Hyperbaric Oxygen Therapy Reduces the Traumatic Brain Injury-Mediated Neuroinflammation Through Enrichment of Prevotella Copri in the Gut of Male Rats.

BACKGROUND: Gastrointestinal dysfunction frequently occurs following traumatic brain injury (TBI) and significantly increases posttraumatic complications. TBI can lead to alterations in gut microbiota. The neuroprotective effects of hyperbaric oxygen (HBO) have not been well recognized after TBI. The study''s aim was to investigate the impact of HBO on TBI-induced dysbiosis in the gut and the pathological changes in the brain following TBI. METHODS: Anesthetized male Sprague-Dawley rats were randomly assigned to three groups: sham surgery plus normobaric air (21% oxygen at 1 atmospheres absolute), TBI (2.0 atm) plus normobaric air, and TBI (2.0 atm) plus HBO (100% oxygen at 2.0 atmospheres absolute) for 60 min immediately after TBI, 24 h later, and 48 h later. The brain injury volume, tumor necrosis factor-α expression in microglia and astrocytes, and neuronal apoptosis in the brain were subsequently determined. The V3-V4 regions of 16S ribosomal rRNA in the fecal samples were sequenced, and alterations in the gut microbiome were statistically analyzed. All parameters were evaluated on the 3rd day after TBI. RESULTS: Our results demonstrated that HBO improved TBI-induced neuroinflammation, brain injury volume, and neuronal apoptosis. HBO appeared to increase the abundance of aerobic bacteria while inhibiting anaerobic bacteria. Intriguingly, HBO reversed the TBI-mediated decrease in Prevotella copri and Deinococcus spp., both of which were negatively correlated with neuroinflammation and brain injury volume. TBI increased the abundance of these gut bacteria in relation to NOD-lik0065 receptor signaling and the proteasome pathway, which also exhibited a positive correlation trend with neuro inflammation and apoptosis. The abundance of Prevotella copri was negatively correlated with NOD-like receptor signaling and the Proteasome pathway. CONCLUSIONS: Our study demonstrated how the neuroprotective effects of HBO after acute TBI might act through reshaping the TBI-induced gut dysbiosis and reversing the TBI-mediated decrease of Prevotella copri.

Machine Learning Algorithm Predicts Mortality Risk in Intensive Care Unit for Patients with Traumatic Brain Injury.

BACKGROUND: Numerous mortality prediction tools are currently available to assist patients with moderate to severe traumatic brain injury (TBI). However, an algorithm that utilizes various machine learning methods and employs diverse combinations of features to identify the most suitable predicting outcomes of brain injury patients in the intensive care unit (ICU) has not yet been well-established. METHOD: Between January 2016 and December 2021, we retrospectively collected data from the electronic medical records of Chi Mei Medical Center, comprising 2260 TBI patients admitted to the ICU. A total of 42 features were incorporated into the analysis using four different machine learning models, which were then segmented into various feature combinations. The predictive performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and validated using the Delong test. RESULT: The AUC for each model under different feature combinations ranged from 0.877 (logistic regression with 14 features) to 0.921 (random forest with 22 features). The Delong test indicated that the predictive performance of the machine learning models is better than that of traditional tools such as APACHE II and SOFA scores. CONCLUSION: Our machine learning training demonstrated that the predictive accuracy of the LightGBM is better than that of APACHE II and SOFA scores. These features are readily available on the first day of patient admission to the ICU. By integrating this model into the clinical platform, we can offer clinicians an immediate prognosis for the patient, thereby establishing a bridge for educating and communicating with family members.

A Computer-Assisted System for Early Mortality Risk Prediction in Patients with Traumatic Brain Injury Using Artificial Intelligence Algorithms in Emergency Room Triage.

Traumatic brain injury (TBI) remains a critical public health challenge. Although studies have found several prognostic factors for TBI, a useful early predictive tool for mortality has yet to be developed in the triage of the emergency room. This study aimed to use machine learning algorithms of artificial intelligence (AI) to develop predictive models for TBI patients in the emergency room triage. We retrospectively enrolled 18,249 adult TBI patients in the electronic medical records of three hospitals of Chi Mei Medical Group from January 2010 to December 2019, and undertook the 12 potentially predictive feature variables for predicting mortality during hospitalization. Six machine learning algorithms including logistical regression (LR) random forest (RF), support vector machines (SVM), LightGBM, XGBoost, and multilayer perceptron (MLP) were used to build the predictive model. The results showed that all six predictive models had high AUC from 0.851 to 0.925. Among these models, the LR-based model was the best model for mortality risk prediction with the highest AUC of 0.925; thus, we integrated the best model into the existed hospital information system for assisting clinical decision-making. These results revealed that the LR-based model was the best model to predict the mortality risk in patients with TBI in the emergency room. Since the developed prediction system can easily obtain the 12 feature variables during the initial triage, it can provide quick and early mortality prediction to clinicians for guiding deciding further treatment as well as helping explain the patient's condition to family members.