Abstract
A closed-loop system for automated control of mechanical ventilation, Autopilot-BT, will be enhanced [1]. It must be able to adapt to diverse disease patterns. The Autopilot-BT is based on fuzzy logic, which can model complex systems using expert knowledge. The expert knowledge was acquired by a specifically designed questionnaire (Figure (Figure1a1a). Figure 1 (a) Questionnaire for ARDS. (b) Membership functions for healthy and ARDS. Methods Exemplarily we will focus on the respiratory rate (RR) controller, responsible for the arterial partial pressure of carbon dioxide/end-tidal carbon dioxide pressure (etCO2) control. The etCO2 values are classified into seven different fuzzy sets ranging from 'extreme hyperventilation' to 'extreme hypoventilation'. For different diseases such as chronic obstructive pulmonary disease or acute respiratory distress syndrome (ARDS), every clinician assigns given etCO2 values to a ventilation status. By averaging over all assignments of the clinicians, new targets and limits for each disease are obtained. Afterwards the new target and limit areas were implemented in a new fuzzy system controlling the RR.
Highlights
The aim of this study was to elucidate the impact of ICU-acquired infection on ICU and hospital mortality
The goal from this study is to evaluate weaning predictor indexes in patients during weaning from mechanical ventilation (MV)
This study aims to evaluate the effects of the threshold in such situations
Summary
The aim of this study was to elucidate the impact of ICU-acquired infection on ICU and hospital mortality. Methods A total of 48 community patients (36 men, 11 women, age 50.17 ± 17.974 years, APACHE II score 13.51 ± 6.153) who were expected to stay in the ICU for >5 days were included in this study. Specific examples of feedback are as follows: ‘good update of management plan reinforces need for taking into account concurrent medication when resuscitating patients’, ‘nice simple messages with good starting points for trying to deal with these complicated patients’, ‘useful data on risk of recurrence as this is a question often asked by patients’ This feedback was encouraging as it showed how the primary care professionals planned to change their practice to improve patient outcomes as a result of the learning. The course was considered excellent by 63% of the participants and good by 36%
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