Desflurane consumption during automated closed-circuit delivery is higher than when a conventional anesthesia machine is used with a simple vaporizer-O2-N2O fresh gas flow sequence

The Zeus® (Dräger, Lübeck, Germany), an automated closed-circuit anesthesia machine, uses high fresh gas flows (FGF) to wash-in the circuit and the lungs, and intermittently flushes the system to remove unwanted N₂. We hypothesized this could increase desflurane consumption to such an extent that agent consumption might become higher than with a conventional anesthesia machine (Anesthesia Delivery Unit [ADU®], GE, Helsinki, Finland) used with a previously derived desflurane-O₂-N₂O administration schedule that allows early FGF reduction.Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Do distribution volumes and clearances relate to tissue volumes and blood flows? A computer simulation

BACKGROUND: Kinetics of inhaled agents are often described by physiological models. However, many pharmacokinetic concepts, such as context-sensitive half-times, have been developed for drugs described by classical compartmental models. We derived classical compartmental models that describe the course of the alveolar concentrations (F(A)) generated by the physiological uptake and distribution models used by the Gas Man(® )program, and describe how distribution volumes and clearances relate to tissue volumes and blood flows. METHODS: Gas Man(® )was used to generate F(A )vs. time curves during the wash-in and wash-out period of 115 min each with a high fresh gas flow (8 L.min(-1)), a constant alveolar minute ventilation (4 L.min(-1)), and a constant inspired concentration (F(I)) of halothane (0.75%), isoflurane (1.15%), sevoflurane (2%), or desflurane (6%). With each of these F(I), simulations were ran for a 70 kg patient with 5 different cardiac outputs (CO) (2, 3, 5, 8 and 10 L.min(-1)) and for 5 patients with different weights (40, 55, 70, 85, and 100 kg) but the same CO (5 L.min(-1)). Two and three compartmental models were fitted to F(A )of the individual 9 runs using NONMEM. After testing for parsimony, goodness of fit was evaluated using median prediction error (MDPE) and median absolute prediction error (MDAPE). The model was tested prospectively for a virtual 62 kg patient with a cardiac output of 4.5 L.min(-1 )for three different durations (wash-in and wash-out period of 10, 60, and 180 min each) with an F(I )of 1.5% halothane, 1.5% isoflurane, sevoflurane 4%, or desflurane 12%. RESULTS: A three-compartment model fitted the data best (MDPE = 0% and MDAPE ≤ 0.074%) and performed equally well when tested prospectively (MDPE ≤ 0.51% and MDAPE ≤ 1.51%). The relationship between CO and body weight and the distribution volumes and clearances is complex. CONCLUSION: The kinetics of anesthetic gases can be adequately described e by a mammilary compartmental model. Therefore, concepts that are traditionally thought of as being applicable to the kinetics of intravenous agents can be equally well applied to anesthetic gases. Distribution volumes and clearances cannot be equated to tissue volumes and blood flows respectively

Can modern infrared analyzers replace gas chromatography to measure anesthetic vapor concentrations?

<p>Abstract</p> <p>Background</p> <p>Gas chromatography (GC) has often been considered the most accurate method to measure the concentration of inhaled anesthetic vapors. However, infrared (IR) gas analysis has become the clinically preferred monitoring technique because it provides continuous data, is less expensive and more practical, and is readily available. We examined the accuracy of a modern IR analyzer (M-CAiOV compact gas IR analyzer (General Electric, Helsinki, Finland) by comparing its performance with GC.</p> <p>Methods</p> <p>To examine linearity, we analyzed 3 different concentrations of 3 different agents in O₂: 0.3, 0.7, and 1.2% isoflurane; 0.5, 1, and 2% sevoflurane; and 1, 3, and 6% desflurane. To examine the effect of carrier gas composition, we prepared mixtures of 1% isoflurane, 1 or 2% sevoflurane, or 6% desflurane in 100% O₂(= O₂group); 30%O₂+ 70%N₂O (= N₂O group), 28%O₂+ 66%N₂O + 5%CO₂(= CO₂group), or air. To examine consistency between analyzers, four different M-CAiOV analyzers were tested.</p> <p>Results</p> <p>The IR analyzer response in O₂is linear over the concentration range studied: IR isoflurane % = -0.0256 + (1.006 * GC %), R = 0.998; IR sevoflurane % = -0.008 + (0.946 * GC %), R = 0.993; and IR desflurane % = 0.256 + (0.919 * GC %), R = 0.998. The deviation from GC calculated as (100*(IR-GC)/GC), in %) ranged from -11 to 11% for the medium and higher concentrations, and from -20 to +20% for the lowest concentrations. No carrier gas effect could be detected. Individual modules differed in their accuracy (p = 0.004), with differences between analyzers mounting up to 12% of the medium and highest concentrations and up to 25% of the lowest agent concentrations.</p> <p>Conclusion</p> <p>M-CAiOV compact gas IR analyzers are well compensated for carrier gas cross-sensitivity and are linear over the range of concentrations studied. IR and GC cannot be used interchangeably, because the deviations between GC and IR mount up to ± 20%, and because individual analyzers differ unpredictably in their performance.</p

In vitro efficiency of 16 different Ca(OH)(2) based CO2 absorbent brands

Data directly comparing CO2 absorbents tested in identical and clinically relevant conditions are scarce or non-existent. We therefore tested and compared the efficiency of 16 different brands of Ca(OH)2 based CO2 absorbents used as loose fill or a cartridge in a refillable canister under identical low flow conditions. CO2 absorbents efficiency was tested by flowing 160 mL/min CO2 into the tip of a 2 L balloon that was ventilated with an ADU anesthesia machine (GE, Madison, WI, USA) with a tidal volume of 500 mL and a respiratory rate of 10/min while running an O2/air FGF of 300 mL/min. After the 1020 mL refillable container was filled with a known volume of CO2 absorbent (derived from weighing the initial canister content and the product's density), the time for the inspired CO2 concentration (FICO2) to rise to 0.5% was measured. This test was repeated 4 times for each product. Because the two SpiraLith Ca® products (one with and one without indicator) are delivered as a cartridge, they had to be tested using their proprietary canister. The time (min) for FICO2 to reach 0.5% was normalized to 100 mL of product, and defined as the efficiency, which was compared amongst the different brands using ANOVA. Efficiency ranged from 50 to 100 min per 100 mL of product, and increased with increasing NaOH content (a catalyst), the exception being SpiraLith Ca® cartridge with color indicator (performing as well as the most efficient granular products) and the SpiraLith Ca® cartridge without color indicator (outperforming all others). Results indicated a spherical or bullet shape is less efficient in absorbing CO2 than broken fragments or cylinders, which in turn is less efficient than a hemispherical (disc) shape, which is in turn less efficient than a solid cartridge with a molded channel geometry. The efficiency of Ca(OH)2 based CO2 absorbent differs up to 100% on a volume basis. Macroscopic arrangement (cylindrical wrap with preformed channels versus granules), chemical composition (NaOH content), and granular shape all affect efficiency per volume of product. The data can be used to compare costs of the different products.status: publishe

Mathematical method to build an empirical model for inhaled anesthetic agent wash-in

<p>Abstract</p> <p>Background</p> <p>The wide range of fresh gas flow - vaporizer setting (FGF - F_D) combinations used by different anesthesiologists during the wash-in period of inhaled anesthetics indicates that the selection of FGF and F_Dis based on habit and personal experience. An empirical model could rationalize FGF - F_Dselection during wash-in.</p> <p>Methods</p> <p>During model derivation, 50 ASA PS I-II patients received desflurane in O₂with an ADU^®anesthesia machine with a random combination of a fixed FGF - F_Dsetting. The resulting course of the end-expired desflurane concentration (F_A) was modeled with Excel Solver, with patient age, height, and weight as covariates; NONMEM was used to check for parsimony. The resulting equation was solved for F_D, and prospectively tested by having the formula calculate F_Dto be used by the anesthesiologist after randomly selecting a FGF, a target F_A(F_At), and a specified time interval (1 - 5 min) after turning on the vaporizer after which F_Athad to be reached. The following targets were tested: desflurane F_At3.5% after 3.5 min (n = 40), 5% after 5 min (n = 37), and 6% after 4.5 min (n = 37).</p> <p>Results</p> <p>Solving the equation derived during model development for F_Dyields F_D=-(e^{(-FGF*-0.23+FGF*0.24)}*(e^(FGF*-0.23)*F_At*Ht*0.1-e^(FGF*-0.23)*FGF*2.55+40.46-e^(FGF*-0.23)*40.46+e^{(FGF*-0.23+Time/-4.08)}*40.46-e^(Time/-4.08)*40.46))/((-1+e^(FGF*0.24))*(-1+e^(Time/-4.08))*39.29). Only height (Ht) could be withheld as a significant covariate. Median performance error and median absolute performance error were -2.9 and 7.0% in the 3.5% after 3.5 min group, -3.4 and 11.4% in the 5% after 5 min group, and -16.2 and 16.2% in the 6% after 4.5 min groups, respectively.</p> <p>Conclusions</p> <p>An empirical model can be used to predict the FGF - F_Dcombinations that attain a target end-expired anesthetic agent concentration with clinically acceptable accuracy within the first 5 min of the start of administration. The sequences are easily calculated in an Excel file and simple to use (one fixed FGF - F_Dsetting), and will minimize agent consumption and reduce pollution by allowing to determine the lowest possible FGF that can be used. Different anesthesia machines will likely have different equations for different agents.</p