Background: Postoperative delirium is a common complication characterized by confusion, inattentiveness and other mental symptoms. It is still unclear whether the use of electroencephalogram (EEG) monitoring during surgery can decrease the incidence of postoperative delirium. The purpose of this study was to evaluate the effectiveness of EEG guided anesthesia on postoperative delirium (POD) based on randomized controlled trials (RCTs).Methods: The electronic databases of Ovid MEDLINE, PubMed, EMBASE, Cochrane Library database, CNKI and other local databases were systematically searched for RCTs from their inception until October 2019. The odds ratios (ORs) and the mean differences (MDs) with a 95% confidence intervals (CIs) were calculated to evaluate the correlation between EEG and itemized categories and continuous variable, respectively.Results: Seven RCTs with 3859 patients were included in the final analysis. The summary OR indicated that patients receiving EEG monitoring had a lower incidence rate of postoperative delirium (OR: 0.65; 95% CI: 0.46-0.92; P = 0.01). In addition, no significant difference was found between the EEG monitoring group and the routine care group with respects to the length of hospitalization (MD: -0.59; 95%CI: -1.26 to 0.07; P=0.08).Conclusions: The findings of this study indicated that intraoperative use of electroencephalogram monitoring could decrease the risk of postoperative delirium. But for high risk patients, we should take a multi-component strategy to prevent delirium. Further large-scale, randomized controlled trials should be conducted to verify the treatment effect of intraoperative use of electroencephalogram monitoring on patients.
Abstract. The airport security inspection network model based on the Petri Net is established to identify the bottleneck of the airport security inspection system. A reach ability graph is set up to represent the process of the security inspection. We can get an isomorphic Markov Chain by simplifying the reach ability graph. Thus, the number of the token of each place is calculated by using the given data. As a result, the bottleneck is identified. We also calculated the throughput of each checkpoint by using the given data, the bottleneck is also identified. The bottlenecks found in the two ways are the same, showing that the model's accuracy is qualified.
Summary:In this paper, we firstly consider the impact of the accident on the problem of toll square traffic and establish a prediction model of traffic conflict to get an accident rate. Then, these parameters are applied to cellular automata model based on the cellular theory, and MATLAB is used to simulate the cell model. According to the result of simulation, we are to analyze and evaluate the toll square, and put forward a kind of ideal merging mode. IntroduceToll square is a vital part of the high way. Its operating efficiency directly affects the level of highway transportation. Low operating efficiency could cause congestion, and even arise traffic accidents. Increasing the toll speed of toll square on the premise of ensuring security and economy is very advantageous for drivers and toll square managers. Therefore, it is necessary to explore how to make rational construction to improve operating efficiency of the toll square. In this paper, we will study the best merging mode of the toll square. The Model of Traffic Conflict PredictionWe build a model of traffic conflict prediction based on different factors. We use the running speed, traffic, traffic composition as a variable for building the model. Then, these independent variables were analyzed for correlation.According
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.