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Objective: The objective is to determine whether unsupervised machine learning identifies traumatic brain injury (TBI) phenotypes with unique clinical profiles., Methods: Pilot self-reported survey data of over 10,000 adults were collected from the Centers for Disease Control and Prevention (CDC)'s National Concussion Surveillance System (NCSS). Respondents who self-reported a head injury in the past 12 months ( n  = 1,364) were retained and queried for injury, outcome, and clinical characteristics. An unsupervised machine learning algorithm, partitioning around medoids (PAM), that employed Gower's dissimilarity matrix, was used to conduct a cluster analysis., Results: PAM grouped respondents into five TBI clusters (phenotypes A-E). Phenotype C represented more clinically severe TBIs with a higher prevalence of symptoms and association with worse outcomes. When compared to individuals in Phenotype A, a group with few TBI-related symptoms, individuals in Phenotype C were more likely to undergo medical evaluation (odds ratio [OR] = 9.8, 95% confidence interval[CI] = 5.8-16.6), have symptoms that were not currently resolved or resolved in 8+ days (OR = 10.6, 95%CI = 6.2-18.1), and more likely to report at least moderate impact on social (OR = 54.7, 95%CI = 22.4-133.4) and work (OR = 25.4, 95%CI = 11.2-57.2) functioning., Conclusion: Machine learning can be used to classify patients into unique TBI phenotypes. Further research might examine the utility of such classifications in supporting clinical diagnosis and patient recovery for this complex health condition.

Background: Traumatic brain injury (TBI) is a leading cause of homicide-related death in the United States. Penetrating TBI associated with firearms is a unique injury with an exceptionally high mortality rate that requires specialized neurocritical trauma care., Objective: To report incidence patterns of firearm-related and nonfirearm-related TBI homicides in the United States between 2000 and 2019 by demographic characteristics to provide foundational data for prevention and treatment strategies., Methods: Data were obtained from multiple cause of death records from the National Vital Statistics System using Centers for Disease Control and Prevention's Wide-Ranging Online Data for Epidemiologic Research database for the years 2000 to 2019. Number, age-adjusted rates, and percent of firearm and nonfirearm-related TBI homicides by demographic characteristics were calculated. Temporal trends were also evaluated., Results: During the study period, there were 77 602 firearm-related TBI homicides. Firearms were involved in the majority (68%) of all TBI homicides. Overall, men, people living in metro areas, and non-Hispanic Black persons had higher rates of firearm-related TBI homicides. The rate of nonfirearm-related TBI homicides declined by 40%, whereas the rate of firearm-related TBI homicides only declined by 3% during the study period. There was a notable increase in the rate of firearm-related TBI homicides from 2012/2013 through 2019 for women (20%) and nonmetro residents (39%)., Conclusion: Firearm-related violence is an important public health problem and is associated with the majority of TBI homicide deaths in the United States. The findings from this study may be used to inform prevention and guide further research to improve treatment strategies directed at reducing TBI homicides involving firearms., (Copyright © Congress of Neurological Surgeons 2023. All rights reserved.)

Despite considerable efforts to advance the science surrounding traumatic brain injury (TBI), formal efforts supporting the current and future implementation of scientific findings within clinical practice and healthcare policy are limited. While many and varied guidelines inform the clinical management of TBI across the spectrum, clinicians and healthcare systems are not broadly adopting, implementing, and/or adhering to them. As part of the Brain Trauma Blueprint TBI State of the Science, an expert workgroup was assembled to guide this review article, which describes: (1) possible etiologies of inadequate adoption and implementation; (2) enablers to successful implementation strategies; and (3) strategies to mitigate the barriers to adoption and implementation of future research.

It is widely appreciated that the spectrum of traumatic brain injury (TBI), mild through severe, contains distinct clinical presentations, variably referred to as subtypes, phenotypes, and/or clinical profiles. As part of the Brain Trauma Blueprint TBI State of the Science, we review the current literature on TBI phenotyping with an emphasis on unsupervised methodological approaches, and describe five phenotypes that appear similar across reports. However, we also find the literature contains divergent analysis strategies, inclusion criteria, findings, and use of terms. Further, whereas some studies delineate phenotypes within a specific severity of TBI, others derive phenotypes across the full spectrum of severity. Together, these facts confound direct synthesis of the findings. To overcome this, we introduce PhenoBench, a freely available code repository for the standardization and evaluation of raw phenotyping data. With this review and toolset, we provide a pathway toward robust, data-driven phenotypes that can capture the heterogeneity of TBI, enabling reproducible insights and targeted care.

When the fourth edition of the Brain Trauma Foundation's Guidelines for the Management of Severe Traumatic Brain Injury were finalized in late 2016, it was known that the results of the RESCUEicp (Trial of Decompressive Craniectomy for Traumatic Intracranial Hypertension) randomized controlled trial of decompressive craniectomy would be public after the guidelines were released. The guideline authors decided to proceed with publication but to update the decompressive craniectomy recommendations later in the spirit of "living guidelines," whereby topics are updated more frequently, and between new editions, when important new evidence is published. The update to the decompressive craniectomy chapter presented here integrates the findings of the RESCUEicp study as well as the recently published 12-mo outcome data from the DECRA (Decompressive Craniectomy in Patients With Severe Traumatic Brain Injury) trial. Incorporation of these publications into the body of evidence led to the generation of 3 new level-IIA recommendations; a fourth previously presented level-IIA recommendation remains valid and has been restated. To increase the utility of the recommendations, we added a new section entitled Incorporating the Evidence into Practice. This summary of expert opinion provides important context and addresses key issues for practitioners, which are intended to help the clinician utilize the available evidence and these recommendations. The full guideline can be found at: https://braintrauma.org/guidelines/guidelines-for-the-management-of-severe-tbi-4th-ed#/., (© Congress of Neurological Surgeons 2020.)

The "Guidelines for the Management of Pediatric Severe Traumatic Brain Injury, Third Edition: Update of the Brain Trauma Foundation Guidelines" published in Pediatric Critical Care Medicine in 2019 provides new and updated recommendations applicable to the emergency department management of children with severe traumatic brain injury. Practice-changing takeaways include specific recommendations for administration of 3% hypertonic saline, administration of seizure prophylaxis, and avoiding hyperventilation.

Objective: To provide a review of evidence and consensus-based description of healthcare and educational service delivery and related recommendations for children with traumatic brain injury., Methods: Literature review and group discussion of best practices in management of children with traumatic brain injury (TBI) was performed to facilitate consensus-based recommendations from the American Congress on Rehabilitation Medicine's Pediatric and Adolescent Task Force on Brain Injury. This group represented pediatric researchers in public health, medicine, psychology, rehabilitation, and education., Results: Care for children with TBI in healthcare and educational systems is not well coordinated or integrated, resulting in increased risk for poor outcomes. Potential solutions include identifying at-risk children following TBI, evaluating their need for rehabilitation and transitional services, and improving utilization of educational services that support children across the lifespan., Conclusion: Children with TBI are at risk for long-term consequences requiring management as well as monitoring following the injury. Current systems of care have challenges and inconsistencies leading to gaps in service delivery. Further efforts to improve knowledge of the long-term TBI effects in children, child and family needs, and identify best practices in pathways of care are essential for optimal care of children following TBI.