Abstract
INTRODUCTION
There is paucity of research addressing how health care decisions are made in cases of severe traumatic brain injury. This study aims to identify the demographic and clinical ...factors associated with withdrawal care in the setting of severe traumatic brain injury.
METHODS
This is a retrospective study using the Trauma Quality Improvement Program database from 2013 to 2015. Patients with severe traumatic brain injury (GCS 3-8, AIS 2-5) were included. Univariate and multivariate analyses with adjusted P-values were performed for descriptive statistics. A logistic regression analysis was used to identify demographic and clinical factors associated with withdrawal of care.
RESULTS
The analysis included 7869 (21%) patients who withdrew and 30 080 (79%) who did not. More than half of those withdrawn were at a University hospital. Patients undergoing withdrawal of care were older (57.6 ± 20.6 vs 42.9 ± 18.8 yr) and 69% were male, 82% were white, 43% were from the Southern United States and 31% had private insurance. Black or other races were less likely to undergo withdrawal of care as compared to white (odds ratio OR 0.7, 95% confidence interval CI 0.6-0.7 and 0.8, 95% CI 0.8-0.9, respectively). Injury severity score (ISS) was significantly different between those who underwent withdrawal and those who did not, 28.2 ± 12.3 vs 26.1 ± 12.1. The presence of epidural or subdural hematoma was also significantly different between the 2 groups (78% vs 72%). Those who did not have a craniotomy were less likely to undergo withdrawal and those who did not have a hematoma were less likely to undergo withdrawal (OR 0.7, 95% CI 0.7-0.8 and 0.8, 95% CI 0.7-0.9, respectively). The average ICU length of stay and ventilation days were shorter for patients who underwent withdrawal.
CONCLUSION
Age, ISS, craniotomy, race (white), and type of insurance (Medicare) were each positive predictors for withdrawal of care. By contrast, region (Southern United States), race (black), and being functionally independent were negative predictors.
Abstract
INTRODUCTION
Following traumatic brain injury (TBI), midline shift is often caused by space occupying lesions leading to increased intracranial pressure and worsened morbidity and mortality. ...Outcome has been studied in this population; recovery trajectory in these patients has not been reported. We utilized the COBRIT trial to analyze subject recovery over time depending on degree of midline shift at presentation.
METHODS
Subject data from the COBRIT trial were stratified into groups of midline shift, and outcome measures were analyzed at 30, 90, and 180 d after injury. Recovery trajectory analysis was performed identifying patients with outcome measures at all time points, analyzing the degree of recovery based on midline shift at presentation.
RESULTS
There were 896, 1196, and 895 subjects with adequate outcome data at 30, 90, and 180 d, respectively. Rates of favorable outcome (GOS-E 4-8) at 6 mo after injury were 87% (no midline shift), 79% (1-5 mm shift), 64% (6-10 mm shift), and 47% (>10 mm shift). The mean improvement from unfavorable outcome (GOS-E 2-3) to favorable outcome (GOS-E 4-8) from 1 to 6 mo in all groups was 20%. The mean GOS-E of subjects in the 6 to 10 mm group crosses from unfavorable outcome into favorable outcome at 90 d, and the mean GOS-E of subjects in the >10 mm group nearly reaches the threshold of favorable outcome by 180 d after injury.
CONCLUSION
In this secondary analysis of the COBRIT trial, TBI subjects with <10mm of midline shift on admission head CT had significantly improved functional outcomes through 180 d after injury compared to those with greater than 10 mm of midline shift; however, nearly 50% of patients with 10 mm of midline shift will achieve a favorable outcome (GOS-E 4-8) by 6 mo after injury. Subjects with a unfavorable outcome (GOS-E 2-3) at 30 d should continue close observation as approximately 20% will improve to a favorable outcome by 6 mo after injury.
Post-traumatic seizures (PTSs) contribute to morbidity after traumatic brain injury (TBI). Early PTS are rare in combat casualties sustaining TBI, but the prevalence of late PTS is poorly described. ...We sought to define the prevalence and risk factors of late PTS in combat casualties with computed tomography evidence of TBI.
From 2010 to 2015, 687 combat casualties were transferred to a military treatment facility and included in the Department of Defense Trauma Registry. 71 patients with radiographic evidence of TBI were analyzed. Data collection included demographics, injury characteristics, interventions, medications, and outcomes.
Of the 71 patients with evidence of TBI, 66 patients survived hospitalization and were followed. No patients had early PTS, and most received antiepileptic drugs (AEDs) for prophylaxis. At a median follow-up of 7.4 y, late PTS occurred in 25.8% of patients. Patients with late PTS were more severely injured (median Injury severity score 30 versus 24, P = 0.005) and required more blood products (18 units versus 2, P = 0.045). Patients with late PTS were more likely to have had a penetrating TBI (76.5% versus 38.8%, P = 0.01), multiple types of intracranial hemorrhage (94.1% versus 63.3%, P = 0.02), and cranial decompression (76.5% versus 28.6%, P = 0.001). Six-month Glasgow outcome scores were worse (3.5 versus 4.1 P = 0.001) in the late PTS population. No significant relationship was observed between administration of AEDs for early PTS prophylaxis and late PTS.
Combat casualties with TBI suffering late PTS are more severely injured and require more blood products. Penetrating TBI, intracranial hemorrhage, and need for cranial decompression are correlated with late PTS, and associated with worse Glasgow Outcome Score. The administration of prophylactic AEDs for early PTS was not associated with a difference in rates of late PTS.
Traumatic brain injury (TBI) is associated with a high social and financial burden due to persisting (severe) disabilities. The consequences of TBI after intensive care unit (ICU) admission are ...generally measured with global disability screeners such as the Glasgow Outcome Scale-Extended (GOSE), which may lack precision. To improve outcome measurement after brain injury, a comprehensive clinical outcome assessment tool called the Minimal Dataset for Acquired Brain Injury (MDS-ABI) was recently developed. The MDS-ABI covers 12 life domains (demographics, injury characteristics, comorbidity, cognitive functioning, emotional functioning, energy, mobility, self-care, communication, participation, social support, and quality of life), as well as informal caregiver capacity and strain. In this cross-sectional study, we used the MDS-ABI among formerly ICU admitted patients with TBI to explore the relationship between dichotomized severity of TBI and long-term outcome. Our objectives were to: 1) summarize demographics, clinical characteristics, and long-term outcomes of patients and their informal caregivers, and 2) compare differences between long-term outcomes in patients with mild-moderate TBI and severe TBI based on Glasgow Coma Scale (GCS) scores at admission. Participants were former patients of a Dutch university hospital (total
= 52; mild-moderate TBI
= 23; severe TBI
= 29) and their informal caregivers (
= 45). Hospital records were evaluated, and the MDS-ABI was administered during a home visit. On average 3.2 years after their TBI, 62% of the patients were cognitively impaired, 62% reported elevated fatigue, and 69% experienced restrictions in ≥2 participation domains (most frequently work or education and going out). Informal caregivers generally felt competent to provide necessary care (81%), but 31% experienced a disproportionate caregiver burden. All but four patients lived at home independently, often together with their informal caregiver (81%). Although the mild-moderate TBI group and the severe TBI group had significantly different clinical trajectories, there were no persisting differences between the groups for patient or caregiver outcomes at follow-up. As a large proportion of the patients experienced long-lasting consequences beyond global disability or independent living, clinicians should implement a multi-domain outcome set such as the MDS-AB to follow up on their patients.
Among the multiple kinds of neuronal cell death triggered by traumatic brain injury (TBI), ferroptosis, an iron-dependent lipid peroxidative regulatory cell death, has a critical role. Peroxisome ...proliferator-activated receptor-γ (PPARγ) is a nuclear transcription factor that regulates lipid metabolism and suppresses neuronal inflammation. However, the role of PPARγ in neuronal ferroptosis induced by TBI remains unclear. Here, we investigated the regulatory effect of PPARγ on neuronal ferroptosis in a weight-drop TBI model in vivo and an RAS-selective lethal 3 (RSL3)-activated ferroptotic neuronal model in vitro. PPARγ was mainly localized in the nucleus of neurons and was decreased in both the in vivo TBI model and the in vitro ferroptotic neuronal model. The addition of a specific agonist, pioglitazone, activated PPARγ, which protected neuronal function post-TBI in vivo and increased the viability of ferroptotic neurons in vitro. Further investigation suggested that PPARγ probably attenuates neuronal ferroptosis by downregulating cyclooxygenase-2 (COX2) protein expression levels in vivo and in vitro. This study revealed the relationship among PPARγ, ferroptosis and TBI and identified a potential target for comprehensive TBI treatment.
•PPARγ is mainly distributed in the nucleus of neurons.•TBI causes downregulation of PPARγ expression with activation of ferroptosis activation.•Pioglitazone reduces neuronal loss after TBI by inhibiting ferroptosis.•PPARγ reduces neuronal ferroptosis by blocking COX2 expression.
Abstract
Introduction
Daytime sleepiness is among the most frequent self-reported complaints by individuals who have sustained a mild traumatic brain injury (mTBI). Previous research demonstrates ...reduced vigilance and processing speed following mTBI. It has yet to be determined, however, if sustaining a mTBI alone, or the combination of daytime sleepiness and brain injury more greatly impacts cognitive function. The goal of this preliminary analysis was to determine the association between vigilance, daytime sleepiness, and mTBI.
Methods
A total of 137 adults (Mage = 24.89±7.2; 83 females) participated in the study, including 33 healthy controls (HCs) and 104 individuals with a documented mTBI within the preceding 12 months. Daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS), while daytime vigilance was measured using the Psychomotor Vigilance Task (PVT). To assess the effect of mTBI and daytime sleepiness on vigilance, we fit a Poisson regression to the number of lapses on the PVT, with group and ESS scores as predictors.
Results
ESS scores were significantly higher (p<.001) and there were significantly more PVT lapses (p=.03) in those with a recent mTBI, compared to HCs. For those with mTBI, the rate of lapses increased by 7.5% for every 1-point increase in ESS score (p< .001). Furthermore, when compared to HCs, the PVT lapse rate was 1.8x higher for individuals with a history of mTBI (p< 0.001), after controlling for ESS scores.
Conclusion
Daytime sleepiness was negatively associated with sustained vigilance for all participants. However, the magnitude of this association was roughly twice as high in individuals who had sustained a mTBI in the previous year. These findings provide evidence of a significant compounding effect of daytime sleepiness and brain injury on sustained vigilant attention. Clinical evaluation of mTBI would benefit from routine assessment of daytime sleepiness.
Support
USAMRMC grant (W81XWH-12–0386).
Abstract
Introduction
Special Operations Forces (SOF) is an umbrella term which encompasses over a dozen specialized communities across all military branches. Little is known about potential ...differences in demographic and health characteristics, including sleep, between SOF vs. non-SOF service members. We leveraged existing longitudinal studies of those with history of TBI to examine differences between SOF and non-SOF in the dataset.
Methods
We conducted a retrospective analysis of data from the VA TBI Model Systems, a multi-center longitudinal study of outcomes following TBI rehabilitation. Participants were included if SOF status was known (N = 261). Differences between groups on variables of interest were then classified as “Immaterial”, “Minor,” and “Important” based on either prevalence (categorical data) or degree of difference (continuous data).
Results
Of included participants, 68 (26%) were identified as SOF and 193 (74%) as non-SOF. SOF were more highly educated and more likely to have history of mild TBI. There were multiple “important” differences in co-morbidity prevalence. SOF participants were more likely to be diagnosed with sleep apnea (36% SOF vs 12% non-SOF). They were also more likely to have been diagnosed with chronic pain, a cardiac condition, high blood cholesterol, and/or osteoarthritis.
Conclusion
SOF participants differed from non-SOF in a multiple important ways, suggesting this is a different and medically complex population. The most striking finding was that SOF personnel had a significantly greater rate of sleep apnea, relative to non-SOF. The mechanism underlying this difference is not known but may relate to training, blast exposure, weapons use, and mission demands. Further investigation regarding mechanisms, prevalence, and treatment of OSA in the SOF community is needed.
Support
This research was sponsored by VHA Central Office VA TBI Model Systems Program of Research; Subcontract from General Dynamics Information Technology (W91YTZ-13-C-0015; HT0014-19-C-0004).
Abstract
Introduction
Mild Traumatic brain injuries (mTBIs) affect ~1–3 million people per year in the US alone. Mild TBIs can have lasting (>1 year) impacts on emotional reactivity and regulation. ...Sleep has also been shown to be significantly altered in individuals with a mTBI, even when tested over a year since the injury. Sleep quality is strongly linked with emotional stability and emotional memory. Therefore, one possible mediating factor between emotional reactivity and mTBIs is sleep. Reduced sleep quality following a mTBI may impair the emotional regulation that typically occurs across sleep. Thus, increasing total sleep time through a nap may help to alleviate some of the emotional symptoms. This study assessed whether individuals with a chronic mTBI showed differences in brain activity associated with emotional regulatory circuits, performance on an emotional reactivity task, and sleep physiology across a nap compared to controls.
Methods
Participants were 53 young adults (mTBI nap group: n=9; control nap group: n=16; mTBI wake group: n=11; control wake group: n=17). Following a nap, or an equivalent bout of wake (both recorded with polysomnography), participants completed an emotional Go/No-Go task in which they were asked to respond when a particular emotional valence was presented (neutral, fearful, or happy), and withhold a response when a different valence was presented.
Results
There was a significant main effect of emotion on reaction time (F(2, 98)=26.55, p < 0.001). Participants were slowest to respond to the neutral images. There was also a significant three way interaction between emotion, group, and condition (F(2,98)=4.085, p = 0.02).
Conclusion
While these results are preliminary, they support that both napping and mTBIs may impact emotional reactivity. Further, napping may help alleviate some of the chronic emotional dysregulation associated with mTBIs.
Support
Zampell Family Faculty Fellow
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