Predictors of Mortality in Traumatic Intracranial Hemorrhage: A National Trauma Data Bank Study

Development and Validation of a Novel Classification System and Prognostic Model for Open Traumatic Brain Injury: A Multicenter Retrospective Study

INTRODUCTION

Open traumatic brain injury (OTBI) is associated with high mortality and morbidity; however, the classification of these injuries and the determination of patient prognosis remain uncertain, hindering the selection of optimal treatment strategies. This study aimed to develop and validate a novel OTBI classification system and a prognostic model for poor prognosis.

METHODS

This retrospective study included patients with isolated OTBI who received treatment at three large medical centers in China between January 2020 and June 2022 as the training set. Data on patients with OTBI collected at the Fuzong Clinical Medical College of Fujian Medical University between July 2022 and June 2023 were used as the validation set. Clinical parameters, including clinical data at admission, radiological and laboratory findings, details of surgical methods, and prognosis were collected. Prognosis was assessed through a dichotomized Glasgow Outcome Scale (GOS). A novel OTBI classification was proposed, categorizing patients based on a combination of intracranial hematoma and midline shift observed on imaging, and logistic regression analyses were performed to identify risk factors associated with poor prognosis and to investigate the association between the novel OTBI classification and prognosis. Finally, a nomogram suitable for clinical application was established and validated.

RESULTS

Multivariable logistic regression analysis identified OTBI classification type C (p < 0.001), a Glasgow Coma Scale score (GCS) ≤ 8 (p < 0.001), subarachnoid hemorrhage (SAH) (p = 0.004), subdural hematoma (SDH) (p = 0.011), and coagulopathy (p = 0.020) as independent risk factors for poor prognosis. The addition of the OTBI classification to a model containing all the other identified prognostic factors improved the predictive ability of the model (Z = 1.983; p = 0.047). In the validation set, the model achieved an area under the curve (AUC) of 0.917 [95% confidence interval (CI) = 0.864-0.970]. The calibration curve closely approximated the ideal curve, indicating strong predictive performance of the model.

CONCLUSIONS

The implementation of our proposed OTBI classification system and its use alongside the other prognostic factors identified here may improve the prediction of patient prognosis and aid in the selection of the most suitable treatment strategies.

Death risk prediction model for patients with non-traumatic intracerebral hemorrhage

BACKGROUND

This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.

METHODS

1274 ICH patients who met the specified inclusion and exclusion criteria were analyzed retrospectively in the MIMIC IV 3.0 database. Patients were randomly divided into training, validation, and testing datasets in a ratio of 6:2:2 based on the outcome distribution. Data from the Second Hospital of Lanzhou University were used as an external validation set. This study used LASSO regression and multivariable logistic regression analysis to screen for features. We then employed XGBoost to construct a machine-learning model. The model's performance was evaluated using ROC curve analysis, calibration curve analysis, clinical decision curve analysis, sensitivity, specificity, accuracy, and F1 score. Conclusively, the SHapley Additive exPlanations (SHAP) method was employed to interpret the model's predictions.

RESULTS

Deaths occurred in 572 out of the 1274 ICH cases included in the study, resulting in an incidence rate of 44.9%. The XGBoost model achieved a high AUC when predicting deaths in ICH patients (train: 0.814, 95%CI: 0.784 - 0.844; validation: 0.715, 95%CI: 0.653 - 0.777; test: 0.797, 95%CI: 0.743 - 0.851). The importance of SHAP variables in the model ranked from high to low was: 'GCS motor', 'Age', 'GCS eyes', 'Low density lipoprotein (LDL)', ' Albumin', ' Atrial fibrillation', and 'Gender'. The XGBoost model demonstrated good predictive performance in both the validation and external validation datasets.

CONCLUSIONS

The XGBoost machine learning model we built has demonstrated strong performance in predicting the risk of death from ICH. Furthermore, the SHAP provides the possibility of interpreting machine learning results.

Survival analysis of famotidine administration routes in non-traumatic intracerebral hemorrhage patients: based on the MIMIC-IV database

OBJECTIVE

This study compared the survival outcomes of non-traumatic intracerebral hemorrhage (ICH) patients with different famotidine administration routes and explored related risk factors.

METHODS

Data from ICH patients between 2008-2019 were extracted from the MIMIC-IV database. Survival differences between patients with intravenous (IV) and non-intravenous (Non-IV) famotidine administration were analyzed using Cox analysis and Kaplan-Meier survival curves.

RESULTS

The study included 351 patients, with 109 in the IV group and 84 in the Non-IV group after PSM. Cox analysis revealed that survival was significantly associated with age (HR = 1.031, 95%CI:1.011-1.050, p = 0.002), chloride ions (HR = 1.061, 95%CI:1.027-1.096, p < 0.001), BUN (HR = 1.034, 95%CI:1.007-1.062, p = 0.012), ICP (HR = 1.059, 95%CI:1.027-1.092, p < 0.001), RDW (HR = 1.156, 95%CI:1.030-1.299, p = 0.014), mechanical ventilation (HR = 2.526, 95%CI:1.341-4.760, p = 0.004), antibiotic use (HR = 0.331, 95%CI:0.144-0.759, p = 0.009), and Non-IV route (HR = 0.518, 95%CI:0.283-0.948, p = 0.033). Kaplan-Meier curves showed a significantly higher 30-day survival rate in the Non-IV group (p = 0.011), particularly in patients with normal ICP (HR = 0.518, 95%CI:0.283-0.948, p = 0.033).

CONCLUSION

Non-IV famotidine administration significantly improves 30-day survival of ICH patients, especially for those with normal ICP, compared to IV administration.

Multicenter Study Examining Temporal Trends in Traumatic Intracranial Hemorrhage Over Six Years Using Joinpoint Regression

The aging US population has altered the epidemiology of traumatic injury, but there are few studies examining changing patterns of traumatic intracranial hemorrhage (tICH). We examined temporal changes in incidence, demographics, severity, management, and outcomes of tICH among trauma admissions at six US Level I trauma centers over 6 years (July 1, 2016-June 30, 2022). Patients with tICH (subdural, epidural, subarachnoid, and intracerebral hemorrhage) were identified by 10th revision of the International Statistical Classification of Diseases diagnosis codes. Temporal trends were examined over 12 six-month intervals using joinpoint regression and reported as biannual percent change (BPC); models without joinpoints are described as linear trends over time. There were 67,514 trauma admissions over 6 years and 11,935 (17.7%) patients had a tICH. The proportion of tICH injuries significantly increased 2.6% biannually from July 2016 to July 2019 (BPC = 2.6, p = 0.04), then leveled off through June 2022 (BPC = -0.9, p = 0.19). Similarly, the proportion of geriatric patients (≥65 years old) increased 2.4% biannually from July 2016 to July 2019 (BPC = 2.4, p = 0.001) as did injuries due to falls (BPC = 2.2, p = 0.01). Three of the four most prevalent comorbidities significantly increased: hypertension linearly increased 2.1% biannually, functional dependence increased 25.5% biannually through June 2019, and chronic anticoagulant use increased 19.0% biannually through June 2019 and then 3.1% thereafter. There were no trends in the rates of neurosurgical intervention (BPC = -0.89, p = 0.40), ED Glasgow coma score 3-8 (BPC = -0.4, p = 0.77), or presence of severe extracranial injuries (BPC = -0.7, p = 0.45). In-hospital mortality linearly declined 2.6% biannually (BPC = 2.6, p = 0.05); however, there was a 10.3% biannual linear increase in discharge to hospice care (BPC = 10.3, p < 0.001). These results demonstrate the incidence of tICH admissions is temporally increasing, and the population is growing older with more comorbidities and injuries from falls. Yet, traumatic brain injury severity and neurosurgical management are unchanged. The shift from in-patient death to hospice care suggests an increased need for palliative care services.

Machine learning predictors of risk of death within 7 days in patients with non-traumatic subarachnoid hemorrhage in the intensive care unit: A multicenter retrospective study

Non-traumatic subarachnoid hemorrhage (SAH) is a critical neurosurgical emergency with a high mortality rate, imposing a significant burden on both society and families. Accurate prediction of the risk of death within 7 days in SAH patients can provide valuable information for clinicians, enabling them to make better-informed medical decisions. In this study, we developed six machine learning models using the MIMIC III database and data collected at our institution. These models include Logistic Regression (LR), AdaBoosting (AB), Multilayer Perceptron (MLP), Bagging (BAG), Gradient Boosting Machines (GBM), and Extreme Gradient Boosting (XGB). The primary objective was to identify predictors of death within 7 days in SAH patients admitted to intensive care units. We employed univariate and multivariate logistic regression as well as Pearson correlation analysis to screen the clinical variables of the patients. The initially screened variables were then incorporated into the machine learning models, and the performance of these models was evaluated. Furthermore, we compared the performance differences among the six models and found that the MLP model exhibited the highest performance with an AUC of 0.913. In this study, we conducted risk factor analysis using Shapley values to identify the factors associated with death within 7 days in patients with SAH. The risk factors we identified include Gcsmotor, bicarbonate, wbc, spo2, heartrate, age, nely, glucose, aniongap, GCS, rbc, sysbp, sodium, and gcseys. To provide clinicians with a useful tool for assessing the risk of death within 7 days in SAH patients, we developed a web calculator based on the MLP machine learning model.

Early surgery versus conservative treatment in patients with traumatic intracerebral hematoma: a CENTER-TBI study

PURPOSE

Evidence regarding the effect of surgery in traumatic intracerebral hematoma (t-ICH) is limited and relies on the STITCH(Trauma) trial. This study is aimed at comparing the effectiveness of early surgery to conservative treatment in patients with a t-ICH.

METHODS

In a prospective cohort, we included patients with a large t-ICH (< 48 h of injury). Primary outcome was the Glasgow Outcome Scale Extended (GOSE) at 6 months, analyzed with multivariable proportional odds logistic regression. Subgroups included injury severity and isolated vs. non-isolated t-ICH.

RESULTS

A total of 367 patients with a large t-ICH were included, of whom 160 received early surgery and 207 received conservative treatment. Patients receiving early surgery were younger (median age 54 vs. 58 years) and more severely injured (median Glasgow Coma Scale 7 vs. 10) compared to those treated conservatively. In the overall cohort, early surgery was not associated with better functional outcome (adjusted odds ratio (AOR) 1.1, (95% CI, 0.6-1.7)) compared to conservative treatment. Early surgery was associated with better outcome for patients with moderate TBI and isolated t-ICH (AOR 1.5 (95% CI, 1.1-2.0); P value for interaction 0.71, and AOR 1.8 (95% CI, 1.3-2.5); P value for interaction 0.004). Conversely, in mild TBI and those with a smaller t-ICH (< 33 cc), conservative treatment was associated with better outcome (AOR 0.6 (95% CI, 0.4-0.9); P value for interaction 0.71, and AOR 0.8 (95% CI, 0.5-1.0); P value for interaction 0.32).

CONCLUSIONS

Early surgery in t-ICH might benefit those with moderate TBI and isolated t-ICH, comparable with results of the STITCH(Trauma) trial.

Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis

PURPOSE

With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI.

METHODOLOGY

We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity.

RESULT

A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance.

CONCLUSION

According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.

Injury characteristics and predictors of mortality in patients undergoing pancreatic excision after abdominal trauma: A National Trauma Data Bank (NTDB) study

Pancreatic tumors and pancreatitis are the main indications for pancreatic excision (PE). However, little is known about this type of intervention in the context of traumatic injuries. Surgical care for traumatic pancreatic injuries is challenging because of the location of the organ and the lack of information on trauma mechanisms, vital signs, hospital deposition characteristics, and associated injuries. This study examined the demographics, vital signs, associated injuries, clinical outcomes, and predictors of in-hospital mortality in patients with abdominal trauma who had undergone PE. Following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines, we analyzed the National Trauma Data Bank and identified patients who underwent PE for penetrating or blunt trauma after an abdominal injury. Patients with significant injuries in other regions (abbreviated injury scale score ≥ 2) were excluded. Of the 403 patients who underwent PE, 232 had penetrating trauma (PT), and 171 had blunt trauma (BT). The concomitant splenic injury was more prevalent in the BT group; however, the frequency of splenectomy was comparable between groups. In particular, concomitant kidney, small intestine, stomach, colon, and liver injuries were more common in the PT group (all P < .05). Most injuries were observed in the pancreatic body and tail regions. The trauma mechanisms also differed between the groups, with motor vehicles accounting for most of the injuries in the BT group and gunshots accounting for most of the injuries in the PT group. In the PT group, major liver lacerations were approximately 3 times more common (P < .001). The in-hospital mortality rate was 12.4%, with no major differences between the PT and BT groups. Furthermore, there was no difference between BT and PT with respect to the location of the injuries in the pancreas, with the pancreatic tail and body accounting for almost 65% of injuries. Systolic blood pressure, Glasgow Coma Scale score, age, and major liver laceration were revealed by logistic regression as independent predictors of mortality, although trauma mechanisms and intent were not linked to mortality risk.

Identifying predictive factors for mortality in patients with TBI at a neurosurgery department

Traumatic brain injury (TBI) can have severe consequences in most cases. Many therapeutic and neurosurgical strategies have been improved to optimize patient outcomes. However, despite adequate surgery and intensive care, death can still occur during hospitalization. TBI often results in protracted hospital stays in neurosurgery departments, indicating the severity of brain injury. Several factors related to TBI are predictive of longer hospital stays and in-hospital mortality rates. This study aimed to identify predictive factors for intrahospital days of death due to TBI. This was a longitudinal, retrospective, analytical, observational study that included 70 TBI-related deaths admitted to the Neurosurgery Clinic in Cluj-Napoca for a period of four years (January 2017 to December 2021) using a cohort model. We identified some clinical data related to intrahospital death after TBI. The severity of TBI was classified as mild (n=9), moderate(n=13), and severe (n=48) and was associated with significantly fewer hospital days (p=0.009). Patients with associated trauma, such as vertebro-medullary or thoracic trauma, were more likely to die after a few days of hospitalization (p=0.007). Surgery applied in TBI was associated with a higher median number of days until death compared to conservative treatment. A low GCS was an independent predictive factor for early intrahospital mortality in patients with TBI. In conclusion, clinical factors such as the severity of injury, low GCS, and polytrauma are predictive of early intrahospital mortality. Surgery was associated with prolonged hospitalization.

Epidemiological and clinical characteristics predictive of ICU mortality of patients with traumatic brain injury treated at a trauma referral hospital - a cohort study

BACKGROUND

Traumatic brain injury (TBI) has substantial physical, psychological, social and economic impacts, with high rates of morbidity and mortality. Considering its high incidence, the aim of this study was to identify epidemiological and clinical characteristics that predict mortality in patients hospitalized for TBI in intensive care units (ICUs).

METHODS

A retrospective cohort study was carried out with patients over 18 years old with TBI admitted to an ICU of a Brazilian trauma referral hospital between January 2012 and August 2019. TBI was compared with other traumas in terms of clinical characteristics of ICU admission and outcome. Univariate and multivariate analyses were used to estimate the odds ratio for mortality.

RESULTS

Of the 4816 patients included, 1114 had TBI, with a predominance of males (85.1%). Compared with patients with other traumas, patients with TBI had a lower mean age (45.3 ± 19.1 versus 57.1 ± 24.1 years, p < 0.001), higher median APACHE II (19 versus 15, p < 0.001) and SOFA (6 versus 3, p < 0.001) scores, lower median Glasgow Coma Scale (GCS) score (10 versus 15, p < 0.001), higher median length of stay (7 days versus 4 days, p < 0.001) and higher mortality (27.6% versus 13.3%, p < 0.001). In the multivariate analysis, the predictors of mortality were older age (OR: 1.008 [1.002-1.015], p = 0.016), higher APACHE II score (OR: 1.180 [1.155-1.204], p < 0.001), lower GCS score for the first 24 h (OR: 0.730 [0.700-0.760], p < 0.001), greater number of brain injuries and presence of associated chest trauma (OR: 1.727 [1.192-2.501], p < 0.001).

CONCLUSION

Patients admitted to the ICU for TBI were younger and had worse prognostic scores, longer hospital stays and higher mortality than those admitted to the ICU for other traumas. The independent predictors of mortality were older age, high APACHE II score, low GCS score, number of brain injuries and association with chest trauma.

Artificial intelligence and machine learning for hemorrhagic trauma care

Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumatic hemorrhage. A literature search was carried out on PubMed and Google scholar. Titles and abstracts were screened and, if deemed appropriate, the full articles were reviewed. We included 89 studies in the review. These studies could be grouped into five areas: (1) prediction of outcomes; (2) risk assessment and injury severity for triage; (3) prediction of transfusions; (4) detection of hemorrhage; and (5) prediction of coagulopathy. Performance analysis of ML in comparison with current standards for trauma care showed that most studies demonstrated the benefits of ML models. However, most studies were retrospective, focused on prediction of mortality, and development of patient outcome scoring systems. Few studies performed model assessment via test datasets obtained from different sources. Prediction models for transfusions and coagulopathy have been developed, but none is in widespread use. AI-enabled ML-driven technology is becoming integral part of the whole course of trauma care. Comparison and application of ML algorithms using different datasets from initial training, testing and validation in prospective and randomized controlled trials are warranted for provision of decision support for individualized patient care as far forward as possible.

The Impact of Frailty on Traumatic Brain Injury Outcomes: An Analysis of 691 821 Nationwide Cases

BACKGROUND

Frailty, a decline in physiological reserve, prognosticates poorer outcomes for several neurosurgical conditions. However, the impact of frailty on traumatic brain injury outcomes is not well characterized.

OBJECTIVE

To analyze the association between frailty and traumatic intracranial hemorrhage (tICH) outcomes in a nationwide cohort.

METHODS

We identified all adult admissions for tICH in the National Trauma Data Bank from 2007 to 2017. Frailty was quantified using the validated modified 5-item Frailty Index (mFI-5) metric (range = 0-5), with mFI-5 ≥2 denoting frailty. Analyzed outcomes included in-hospital mortality, favorable discharge disposition, complications, ventilator days, and intensive care unit (ICU) and total length of stay (LOS). Multivariable regression assessed the association between mFI-5 and outcomes, adjusting for patient demographics, hospital characteristics, injury severity, and neurosurgical intervention.

RESULTS

A total of 691 821 tICH admissions were analyzed. The average age was 57.6 years. 18.0% of patients were frail (mFI-5 ≥ 2). Between 2007 and 2017, the prevalence of frailty grew from 7.9% to 21.7%. Frailty was associated with increased odds of mortality (odds ratio [OR] = 1.36, P < .001) and decreased odds of favorable discharge disposition (OR = 0.72, P < .001). Frail patients exhibited an elevated rate of complications (OR = 1.06, P < .001), including unplanned return to the ICU (OR = 1.55, P < .001) and operating room (OR = 1.17, P = .003). Finally, frail patients experienced increased ventilator days (+12%, P < .001), ICU LOS (+11%, P < .001), and total LOS (+13%, P < .001). All associations with death and disposition remained significant after stratification for age, trauma severity, and neurosurgical intervention.

CONCLUSION

For patients with tICH, frailty predicted higher mortality and morbidity, independent of age or injury severity.

A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms

IMPORTANCE

An adequate system for triaging patients with head trauma in prehospital settings and choosing optimal medical institutions is essential for improving the prognosis of these patients. To our knowledge, there has been no established way to stratify these patients based on their head trauma severity that can be used by ambulance crews at an injury site.

OBJECTIVES

To develop a prehospital triage system to stratify patients with head trauma according to trauma severity by using several machine learning techniques and to evaluate the predictive accuracy of these techniques.

DESIGN, SETTING, AND PARTICIPANTS

This single-center retrospective cohort study was conducted by reviewing the electronic medical records of consecutive patients who were transported to Tokyo Medical and Dental University Hospital in Japan from April 1, 2018, to March 31, 2021. Patients younger than 16 years with cardiopulmonary arrest on arrival or with a significant amount of missing data were excluded.

MAIN OUTCOMES AND MEASURES

Machine learning-based prediction models to detect the presence of traumatic intracranial hemorrhage were constructed. The predictive accuracy of the models was evaluated with the area under the receiver operating curve (ROC-AUC), area under the precision recall curve (PR-AUC), sensitivity, specificity, and other representative statistics.

RESULTS

A total of 2123 patients (1527 male patients [71.9%]; mean [SD] age, 57.6 [19.8] years) with head trauma were enrolled in this study. Traumatic intracranial hemorrhage was detected in 258 patients (12.2%). Among several machine learning algorithms, extreme gradient boosting (XGBoost) achieved the mean (SD) highest ROC-AUC (0.78 [0.02]) and PR-AUC (0.46 [0.01]) in cross-validation studies. In the testing set, the ROC-AUC was 0.80, the sensitivity was 74.0% (95% CI, 59.7%-85.4%), and the specificity was 74.9% (95% CI, 70.2%-79.3%). The prediction model using the National Institute for Health and Care Excellence (NICE) guidelines, which was calculated after consultation with physicians, had a sensitivity of 72.0% (95% CI, 57.5%-83.8%) and a specificity of 73.3% (95% CI, 68.7%-77.7%). The McNemar test revealed no statistically significant differences between the XGBoost algorithm and the NICE guidelines for sensitivity or specificity (P = .80 and P = .55, respectively).

CONCLUSIONS AND RELEVANCE

In this cohort study, the prediction model achieved a comparatively accurate performance in detecting traumatic intracranial hemorrhage using only the simple pretransportation information from the patient. Further validation with a prospective multicenter data set is needed.

Risk factor guided early discharge and potential resource allocation benefits in patients with traumatic subarachnoid hemorrhage

OBJECTIVES

We sought to develop screening criteria predicting the lack of poor neurological outcomes in patients presenting with traumatic subarachnoid hemorrhage (tSAH), while evaluating their potential to improve resource-allocation in these cases.

METHODS

We retrospectively reviewed patients presenting with tSAH to the emergency department (ED) of a tertiary care institution from 2016-2018. We defined good neurological outcomes as patients with stable/improving neurological status, did not require neurosurgical intervention, no expanding bleed, and no hospital readmission. Univariate and multivariate models were generated to predict risk factors inversely associated with good neurological outcome.

RESULTS

167 patients presented with tSAH from 2016-2018. The presence of depressed skull fracture, concomitant spinal fracture, low GCS, cranial nerve palsies, disorientation, concomitant hemorrhages, midline shift (MLS), elevated INR, and emergent medical intervention were inversely correlated with likelihood of good neurological outcome upon univariate analysis. Multivariate regression demonstrated that midline shift [OR=0.22 (0.05-0.89), p=0.04], GCS <13 [OR=0.22 (0.05-0.99), p=0.05], elevated INR [OR=0.18 (0.03-0.85), p=0.04], and emergent medical intervention [OR=0.18 (0.04-0.63), p=0.01] were independently associated with lower likelihood of good neurological outcome. 46 patients without any factors had good outcomes but were held in the ED or admitted to the hospital. These patients - if instead discharged directly - translated to a potential cost savings of $179,172.

CONCLUSIONS

In our study we found multiple risk factors inversely associated with good neurological outcome, namely low GCS, midline shift, emergent medical intervention, and INR ≥ 1.4. Our findings may aid clinicians in determining which tSAH patients are candidates for safe early discharge.

A Novel Gating Mechanism of Aquaporin-4 Water Channel Mediated by Blast Shockwaves for Brain Edema

As the principal water channel in the brain, aquaporin-4 (AQP4) plays a vital role in brain edema, but its role in blast brain edema is unclear. On the basis of molecular simulations, we reveal the atomically detailed picture of AQP4 in response to blast shockwaves. The results show that the shockwave alone closes the AQP4 channel; however, shock-induced bubble collapse opens it. The jet from bubble collapse forcefully increases the distance between helices and the tilt angles of six helices relative to the membrane vertical direction in a very short time. The average channel size increases about 2.6 times, and the water flux rate is nearly 20 times higher than for normal states. It is responsible for abnormal water transport and a potential cause of acute blast brain edema. Additionally, the open AQP4 channel quickly returns to its normal state, which is in turn helpful for edema absorption. Thus, a novel gating mechanism for AQP4 related to the secondary structure change has been provided, which is different from the previous residue-mediated gating mechanism.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Management and Outcome of Traumatic Intracerebral Hemorrhage in 79 Infants and Children from a Single Level 1 Trauma Center

OBJECTIVE

Traumatic brain injury is a leading form of pediatric trauma and a frequent cause of mortality and acquired neurological impairment in children. The aim of this study was to present the severity and outcomes of traumatic intracerebral bleeding in children and adolescence.

METHODS

Seventy-nine infants and children with intracerebral bleedings were treated between 1992 and 2020 at a single level 1 trauma center. Data regarding accident, treatment and outcomes were collected retrospectively. The Glasgow Outcome Scale was used to classify the outcome at hospital discharge and at follow-up visits. CT scans of the brain were classified according to the Rotterdam score.

RESULTS

In total, 41 (52%) patients with intracerebral bleedings were treated surgically, and 38 (48%) patients were treated conservatively; in 15% of the included patients, delayed surgery was necessary. Patients presenting multiple trauma (p < 0.04), higher ISS (p < 0.01), poor initial neurological status (p < 0.001) and a higher Rotterdamscore (p = 0.038) were significantly more often treated surgically. Eighty-three percent of patients were able to leave the hospital, and out of these patients, about 60% showed good recovery at the latest follow-up visit. Overall, 11 patients (14%) died.

CONCLUSION

The findings in this study verified intracerebral bleeding as a rare but serious condition. Patients presenting with multiple traumas, higher initial ISS, poor initial neurological status and a higher Rotterdamscore were more likely treated by surgery.

TRIAL REGISTRATION

(researchregistry 2686).