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.

Background: Ceftriaxone is a β-lactam antibiotic used to treat central nervous system infections. Whether the neuroprotective effects of ceftriaxone after TBI are mediated by attenuating neuroinflammation but not its antibacterial actions is not well established. Methods: Anesthetized male Sprague–Dawley rats were divided into sham-operated, TBI + vehicle, and TBI + ceftriaxone groups. Ceftriaxone was intraperitoneally injected at 0, 24, and 48 h with 50 or 250 mg/kg/day after TBI. During the first 120 min after TBI, we continuously measured heart rate, arterial pressure, intracranial pressure (ICP), and cerebral perfusion pressure. The infarct volume was measured by TTC staining. Motor function was measured using the inclined plane. Glutamate transporter 1 (GLT-1), neuronal apoptosis and TNF-α expression in the perilesioned cortex were investigated using an immunofluorescence assay. Bacterial evaluation was performed by Brown and Brenn’s Gram staining. These parameters above were measured at 72 h after TBI. Results: Compared with the TBI + vehicle group, the TBI + ceftriaxone 250 mg/kg group showed significantly lower ICP, improved motor dysfunction, reduced body weight loss, decreased infarct volume and neuronal apoptosis, decreased TBI-induced microglial activation and TNF-α expression in microglia, and increased GLT-1 expression in neurons and microglia. However, the grades of histopathological changes of antibacterial effects are zero. Conclusions: The intraperitoneal injection of ceftriaxone with 250 mg/kg/day for three days may attenuate TBI by increasing GLT-1 expression and reducing neuroinflammation and neuronal apoptosis, thereby resulting in an improvement in functional outcomes, and this neuroprotective effect is not related to its antibacterial effects.

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.

Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial intelligence (AI) machine learning algorithms. A retrospective analysis was conducted on electronic medical records of SICH patients aged 20 and above, admitted to Chi Mei Medical Center’s intensive care unit between January 2016 and December 2021. The study utilized 37 features related to mortality. Predictive models were developed using logistic regression, Random forest, LightGBM, XGBoost, and Multi-layer Perceptron (MLP), with assessments of feature importance, and Area under the curve (AUC). A total of 1451 SICH patients were enrolled. Factors associated with mortality included lower initial GCS scores (p < 0.001), pupillary changes (P < 0.001), kidney disease (p < 0.001), and respiratory failure requiring intubation (p < 0.001). Negative correlations were observed between mortality and pupil light reflexes, as well as GCS components E(r=-0.4602), V (r=-0.4132), M(r=-0.4082). Positive correlations were identified with vasopressors (r = 0.4464), FiO2 (r = 0.3901), and sedative-hypnotic drugs (r = 0.1178). XGBoost demonstrated the best predictive performance (AUC = 0.913), outperforming LR (0.899), RF (0.905), LightGBM (0.909), and MLP (0.892). The XGBoost model, utilizing both 18 and 36 features, continues to outperform both the Acute Physiology and Chronic Health Evaluation (APACHE II) (p < 0.001) and Sequential Organ Failure Assessment (SOFA) scoring systems (p < 0.001). This study successfully developed an AI mortality prediction model for SICH patients, with XGBoost exhibiting superior performance. The model, incorporating 18 key features, has been integrated into clinical practice assisting clinicians in treatment decisions and communication with patients’ families.

Background: The aim of this study was to investigate whether AMN082 exerts its neuroprotective effect by attenuating glutamate receptor-associated neuronal apoptosis and improving functional outcomes after traumatic brain injury (TBI). Methods: Anesthetized male Sprague–Dawley rats were divided into the sham-operated, TBI + vehicle, and TBI + AMN082 groups. AMN082 (10 mg/kg) was intraperitoneally injected 0, 24, or 48 h after TBI. In the 120 min after TBI, heart rate, mean arterial pressure, intracranial pressure (ICP), and cerebral perfusion pressure (CPP) were continuously measured. Motor function, the infarct volume, neuronal nitrosative stress-associated apoptosis, and N-methyl-d-aspartate receptor 2A (NR2A) and NR2B expression in the pericontusional cortex were measured on the 3rd day after TBI. Results: The results showed that the AMN082-treated group had a lower ICP and higher CPP after TBI. TBI-induced motor deficits, the increase in infarct volume, neuronal apoptosis, and 3-nitrotyrosine and inducible nitric oxide synthase expression in the pericontusional cortex were significantly improved by AMN082 therapy. Simultaneously, AMN082 increased NR2A and NR2B expression in neuronal cells. Conclusions: We concluded that intraperitoneal injection of AMN082 for 3 days may ameliorate TBI by attenuating glutamate receptor-associated nitrosative stress and neuronal apoptosis in the pericontusional cortex. We suggest that AMN082 administration in the acute stage may be a promising strategy for TBI.