Journal of clinical monitoring
-
This second article in a 2-part series on the operation of principal components within Narkomed anesthesia systems describes the function and compensation mechanisms of the Dräger 19.n vaporizer, the operating principles of the anesthesia ventilator-electronic, the structure and mechanics of the pressure limit control, and the 3 basic monitoring systems built into the anesthesia system. Part II of this series builds on the data published in part I (J Clin Monit 1992;8:295-307).
-
Pulse oximeters are known to be inaccurate in the presence of elevated concentrations of carboxyhemoglobin and methemoglobin. This paper attempts to alleviate some of the confusion that exists between fractional and functional saturation, and to clarify the comparison of each with SpO2. A series of theoretical relationships between pulse oximeter reading (SpO2) and actual oxygen saturation (both fractional and functional) is derived using simple absorption theory. ⋯ This consists of a blood circuit containing a model finger, capable of simulating the pulsatile transmission signals through a real finger. Theoretical predictions and experimental results are compared and are found to agree well in the presence of carboxyhemoglobin, but less well with methemoglobin. Possible reasons are discussed.
-
The objective of this study was to evaluate the effect of positive end-expiratory pressure (PEEP) on capnography. ⋯ These results demonstrated that absence of gas flow immediately after the application of PEEP may transiently abolish a capnogram when the lung volume increases.
-
Review
Integration of monitoring for intelligent alarms in anesthesia: neural networks--can they help?
Although there has been a decrease in the number of anesthesia-related critical incidents, there are still opportunities for further improvement. We discuss the potential of integrated monitoring and artificial neural networks as a means of vigilantly watching for patterns in multiple variables to detect incidents and reduce false alarms. ⋯ We present artificial neural networks as an approach that is more suited to the type of multivariable monitoring and pattern recognition required. Along with rule-based artificial intelligence, these now have the potential to help develop innovative monitoring in the operating room.