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

Influence of steep Trendelenburg position and CO2 pneumoperitoneum on cardiovascular, cerebrovascular, and respiratory homeostasis during robotic prostatectomy

The steep (40 degrees) Trendelenburg position optimizes surgical exposure during robotic prostatectomy. The goal of the current study was to investigate the combined effect of this position and CO2 pneumoperitoneum on cardiovascular, cerebrovascular, and respiratory homeostasis during these procedures. Physiological data were recorded during the whole surgical procedure in 31 consecutive patients who underwent robotic endoscopic radical prostatectomy under general anaesthesia. Heart rate, mean arterial pressure, central venous pressure, Sp(o2), Pe'(co2), P-Plat, tidal volume, compliance, and minute ventilation were monitored and recorded. Arterial samples were obtained to determine the arterial-to-end-tidal CO2 tension gradient. Continuous regional cerebral tissue oxygen saturation (Sct(o2)) was determined by near-infrared spectroscopy. Although patients were in the Trendelenburg position, all variables investigated remained within a clinically acceptable range. Cerebral perfusion pressure (CPP) decreased from 77 mm Hg at baseline to 71 mm Hg (P=0.07), and Sct(o2) increased from 70% to 73% (P < 0.001). Pe'(co2) increased from 4.12 to 4.79 kPa (P < 0.001) and the arterial-to-Pe'(co2) tension difference increased from 1.06 kPa in the normal position to a maximum of 1.41 kPa (P < 0.001) after 2 h in the Trendelenburg position. The combination of the prolonged steep Trendelenburg position and CO2 pneumoperitoneum was well tolerated. Haemodynamic and pulmonary variables remained within safe limits. Regional cerebral oxygenation was well preserved and CPP remained within the limits between which cerebral blood flow is usually considered to be maintained by cerebral autoregulation

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

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

Theoretical effect of hyperventilation on speed of recovery and risk of rehypnotization following recovery - a GasMan^® simulation

<p>Abstract</p> <p>Background</p> <p>Hyperventilation may be used to hasten recovery from general anesthesia with potent inhaled anesthetics. However, its effect may be less pronounced with the newer, less soluble agents, and it may result in rehypnotization if subsequent hypoventilation occurs because more residual anesthetic will be available in the body for redistribution to the central nervous system. We used GasMan^® simulations to examine these issues.</p> <p>Methods</p> <p>One MAC of isoflurane, sevoflurane, or desflurane was administered to a fictitious 70 kg patient for 8 h with normoventilation (alveolar minute ventilation [V_A] 5 L.min^-1), resulting in full saturation of the vessel rich group (VRG) and >95% saturation of the muscle group. After 8 h, agent administration was stopped, and fresh gas flow was increased to 10 L.min^-1 to avoid rebreathing. At that same time, we continued with one simulation where normoventilation was maintained, while in a second simulation hyperventilation was instituted (10 L.min^-1). We determined the time needed for the partial pressure in the VRG (F_VRG; representing the central nervous system) to reach 0.3 MAC (MACawake). After reaching MACawake in the VRG, several degrees of hypoventilation were instituted (V_A of 2.5, 1.5, 1, and 0.5 L.min^-1) to determine whether F_VRG would increase above 0.3 MAC(= rehypnotization).</p> <p>Results</p> <p>Time to reach 0.3 MAC in the VRG with normoventilation was 14 min 42 s with isoflurane, 9 min 12 s with sevoflurane, and 6 min 12 s with desflurane. Hyperventilation reduced these recovery times by 30, 18, and 13% for isoflurane, sevoflurane, and desflurane, respectively. Rehypnotization was observed with V_A of 0.5 L.min^-1 with desflurane, 0.5 and 1 L.min^-1 with sevoflurane, and 0.5, 1, 1.5, and 2.5 L.min^-1 with isoflurane. Only with isoflurane did initial hyperventilation slightly increase the risk of rehypnotization.</p> <p>Conclusions</p> <p>These GasMan^® simulations confirm that the use of hyperventilation to hasten recovery is marginally beneficial with the newer, less soluble agents. In addition, subsequent hypoventilation results in rehypnotization only with more soluble agents, unless hypoventilation is severe. Also, initial hyperventilation does not increase the risk of rehypnotization with less soluble agents when subsequent hypoventilation occurs. Well-controlled clinical studies are required to validate these simulations.</p