictors with p < 0.10 were considered for multivariable regression. Adjusted odds ratios (AOR) were reported for outcome predictors. Significance was assessed at p < 0.05. Subjects who completed GOSE at 3- and 6-month were 211 (GOSE < 8: 60%) and 185 (GOSE < 8: 65%). Risk factors for 6-month GOSE < 8 included less education (AOR = 0.85 per-year increase, 95% CI: (0.74–0.98)), prior psychiatric history (AOR = 3.75 (1.73–8.12)), Asian/minority race (American Indian/Alaskan/Hawaiian/Pacific Islander) (AOR = 23.99 (2.93–196.84)), and Hispanic ethnicity (AOR = 3.48 (1.29–9.37)). Risk factors for 3-month GOSE < 8 were similar with the addition of injury by assault predicting poorer outcome (AOR = 3.53 (1.17–10.63)). In mTBI patients seen in urban trauma center EDs with negative CT, education, injury by assault, Asian/minority race, and prior psychiatric history emerged as risk factors for prolonged disability.

Individualizing the definition of seizure clusters based on temporal clustering analysis

OBJECTIVE

Seizure clusters are often encountered in people with poorly controlled epilepsy. Detection of seizure clusters is currently based on simple clinical rules, such as two seizures separated by four or fewer hours or multiple seizures in 24 h. Current definitions fail to distinguish between statistically significant clusters and those that may result from natural variation in the person's seizures. Ability to systematically define when a seizure cluster is significant for the individual carries major implications for treatment. However, there is no uniform consensus on how to define seizure clusters. This study proposes a principled statistical approach to defining seizure clusters that addresses these issues.

METHODS

A total of 533,968 clinical seizures from 1,748 people with epilepsy in the Seizure Tracker™ seizure diary database were used for algorithm development. We propose an algorithm for automated individualized seizure cluster identification combining cumulative sum change-point analysis with bootstrapping and aberration detection, which provides a new approach to personalized seizure cluster identification at user-specified levels of clinical significance. We develop a standalone user interface to make the proposed algorithm accessible for real-time seizure cluster identification (ClusterCalc™). Clinical impact of systematizing cluster identification is demonstrated by comparing empirically-defined clusters to those identified by routine seizure cluster definitions. We also demonstrate use of the Hurst exponent as a standardized measure of seizure clustering for comparison of seizure clustering burden within or across patients.

RESULTS

Seizure clustering was present in 26.7 % (95 % CI, 24.5-28.7 %) of people with epilepsy. Empirical tables were provided for standardizing inter- and intra-patient comparisons of seizure cluster tendency. Using the proposed algorithm, we found that 37.7-59.4 % of seizures identified as clusters based on routine definitions had high probability of occurring by chance. Several clusters identified by the algorithm were missed by conventional definitions. The utility of the ClusterCalc algorithm for individualized seizure cluster detection is demonstrated.

SIGNIFICANCE

This study proposes a principled statistical approach to individualized seizure cluster identification and demonstrates potential for real-time clinical usage through ClusterCalc. Using this approach accounts for individual variations in baseline seizure frequency and evaluates statistical significance. This new definition has the potential to improve individualized epilepsy treatment by systematizing identification of unrecognized seizure clusters and preventing unnecessary intervention for random events previously considered clusters.

Traumatic brain injury and sleep disturbances in combat-exposed service members and veterans: Where to go next?

OBJECTIVE

To synthesize the current evidence on sleep disturbances in military service members (SMs) and veterans with traumatic brain injury (TBI).

METHODS

An electronic literature search first identified abstracts published from 2008-2018 inclusively referencing sleep, TBI, and military personnel from Operation Enduring Freedom, Operation Iraqi Freedom, Operation New Dawn, and Persian Gulf veterans. Selection criteria eliminated studies on non-combat TBI, open or penetrating injuries, and articles where the relationship between sleep and TBI was not directly examined. Articles on all military branches and components, those currently serving and veterans-ranging from medical chart reviews to clinical trials, were included. Forty-one articles were selected for full text-review.

RESULTS

Twenty-four papers estimated the prevalence of sleep disturbances in TBI. Eight studies demonstrated the contribution of common co-occurring conditions, most notably posttraumatic stress disorder, to the relationship between disrupted sleep and TBI. Ten studies differentiated sleep profiles between military SMs and veterans with and without acute TBI and detected significant differences in sleep disturbances across the course of injury. Longitudinal studies were scarce but helped to establish the temporal relationship between sleep disturbances and TBI and isolate sleep-related mechanisms influencing TBI prognosis. Only three studies reported on interventions for improving sleep quality and TBI symptoms. Systematic research testing assessments and interventions that target sleep disturbances for improving sleep, TBI symptoms, and long-term functional outcomes were identified as critical knowledge gaps.

CONCLUSION

Findings unequivocally establish that sleep disturbances are highly prevalent in SMs and veterans with TBI. However, studies testing the effectiveness of treatments for improving sleep in military groups with TBI have been limited and their results inconsistent. This review highlights a critical opportunity for advancing military medicine through future research aimed at identifying and testing sleep-focused treatments in SMs and veterans with combat-related TBI.

Translingual Neurostimulation for the Treatment of Chronic Symptoms Due to Mild-to-Moderate Traumatic Brain Injury

Objective

To compare the efficacy of high- and low-frequency noninvasive translingual neurostimulation (TLNS) plus targeted physical therapy (PT) for treating chronic balance and gait deficits due to mild-to-moderate traumatic brain injury (mmTBI).

Design

Participants were randomized 1:1 in a 26-week double-blind phase 1/2 study (NCT02158494) with 3 consecutive treatment stages: in-clinic, at-home, and no treatment. Arms were high-frequency pulse (HFP) and low-frequency pulse (LFP) TLNS.

Setting

TLNS plus PT training was initiated in-clinic and then continued at home.

Participants

Participants (N=44; 18-65y) from across the United States were randomized into the HFP and LFP (each plus PT) arms. Forty-three participants (28 women, 15 men) completed at least 1 stage of the study. Enrollment requirements included an mmTBI ≥1 year prior to screening, balance disorder due to mmTBI, a plateau in recovery with current PT, and a Sensory Organization Test (SOT) score ≥16 points below normal.

Interventions

Participants received TLNS (HFP or LFP) plus PT for a total of 14 weeks (2 in-clinic and 12 at home), twice daily, followed by 12 weeks without treatment.

Main Outcome Measures

The primary endpoint was change in SOT composite score from baseline to week 14. Secondary variables (eg, Dynamic Gait Index [DGI], 6-minute walk test [6MWT]) were also collected.

Results

Both arms had a significant (P<.0001) improvement in SOT scores from baseline at weeks 2, 5, 14 (primary endpoint), and 26. DGI scores had significant improvement (P<.001-.01) from baseline at the same test points; 6MWT evaluations after 2 weeks were significant. The SOT, DGI, and 6MWT scores did not significantly differ between arms at any test point. There were no treatment-related serious adverse events.

Conclusions

Both the HFP+PT and LFP+PT groups had significantly improved balance scores, and outcomes were sustained for 12 weeks after discontinuing TLNS treatment. Results between arms did not significantly differ from each other. Whether the 2 dosages are equally effective or whether improvements are because of provision of PT cannot be conclusively established at this time.

Artificial intelligence as an emerging technology in the current care of neurological disorders

BACKGROUND

Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind.

DISCUSSION

The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community.

CONCLUSION

AI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner.

Mild, moderate and severe: terminology implications for clinical and experimental traumatic brain injury

PURPOSE OF REVIEW

When describing clinical or experimental traumatic brain injury (TBI), the adjectives 'mild,' 'moderate' and 'severe' are misleading. 'Mild' clinical TBI frequently results in long-term disability. 'Severe' rodent TBI actually resembles mild or complicated mild clinical TBI.

RECENT FINDINGS

Many mild TBI patients appear to have recovered completely but have postconcussive symptoms, deficits in cognitive and executive function and reduced cerebral blood flow. After moderate TBI, 31.8% of patients died or were discharged to skilled nursing or hospice. Among survivors of moderate and severe TBI, 44% were unable to return to work. On MRI, 88% of mild TBI patients have evidence of white matter damage, based on measurements of fractional anisotropy and mean diffusivity/apparent diffusion coefficient. After sports concussion, clinically recovered patients have abnormalities in functional connectivity on functional MRI. Methylphenidate improved fatigue and cognitive impairment and, combined with cognitive rehabilitation, improved memory and executive functioning. In comparison to clinical TB, because the entire spectrum of experimental rodent TBI, although defined as moderate or severe, more closely resembles mild or complicated mild clinical TBI.

SUMMARY

Many patients after mild or moderate TBI suffer long-term sequelae and should be considered a major target for translational research. Treatments that improve outcome in rodent TBI, even when the experimental injuries are defined as severe, might be most applicable to mild or moderate TBI.

Artificial intelligence for understanding concussion: Retrospective cluster analysis on the balance and vestibular diagnostic data of concussion patients

OBJECTIVES

We propose a bottom-up, machine-learning approach, for the objective vestibular and balance diagnostic data of concussion patients, to provide insight into the differences in patients' phenotypes, independent of existing diagnoses (unsupervised learning).

METHODS

Diagnostic data from a battery of validated balance and vestibular assessments were extracted from the database of the Swiss Concussion Center. The desired number of clusters within the patient database was estimated using Calinski-Harabasz criteria. Complex (self-organizing map, SOM) and standard (k-means) clustering tools were used, and the formed clusters were compared.

RESULTS

A total of 96 patients (81.3% male, age (median [IQR]): 25.0[10.8]) who were expected to suffer from sports-related concussion or post-concussive syndrome (52[140] days between diagnostic testing and the concussive episode) were included. The cluster evaluation indicated dividing the data into two groups. Only the SOM gave a stable clustering outcome, dividing the patients in group-1 (n = 38) and group-2 (n = 58). A large significant difference was found for the caloric summary score for the maximal speed of the slow phase, where group-1 scored 30.7% lower than group-2 (27.6[18.2] vs. 51.0[31.0]). Group-1 also scored significantly lower on the sensory organisation test composite score (69.0[22.3] vs. 79.0[10.5]) and higher on the visual acuity (-0.03[0.33] vs. -0.14[0.12]) and dynamic visual acuity (0.38[0.84] vs. 0.20[0.20]) tests. The importance of caloric, SOT and DVA, was supported by the PCA outcomes. Group-1 tended to report headaches, blurred vision and balance problems more frequently than group-2 (>10% difference).

CONCLUSION

The SOM divided the data into one group with prominent vestibular disorders and another with no clear vestibular or balance problems, suggesting that artificial intelligence might help improve the diagnostic process.

GCS 15: when mild TBI isn't so mild

OBJECTIVE

The present study characterizes patients with the mildest of mild traumatic brain injury (TBI), as defined by a Glasgow coma score (GCS) of 15.

METHODS

This is an IRB approved observational cohort study of adult patients who presented to the emergency department of a Level-1 trauma center, with the primary diagnosis of TBI and a GCS score of 15 on arrival. Data collected included demographic variables such as age, gender, race, mechanisms of injury, signs and symptoms including associated vomiting, seizures, loss of consciousness (LOC), alteration of consciousness (AOC), and post-traumatic amnesia (PTA).Pre- hospital GCS, Emergency Department (ED) GCS, and results of brain CT scans were also collected as well as patient centered outcomes including hospital or intensive care unit (ICU) admission, neurosurgical intervention, and in hospital death. Data were stored in REDCap (Research Electronic Data Capture), a secure, web- based application. Descriptive and inferential analysis was done using JMP 14.0 for the Mac.

RESULTS

Univariate predictors of hospital admission included LOC, AOC, and PTA, all p < 0.0001. Patients admitted to ICU were significantly more likely to be on an antiplatelet or anticoagulant (P < 0.0001), have experienced PTA (p = 0.0025), LOC (p < 0.0001), or have an abnormal brain CT (p < 0.0001). Patients who died in the hospital were significantly more likely to be on an antiplatelet or anticoagulant (P = 0.0005. All who died in the hospital had intracranial hemorrhage on ED head CT, despite having presented to the ED with GCS of 15. Patients were also significantly more likely to have had vomiting (p < 0.0001). Patients who underwent neurosurgical intervention were significantly more likely to be male (P = 0.0203), to be on an antiplatelet or anticoagulant (P = < 0.0001) likely to have suffered their TBI from a fall (P = 0.0349), and experienced vomiting afterwards (P = 0.0193).

CONCLUSIONS

This study underscores: 1) the importance of neuroimaging in all patients with TBI, including those with a GCS 15. Fully 10% of our cohort was not imaged. Extrapolating, these would represent 2.5% bleeds, and 1.47% fractures. 2) The limitations of GCS in classifying TBI, as patients with even the mildest of mild TBI have a high frequency of gross CT abnormalities.

A Cross-Study Analysis for Reproducible Sub-classification of Traumatic Brain Injury

Objective: To identify reproducible sub-classes of traumatic brain injury (TBI) that correlate with patient outcomes.

Methods: Two TBI datasets from the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System were utilized, Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot and Citicoline Brain Injury Treatment Trial (COBRIT). Patients included in these analyses had closed head injuries with Glasgow Comas Scale (GCS) scores of 13–15 at arrival at the Emergency Department (ED). Sparse hiearchical clustering was applied to identify TBI sub-classes within each dataset. The reproducibility of the sub-classes was evaluated by investigating similarities in clinical variable profiles and patient outcomes in each sub-class between the two datasets, as well as by using a statistical metric called in-group proportion (IGP).

Results: Seven TBI sub-classes were identified in the first dataset. There were between-class differences in patient outcomes at 90 days (Glasgow Outcome Scale Extended (GOSE): p < 0.001) and 180 days (Trail Making Test (TMT): p = 0.03). Four of seven sub-classes were reproducible in the second dataset with very high IGPs (94, 100, 99, 97%). Seven TBI sub-classes were also identified in the second dataset. There were significant between-class differences in patient outcomes at 180 days (GOSE: p = 0.024; Brief Symptom Inventory (BSI) p = 0.007; TMT: p < 0.001). Three of seven sub-classes were reproducible in the second dataset with very high IGPs (100% for all).

Conclusions: Reproducible TBI sub-classes were identified across two independent datasets, suggesting that these sub-classes exist in a general population. Differences in patient outcomes according to sub-class assignment suggest that this sub-classification could be used to guide post-TBI prognosis.