Richard A. French

Changing patterns in anesthetic fresh gas flow rates over 5 years in a teaching hospital.

BACKGROUND:Reducing anesthetic fresh gas flows can reduce volatile anesthetic consumption without affecting drug delivery to the patient. Delivery systems with electronic flow transducers permit the simple and accurate collection of fresh gas flow information. In a 2001 audit of fresh gas flow, we found little response to interventions designed to foster more efficient use of fresh gas. We compared current practice with our earlier results. METHODS:Flow data were collected in areas with a mix of general and acute surgery in March and November 2001, and again during 2006, by recording directly from the Datex ADU to a computer every 10 s. We extracted the distribution of flow rates when a volatile anesthetic was being administered. Data collection in March 2001 and 2006 was not advertised. RESULTS:In 2001, the mean flow rates were 1.95 and 2.1 L/min with a median flow of 1.5 L/min. In 2006, the mean was 1.27 and the median in the range 0.5-1.0 L/min. Isoflurane use decreased from 47% in 2001 to 4% in 2006. CONCLUSIONS:Fresh gas flows used in our department have decreased by 35% over 4 years. Although the absolute change in flow rate is not large, this represents potential annual savings of more than

The effect of a model-based predictive display on the control of end-tidal sevoflurane concentrations during low-flow anesthesia

US130,000. This occurred without specific initiatives, suggesting an evolution in practice towards lower fresh gas flow. Improvements in equipment and monitoring, including a locally developed system, which displays forward predictions of end-tidal and effect-site vapor concentrations, may be factors in this change.

Predictive accuracy of a model of volatile anesthetic uptake

We have shown that a multicompartment model accurately predicts end-tidal (ET) sevoflurane (sevo) and isoflurane concentrations. The model has been adapted to use real-time fresh gas flow and vaporizer settings to display a 10-min prediction of ET sevo concentrations. In this study, we evaluated the effect of the predictive display on the speed and accuracy of changes in ET sevo by the anesthesiologist. Fifteen patients were studied in whom sevo-based anesthesia was expected to last more than 2 h. Four step changes of target ET concentration (+0.5, +1.0, −1.0, and −0.5 vol%) were made either unaided or with the prediction display. Fresh gas flow was 1 L/min. Response time, maximum overshoot, and stability in the 5 min after the target was achieved were compared by using two-tailed paired Student’s t-tests. Changes were made on average 1.5–2.3 times faster with the predictive display than without it. These differences were statistically significant (P < 0.05) for the +0.5, +1.0, and −0.5 vol% step changes but not for the −1.0 vol% change. There were no differences in the degree of overshoot or stability. These differences are comparable to those seen with an automatic feedback control system. This system may simplify the administration of volatile anesthesia and the use of low-flow anesthesia.

A breathing circuit disconnection detected by anesthetic agent monitoring

A computer program that models anesthetic uptake and distribution has been in use in our department for 20 yr as a teaching tool. New anesthesia machines that electronically measure fresh gas flow rates and vaporizer settings allowed us to assess the performance of this model during clinical anesthesia. Gas flow, vaporizer settings, and end-tidal concentrations were collected from the anesthesia machine (Datex S/5 ADU) at 10-s intervals during 30 elective anesthetics. These were entered into the uptake model. Expired anesthetic vapor concentrations were calculated and compared with actual values as measured by the patient monitor (Datex AS/3). Sevoflurane was used in 16 patients and isoflurane in 14 patients. For all patients, the median performance error was −0.24%, the median absolute performance error was 13.7%, divergence was 2.3%/h, and wobble was 3.1%. There was no significant difference between sevoflurane and isoflurane. This model predicted expired concentrations well in these patients. These results are similar to those seen when comparing calculated and actual propofol concentrations in propofol infusion systems and meet published guidelines for the accuracy of models used in target-controlled anesthesia systems. This model may be useful for predicting responses to changes in fresh gas and vapor settings.

Sevoflurane end-tidal to effect-site equilibration in women determined by response to laryngeal mask airway insertion.

PurposeTo describe a case involving a spontaneously breathing patient where a circuit disconnection was detected by a change in monitored anesthetic agent parameters.Clinical featuresA patient undergoing shoulder surgery was breathing spontaneously from a circle type anesthesia circuit via a laryngeal mask. A disconnection occurred between the heat and moisture exchanger (HME) and the circle system’s Y-piece. As the gas sampling port was integrated into the HME a near normal pattern of CO2 continued to be displayed. The disconnection was noted because of a change in the graphical display of the volatile agent concentration.ConclusionsAnesthetic circuit disconnection can be difficult to detect, especially in the spontaneously breathing patient. Capnometry may not detect a disconnection on the machine side of the gas sampling port. Changes in oxygen and volatile agent concentrations may provide an early indication of these types of disconnection.RésuméObjectifDécrire un cas de déconnexion du circuit, impliquant un patient en respiration spontanée, détectée par un changement de paramètres des anesthésiques sous monitorage.Éléments cliniquesOn a utilisé, pour une intervention chirurgicale à l’épaule, une anesthésie en respiration spontanée avec masque laryngé et circuit cercle. Une déconnexion est survenue entre l’échangeur de chaleur et d’humidité (ECH) et la pièce en Y du circuit cercle. Comme le site d’échantillonnage du gaz est intégré à l’ECH, l’affichage quasi normal de C02 s’est poursuivi. La déconnexion a été remarquée grâce à un changement dans le graphique de concentration de l’agent volatil.ConclusionUne déconnexion du circuit anesthésique peut être difficile à détecter, surtout si le patient respire spontanément. La capnométrie pourrait n’être d’aucun secours pour une déconnexion du côté de l’appareil oú se trouve le site d’échantillonnage. Des changements de concentrations d’oxygène et d’anesthésique volatil peuvent alors fournir une indication précoce de ce type de déconnexion.

Sustaining a Reduction in Fresh Gas Flow Rates

BACKGROUND:End-tidal concentrations (CET) have been used to guide delivery of inhaled anesthetic drugs for many years. Effect-site concentrations (Ceff) are a frequently used guide to therapy with IV drugs and should also be of benefit with inhaled drugs, especially during periods of rapid change. For Ceff to be useful, the appropriate levels required for any given end point, and the delay between central compartment and effect, need to be defined. In this study, we explored these relationships for the effect of response to insertion of the classic laryngeal mask airway (cLMA) and compared the utility of CET and Ceff-guided cLMA insertion. METHOD:We studied 30 ASA physical status I or II patients in whom induction with sevoflurane alone and use of the cLMA were appropriate. After oxygen administration from a circle system with a total gas flow of 6 L/min, the sevoflurane vaporizer dial was set to 6%. cLMA insertion was attempted at a predetermined Ceff calculated in real time based on measured CET. Target levels were chosen using up-and-down methodology. The initial value was 2.5 vol% with a step size of 0.2 vol%. Subjects showing a gross motor response were responders, and the target was increased for the next subject. Those without such a response were nonresponders, and the target was decreased for the next subject. Data collection continued until after 7 transitions from nonresponder to responder. For each subject, after the first transition, we calculated a Ceff time series from the measured CET time series for 11 t1/2ke0 values between 0.5 and 5.0 minutes. We combined data from 2 studies of equilibrium 50% effective concentration (EC50) for LMA insertion to derive a pooled EC50 of 2.17%. We determined graphically the t1/2ke0 that gave a mean EC50 of 2.17% in our subjects. We constructed receiver operator characteristic curves to compare the utility of CET and Ceff-guided cLMA insertion. RESULTS:The 30 patients studied were all women, ASA physical status I or II, aged between 22 and 66 years (mean 38). Consciousness was lost after 99.2 (SD 11.1) seconds, and the target for cLMA insertion reached after 256 (57) seconds. The optimum t1/2ke0 was 2.25 minutes (95% confidence interval, 2.0–2.5 minutes). The area under the receiver operator characteristic curves was significantly different at 0.87 (SE 0.06) for Ceff and 0.63 (0.11) for CET. CONCLUSIONS:This study confirmed that real-time calculation and display of Ceff based on measured CET values are feasible. We determined the optimum t1/2ke0 for sevoflurane for the effect of cLMA insertion as 2.25 minutes, similar to that determined for loss of consciousness using the raw electroencephalogram. We also showed that Ceff is a more reliable (P < 0.05) guide to successful cLMA insertion than CET.

The development of a system to guide volatile anaesthetic administration.

In Reply: We sincerely appreciate the comments by Drs. Kennedy and French related to our study1 aimed at reducing fresh gas flow (FGF) by using a decision support system. We believe that continuous feedback of information is necessary to maintain desired provider behavior and care patterns. This is supported by our finding that when we turned off the FGF reminders, the providers reverted to use the original, preintervention FGF settings within a few months. We read with interest the inhaled anesthetic delivery guidance system developed by Drs. Kennedy and French and its application in precisely controlling the delivery of vapors.2,3 We believe that their guidance system performed a function similar to our decision support system with respect to continuous, near real-time, feedback of FGF to anesthesia providers. Considering this similarity, it was interesting to note that the reduction in mean FGF achieved by Dr. Kennedy et al. was in close agreement with what was observed in our study.1,4 Although continuous feedback was provided by both studies, the mechanisms used were different. Dr. Kennedy et al. developed a specialized software program to interface with the anesthesia machine and implement a predictive model of end-tidal vapor concentration. However, we used an Anesthesia Information Management System–based decision support module called Smart Anesthesia Manager. Smart Anesthesia Manager has the advantage that it can provide decision support not only for FGF but also for a variety of other clinical care items to improve quality of care and patent safety.5