Abstract
Recent interest in the use of low-flow or closed circuit anesthesia has rekindled interest in the pharmacokinetics of inhaled anesthetics. The kinetic properties of inhaled anesthetics are most often modeled by physiologic models because of the abundant information that is available on tissue solubilities and organ perfusion. These models are intuitively attractive because they can be easily understood in terms of the underlying anatomy and physiology. The use of classical compartment modeling, on the other hand, allows modeling of data that are routinely available to the anesthesiologist, and eliminates the need to account for every possible confounding factor at each step of the partial pressure cascade of potent inhaled agents. Concepts used to describe IV kinetics can readily be applied to inhaled agents (e.g., context-sensitive half-time and effect site concentrations).
The interpretation of the FA/FI vs time curve is expanded by reintroducing the concept of the general anesthetic equation—the focus is shifted from “how FA approaches FI” to “what combination of delivered concentration and fresh gas flow (FGF) can be used to attain the desired FA.” When the desired FA is maintained with a FGF that is lower than minute ventilation, rebreathing causes a discrepancy between the concentration delivered by the anesthesia machine (=selected by the anesthesiologist on the vaporizer, FD) and that inspired by the patient. This FD-FI discrepancy may be perceived as “lack of control” and has been the rationale to use a high FGF to ensure the delivered matched the inspired concentration. Also, with low FGF there is larger variability in FD because of interpatient variability in uptake. The FD-FI discrepancy increases with lower FGF because of more rebreathing, and as a consequence the uptake pattern seems to be more reflected in the FD required to keep FA constant. The clinical implication for the anesthesiologist is that with high FGF few FD adjustments have to be made, while with a low FGF FD has to be adjusted according to a pattern that follows the decreasing uptake pattern in the body. The ability to model and predict the uptake pattern of the individual patient and the resulting kinetics in a circle system could therefore help guide the anesthesiologist in the use of low-flow anesthesia with conventional anesthesia machines. Several authors have developed model-based low FGF administration schedules, but biologic variability limits the performance of any model, and therefore end-expired gas analysis is obligatory. Because some fine-tuning based on end-expired gas analysis will always be needed, some clinicians may not be inclined to use very low FGF in a busy operating room, considering the perceived increase in complexity. This practice may be facilitated by the development of anesthesia machines that use closed circuit anesthesia (CCA) with end-expired feedback control—they “black box” these issues (see Chapter 21).
In this chapter, we first explore how and why the kinetic properties of intravenous and inhaled anesthetics have been modeled differently. Next, we will review the method most commonly used to describe the kinetics of inhaled agents, the FA/FI vs time curve that describes how the alveolar (FA) approaches the inspired (FI) fraction (in the gas phase, either “fraction,” “concentration,” or “partial pressure” can be used). Finally, we will reintroduce the concept of the general anesthetic equation to explain why the use of low-flow or closed circuit anesthesia has rekindled interest in the modeling of pharmacokinetics of inhaled anesthetics. Clinical applications of some of these models are reviewed. A basic understanding of the circle system is required, and will be provided in the introduction.
Parts of this chapter appeared in Dr. Jan Hendrickx’s doctoral thesis (Hendrickx 2004).
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© 2008 Springer-Verlag Berlin Heidelberg
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Hendrickx, J.F.A., De Wolf, A. (2008). Special Aspects of Pharmacokinetics of Inhalation Anesthesia. In: Schüttler, J., Schwilden, H. (eds) Modern Anesthetics. Handbook of Experimental Pharmacology, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74806-9_8
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DOI: https://doi.org/10.1007/978-3-540-74806-9_8
Publisher Name: Springer, Berlin, Heidelberg
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