ABSTRACT
Although guidelines and formulas have been developed through clinical practice to define infusion rate and volume, over- and under-resuscitation are still common, followed by increasing morbidity and mortality. In order to establish an effective management for early fluid resuscitation, the clinical decision support system (CDSS) has been established. The CDSS, by utilizing information systems coupled with decision support technology, could provide recommendations for the amount of fluid to be infused based on measured biological response. The results showed that patients treated with CDSS had a significantly lower mortality, increased ventilator-free days, and ICU-free days as compared with those treated with traditional fluid management. This article reviews the concepts as well as the result of recent clinical studies of CDSS for burn patients.
Subject(s)
Humans , Burns , Therapeutics , Decision Support Systems, Clinical , Fluid TherapyABSTRACT
Objective To explore the generative mechanism of bacterial wave growth in view of the open living system of microorganism. Methods With the single strain of bacterium as the subject, the nonlinear method of the dynamic systems and the analyzing method of the numerical simulation were used to study the effect of the changing environment on the bacterial growth. Results A mathematical model of the relationship between the bacterial wave growth and environment was established. Conclusion This model is verified to be scientific and its numerical simulation results are obtained, providing a helpful quantitative analysis for the research on the laws of bacterial wave growth.
ABSTRACT
Objective To explore the generative mechanism of bacterial wave growth in view of the open living system of microorganism. Methods With the single strain of bacterium as the subject, the nonlinear method of the dynamic systems and the analyzing method of the numerical simulation were used to study the effect of the changing environment on the bacterial growth. Results A mathematical model of the relationship between the bacterial wave growth and environment was established. Conclusion This model is verified to be scientific and its numerical simulation results are obtained, providing a helpful quantitative analysis for the research on the laws of bacterial wave growth.