IntroductionThe adverse effects of delayed admission to the intensive care unit (ICU) have been recognized in previous studies. However, the definitions of delayed admission vary across studies. This study proposed a model to define ‘delayed admission’, and explored the effect of ICU waiting time on patients’ outcome.MethodsThis retrospective cohort study included nontraumatic adult patients on mechanical ventilation in the emergency department (ED), from July 2009 to June 2010. The primary outcomes measures were 21-ventilator-day mortality and prolonged hospital stays (over 30 days). Models of Cox regression and logistic regression were used for multivariate analysis. The non-delayed ICU waiting was defined as a period in which the time effect on mortality was not statistically significant in a Cox regression model. To identify a suitable cutoff point between ‘delayed’ and ‘non-delayed’ subsets from the overall data were made based on ICU waiting time and the hazard ratio of ICU waiting hour in each subset was iteratively calculated. The cutoff time was then used to evaluate the impact of delayed ICU admission on mortality and prolonged length of hospital stay.ResultsThe final analysis included 1,242 patients. The time effect on mortality emerged after 4 hours, thus we deduced ICU waiting time in the ED of >4 hours as delayed. By logistic regression analysis, delayed ICU admission affected the outcomes of 21-ventilator-day mortality and prolonged hospital stay, with an odds ratio of 1.41 (95% confidence interval, 1.05 to 1.89) and 1.56 (95% confidence interval, 1.07 to 2.27) respectively.ConclusionsFor patients on mechanical ventilation in the ED, delayed ICU admission is associated with higher probability of mortality and additional resource expenditure. A benchmark waiting time of no more than 4 hours for ICU admission is recommended.

As a branch of the Hydraulic Hybrid Vehicle (HHV) technology, Series Hydraulic Hybrid Vehicle (SHHV) has been an important research object of institutions and automotive manufacturers all over the world. With the flexibility of engine management and regenerative braking characteristics SHHV is expected to be a short-term solution to develop a higher efficiency, cleaner, and safer transportation. In this work, the function and parameter determination of key components for SHHV are discussed. Based on the analytical analysis, the suitable set of component parameters is selected. The model of SHHV is implemented via Simulink/MATLAB mostly based on SimScape toolbox. The proposed model can be used as a development tool to quickly simulate the real hybrid system when it allows applying different parameter sets and in various conditions. The performance of the system is evaluated through some specific cases and the capability of braking energy recovery of the system is also investigated. Simulation results indicate that for a 2.5 ton truck case, more than 86% of braking energy can be captured and more than 72% of that energy can be returned to the kinetic energy of vehicle motion.