Risk Of Traumatic Brain Injury Research Articles

Risk of traumatic brain injury among patients with ADHD and their unaffected siblings.

As the relationship between attention deficit hyperactivity disorder (ADHD) and traumatic brain injury (TBI) is gaining increasing attention, the TBI risk in patients with ADHD, unaffected siblings of ADHD probands, and non-ADHD controls remains unclear. Overall, 18,645 patients with ADHD, 18,880 unaffected siblings of ADHD probands, and 188,800 age-/sex-matched controls were followed up from enrollment to the end of 2011. The cases of TBI and TBI requiring hospitalization were identified during follow-up. Patients with ADHD (hazard ratio [HR]: 1.57) and unaffected siblings (HR: 1.20) had an increased risk of any TBI compared with non-ADHD controls. Surprisingly, the likelihood of developing TBI requiring hospitalization during follow-up was higher in the unaffected siblings group (HR: 1.21) than in the control group, whereas it was lower in the ADHD probands group (HR: 0.86). Patients with ADHD and unaffected siblings of ADHD probands were more likely to develop any TBI during follow-up than controls. Unaffected siblings of patients with ADHD exhibited the highest risk of subsequent TBI requiring hospitalization compared with patients with ADHD and healthy controls. Therefore, TBI risk in patients with ADHD and their unaffected siblings would require further investigation. ADHD diagnosis and ADHD trait are associated with risk of traumatic brain injury (TBI). Both patients with ADHD and their unaffected siblings were more likely to develop TBI during the follow-up compared with the control group. TBI requiring hospitalization occurred more in the sibling group than in the proband group. TBI risk should be closely monitored among unaffected siblings of patients with ADHD.

Quantitative assessment of traumatic brain injury risk in diverse age groups of females: Insights from computational biomechanics

Traumatic Brain Injury (TBI) stands as a multifaceted health concern, exhibiting varying influences across human population. This study delves into the biomechanical complexities of TBI within gender-specific contexts, focusing on females. Our primary objective is to investigate distinctive injury mechanisms and risks associated with females, emphasizing the imperative for tailored investigations within this cohort.By employing Fluid-Structure Interaction (FSI) Analysis, we conducted simulations to quantify biomechanical responses to traumatic forces across diverse age groups of females. The study utilized a scaling technique to create finite element models (FEMs). The young female FEM, based on anthropometric data, showcased a 15 % smaller head geometry compared to the young male FEM. Moreover, while the elderly female FEM closely mirrored the young female FEM in most structural aspects, it showed distinctive features such as brain atrophy and increased cerebrospinal fluid (CSF) layer thickness. Notably, the child female FEM (ages 7–11 years) replicated around 95 % of the young female FEM's geometry. These structural distinctions meticulously captured age-specific variations across our modeled female age groups. It's noteworthy that identical conditions, encompassing impact intensity, loading type, and boundary conditions, were maintained across all FEMs in this biomechanical finite element analysis, ensuring comparative results.The findings unveiled significant variations in frontal and occipital pressures among diverse age groups, highlighting potential age-related discrepancies in TBI susceptibility among females. These variations were primarily linked to differences in anatomical features, including brain volume, CSF thickness, and brain condition, as the same material properties were used in the FEMs. These results were approximately 4.70, 6.33 and 6.43 % in frontal area of brain in diverse age groups of females (young, elderly, and child) respectively compared to young male FEM. Comparing the FEM results between the young female and the elderly female, we observed a decrease in occipital brain pressure at the same point, reducing from 171,993 to 167,793 Pa, marking an approximate 2.5 % decrease. While typically the elderly exhibit greater brain vulnerability compared to the young, our findings showcase a reduction in brain pressure. Notably, upon assessing the relative movement between the brain and the skull at the point located in occipital area, we observed greater relative movement in the elderly (1.8 mm) compared to the young female (1.04 mm). Therefore, brain atrophy increases the range of motion of the brain within the cranial space. The study underscores the critical necessity for nuanced TBI risk assessment tailored to age and gender, emphasizing the importance of age-specific protective strategies in managing TBIs across diverse demographics. Future research employing individual modeling techniques and exploring a wider age spectrum holds promise in refining our understanding of TBI mechanisms and adopting targeted approaches to mitigate TBI in diverse groups.

Association of Cadherin-Related Family Member 1 with Traumatic Brain Injury

The cadherin family plays a pivotal role in orchestrating synapse formation in the central nervous system. Cadherin-related family member 1 (CDHR1) is a photoreceptor-specific calmodulin belonging to the expansive cadherin superfamily. However, its role in traumatic brain injury (TBI) remains largely unknown. CDHR1 expression across various brain tissue sites was analyzed using the GSE104687 dataset. Employing a summary-data-based Mendelian Randomization (SMR) approach, integrated analyses were performed by amalgamating genome-wide association study abstracts from TBI with public data on expressed quantitative trait loci and DNA methylation QTL from both blood and diverse brain tissues. CDHR1 expression and localization in different brain tissues were meticulously delineated using western blotting, immunohistochemistry, and enzyme-linked immunosorbent assay. CDHR1 expression was consistently elevated in the TBI group compared to that in the sham group across multiple tissues. The inflammatory response emerged as a crucial biological mechanism, and pro-inflammatory and anti-inflammatory factors were not expressed in either group. Integrated SMR analyses encompassing both blood and brain tissues substantiated the heightened CDHR1 expression profiles, with methylation modifications emerging as potential contributing factors for increased TBI risk. This was corroborated by western blotting and immunohistochemistry, confirming augmented CDHR1 expression following TBI. This multi-omics-based genetic association study highlights the elevated TBI risk associated with CDHR1 expression coupled with putative methylation modifications. These findings provide compelling evidence for future targeted investigations and offer promising avenues for developing interventional therapies for TBI.Graphical Cadherin-related family member 1 as a potential risk factor for traumatic brain injury.

Concomitant head or neck injury increases risk of traumatic brain injury in facial fracture patients

Concomitant traumatic brain injury (TBI) is common in facial fracture patients and prompt intervention is crucially important to minimize the risk of potential long-term sequalae. In order to achieve rapid diagnosis, the clinicians need to be aware of the risk factors associated with concomitant TBI and facial fractures. Previous literature suggests that a facial fracture can be considered a significant indicator of TBI. Nevertheless, a large data gap remains on specific injury patterns of facial fractures and associated TBI. Therefore, the objective of this study was to estimate and compare the frequency of and risk factors for TBI in patients with and without different types of additional injuries.The retrospective cohort study included 1836 facial fracture patients aged at least 18 years. Outcome variable was TBI with radiological finding in computed tomography or magnetic resonance imaging. The primary predictor variables were associated injury outside the head and neck, associated cranial fracture and associated neck injury.Based on this study, associated cranial fracture increased the risk for TBI 4.7-fold. Patients with associated neck injury had a 2.1-fold risk for TBI. In addition, significant predictors for TBI were increasing age (p=.0004), high energy of injury (p<.0001) and anticoagulant medication (p=.0003).Facial fracture patients with associated injuries in the head and neck region are in significant risk for TBI. In clinical work, multiprofessional evaluation of facial fracture patients should be routine and repeated survey should be targeted especially at high-risk patients to identify TBIs.

176 Repetitive Traumatic Brain Injuries Are Associated With Poorer Outcomes at 1-Year and 3-7 Years After Index Injury

INTRODUCTION: Traumatic brain injury (TBI) patients are at risk for subsequent TBIs. Research on the longitudinal impact of repetitive TBI is limited. METHODS: The 18-center TRACK-TBI study enrolled subjects who presented to hospital and received head computed tomography (CT) within 24-hours of TBI between 2014-2018. Subjects were interviewed regarding post-index TBI during follow-up. Data from subjects aged = 17-years with 1-year or 3-7-year outcomes were extracted. Outcome measures included functional (GOSE:Glasgow Outcome Scale-Extended), post-concussive symptoms (PCS) (RPQ:Rivermead Post-Concussion Symptoms Questionnaire), psychological distress (BSI-18:Brief Symptom Inventory-18), depression (PHQ-9:Patient Health Questionnaire-9), post-traumatic stress disorder (PCL-5:PTSD-Checklist for DSM-5), and quality of life (QOL) (QOLIBRI-OS:Quality of Life After Brain Injury-Overall Scale). Multivariable regressions were adjusted for demographic, socioeconomic, and injury severity factors, and for multiple comparisons. Mean-differences (B), relative risks (RR), and 95% confidence intervals [CI] were reported. RESULTS: In 1,572 subjects, 3.2% and 12.2% sustained post-index TBI at 1-year and 3-7-years, respectively; 2.4% sustained multiple post-index TBIs at 3-7-years. Risk factors included psychiatric history, prior TBI, and extracranial injury severity. At 1-year, post-index TBI was independently associated with incomplete recovery (GOSE&lt;8; RR = 1.75 [1.04-2.94]), decreased QOL (QOLIBRI-OS: B = -15.9 [-22.6, -9.1]), and increased severity of PCS, psychological distress, PTSD, and depression (RPQ: B = 8.1 [4.2-11.9], BSI-18: B = 5.3 [2.1-8.6]; PCL-5: B = 7.8 [3.2-12.4]; PHQ-9: B = 3.0 [1.5-4.4]); effects were conserved at reduced magnitudes at 3-7-years. Multiple (=2 vs. 1) post-index TBIs were associated with greater symptom severities across all outcome measures. CONCLUSIONS: Post-index TBI is associated with incomplete functional recovery, increased PCS, psychological distress, PTSD, and depressive symptom severities, and decreased QOL at 1- and 3-7-years post-injury. Sustaining multiple post-index TBIs further increases symptom severity across all outcome domains. Institutional processes and resources for TBI prevention, education, counseling, and follow-up are greatly needed.

Causal association between years of schooling and the risk of traumatic brain injury: A two-sample mendelian randomization analysis

ObjectiveTo investigate whether the number of years of schooling are causally associated traumatic brain injury (TBI). We aimed to investigate whether the number of years of schooling are causally associated TBI. MethodsWe investigate the prospective causal effect of years of schooling on TBI using summary statistical data. The statistical dataset comprising years of schooling (n = 293,723) from genome-wide association studies (GWASs) deposited in the UK Biobank was used for exposure. We used the following GWAS available in the FinnGen dataset: individuals with TBI (total = 13,165; control = 136,576; number of single nucleotide polymorphisms [SNPs] = 16,380,088). ResultsSeventy significant genome-wide SNPs from GWAS datasets with annotated years of schooling were selected as instrumental variables. The inverse variance weighted method results supported a causal relationship between years of schooling and TBI (odds ratio (OR), 0.78; 95 % confidence interval (CI), 0.62–0.98; P = 0.029). MR-Egger regression showed that polydirectionality was unlikely to bias the results (intercept = 0.007, SE = 0.01, P = 0.484) and demonstrated no causal relationship between years of schooling and TBI (OR, 0.52; 95%CI, 0.17–1.64; P = 0.270). The weighted median method revealed a causal relationship with TBI (OR, 0.73; 95%CI, 0.55–0.98; P = 0.047). A Cochran's Q test and funnel plot did not show heterogeneity nor asymmetry, indicating no directional pleiotropy. ConclusionsThe current investigation yields substantiation of a causal association between years of schooling and TBI development. More years of schooling may be causally associated with a reduced risk of TBI, which has implications for clinical and public health practices and policies.

Factors influencing traumatic brain injuries in maxillofacial fractures: A 12-year retrospective analysis of 2841 patients.

Results of studies investigating the association between traumatic brain injury (TBI) and maxillofacial fractures (MFs) have varied considerably. The present study aimed to evaluate the correlation between TBIs and MFs, as well as the impact of age, sex, trauma mechanism, and season on TBIs. This 12-year retrospective study of 2841 patients used univariate and multivariate logistic regression to assess the association between MFs and other factors impacting TBIs. Among 2841 patients, 1978 TBIs occurred in 829 (29.2%), with intracranial injuries (n = 828) is the most common. Of 829 patients with TBIs, 688 were male and 141 were female, corresponding to a male-to-female ratio of 4.9:1.0. The most common age group was 40-49 years (24.6%). Vehicles (including motor vehicles and electric vehicles) accidents were the primary causes of injuries. Multivariate regression analyses revealed an increased risk for TBIs among males (odds ratio [OR] 0.632, p < 0.001). Patients >40 years of age were at higher risk for TBIs, especially those ≥70 years (OR 3.966, p = 0.001). Vehicle accidents were a high-risk factor for TBIs (OR 6.894, p < 0.001), and winter was the most prevalent season for such injuries (OR 1.559, p = 0.002). Risk for TBI increased by 136.4% in combined midfacial and mandibular fractures (p = 0.016) and by 101.6% in multiple midfacial fractures (p = 0.045). TBIs were less common in single mandibular fractures, notably in single-angle fractures, with a risk of only 0.204-fold. TBIs in MFs were significantly correlated with sex, age, aetiology, season and fracture location. Maxillofacial surgeons and emergency physicians must be aware of the possible association between TBIs and MFs to assess and manage this complicated relationship in a timely manner.

Patterns of concomitant traumatic brain injury and ocular trauma in US service members

BackgroundConcomitant traumatic brain injury (TBI) and ocular trauma (OT) are caused by the same physical mechanisms, which may complicate therapeutic intervention if screening and evaluation of each condition are not...

Impact of prior axonal injury on subsequent injury during brain tissue stretching – A mesoscale computational approach

Epidemiology studies of traumatic brain injury (TBI) show individuals with a prior history of TBI experience an increased risk of future TBI with a significantly more detrimental outcome. But the mechanisms through which prior head injuries may affect risks of injury during future head insults have not been identified. In this work, we show that prior brain tissue injury in the form of mechanically induced axonal injury and glial scar formation can facilitate future mechanically induced tissue injury. To achieve this, we use finite element computational models of brain tissue and a history-dependent pathophysiology-based mechanically-induced axonal injury threshold to determine the evolution of axonal injury and scar tissue formation and their effects on future brain tissue stretching. We find that due to the reduced stiffness of injured tissue and glial scars, the existence of prior injury can increase the risk of future injury in the vicinity of prior injury during future brain tissue stretching. The softer brain scar tissue is shown to increase the strain and strain rate in its vicinity by as much as 40% in its vicinity during dynamic stretching that reduces the global strain required to induce injury by 20% when deformed at 15 s−1 strain rate. The results of this work highlight the need to account for patient history when determining the risk of brain injury.

Association Between Social Determinants of Health and Traumatic Brain Injury: A Scoping Review.

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. Disparities exist in the populations that acquire TBIs, however, with a greater burden and poorer outcomes associated with communities of color and lower socioeconomic status. To combat health inequities such as these, institutions have begun to target social determinants of health (SDoH), which are environmental factors that affect health outcomes and risks. The SDoH may play a role in sustaining a TBI and provide modifiable targets for action to reduce the risk of TBI, especially in high-risk communities. In this study, we describe the existing literature regarding SDoH and their association with sustaining a TBI. We performed a scoping review with a comprehensive search of the Ovid MEDLINE/Embase databases. To summarize the literature, this review adapts the World Health Organization's Commission on SDoH's conceptual framework. Fifty-nine full-text articles, including five focusing on lower and middle-income countries, met our study criteria. Results of the scoping review indicate that several structural determinants of health were associated with TBI risk. Lower educational attainment and income levels were associated with higher odds of TBI. In addition, multiple studies highlight that minority populations were identified as having higher odds of TBI than their White counterparts. Literature highlighting intermediate determinants of health examined in this review describes associations between sustaining a TBI and rurality, work environment, medical conditions, medication/substance use, and adversity. Recommended exploration into lesser-researched SDoH is discussed, and the expansion of this review to other aspects of the TBI continuum is warranted.

Non-Invasive Vagal Nerve Stimulation Pre-Treatment Reduces Neurological Dysfunction After Closed Head Injury in Mice.

Non-invasive vagus nerve stimulation (nVNS) has recently been suggested as a potential therapy for traumatic brain injury (TBI). We previously demonstrated that nVNS inhibits cortical spreading depolarization, the electrophysiological event underlying migraine aura, and is relevant to TBI. Our past work also suggests a role for interleukin-1 beta (IL-1β) in cognitive deficits after closed head injury (CHI) in mice. We show that nVNS pre-treatment suppresses CHI-associated spatial learning and memory impairment and prevents IL-1β activation in injured neurons, but not endothelial cells. In contrast, nVNS administered 10 min after CHI was ineffective. These data suggest that nVNS prophylaxis might ameliorate neuronal dysfunction associated with CHI in populations at high risk for concussive TBI.

Brain deformation estimation with transfer learning for head impact datasets across impact types.

Brain strain and strain rate are effective biomechanics predictors of traumatic brain injury (TBI) caused by head impacts. However, state-of-the-art finite element modeling (FEM) demands considerable computational time, limiting its application in real-time TBI risk monitoring. To accelerate, machine learning head models (MLHMs) were developed to predict brain strain based on head kinematics measurements, but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than 7 s-1 in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

Cerebral microbleeds in traumatic brain injury associated with differential risk factors and clinical outcomes: the PREVENT‐Dementia study

AbstractBackgroundCerebral microbleeds (CMB) are predictive of increased risk of dementia and stroke. Although commonly regarded as vascular markers, CMB can also stem from non‐vascular aetiologies like head injuries or traumatic brain injury (TBI), although these are often overlooked. Therefore, this study examines CMB in relation to TBI, and their differential causes (i.e., risk factors) and consequences (i.e., clinical outcomes).MethodIn 605 healthy middle‐aged adults (aged 40‐59), CMB were rated on 3T susceptibility weighted imaging (SWI) MRI. TBI was assessed using the Brain Injury Screening Questionnaire (BISQ). TBI+ was defined as at least one blow to the head resulting in loss of consciousness (36.0%; n = 217). Memory was assessed using the COGNITO battery. Interaction analyses examined TBI*CMB interactions on hypertension, gait, and memory. Dominance analysis examined the relative contribution of TBI and vascular risk factors on predicting gait disturbances and memory. All models adjusted for sex, age, education, and study site.ResultTBI was related to higher CMB count (t = 2.06, p = .039), and were more common in males (48.3%) than females (28.0%) (Χ2 = 28.91, p&lt;.001). Number of TBI events related to poorer memory (t = ‐2.62, p = .009) and gait disturbances (t = 3.54, p&lt;.001). Interaction analyses demonstrated that hypertension (t = ‐2.26, p = .024), memory (t = 2.70, p = .007) and gait (t = ‐2.29, p = .023) were less closely related to CMB in individuals with greater number of TBI events, relative to those with fewer TBIs. Regionally, these interactions were significant for lobar CMB, but not deep subcortical CMB. Within the TBI+ group, dominance analysis demonstrated that number of TBI events outperformed vascular risk factors in predicting gait disturbances (Contribution to R2: TBI = 52.9%, vascular risk = 32.6%) and memory (TBI = 28.7%, vascular risk = 1.2%).ConclusionCMB appeared to differ aetiologically and clinically in those with and without TBI. In individuals with TBI, TBI itself was the dominant driver of clinical deficits. When compared to CMB of presumed vascular origins cross‐sectionally, TBI‐related CMB may appear to be less detrimental. However, longitudinal analysis is required to determine how TBI‐related CMB differ in clinical trajectory and downstream pathologies. This study highlights the importance of differentiating between CMB of vascular origins vs. TBI in both research and clinically to aid prognosis and treatment decisions.

Cerebral microbleeds in traumatic brain injury associated with differential risk factors and clinical outcomes: the PREVENT‐Dementia study

AbstractBackgroundCerebral microbleeds (CMB) are predictive of increased risk of dementia and stroke. Although commonly regarded as vascular markers, CMB can also stem from non‐vascular aetiologies like head injuries or traumatic brain injury (TBI), although these are often overlooked. Therefore, this study examines CMB in relation to TBI, and their differential causes (i.e., risk factors) and consequences (i.e., clinical outcomes).MethodIn 605 healthy middle‐aged adults (aged 40‐59), CMB were rated on 3T susceptibility weighted imaging (SWI) MRI. TBI was assessed using the Brain Injury Screening Questionnaire (BISQ). TBI+ was defined as at least one blow to the head resulting in loss of consciousness (36.0%; n = 217). Memory was assessed using the COGNITO battery. Interaction analyses examined TBI*CMB interactions on hypertension, gait, and memory. Dominance analysis examined the relative contribution of TBI and vascular risk factors on predicting gait disturbances and memory. All models adjusted for sex, age, education, and study site.ResultTBI was related to higher CMB count (t = 2.06, p = .039), and were more common in males (48.3%) than females (28.0%) (Χ2 = 28.91, p&lt;.001). Number of TBI events related to poorer memory (t = ‐2.62, p = .009) and gait disturbances (t = 3.54, p&lt;.001). Interaction analyses demonstrated that hypertension (t = ‐2.26, p = .024), memory (t = 2.70, p = .007) and gait (t = ‐2.29, p = .023) were less closely related to CMB in individuals with greater number of TBI events, relative to those with fewer TBIs. Regionally, these interactions were significant for lobar CMB, but not deep subcortical CMB. Within the TBI+ group, dominance analysis demonstrated that number of TBI events outperformed vascular risk factors in predicting gait disturbances (Contribution to R2: TBI = 52.9%, vascular risk = 32.6%) and memory (TBI = 28.7%, vascular risk = 1.2%).ConclusionCMB appeared to differ aetiologically and clinically in those with and without TBI. In individuals with TBI, TBI itself was the dominant driver of clinical deficits. When compared to CMB of presumed vascular origins cross‐sectionally, TBI‐related CMB may appear to be less detrimental. However, longitudinal analysis is required to determine how TBI‐related CMB differ in clinical trajectory and downstream pathologies. This study highlights the importance of differentiating between CMB of vascular origins vs. TBI in both research and clinically to aid prognosis and treatment decisions.

Unpowered scooter injuries in children and risk factors for traumatic brain injuries: An 8-year cross-sectional study using a national registry in South Korea

IntroductionHead trauma accounts for a large proportion of unpowered scooter injuries in children. Traumatic brain injury (TBI) is the leading cause of considerable mortality and morbidity in children, who are the main users of unpowered scooters. The aim of this study was to explore the characteristics of unpowered scooter injuries in children and to identify predictors of the occurrence of TBI. MethodsA multicentre observational retrospective study was conducted using the Emergency Department-based Injury In-depth Surveillance (EDIIS) database in South Korea. Children aged 2 to 18 years old with unpowered scooter injuries between 2011 and 2018 were eligible for inclusion in this study, and the primary outcome was TBI defined based on the International Classification of Diseases, 10th Revision (ICD-10) code. ResultsThe annual rate of unpowered scooter injuries per 1,000 injured patients increased throughout the study period from 1.4 in 2011 to 16.4 in 2018 (P for trend < 0.001). Of the 3,892 children who had unpowered scooter injuries, 353 (9.2 %) had TBI. Children were at a higher risk of unpowered scooter TBI if they were aged between 2 and 5 years (adjusted odds ratio [aOR]: 1.37; 95 % confidence interval (CI): 1.09–1.73), were male (aOR: 1.45; 95 % CI: 1.14–1.86), were injured either on sidewalks (aOR: 1.80; 95 % CI: 1.20–2.70) or on driveways (aOR: 2.31; 95 % CI: 1.41–3.79), and experienced a fall (aOR: 1.98; 95 % CI: 1.15–3.43). Additionally, children injured after a blunt force were at a lower risk of TBI (aOR: 0.28; 95 % CI: 0.15–0.53). ConclusionUnpowered scooter injuries in children are increasing in South Korea. It is essential for younger children riding unpowered scooters to wear helmets and for caregivers to actively supervise their children to prevent TBI.

8 Incident TBI among a Nationwide Cohort of US Older Adults

Objective:Traumatic brain injury (TBI), very common in the United States (US) and occurring at highest rates in older adulthood, is a documented risk factor for cognitive impairment and dementia. However, the full scope of the problem is unknown, as comparative incidence of TBI among older adults is poorly characterized. Moreover, the effect of demographics (race/ethnicity, sex) and cognitive and medical status, as well as education, socioeconomic status, and other social determinants of health (SDOH) on TBI risk is not well understood. We aimed to explore the impact of demographics, cognitive and medical status, and SDOH on vulnerability to new TBIs among older adults.Participants and Methods:Enrollees 65 and older in the nationally representative Health and Retirement Study (HRS) who consented to have their survey data linked to Medicare claims and had not experienced a head injury prior to HRS enrollment were studied. We used claims data 2000-2018 to obtain incident TBI diagnoses and harnessed the detailed demographic, cognitive, medical, and SDOH information available in the HRS. Incident TBI was defined using inpatient and outpatient International Classification of Disease (ICD 9 and 10) codes received the same day as an emergency room (ER) visit code and a computed tomography (CT) scan code, occurring after the enrollee’s baseline HRS interview. We calculated descriptive statistics and bivariate associations for TBI status with demographic and SDOH characteristics measured at baseline using sample weights to account for the complex survey design.Results:Of respondents meeting inclusion criteria (n=9273) during the study follow-up period of 18 years, 8.9% received emergency room treatment for a TBI. Older adults who experienced TBI during the study period were more likely to be female (p=0.0006), and white (p=0.0001), to have normal cognition (vs. cognitive impairment or dementia, p=0.0011), higher education (p&lt;0.0001), and higher income (p=0.01). Having lung disease (p=0.0003) or functional impairment (p=0.03) at baseline were protective against experiencing a TBI.Conclusions:Our results suggest that almost 9% of US older adults received ER treatment for a new TBI during the 18-year study period, and that race, sex, and SDOH factors may increase risk for, or be protective against, TBI. This novel investigation into the impact of demographics and SDOH on incident TBI suggests access to care may impact who gets treatment for TBI. Further study is indicated and may lead to opportunities for both targeted intervention (e.g., primary TBI prevention) to groups most at risk as well as identification and mollification of the most relevant structural and contextual factors (e.g., access to care) to reduce risk of TBI among older adults.

Genome-wide association study of traumatic brain injury in U.S. military veterans enrolled in the VA million veteran program.

Large-scale genetic studies of traumatic brain injury (TBI) are lacking; thus, our understanding of the influence of genetic factors on TBI risk and recovery is incomplete. This study aimed to conduct a genome-wide association study (GWAS) of TBI in VA Million Veteran Program (MVP) enrollees. Participants included a multi-ancestry cohort (European, African, and Hispanic ancestries; N = 304,485; 111,494 TBI cases, 192,991 controls). TBI was assessed using MVP survey data and International Classification of Diseases (ICD) codes from the Veterans Health Administration's electronic health record. GWAS was performed using logistic regression in PLINK, and meta-analyzed in METAL. FUMA was used for post-GWAS analysis. Genomic structural equation modeling (gSEM) was conducted to investigate underlying genetic associations with TBI, and bivariate MiXeR was used to estimate phenotype specific and shared polygenicity. SNP-based heritability was 0.060 (SE = 0.004, p = 7.83×10-66). GWAS analysis identified 15 genome-wide significant (GWS) loci at p < 5×10-8. Gene-based analyses revealed 14 gene-wide significant genes; top genes included NCAM1, APOE, FTO, and FOXP2. Gene tissue expression analysis identified the brain as significantly enriched, particularly in the frontal cortex, anterior cingulate cortex, and nucleus accumbens. Genetic correlations with TBI were significant for risk-taking behaviors and psychiatric disorders, but generally not significant for the neurocognitive variables investigated. gSEM analysis revealed stronger associations with risk-taking traits than with psychiatric traits. Finally, the genetic architecture of TBI was similar to polygenic psychiatric disorders. Neurodegenerative disorders including Alzheimer's and Parkinson's disease showed much less polygenicity, however, the proportion of shared variance with TBI was high. This first well-powered GWAS of TBI identified 15 loci including genes relevant to TBI biology, and showed that TBI is a heritable trait with comparable genetic architecture and high genetic correlation with psychiatric traits. Our findings set the stage for future TBI GWASs that focus on injury severity and diversity and chronicity of symptom sequelae.

A - 7 Genome-Wide Association Study of Traumatic Brain Injury in U.S. Military Veterans.

Large-scale genetic studies of traumatic brain injury (TBI) are lacking; thus, our understanding of the influence of genetic factors on TBI risk and recovery is incomplete. This study aimed to conduct a genome-wide association study (GWAS) of TBI in VA Million Veteran Program (MVP) enrollees. Participants included a multi-ancestry cohort (European, African, and Hispanic ancestries; N = 304,485; 111,494 TBI cases, 192,991 controls). TBI was assessed using MVP survey data and ICD codes from electronic health records. GWAS was performed using logistic regression in PLINK, and FUMA was used for post-GWAS analysis. Genetic correlations between TBI and several health domains (risk-taking behaviors, psychiatric disorders, neurocognitive variables, and brain morphometrics) were also evaluated. SNP-based heritability was 0.060 (SE = 0.004, p = 7.83x10-66). GWAS analysis identified 15 genome-wide significant loci at p < 5x10-8. Gene-based analyses revealed 14 gene-wide significant genes, including NCAM1, APOE, FTO, and FOXP2. Gene tissue expression analysis identified the brain as significantly enriched, particularly in the frontal cortex, anterior cingulate cortex, and nucleus accumbens. Genetic correlations with TBI were significant for risk-taking behaviors and psychiatric disorders, but generally non-significant for neurocognitive and brain morphometric variables. The strongest genetic correlation was with PTSD (rg = 0.69, SE = 0.04, p = 2.5x10-75). This first well-powered GWAS of TBI showed that TBI is a heritable trait and identified 15 loci including genes relevant to TBI biology. Findings also revealed high genetic correlations with psychiatric and risk-taking traits, significantly advancing understanding of the genetic basis of TBI. Results set the stage for future TBI GWASs that focus on diversity and chronicity of symptom sequelae.

Longitudinal Recovery Following Repetitive Traumatic Brain Injury

One traumatic brain injury (TBI) increases the risk of subsequent TBIs. Research on longitudinal outcomes of civilian repetitive TBIs is limited. To investigate associations between sustaining 1 or more TBIs (ie, postindex TBIs) after study enrollment (ie, index TBIs) and multidimensional outcomes at 1 year and 3 to 7 years. This cohort study included participants presenting to emergency departments enrolled within 24 hours of TBI in the prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study (enrollment years, February 2014 to July 2020). Participants who completed outcome assessments at 1 year and 3 to 7 years were included. Data were analyzed from September 2022 to August 2023. Postindex TBI(s). Demographic and clinical factors, prior TBI (ie, preindex TBI), and functional (Glasgow Outcome Scale-Extended [GOSE]), postconcussive (Rivermead Post-Concussion Symptoms Questionnaire [RPQ]), psychological distress (Brief Symptom Inventory-18 [BSI-18]), depressive (Patient Health Questionnaire-9 [PHQ-9]), posttraumatic stress disorder (PTSD; PTSD Checklist for DSM-5 [PCL-5]), and health-related quality-of-life (Quality of Life After Brain Injury-Overall Scale [QOLIBRI-OS]) outcomes were assessed. Adjusted mean differences (aMDs) and adjusted relative risks are reported with 95% CIs. Of 2417 TRACK-TBI participants, 1572 completed the outcomes assessment at 1 year (1049 [66.7%] male; mean [SD] age, 41.6 [17.5] years) and 1084 completed the outcomes assessment at 3 to 7 years (714 [65.9%] male; mean [SD] age, 40.6 [17.0] years). At 1 year, a total of 60 participants (4%) were Asian, 255 (16%) were Black, 1213 (77%) were White, 39 (2%) were another race, and 5 (0.3%) had unknown race. At 3 to 7 years, 39 (4%) were Asian, 149 (14%) were Black, 868 (80%) were White, 26 (2%) had another race, and 2 (0.2%) had unknown race. A total of 50 (3.2%) and 132 (12.2%) reported 1 or more postindex TBIs at 1 year and 3 to 7 years, respectively. Risk factors for postindex TBI were psychiatric history, preindex TBI, and extracranial injury severity. At 1 year, compared with those without postindex TBI, participants with postindex TBI had worse functional recovery (GOSE score of 8: adjusted relative risk, 0.57; 95% CI, 0.34-0.96) and health-related quality of life (QOLIBRI-OS: aMD, -15.9; 95% CI, -22.6 to -9.1), and greater postconcussive symptoms (RPQ: aMD, 8.1; 95% CI, 4.2-11.9), psychological distress symptoms (BSI-18: aMD, 5.3; 95% CI, 2.1-8.6), depression symptoms (PHQ-9: aMD, 3.0; 95% CI, 1.5-4.4), and PTSD symptoms (PCL-5: aMD, 7.8; 95% CI, 3.2-12.4). At 3 to 7 years, these associations remained statistically significant. Multiple (2 or more) postindex TBIs were associated with poorer outcomes across all domains. In this cohort study of patients with acute TBI, postindex TBI was associated with worse symptomatology across outcome domains at 1 year and 3 to 7 years postinjury, and there was a dose-dependent response with multiple postindex TBIs. These results underscore the critical need to provide TBI prevention, education, counseling, and follow-up care to at-risk patients.

Machine learning algorithms for predicting outcomes of traumatic brain injury: A systematic review and meta-analysis.

Traumatic brain injury (TBI) is a leading cause of death and disability worldwide. The use of machine learning (ML) has emerged as a key advancement in TBI management. This study aimed to identify ML models with demonstrated effectiveness in predicting TBI outcomes. We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. In total, 15 articles were identified using the search strategy. Patient demographics, clinical status, ML outcome variables, and predictive characteristics were extracted. A small meta-analysis of mortality prediction was performed, and a meta-analysis of diagnostic accuracy was conducted for ML algorithms used across multiple studies. ML algorithms including support vector machine (SVM), artificial neural networks (ANN), random forest, and Naïve Bayes were compared to logistic regression (LR). Thirteen studies found significant improvement in prognostic capability using ML versus LR. The accuracy of the above algorithms was consistently over 80% when predicting mortality and unfavorable outcome measured by Glasgow Outcome Scale. Receiver operating characteristic curves analyzing the sensitivity of ANN, SVM, decision tree, and LR demonstrated consistent findings across studies. Lower admission Glasgow Coma Scale (GCS), older age, elevated serum acid, and abnormal glucose were associated with increased adverse outcomes and had the most significant impact on ML algorithms. ML algorithms were stronger than traditional regression models in predicting adverse outcomes. Admission GCS, age, and serum metabolites all have strong predictive power when used with ML and should be considered important components of TBI risk stratification.