A System Model for Closed-circuit Inhalation Anesthesia I. Computer Study

Anesthesia &Amp; Analgesia

Pace

et al. 2010

Introduction Sevoflurane - remifentanil interaction models that predict responsiveness and response to painful stimuli have been evaluated in patients undergoing elective surgery. Preliminary evaluations of model predictions were found to be consistent with observations in patients anesthetized with sevoflurane, remifentanil and fentanyl. The present study explored the feasibility of adapting the predictions of sevoflurane-remifentanil interaction models to an isoflurane-fentanyl anesthetic. We hypothesized that model predictions adapted for isoflurane and fentanyl are consistent with observed patient responses and are similar to the predictions observed in our prior work with sevoflurane-remifentanil/fentanyl anesthetics. Methods Twenty-five patients scheduled for elective surgery received a fentanyl-isoflurane anesthetic. Model predictions of unresponsiveness were recorded at emergence and predictions of a response to noxious stimulus were recorded when patients first required analgesics in the recovery room. Model predictions were compared to observations with graphical and temporal analyses. Results were also compared to our prior predictions following a sevoflurane-remifentanil/fentanyl anesthetic. Results While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (≥ 99%). Following termination of the anesthetic, model predictions of responsiveness well described the actual fraction of patients observed to be responsive during emergence. Half of the patients awoke within 2 minutes of the 50% model predicted probability of unresponsiveness; 70% awoke within 4 minutes. Similarly, predictions of a response to a noxious stimulus were consistent with the number of patients who required fentanyl in the recovery room. Model predictions following an isoflurane-fentanyl anesthetic were similar to model predictions following a sevoflurane-remifentanil/fentanyl anesthetic. Discussion Results confirmed our study hypothesis; model predictions for unresponsiveness and no response to painful stimuli, adapted to isoflurane-fentanyl, were consistent with observations. These results were similar to our prior study comparing model predictions and patient observations following a sevoflurane-remifentanil/fentanyl anesthetic.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Response Surface Model Predictions of Emergence and Response to Pain in the Recovery Room

Syroid

Anesthesia &Amp; Analgesia

Pace

et al. 2010

“…Pharmacokinetic model development used to predict sevoflurane effect-site concentrations A multi-compartment pharmacokinetic model of inhalation agents developed by Lerou et al(4,5) was used to estimate effect-site concentrations from end-tidal levels. The Lerou model uses fresh gas flow, minute volume, vaporizer settings to predict inhalation agent end-tidal concentrations.…”

mentioning

confidence: 99%

An Evaluation of Remifentanil-Sevoflurane Response Surface Models in Patients Emerging from Anesthesia

Anesthesia &Amp; Analgesia

Syroid

Gupta³

et al. 2010

Introduction We previously reported models that characterized the synergistic interaction between remifentanil and sevoflurane in blunting responses to verbal and painful stimuli. This preliminary study evaluated the ability of these models to predict a return of responsiveness (ROR) during emergence from anesthesia and a response to tibial pressure when patients required analgesics in the recovery room. We hypothesized that model predictions would be consistent with observed responses. We also hypothesized that under non steady state conditions, accounting for the lag time between effect site (Ce) and end tidal (ET) sevoflurane concentrations would improve predictions. Methods Twenty patients received a sevoflurane, remifentanil, and fentanyl anesthetic. Two model predictions of responsiveness were recorded at emergence: an ET based and a Ce based prediction. Similarly two predictions of a response to noxious stimuli were recorded when patients first required analgesics in the recovery room. Model predictions were compared to observations with graphical and temporal analyses. Results While patients were anesthetized, model predictions indicated a high likelihood that patients would be unresponsive (≥ 99%). However, following termination of the anesthetic, models exhibited a wide range of predictions at emergence (1% to 97%). Although wide, the Ce based predictions of responsiveness were better distributed over a percentage ranking of observations than the ET based predictions. For the ET based model, 45% of the patients awoke within 2 minutes of the 50% model predicted probability of unresponsiveness; 65% awoke within 4 minutes. For the Ce based model, 45% of the patients awoke within 1 minute of the 50% model predicted probability of unresponsiveness; 85% awoke within 3.2 minutes. Predictions of a response to a painful stimulus in the recovery room were similar for the Ce and ET based models. Discussion Results confirmed in part our study hypothesis; accounting for the lag time between Ce and ET sevoflurane concentrations improved model predictions of responsiveness but had no effect on predicting a response to a noxious stimulus in the recovery room. These models may be useful in predicting events of clinical interest but large scale evaluations with numerous patients are needed to better characterize model performance.

“… Drug Simulations: Simulations were performed using published pharmacokinetic and inhalation–opioid interaction models . Sevoflurane levels are presented as effect‐site (not end‐tidal) concentrations.…”

Section: Key Concepts Of Drug Display Monitorsmentioning

confidence: 99%

An introduction to predictive modelling of drug concentration in anaesthesia monitors

DeCou

2017

Anaesthesia

Summary A significant amount of anaesthetists’ work involves the prediction of drug effects and interactions to produce a smooth general anaesthetic that minimises drug side effects and promotes rapid emergence. Successfully managing this process requires a basic understanding of drug effects, experience and inevitably some guesswork, since it is difficult (and in some cases impossible) to anticipate all relevant patient and surgical factors. Although data are generally available to allow calculation of plasma drug and effect site concentrations, this is often difficult to apply in complex clinical contexts, particularly when multiple drug types are used. In recent years, manufacturers have developed and incorporated into anaesthetic workstations technologies that use drug pharmacodynamic and pharmacokinetic data to predict drug effects and interactions. Such systems can predict the duration and effects of drugs during anaesthesia and assist the anaesthetist to understand complex drug interactions. With this information available, different drug types, doses and combinations may be tailored in a scientific way to maximise useful effects whilst minimising overdose and side‐effects, particularly in high‐risk patients. Examples are used to illustrate how such systems can be used in practice, and how drug effects and interactions can be simulated to “rehearse” an anaesthetic before any drugs are actually administered. At present only a small number of anaesthetic workstations use this technology, and as yet they are not able to manage all drugs used in anaesthetic practice. However, such systems have the potential to help anaesthetists manage the complexity of their work, and to provide information on predicted drug effects in a way that is useful and relevant to both experienced anaesthetists and trainees.