Mortality In Traumatic Brain Injury Research Articles

Predicting the Risk of In-Hospital Mortality in Traumatic Brain Injury Patients on Invasive Mechanical Ventilation in the Intensive Care Unit: Construction and Validation of an Online Nomogram

To explore mortality risk factors and to construct an online nomogram for predicting in-hospital mortality in traumatic brain injury (TBI) patients receiving invasive mechanical ventilation (IMV) in intensive care unit (ICU). We retrospectively analyzed TBI patients on IMV in ICU from Medical Information Mart for Intensive Care IV database and 2 hospitals. Least absolute shrinkage and selection operation regression and multiple logistic regression were used to detect predictors of in-hospital mortality and to construct an online nomogram. The predictive performance of nomogram was evaluated using area under the receiver operating characteristic curves (AUC), calibration curves, decision curve analysis, and clinical impact curves. Five hundred ten from Medical Information Mart for Intensive Care IV database were enrolled for nomogram construction (80%, n= 408) and internal validation (20%, n= 102). One hundred eighty-five from 2 hospitals were enrolled for external validation. Least absolute shrinkage and selection operation-logistic regression revealed predictors of in-hospital mortality among TBI patients on IMV in ICU included Glasgow Coma Scale (GCS) after ICU admission, Acute Physiology Score III (APS III) after ICU admission, neutrophil and lymphocyte ratio after IMV, blood urea nitrogen after IMV, arterial serum lactate after IMV, and in-hospital tracheotomy. The AUC, calibration curves, decision curve analysis, and clinical impact curves indicated the nomogram had good discrimination, calibration, clinical benefit, and applicability. The multimodel comparisons revealed the nomogram had higher AUC than GCS, APS III, and Simplified Acute Physiology Score II. We constructed and validated an online nomogram based on routinely recorded factors at admission to ICU and at the beginning of IMV to target prediction of in-hospital mortality among TBI patients on IMV in ICU.

Prior traumatic brain injury is a risk factor for in-hospital mortality in moderate to severe traumatic brain injury: a TRACK-TBI cohort study

ObjectivesAn estimated 14–23% of patients with traumatic brain injury (TBI) incur multiple lifetime TBIs. The relationship between prior TBI and outcomes in patients with moderate to severe TBI (msTBI) is...

Prognostic Value of Cardiac Troponin Release in Head Trauma Patients

Abstract Background Traumatic brain injury (TBI) is defined as damage to the brain sustained after the application of external physical force that causes temporary or permanent functional or structural damage to the brain. TBI stands as a major cause of death among youth in industrialized societies. Brain injury can be mild, moderate, and severe. Objective Myocardial dysfunction is frequently described as an underlying cause of mortality in traumatic brain injury (TBI) known as brain-cardiac link. The impact on prognosis of a disease remains uncertain. The present study aimed at investigating the correlation between serum cTnI levels as a biochemical marker of myocardial damage and morbidity, mortality of the patients with mild to moderate TBI. Patients and Methods This study was conducted to investigate the relationship between TBI and cTnI elevation, as well as the prognostic usefulness of this relationship and morbidity, mortality in patients with mild to moderate TBI. It has been claimed that there is a relationship between cardiac injury and mortality rate in TBI patients. Results In the present study, there was a non-significant relation between incidence of mortality and cause & type of injury among studied cases. This study revealed that there is significant relation between incidence of mortality and hospitalization characteristics (length of ICU stay, GCS, troponin level) among studied cases. However, there is a non-significant relation between incidence of mortality and length of hospital stay, AIS, GOS and troponin release. Results of binary logistic regression for prediction of mortality among studied cases were significant in GCS and troponin; however, were non-significant in length of ICU. Regarding validity of troponin in prediction of mortality among studied cases in the present study population, AUC of troponin was 0.912 (95% CI: 0.756-1.0) with cutoff point 0.265 (P < 0.001). Conclusion A non-significant relation was found between release of troponin and cause & type of injury among the studied cases. In addition, there was a non-significant relation between troponin level and mode & type of injury. Increased cTnI levels could be a predictor of mortality among patients with TBI.

EASIX is an effective marker in predicting mortality of traumatic brain injury patients

BackgroundThe Endothelial Activation and Stress Index (EASIX) is a novel marker of endothelial injury and correlates with survival of various patients. The endothelial dysfunction plays an important role on the pathophysiological process of traumatic brain injury (TBI). This study was designed to explore the prognostic value of EASIX on TBI patients.Methods358 TBI patients hospitalized in the West China hospital between October 2018 and October 2022 were enrolled for this study. The EASIX was calculated based on the formula: lactate dehydrogenase (U/L) × creatinine (mg/dL)/platelets (109 cells/L). The univariate and multivariate logistic regression with forward method was performed to explore the association between EASIX and mortality. A prognostic model was developed combining significant risk factors in the multivariate logistic regression. The receiver operating characteristic (ROC) curve was used to compare the predictive accuracy of the EASIX and the developed model.ResultsThe 30-day mortality of enrolled 358 TBI patients was 51.1%. Non-survivors had higher EASIX than survivors (p < 0.001). The multivariate logistic regression confirmed seven risk factors for mortality of TBI including injury mechanism (p = 0.010), GCS (p < 0.001), glucose (p < 0.001), EASIX (p = 0.017), subdural hematoma (p = 0.012), coagulopathy (p = 0.001). The AUC of EASIX, SOFA, GCS was 0.747, 0.748 and 0.774, respectively. The AUC of developed predictive model was 0.874 with the sensitivity of 0.913 and specificity of 0.686.ConclusionsThe EASIX is a reliable marker for predicting mortality of TBI patients. The predictive model incorporating EASIX is helpful for clinicians to evaluate the mortality risk of TBI patients.

Ionized hypocalcemia as a prognostic factor of early mortality in traumatic brain injury

ABSTRACT Traumatic brain injury (TBI) is a common neurological condition. It can affect mental, physical, and cognitive functions and is a common cause of mortality. Electrolytes imbalance is common in those patients. Effective therapy of electrolytes abnormalities improves neurological outcome and reduces mortality. Therefore, we studied the predictive significance of ionized hypocalcemia for early mortality in TBI patients. Patients and method This observational study in the intensive care units of the Ain Shams University hospitals was done on sixty adult patients with TBI of both sexes, divided into two groups: group A (ionized hypocalcemic patients), and group B (normocalcemic patients). On admission, medical history, physical examination including neurological evaluation, and appropriate investigations were performed. Ionized calcium was measured at the start of the 28-day study and then every five days after that. Results There was no statistically significant difference between the two groups for demographic data, brain injury type, or the number of intubated patients (p > 0.05). There was a statistically significant difference for diffuse axonal damage, subdural hemorrhage, cerebral edema, GCS, and mean blood pressure (p < 0.001). Serum ionized calcium in group A was significantly lower than in group B on the day of admission and day five (p < 0.001). On the day of admission, there was a significant difference in serum sodium (p < 0.05). In both groups, there was a significant difference in the ICU stay as regards survivors (p < 0.001). There was a statistically significant difference in 28-day mortality between the two groups (p = 0.001). Conclusion In TBI patients, ionized hypocalcemia on admission and on day five, hypernatremia and disturbed conscious level are strongly linked to greater fatality rates.

Interaction of high-fat diet and brain trauma alters adipose tissue macrophages and brain microglia associated with exacerbated cognitive dysfunction

Obesity increases the morbidity and mortality of traumatic brain injury (TBI). Detailed analyses of transcriptomic changes in the brain and adipose tissue were performed to elucidate the interactive effects between high-fat diet-induced obesity (DIO) and TBI. Adult male mice were fed a high-fat diet (HFD) for 12 weeks prior to experimental TBI and continuing after injury. High-throughput transcriptomic analysis using Nanostring panels of the total visceral adipose tissue (VAT) and cellular components in the brain, followed by unsupervised clustering, principal component analysis, and IPA pathway analysis were used to determine shifts in gene expression patterns and molecular pathway activity. Cellular populations in the cortex and hippocampus, as well as in VAT, during the chronic phase after combined TBI-HFD showed amplification of central and peripheral microglia/macrophage responses, including superadditive changes in selected gene expression signatures and pathways. Furthermore, combined TBI and HFD caused additive dysfunction in Y-Maze, Novel Object Recognition (NOR), and Morris water maze (MWM) cognitive function tests. These novel data suggest that HFD-induced obesity and TBI can independently prime and support the development of altered states in brain microglia and VAT, including the disease-associated microglia/macrophage (DAM) phenotype observed in neurodegenerative disorders. The interaction between HFD and TBI promotes a shift toward chronic reactive microglia/macrophage transcriptomic signatures and associated pro-inflammatory disease-altered states that may, in part, underlie the exacerbation of cognitive deficits. Thus, targeting of HFD-induced reactive cellular phenotypes, including in peripheral adipose tissue immune cell populations, may serve to reduce microglial maladaptive states after TBI, attenuating post-traumatic neurodegeneration and neurological dysfunction.

Traumatic brain injury incidence and mortality: a large population-based study.

Traumatic brain injury (TBI) is a severe condition that represents a major global public health concern. Provide a comprehensive epidemiological outlook encompassing TBI incidence, healthcare provision and mortality. Population-based study in Veneto (4.9 million inhabitants), Italy, from 2012 to 2021. Hospital discharge and mortality records were used to assess incidence and mortality. Kaplan-Meier survival estimator and Cox regression models were fitted to investigate determinants of mortality. Between 2012 and 2021, there were 37,487 incident TBI cases, corresponding to an age-standardized rate of 77.30/100,000 people (95% CI 76.52-78.09), higher among males, with an exponential growth after age 70. Leading causes were domestic (33.1%) and traffic accidents (17.7%), the first predominating among the elderly and children, while the latter in males 15-24 and older people. After rates stably declined between 2012 and 2019, the study captured a sharp decrease especially for traffic and occupational accidents in males, due to COVID-19 lockdown in 2020. Overall, 48.9% TBI patients were hospitalized in a specialized trauma center, with 2.6% requiring a transfer after accessing a spoke hospital. Over a 3.7 years median follow-up, 16,145 deaths were recorded, with higher mortality for those undergoing neurosurgical interventions, regardless of their access point. Risks of death increased with age, male gender, and comorbidities. TBI incidence is characterized by distinct patterns, affecting particularly older individuals and males. Minimal hospital transfers with comparable survival irrespective of access point suggests an effective patient management within the network. The study underscores the critical need for acute-phase support and prolonged care strategies for older TBI patients.

Effect of Daytime versus Nighttime on Prehospital Care and Outcomes after Severe Traumatic Brain Injury.

Background/Objectives: Severe traumatic brain injury (TBI) is a frequent cause of morbidity and mortality worldwide. In the Netherlands, suspected TBI is a criterion for the dispatch of the physician-staffed helicopter emergency medical services (HEMS) which are operational 24 h per day. It is unknown if patient outcome is influenced by the time of day during which the incident occurs. Therefore, we investigated the association between the time of day of the prehospital treatment of severe TBI and 30-day mortality. Methods: A retrospective analysis of prospectively collected data from the BRAIN-PROTECT study was performed. Patients with severe TBI treated by one of the four Dutch helicopter emergency medical services were included and followed up to one year. The association between prehospital treatment during day- versus nighttime, according to the universal daylight period, and 30-day mortality was analyzed with multivariable logistic regression. A planned subgroup analysis was performed in patients with TBI with or without any other injury. Results: A total of 1794 patients were included in the analysis, of which 1142 (63.7%) were categorized as daytime and 652 (36.3%) as nighttime. Univariable analysis showed a lower 30-day mortality in patients with severe TBI treated during nighttime (OR 0.74, 95% CI 0.60-0.91, p = 0.004); this association was no longer present in the multivariable model (OR 0.82, 95% CI 0.59-1.16, p = 0.262). In a subgroup analysis, no association was found between mortality rates and the time of prehospital treatment in patients with combined injuries (TBI and any other injury). Patients with isolated TBI had a lower mortality rate when treated during nighttime than when treated during daytime (OR 0.51, 95% CI 0.34-0.76, p = 0.001). Within the whole cohort, daytime versus nighttime treatments were not associated with differences in functional outcome defined by the Glasgow Outcome Scale. Conclusions: In the overall study population, no difference was found in 30-day mortality between patients with severe TBI treated during day or night in the multivariable model. Patients with isolated severe TBI had lower mortality rates at 30 days when treated at nighttime.

Severe traumatic brain injury and acute kidney injury patients: factors associated with in-hospital mortality and unfavorable outcomes

ABSTRACT Objective The purpose of this study was to identify the occurrence of AKI, and factors associated with in-hospital mortality and unfavorable outcomes in patients with severe traumatic brain injury (TBI) and acute kidney injury (AKI) severity. Method A retrospective cohort study which analyzed data with severe TBI between 2013 and 2017. We examined demographic and clinical information, and outcome by in-hospital mortality, and the Glasgow Outcome Scale six months after TBI. We associated factors to in-hospital mortality and unfavorable outcome in severe TBI and AKI with an association test. Results A total of 219 patients were selected, 39.3% had an AKI, and several factors associated with AKI occurrence after severe TBI. Stage 2 or 3 of AKI (OR 12.489; 95% CI = 4.45–37.94) were independent risk for both outcomes in multivariable models, severity injury by the New Trauma Injury Severity Score (OR 0.97; 95% CI = 0.96–0.99) for mortality, and the New Injury Severity Score (OR1.07; 95% CI = 1.04–1.10) and Trauma and Injury Severity Score (OR = 0.98; 95% CI = 0.965–0.997) for unfavorable outcome. Conclusion The findings of our study confirmed that AKI severity and severity of injury was also related to increased mortality and unfavorable outcome after severe TBI.

Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury

BackgroundTraumatic brain injury (TBI) is a major cause of death and functional disability in the general population. The nomogram is a clinical prediction tool that has been researched for a wide range of medical conditions. The purpose of this study was to identify prognostic factors associated with in-hospital mortality. The secondary objective was to develop a clinical nomogram for TBI patients' in-hospital mortality based on prognostic factors. MethodsA retrospective cohort study was conducted to analyze 14,075 TBI patients who were admitted to a tertiary hospital in southern Thailand. The total dataset was divided into the training and validation datasets. Several clinical characteristics and imaging findings were analyzed for in-hospital mortality in both univariate and multivariable analyses using the training dataset. Based on binary logistic regression, the nomogram was developed and internally validated using the final predictive model. Therefore, the predictive performances of the nomogram were estimated by the validation dataset. ResultsPrognostic factors associated with in-hospital mortality comprised age, hypotension, antiplatelet, Glasgow coma scale score, pupillary light reflex, basilar skull fracture, acute subdural hematoma, subarachnoid hemorrhage, midline shift, and basal cistern obliteration that were used for building nomogram. The predictive performance of the nomogram was estimated by the training dataset; the area under the receiver operating characteristic curve (AUC) was 0.981. In addition, the AUCs of bootstrapping and cross-validation methods were 0.980 and 0.981, respectively. For the temporal validation with an unseen dataset, the sensitivity, specificity, accuracy, and AUC of the nomogram were 0.90, 0.88, 0.88, and 0.89, respectively. ConclusionA nomogram developed from prognostic factors had excellent performance; thus, the tool had the potential to serve as a screening tool for prognostication in TBI patients. Furthermore, future research should involve geographic validation to examine the predictive performances of the clinical prediction tool.

Trends in Traumatic Brain Injury Mortality in the US

This cohort study characterizes US trends in traumatic brain injury–related mortality by age, sex, and race and ethnicity.

Effect of the COVID-19 Pandemic on the Epidemiology of Pediatric Traumatic Brain Injury in Brazil.

Aim In response to the coronavirus 2019 disease (COVID-19) pandemic, governments worldwide implemented measures to prevent infection, resulting in restricted school activities, restricted children's freedom of movement, and increased risk of violence and injuries at home, including traumatic brain injury (TBI), among children. In Brazil, the consequences of the COVID-19 pandemic on the causes, severity, and mortality of pediatric TBI have not yet been investigated. Thus, our study aimed to determine whether the COVID-19 pandemic has affected the epidemiology of pediatric TBI among Brazilian children. Materials and methods We investigated the patients with TBI aged <18 years who visited a tertiary trauma center in Brazil in 2019 and 2020. TBI-related variables, such as classification, mechanism, clinical manifestations, need for intervention, morbidity, and mortality, were recorded. Furthermore, we used a nationwide databank to collect information on mortality from external causes of trauma and violence in the pediatric population in 2019 and 2020. The Mann-Whitney test was used to compare quantitative variables related to the mechanisms and severity of TBI in both periods in order to determine the impact of the COVID-19 pandemic. Results Of the patients with traumatic brain injury, 1371 visited the trauma center in 2019 and 1052 in 2020. No difference was noted in the incidence rate of abusive head trauma between these periods (p=0.142) or in mortality from violence in Brazil. Recreational causes of pediatric TBI increased during the first year of the COVID-19 pandemic in Brazil and falls from bicycles significantly increased during the pandemic (p<0.001). Conclusion A global reduction in pediatric admissions to emergency rooms as well as no impact on mortality and severity of pediatric TBI were observed during the COVID-19 pandemic in Brazil. Additionally, a public education program regarding child safety during recreational activities, particularly how to avoid falls from bicycles was recommended.

Effect of antiplatelet and anticoagulant medication use on injury severity and mortality in patients with traumatic brain injury treated in the intensive care unit

BackgroundAntiplatelet and anticoagulant medication are increasingly common and can increase the risks of morbidity and mortality in traumatic brain injury (TBI) patients. Our study aimed to quantify the association of antiplatelet or anticoagulant use in intensive care unit (ICU)–treated TBI patients with 1-year mortality and head CT findings.MethodWe conducted a retrospective, multicenter observational study using the Finnish Intensive Care Consortium database. We included adult TBI patients admitted to four university hospital ICUs during 2003–2013. The patients were followed up until the end of 2016. The national drug reimbursement database provided information on prescribed medication for our study. We used multivariable logistic regression models to assess the association between TBI severity, prescribed antiplatelet and anticoagulant medication, and their association with 1-year mortality.ResultsOf 3031 patients, 128 (4%) had antiplatelet and 342 (11%) anticoagulant medication before their TBI. Clopidogrel (2%) and warfarin (9%) were the most common antiplatelets and anticoagulants. Three patients had direct oral anticoagulant (DOAC) medication. The median age was higher among antiplatelet/anticoagulant users than in non-users (70 years vs. 52 years, p < 0.001), and their head CT findings were more severe (median Helsinki CT score 3 vs. 2, p < 0.05). In multivariable analysis, antiplatelets (OR 1.62, 95% CI 1.02–2.58) and anticoagulants (OR 1.43, 95% CI 1.06–1.94) were independently associated with higher odds of 1-year mortality. In a sensitivity analysis including only patients over 70, antiplatelets (OR 2.28, 95% CI 1.16–4.22) and anticoagulants (1.50, 95% CI 0.97–2.32) were associated with an increased risk of 1-year mortality.ConclusionsBoth antiplatelet and anticoagulant use before TBI were risk factors in our study for 1-year mortality. Antiplatelet and anticoagulation medication users had a higher radiological intracranial injury burden than non-users defined by the Helsinki CT score. Further investigation on the effect of DOACs on mortality should be done in ICU–treated TBI patients.

Association of Central Nervous System-Affecting Medications With Occurrence and Short-Term Mortality of Traumatic Brain Injury.

The use of medications commonly prescribed after traumatic brain injury (TBI) has been little studied before TBI. This study examined the association between the use of medications that affect the central nervous system (CNS) and the occurrence and short-term mortality of TBI. Mandatory Finnish registries were used to identify TBI admissions, fatal TBIs, and drug purchases during 2005-2018. Patients with TBI were 1:1 matched to nontrauma control patients to investigate the association between medications and the occurrence of TBI and 30-day mortality after TBI. Number needed to harm (NNH) was calculated for all medications. The cohort included 59 606 patients with TBI and a similar number of control patients. CNS-affecting drugs were more common in patients with TBI than in controls [odds ratio = 2.07 (2.02-2.13), P < .001)]. Benzodiazepines were the most common type of medications in patients with TBI (17%) and in controls (11%). The lowest NNH for the occurrence of TBI was associated with benzodiazepines (15.4), selective serotonin uptake inhibitors (18.5), and second-generation antipsychotics (25.8). Eight percent of the patients with TBI died within 30 days. The highest hazard ratios (HR) and lowest NNHs associated with short-term mortality were observed with strong opioids [HR = 1.41 (1.26-1.59), NNH = 33.1], second-generation antipsychotics [HR = 1.36 (1.23-1.50), NNH = 37.1], and atypical antidepressants [HR = 1.17 (1.04-1.31), NNH = 77.7]. Thirty-seven percent of patients with TBI used at least 1 CNS-affecting drug. This proportion was significantly higher than in the control population (24%). The highest risk and lowest NNH for short-term mortality were observed with strong opioids, second-generation antipsychotics, and atypical antidepressants. The current risks underscore the importance of weighing the benefits and risks before prescribing CNS-affecting drugs in patients at risk of head injury.

Liver fibrosis score is associated with the mortality of traumatic brain injury patients.

The fibrosis-4 score is a marker of liver fibrosis and has been confirmed to be associated with the prognosis of various diseases. There is no study exploring the prognostic value of the fibrosis-4 score in traumatic brain injury (TBI) patients. We design this study to explore the association between the fibrosis-4 score and mortality from TBI. TBI patients from the Medical Information Mart for Intensive Care-III database were extracted for the study. Univariate and multivariate logistic regressions were sequentially performed to analyze the association between fibrosis-4 and mortality in TBI. The area under the receiver operating characteristic curve (AUC) was drawn to evaluate the prognostic value of fibrosis-4 and other scores. A total of 1018 TBI patients were included, with a 30-day mortality of 24.2%. Non-survivors had older age, lower Glasgow Coma Scale (GCS), and higher injury severity score (ISS) than survivors. The aspartate aminotransferase platelet ratio index (APRI) and fibrosis-4 score were significantly higher in non-survivors. Univariate logistic regression showed that age, GCS, ISS, white blood cell, hemoglobin, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants were associated with the mortality of TBI patients. Multivariate logistic regression presented that age, GCS, ISS, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants were independent risk factors of mortality in TBI patients after adjusting for confounding effects. The AUC of the GCS, ISS, APRI, and fibrosis-4 score for predicting mortality was 0.711, 0.625, 0.592, and 0.627, respectively. Composed of age, GCS, ISS, fibrosis-4 score, subarachnoid hemorrhage, and anticoagulants, the predictive model had the highest AUC value of 0.790. The fibrosis-4 score is an independent risk factor for mortality in TBI. The model incorporating fibrosis-4 performs well in predicting the prognosis of TBI patients.

Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department.

Accurate prediction of in-hospital mortality is essential for better management of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been shown to be effective in predicting clinical outcomes. This study aimed to identify predictors of in-hospital mortality in TBI patients using ML algorithms. A retrospective study was performed using data from both the trauma registry and electronic medical records among TBI patients admitted to the Hamad Trauma Center in Qatar between June 2016 and May 2021. Thirteen features were selected for four ML models including a Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XgBoost), to predict the in-hospital mortality. A dataset of 922 patients was analyzed, of which 78% survived and 22% died. The AUC scores for SVM, LR, XgBoost, and RF models were 0.86, 0.84, 0.85, and 0.86, respectively. XgBoost and RF had good AUC scores but exhibited significant differences in log loss between the training and testing sets (% difference in logloss of 79.5 and 41.8, respectively), indicating overfitting compared to the other models. The feature importance trend across all models indicates that aPTT, INR, ISS, prothrombin time, and lactic acid are the most important features in prediction. Magnesium also displayed significant importance in the prediction of mortality among serum electrolytes. SVM was found to be the best-performing ML model in predicting the mortality of TBI patients. It had the highest AUC score and did not show overfitting, making it a more reliable model compared to LR, XgBoost, and RF.

Neuropharmacological insight into preventive intervention in posttraumatic epilepsy based on regulating glutamate homeostasis.

Posttraumatic epilepsy (PTE) is one of the most critical complications of traumatic brain injury (TBI), significantly increasing TBI patients' neuropsychiatric symptoms and mortality. The abnormal accumulation of glutamate caused by TBI and its secondary excitotoxicity are essential reasons for neural network reorganization and functional neural plasticity changes, contributing to the occurrence and development of PTE. Restoring glutamate balance in the early stage of TBI is expected to play a neuroprotective role and reduce the risk of PTE. To provide a neuropharmacological insight for drug development to prevent PTE based on regulating glutamate homeostasis. We discussed how TBI affects glutamate homeostasis and its relationship with PTE. Furthermore, we also summarized the research progress of molecular pathways for regulating glutamate homeostasis after TBI and pharmacological studies aim to prevent PTE by restoring glutamate balance. TBI can lead to the accumulation of glutamate in the brain, which increases the risk of PTE. Targeting the molecular pathways affecting glutamate homeostasis helps restore normal glutamate levels and is neuroprotective. Taking glutamate homeostasis regulation as a means for new drug development can avoid the side effects caused by direct inhibition of glutamate receptors, expecting to alleviate the diseases related to abnormal glutamate levels in the brain, such as PTE, Parkinson's disease, depression, and cognitive impairment. It is a promising strategy to regulate glutamate homeostasis through pharmacological methods after TBI, thereby decreasing nerve injury and preventing PTE.

Predicting inpatient mortality in pediatric traumatic brain injury: insights from a national database.

The purpose of this study was to determine factors significantly associated with mortality and length of stay (LOS) in admissions to the pediatric intensive care unit (PICU) for traumatic brain injury (TBI). A cross-sectional, retrospective cohort study that identified PICU admissions with TBI from forty-nine hospitals in the USA using the Pediatric Health Information System database from 2016 to 2021. Univariable analyses comparing those who did and did not experience mortality were performed. The following regression analyses were conducted: logistic regression with mortality as dependent variable; linear regression with LOS as the dependent variable; logistic regression with mortality as the dependent variable but only included patients with cerebral edema; and linear regression with LOS as the dependent variable but only included patients who survived. From the regression analysis for mortality in all TBI patients was utilized to develop a mortality risk score. A total of 3041 admissions were included. Those with inpatient mortality (18.5%) tended to be significantly younger (54 vs. 92months, p < 0.01), have < 9 pediatric Glasgow Coma Scale on admission (100% vs. 52.9%, p < 0.01) and more likely to experience acute renal, hepatic and respiratory failure, acidosis, central diabetes insipidus, hyperkalemia, and hypocalcemia. Regression analysis identified that pediatric Glasgow Coma Scale, alkalosis and cardiac arrest significantly increased risks of mortality. The TBI mortality risk score had an area under the curve of 0.89 to identify those with mortality; a score of 6 ≤ was associated with 88% mortality. Patients admitted to the PICU with TBI have 18.5% risk of inpatient mortality with most occurring the first 48h and these are characterized with greater multisystem organ dysfunction, received medical and mechanical support. TBI mortality risk score suggested is a practical tool to identify patients with an increase likelihood to die.

PREVALENCE OF ANATOMICAL VARIATIONS OF CIRCLE OF WILLIS IN SOUTH INDIAN POPULATION SUCCUMBING TO ISOLATED TRAUMATIC BRAIN INJURY - A CADAVERIC STUDY

Introduction: The Circle of Willis (COW) is a large arterial anastomotic ring present at the basal cistern of the brain, uniting the internal carotid and the vertebro-basilar system. Sir Thomas Willis was the first to describe the importance of the circle in maintaining collateral flow. It was observed that there is very little mixing of blood between the collateral branches of the circle. These collaterals may however open up during occlusive episodes of the proximal feeding vessels or in case of severe stress such as stroke and traumatic brain injury (TBI) in order to maintain metabolic activity in the brain. This phenomenon is affected to various degrees in the presence of anomalies. The prevalence of anomalies of COW in the general population is up to 75.72% was demonstrated in other studies. Materials and Methodology: This is a cross sectional observational study. 20 consecutive patients above 18 years of age succumbed to isolated TBI who were subjected to medico-legal autopsy in our hospital were included. The anomalies were classified as per the criteria laid down by Stojanovi´c et al. Results: 9 Normal variants (Type A) of COW (45%) was found. The most common type of anomaly was Type C which was noted in 4 cases (20%). Conclusion: Anatomical variations of COW has proven association in the areas of stroke, aneurysm, migraine, carotid endarterectomy and ageing brains. Further studies are needed to determine whether the anomalies of COW really contribute to the mortality in TBI.

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram.

Traumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality. Data were extracted from an online database called "Multiparameter Intelligent Monitoring in Intensive Care IV" (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA). The eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions. The proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients.