Journal of clinical monitoring and computing
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J Clin Monit Comput · Jan 2002
Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia.
Until now, the knowledge of combining anesthetics to obtain an adequate level of anesthesia and to economize wakeup time has been empirical and difficult to represent in quantitative models. Since there is no reason to expect that the effect of non-opioid and opioid anesthetics can be modeled in a simple linear manner, the use of a new computational approach with Bayesian belief network software is demonstrated. ⋯ Model building and evaluation with Bayesian networks does not depend on underlying linear relationships. Bayesian relationships represent true features of the represented data sample. Data may be sparse, uncertain, stochastic, or imprecise. Multiple platform software that is easy to use is increasingly available. Bayesian networks promise to be versatile tools for building valid, nonlinear, predictive instruments to further gain insight into the complex interaction of anesthetics.
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Inadvertent sleep episodes are a recognized complication of sleep deprivation. Although such events can be life threatening, no system currently exists to detect and prevent sleep onset. Because sleep shares electroencephalographic similarities with the anesthetized state, we hypothesized that the BIS monitor, a currently available EEG-based monitor of anesthetic depth, would detect the onset of physiologic sleep. To test our hypothesis, we monitored volunteers during the transition from waking to sleep. ⋯ Although variability in the BIS value marking sleep onset was noted, the BIS monitor detected all episodes of sleep onset in our testing regimen. We conclude that a threshold BIS value can be defined to allow the BIS monitor to detect sleep onset.
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In mechanically ventilated patients the expiratory time constant provides information about respiratory mechanics. In the present study a new method, fuzzy clustering, is proposed to determine expiratory time constants. Fuzzy clustering differs from other methods since it neither interferes with expiration nor presumes any functional relationship between the variables analysed. Furthermore, time constant behaviour during expiration can be assessed, instead of an average time constant. The time constants obtained with fuzzy clustering are compared to time constants conventionally calculated from the same expirations. ⋯ In mechanically ventilated patients, expiratory time constant behaviour can be accurately assessed by fuzzy clustering. A good correlation was found between time constants obtained with fuzzy clustering and time constants obtained by conventional analysis. On the basis of the time constants obtained with fuzzy clustering, a clear distinction was made between patients with and without
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J Clin Monit Comput · Jan 2002
Application of artificial neural networks as an indicator of awareness with recall during general anaesthesia.
Awareness with recall is a rare but serious complication of general anaesthesia with an incidence ranging from 0.1%-0.7%. In the absence of a reliable depth-of-anaesthesia monitor, attempts have been made to predict awareness from intraoperative haemodynamic monitoring data, with little success. Artificial neural networks can sometimes detect relationships between input and output variables even when conventional methods fail. Therefore, we subjected standard intraoperative monitoring data to both artificial neural models and conventional statistical methods in an attempt to predict awareness with recall. ⋯ A prediction indicating awareness by the network is very suggestive of true awareness and recall. Blood pressure and heart rate are significantly higher on average in patients with awareness than in patients without. In an individual patient, however, none of our artificial neural models can detect awareness sufficiently reliably.
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J Clin Monit Comput · Jan 2002
Comparative StudyMonitoring xenon in the breathing circuit with a thermal conductivity sensor. Comparison with a mass spectrometer and implications on monitoring other gases.
To test the accuracy of a thermal conductivity xenon sensor in vitro and in vivo and to test the effect of xenon on other anesthetic gas analyzers as determined by a mass spectrometry gold standard. ⋯ Thermal conductivity is a clinically useful technique to measure xenon in the breathing circuit despite its statistically significant but clinically irrelevant error compared to mass spectrometry. Other gases of interest have to be measured with selected monitors explicitly approved or tested for use with xenon.