Impact of grouping complications on mortality in traumatic brain injury: A nationwide population-based study

Modeling of the brain-lung axis using organoids in traumatic brain injury: an updated review

Clinical outcome after traumatic brain injury (TBI) is closely associated conditions of other organs, especially lungs as well as degree of brain injury. Even if there is no direct lung damage, severe brain injury can enhance sympathetic tones on blood vessels and vascular resistance, resulting in neurogenic pulmonary edema. Conversely, lung damage can worsen brain damage by dysregulating immunity. These findings suggest the importance of brain-lung axis interactions in TBI. However, little research has been conducted on the topic. An advanced disease model using stem cell technology may be an alternative for investigating the brain and lungs simultaneously but separately, as they can be potential candidates for improving the clinical outcomes of TBI.In this review, we describe the importance of brain-lung axis interactions in TBI by focusing on the concepts and reproducibility of brain and lung organoids in vitro. We also summarize recent research using pluripotent stem cell-derived brain organoids and their preclinical applications in various brain disease conditions and explore how they mimic the brain-lung axis. Reviewing the current status and discussing the limitations and potential perspectives in organoid research may offer a better understanding of pathophysiological interactions between the brain and lung after TBI.

The Impact of Pulmonary Disorders on Neurological Health (Lung-Brain Axis)

The brain and lungs, vital organs in the body, play essential roles in maintaining overall well-being and survival. These organs interact through complex and sophisticated bi-directional pathways known as the 'lung-brain axis', facilitated by their close proximity and neural connections. Numerous studies have underscored the mediation of the lung-brain axis by inflammatory responses and hypoxia-induced damage, which are pivotal to the progression of both pulmonary and neurological diseases. This review aims to delve into how pulmonary diseases, including acute/chronic airway diseases and pulmonary conditions, can instigate neurological disorders such as stroke, Alzheimer's disease, and Parkinson's disease. Additionally, we highlight the emerging research on the lung microbiome which, drawing parallels between the gut and lungs in terms of microbiome contents, may play a significant role in modulating brain health. Ultimately, this review paves the way for exciting avenues of future research and therapeutics in addressing respiratory and neurological diseases.

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.

Prognosis prediction in traumatic brain injury patients using machine learning algorithms

Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.

TBI and risk of death in military veterans over 14 years: Injury severity, timing, and cause of death

The objective of this study was to determine the association of traumatic brain injury (TBI) with mortality in military veterans and whether this association differs as a function of TBI severity, timing, and cause of death. This national cohort study used U.S. Department of Veterans Affairs' (VA) data of patients 18 years and older with TBI diagnoses (N = 213,290) and 1:1 propensity-matched comparison random sample of patients without TBI (N = 213,290). The main outcome measure was mortality within 6 months of TBI diagnosis and longer-term (after 6 months). Cox proportional hazards models were used to examine risk of all-cause mortality according to TBI severity and Fine-Gray proportional hazards regression to examine time to cause-specific mortality, accounting for competing risk of other deaths. For patients with moderate-to-severe TBI (compared with no TBI), hazard ratios (HRs) for mortality were highest within first 6 months of injury (fully-adjusted HR: 2.42, 95% CI: 2.32-2.53); for mild TBIs, HRs for mortality were lower and relatively constant over time (fully-adjusted HR within first 6 months: 1.33, 95% CI: 1.26-1.39). Veterans with mild and moderate-to-severe TBI had higher risk of future death over short term for 9 out 10 of the U.S. leading causes of death, with only unintentional injury, stroke, and suicide showing differences by TBI severity. Associations attenuated significantly from within to after 6 months TBI diagnosis. These findings indicate that adults with TBI are at increased risk of majority of leading causes of death, with differential risk by TBI severity and timing of death.

Effect of Cluster Nursing Based on Risk Management Strategy on Urinary Tract Infection in Patients With Severe Craniocerebral Injury

Objective

To observe the effect of cluster nursing based on risk management strategy in the management of urinary tract infection in patients with severe craniocerebral injury.

Methods

A total of 116 patients with severe craniocerebral injury who were admitted to our hospital from March 2019 to March 2021 were included. They were divided into the control group (58 patients) and the observation group (58 patients). The control group received routine nursing care and the observation group received cluster nursing based on risk management strategy. The incidence of catheter-associated urinary tract infection (CAUTI), the results of bacterial culture on the surface of the urinary catheter, the incidence of nursing risk events, the duration of placing the urinary catheter, the length of hospital stay, and hospital costs as well as the patient satisfaction score were compared between the two groups. The knowledge, attitude, and practice scale for prevention of catheter infection and the competence evaluation scale of nurses were used to evaluate the sense-control ability and core competence of the interveners.

Results

The total incidence of CAUTI in the observation group was (6.90%) lower than that in the control group (20.69%) (p < 0.05). The bacterial culture results on the catheter surface of patients in the observation group before and after 6 and 12 h of catheter cleaning were better than those of patients in the control group (p < 0.05). The duration of indwelling urinary catheter, hospitalization time, and hospitalization expenses of patients in the observation group were lower than those of patients in the control group (p < 0.05). The incidence rate of nursing risk events in the observation group was (1.72%) lower than that in the control group (11.86%) (p < 0.05). The overall satisfaction score of patients and the control and core ability scores of nursing staff in the observation group were higher than those in the control group (p < 0.05).

Conclusion

Cluster nursing based on risk management strategy can effectively reduce the incidence of nursing risk events and the probability of UTI in patients with severe craniocerebral injury, shorten the duration of indwelling urinary catheter and hospitalization.

Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF.

Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1.

Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05].

Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

Beyond the Brain: Peripheral Interactions after Traumatic Brain Injury

Traumatic brain injury (TBI) is a leading cause of death and disability, and there are currently no pharmacological treatments known to improve patient outcomes. Unquestionably, contributing toward a lack of effective treatments is the highly complex and heterogenous nature of TBI. In this review, we highlight the recent surge of research that has demonstrated various central interactions with the periphery as a potential major contributor toward this heterogeneity and, in particular, the breadth of research from Australia. We describe the growing evidence of how extracranial factors, such as polytrauma and infection, can significantly alter TBI neuropathology. In addition, we highlight how dysregulation of the autonomic nervous system and the systemic inflammatory response induced by TBI can have profound pathophysiological effects on peripheral organs, such as the heart, lung, gastrointestinal tract, liver, kidney, spleen, and bone. Collectively, this review firmly establishes TBI as a systemic condition. Further, the central and peripheral interactions that can occur after TBI must be further explored and accounted for in the ongoing search for effective treatments.

The effect of EBN combined with integrated hierarchical accountability nursing on patients with severe pneumonia

OBJECTIVE

To explore the effects of evidence-based nursing (EBN) combined with integrated hierarchical accountability nursing on patients with severe pneumonia (SP).

METHODS

72 SP patients admitted to our hospital from March 2019 to March 2020 were recruited as the study cohort and randomly divided into control group (36 patients) or research group (36 patients). The control group underwent conventional nursing, and the research group underwent EBN combined with integrated hierarchical accountability nursing plus. The patients' respiratory function, inflammatory factor levels, hospital stay durations, mechanical ventilation times, complication rates, and nursing satisfaction levels were compared between the two groups.

RESULTS

Before the nursing, there were no significant differences in the FVC, TLC, MVV, or VC levels between the two groups (P>0.05). After the nursing, the FVC, TLC, MVV, and VC levels in the research group were all lower than they were in the control group (all P<0.05). Before the nursing, the WBC, CRP, and PCT levels in the two groups were similar (P>0.05). After the nursing, the WBC, CRP, and PCT levels in the research group were significantly lower than they were in the control group (P<0.05). The hospital stay durations and mechanical ventilation times in the research group were shorter than they were in the control group (P<0.05). The complication rate in the research group was lower than it was in the control group (5.56% vs. 27.78% P<0.05). The nursing satisfaction level in the research group was higher than it was in the control group (97.22% vs. 77.78% P<0.05).

CONCLUSION

EBN combined with integrated hierarchical accountability nursing has a good application effect on patients with SP. It can significantly improve patients' respiratory function and inflammatory factor levels, reduce the duration of patient hospital stays, reduce their mechanical ventilation times, and complication rate, and enhance their satisfaction with the nursing. Thus, it is worthy of further promotion.

The risk of pulmonary tuberculosis after traumatic brain injury

After traumatic brain injury (TBI), an inflammatory response in the brain might affect the immune system. The risk of pulmonary infection reportedly increases in patients with TBI. We aimed to evaluate the risk of tuberculosis (TB) in patients with TBI in Taiwan. All participants were selected from the intensive care unit (ICU). Patients with TBI were defined as patients in ICU with intracranial injury, and comparison cohort were patients in ICU without TBI diagnosis. There was a significant difference in TB risk between the patients with TBI and the comparison cohort according to age and the Charlson’s comorbidity index (CCI) score. Thus, we divided patients based on CCI into three groups for further analysis: mild (CCI = 0), moderate (CCI = 1/2), severe (CCI > 2). Mild-CCI group had a lower TB incidence rate (0.74%) and longer time to TB development (median: 2.43) than the other two groups. Moderate-CCI group had 1.52-fold increased risk of TB infection (p < 0.0001) compared with mild-CCI group. In the severe-CCI group, patients aged ≥ 80 years had 1.91-fold risk of TB compared with mild-CCI group (p = 0.0481). Severe-CCI group had significantly higher mortality than the mild-CCI group (p = 0.0366). Patients with TBI and more comorbidities had higher risk of TB infection with higher mortality rate.

The effect of age and sex on outcomes following isolated moderate to severe traumatic brain injury

PURPOSE

The impact of female sex on traumatic brain injury (TBI) outcomes remains controversial. The combined impact of age and sex on TBI outcomes must be clarified. We hypothesized that females have better outcomes than males in the premenopausal age group.

METHODS

Data from the 2007-2016 National Trauma Data Bank of the Committee on Trauma-American College of Surgeons were used. Of a total of 686,549 patients with moderate to severe TBI (AIS ≥ 3), 251,491 were female. Comparison analyses of clinical characteristics and outcomes between females and males were conducted at different age groups: < 45 years, 45-55, and > 55 years. Logistic regressions were performed to assess the impact of age and female sex on mortality and complications.

RESULTS

Mortality rate between females and males aged < 45 and 45-55 years was similar, but significantly reduced in the > 55 years group. After multivariate logistic regression analysis controlling for multiple confounding factors, we found that females aged > 55 years had markedly decreased risk of mortality (AOR: 0.857, 95% CI 0.835-0.879, p < 0.001) and complications.

CONCLUSION

Female patients in the postmenopausal stage have better outcomes following TBI than males, but pre- and perimenopausal females do not, suggesting that female sexual hormones may not provide a significant protective effect on clinical outcomes following isolated moderate to severe TBI.

Experimental traumatic brain injury does not lead to lung infection

Traumatic brain injury (TBI) patients often experience post-traumatic infections, especially in the lung. Pulmonary infection is associated with unfavorable outcomes and increased mortality rates in TBI patients; however, our understanding of the underlying mechanisms is poor. Here we used a lateral fluid percussion injury (LFPI) model in rats to investigate whether TBI could lead to spontaneous lung infection. Analysis of bacterial load in lung tissue indicated no occurrence of spontaneous lung infection at 24 h, 48 h, and 7 d following LFPI. This may suggest that exogenous infectious agents play a crucial role in post-TBI infection in patients.

Incidence and Effect of Diabetes Insipidus in the Acute Care of Patients with Severe Traumatic Brain Injury

BACKGROUND

Literature on diabetes insipidus (DI) after severe traumatic brain injury (TBI) is scarce. Some studies have reported varying frequencies of DI and have showed its association with increased mortality, suggesting it as a marker of poor outcome. This knowledge gap in the acute care consequences of DI in severe TBI patients led us to conceive this study, aimed at identifying risk factors and quantifying the effect of DI on short-term functional outcomes and mortality.

METHODS

We assembled a historic cohort of adult patients with severe TBI (Glasgow Coma Scale ≤ 8) admitted to the intensive care unit (ICU) of a tertiary-care university hospital over a 6-year period. Basic demographic characteristics, clinical information, imaging findings, and laboratory results were collected. We used logistic regression models to assess potential risk factors for the development of DI, and the association of this condition with death and unfavorable functional outcomes [modified Rankin scale (mRS)] at hospital discharge.

RESULTS

A total of 317 patients were included in the study. The frequency of DI was 14.82%, and it presented at a median of 2 days (IQR 1-3) after ICU admission. Severity according to the Abbreviated Injury Scale (AIS) score of the head, intracerebral hemorrhage, subdural hematoma, and skull base fracture was suggested as risk factors for DI. Diagnosis of DI was independently associated death (OR 4.34, CI 95% 1.92-10.11, p = 0.0005) and unfavorable outcome (modified Rankin Scale = 4-6) at discharge (OR 7.38; CI 95% 2.15-37.21, p = 0.0047).

CONCLUSIONS

Diabetes insipidus is a frequent and early complication in patients with severe TBI in the ICU and is strongly associated with increased mortality and poor short-term outcomes. We provide clinically useful risk factors that will help detect DI early to improve prognosis and therapy of patients with severe TBI.

Role of the PGE2 receptor subtypes EP1, EP2, and EP3 in repetitive traumatic brain injury

Aims

The goal was to explore the signaling pathways of PGE₂ to investigate therapeutic effects against secondary injuries following TBI.

Methods

Young (4.9 ± 1.0 months) and aged (20.4 ± 1.4 months) male wild type (WT) C57BL/6 and PGE₂ EP1, 2, and 3 receptor knockout mice were selected to either receive sham or repetitive concussive head injury. Immunohistochemistry protocols with Iba1 and GFAP were performed to evaluate microgliosis and astrogliosis in the hippocampus, two critical components of neuroinflammation. Passive avoidance test measured memory function associated with the hippocampus.

Results

No differences in hippocampal microgliosis were found when aged EP2^−/− and EP3^−/− mice were compared with aged WT mice. However, the aged EP1^−/− mice had 69.2 ± 7.5% less hippocampal microgliosis in the contralateral hemisphere compared with WT aged mice. Compared with aged EP2^−/− and EP3^−/−, EP1^−/− aged mice had 78.9 ± 5.1% and 74.7 ± 6.2% less hippocampal microgliosis in the contralateral hemisphere. Within the EP1^−/− mice, aged mice had 90.7 ± 2.7% and 81.1 ± 5.6% less hippocampal microgliosis compared with EP1^−/− young mice in the contralateral and ipsilateral hemispheres, respectively. No differences were noted in all groups for astrogliosis. There was a significant difference in latency time within EP1^−/−, EP2^−/−, and EP3^−/− on day 1 and day 2 in aged and young mice.

Conclusion

These findings demonstrate that the PGE₂ EP receptors may be potential therapeutic targets to treat repetitive concussions and other acute brain injuries.