Risk factors and predictive model of cerebral edema after road traffic accidents-related traumatic brain injury.

PURPOSE: Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries. METHODS: This case-control study included 218 patients with TBI caused by RTAs. The cohort was divided into CE and non-CE groups, according to CT results within 7 days. Demographic data, imaging data, and clinical data were collected and analyzed. Quantitative variables that follow normal distribution were presented as mean ± standard deviation, those that do not follow normal distribution were presented as median (Q1, Q3). Categorical variables were expressed as percentages. The Chi-square test and logistic regression analysis were used to identify risk factors for CE. Logistic curve fitting was performed to predict the time to secondary CE in TBI patients with different levels of injuries. The efficacy of the model was evaluated using the receiver operator characteristic curve. RESULTS: According to the study, almost half (47.3%) of the patients were found to have CE. The risk factors associated with CE were bilateral frontal lobe contusion, unilateral frontal lobe contusion, cerebral contusion, subarachnoid hemorrhage, and abbreviated injury scale (AIS). The odds ratio values for these factors were 7.27 (95% confidence interval (CI): 2.08 - 25.42, p = 0.002), 2.85 (95% CI: 1.11 - 7.31, p = 0.030), 2.62 (95% CI: 1.12 - 6.13, p = 0.027), 2.44 (95% CI: 1.25 - 4.76, p = 0.009), and 1.5 (95% CI: 1.10 - 2.04, p = 0.009), respectively. We also observed that patients with mild/moderate TBI (AIS ≤ 3) had a 50% probability of developing CE 19.7 h after injury (χ2 = 13.82, adjusted R2 = 0.51), while patients with severe TBI (AIS > 3) developed CE after 12.5 h (χ2 = 18.48, adjusted R2 = 0.54). Finally, we conducted a receiver operator characteristic curve analysis of CE time, which showed an area under the curve of 0.744 and 0.672 for severe and mild/moderate TBI, respectively. CONCLUSION: Our study found that the onset of CE in individuals with TBI resulting from RTAs was correlated with the severity of the injury. Specifically, those with more severe injuries experienced an earlier onset of CE. These findings suggest that there is a critical time window for clinical intervention in cases of CE secondary to TBI.

ONS resolution prediction based on Rasch model.

IoT (Internet of Things) involves a wide range of fields, and its application scenarios are complex and diverse. Failure of security defense in any link of IoT may lead to huge information leakage and immeasurable losses. IoT security problem is affecting and restricting its application prospect, and has become one of the hotspots in the field of IoT. ONS (Object Naming Service) is responsible for mapping function from EPC code information to URI (Uniform Resource Identifier). The security mechanism of ONS has been extensively studied by more and more scholars in recent years. The purpose of this paper is to apply Rasch, a famous psychological model, to ONS resolution security technology. Through observing the past resolution result, the ability of ONS resolution and the difficulty of EPC code can be calculated. With the difference between the ability of ONS resolution and the difficulty of EPC code, this model can predict the probability of the ONS future resolution to achieve the purpose of privacy protection in IoT addressing. Through simulation and Ministep software, the feasibility of the model is verified.