Immobility

Surgical anaesthesia usually requires hypnosis, antinociception (ensuring blood pressure and heart rate control) and immobility with varying degrees of muscle relaxation. However, the relative contribution of these three components to the state of anaesthesia may vary between different anaesthesias and surgical procedures. While volatile anaesthetics may be used to produce anaesthesia in and by themselves, most often anaesthesia is produced by a combination of drugs. Anaesthesia produced by the concomitant use of hypnotics, analgesics and neuromuscular blocking drugs is called ‘balanced anaesthesia’.Surgical anaesthesia usually requires hypnosis, antinociception (ensuring blood pressure and heart rate control) and immobility with varying degrees of muscle relaxation. However, the relative contribution of these three components to the state of anaesthesia may vary between different anaesthesias and surgical procedures. While volatile anaesthetics may be used to produce anaesthesia in and by themselves, most often anaesthesia is produced by a combination of drugs. Anaesthesia produced by the concomitant use of hypnotics, analgesics and neuromuscular blocking drugs is called ‘balanced anaesthesia’. © Pedro L. Gambús and Jan F.A. Hendrickx 2020.Peer reviewe

Performance of an Iterative Two-stage Bayesian Technique for Population Pharmacokinetic Analysis of Rich Data Sets

Purpose. To test the suitability of an Iterative Two-Stage Bayesian (ITSB) technique for population pharmacokinetic analysis of rich data sets, and to compare ITSB with Standard Two-Stage (STS) analysis and nonlinear Mixed Effect Modeling (MEM). Materials and Methods. Data from a clinical study with rapacuronium and data generated by Monte Carlo simulation were analyzed by an ITSB technique described in literature, with some modifications, by STS, and by MEM (using NONMEM). The results were evaluated by comparing the mean error (accuracy) and root mean squared error (precision) of the estimated parameter values, their interindividual standard deviation, correlation coefficients, and residual standard deviation. In addition, the influence of initial estimates, number of subjects, number of measurements, and level of residual error on the performance of ITSB were investigated. Results. ITSB yielded best results, and provided precise and virtually unbiased estimates of the population parameter means, interindividual variability, and residual standard deviation. The accuracy and precision of STS was poor, whereas ITSB performed better than MEM. Conclusions. ITSB is a suitable technique for population pharmacokinetic analysis of rich data sets, and in the presented data set it is superior to STS and MEM

Population pharmacodynamic modeling using the sigmoid E-max model : influence of inter-individual variability on the steepness of the concentration-effect relationship : a simulation study

The relationship between the concentration of a drug and its pharmacological effect is often described by empirical mathematical models. We investigated the relationship between the steepness of the concentration-effect relationship and inter-individual variability (IIV) of the parameters of the sigmoid E-max model, using the similarity between the sigmoid E-max model and the cumulative log-normal distribution. In addition, it is investigated whether IIV in the model parameters can be estimated accurately by population modeling. Multiple data sets, consisting of 40 individuals with 4 binary observations in each individual, were simulated with varying values for the model parameters and their IIV. The data sets were analyzed using Excel Solver and NONMEM. An empirical equation (Eq. (11)) was derived describing the steepness of the population-predicted concentration-effect profile (gamma*) as a function of gamma and IIV in C50 and gamma, and was validated for both binary and continuous data. The tested study design is not suited to estimate the IIV in C50 and gamma with reasonable precision. Using a naive pooling procedure, the population estimates gamma* are significantly lower than the value of gamma used for simulation. The steepness of the population-predicted concentration-effect relationship (gamma*) is less than that of the individuals (gamma). Using gamma*, the population-predicted drug effect represents the drug effect, for binary data the probability of drug effect, at a given concentration for an arbitrary individual

Mechanism-based pharmacodynamic model for propofol haemodynamic effects in healthy volunteers☆

Background: The adverse haemodynamic effects of the intravenous anaesthetic propofol are well known, yet few empirical models have explored the dose-response relationship. Evidence suggests that hypotension during general anaesthesia is associated with postoperative mortality. We developed a mechanism-based model that quantitatively characterises the magnitude of propofol-induced haemodynamic effects during general anaesthesia. Methods: Mean arterial pressure (MAP), heart rate (HR) and pulse pressure (PP) measurements were available from 36 healthy volunteers who received propofol in a step-up and step-down fashion by target-controlled infusion using the Schnider pharmacokinetic model. A mechanistic pharmacodynamic model was explored based on the Snelder model. To benchmark the performance of this model, we developed empirical models for MAP, HR, and PP. Results: The mechanistic model consisted of three turnover equations representing total peripheral resistance (TPR), stroke volume (SV), and HR. Propofol-induced changes were implemented by E-max models on the zero-order production rates of the turnover equations for TPR and SV. The estimated 50% effective concentrations for propofol-induced changes in TPR and SV were 2.96 and 0.34 mu g ml(-1), respectively. The goodness-of-fit for the mechanism-based model was indistinguishable from the empirical models. Simulations showed that predictions from the mechanism-based model were similar to previously published MAP and HR observations. Conclusions: We developed a mechanism-based pharmacodynamic model for propofol-induced changes in MAP, TPR, SV, and HR as a potential approach for predicting haemodynamic alterations

General purpose models for intravenous anesthetics, the next generation for target-controlled infusion and total intravenous anesthesia?

PURPOSE OF REVIEW: There are various pharmacokinetic-dynamic models available, which describe the time course of drug concentration and effect and which can be incorporated into target-controlled infusion (TCI) systems. For anesthesia and sedation, most of these models are derived from narrow patient populations, which restricts applicability for the overall population, including (small) children, elderly, and obese patients. This forces clinicians to select specific models for specific populations. RECENT FINDINGS: Recently, general purpose models have been developed for propofol and remifentanil using data from multiple studies and broad, diverse patient groups. General-purpose models might reduce the risks associated with extrapolation, incorrect usage, and unfamiliarity with a specific TCI-model, as they offer less restrictive boundaries (i.e., the patient "doesn't fit in the selected model") compared with the earlier, simpler models. Extrapolation of a model can lead to delayed recovery or inadequate anesthesia. If multiple models for the same drug are implemented in the pump, it is possible to select the wrong model for that specific case; this can be overcome with one general purpose model implemented in the pump. SUMMARY: This article examines the usability of these general-purpose models in relation to the more traditional models.</p

Pharmacodynamic mechanism-based interaction model for the haemodynamic effects of remifentanil and propofol in healthy volunteers

BACKGROUND: Propofol and remifentanil are frequently combined for the induction and maintenance of general anaesthesia. Both propofol and remifentanil cause vasodilation and potentially reduce arterial BP. We aimed to develop a mechanism-based model that characterises the haemodynamic interactions between remifentanil and propofol.METHODS: Data from two clinical trials in healthy volunteers were analysed using remifentanil-alone, propofol-alone, and combination groups. We evaluated remifentanil effects on haemodynamics using a previously developed mechanism-based haemodynamic model of propofol. The interaction between propofol and remifentanil was explored using the principles of the general pharmacodynamic interaction (GPDI) model.RESULTS: Remifentanil alone increased the dissipation rate of total peripheral resistance by 50% at 3.0 ng ml-1. Additionally, the dissipation rates of HR and stroke volume were attenuated by 4.8% and 4.9% per 1 ng ml-1 increase in remifentanil concentration, respectively. The maximal effect of propofol alone in decreasing the production rate of total peripheral resistance was 78%, which decreased to 32% when combined with remifentanil 4 ng ml-1. The effects of remifentanil on HR and stroke volume were attenuated by propofol with maximum decreases of 11.9% and 21.2%, respectively. Goodness-of-fit plots and prediction-corrected visual predictive check plots showed good predictive performance of the models.CONCLUSIONS: The structure of the previous mechanism-based haemodynamic model for propofol was able to describe the effects of remifentanil alone on haemodynamic variables. The GPDI model provided a good framework for characterising the pharmacodynamic interaction between remifentanil and propofol on haemodynamic properties.CLINICAL TRIAL REGISTRATION: NCT02043938; NCT03143972.</p

Comment on Morse et al. A Universal Pharmacokinetic Model for Dexmedetomidine in Children and Adults. J. Clin. Med. 2020, 9, 3480

We read with interest the recently published manuscript [...]

Bayesian statistics in anesthesia practice:a tutorial for anesthesiologists

This narrative review intends to provide the anesthesiologist with the basic knowledge of the Bayesian concepts and should be considered as a tutorial for anesthesiologists in the concept of Bayesian statistics. The Bayesian approach represents the mathematical formulation of the idea that we can update our initial belief about data with the evidence obtained from any kind of acquired data. It provides a theoretical framework and a statistical method to use pre-existing information within the context of new evidence. Several authors have described the Bayesian approach as capable of dealing with uncertainty in medical decision-making. This review describes the Bayes theorem and how it is used in clinical studies in anesthesia and critical care. It starts with a general introduction to the theorem and its related concepts of prior and posterior probabilities. Second, there is an explanation of the basic concepts of the Bayesian statistical inference. Last, a summary of the applicability of some of the Bayesian statistics in current literature is provided, such as Bayesian analysis of clinical trials and PKPD modeling

A response surface model approach for continuous measures of hypnotic and analgesic effect during sevoflurane-remifentanil interaction: quantifying the pharmacodynamic shift evoked by stimulation

Background: The authors studied the interaction between sevoflurane and remifentanil on bispectral index (BIS), state entropy (SE), response entropy (RE), Composite Variability Index, and Surgical Pleth Index, by using a response surface methodology. The authors also studied the influence of stimulation on this interaction. Methods: Forty patients received combined concentrations of remifentanil (0 to 12 ng/ml) and sevoflurane (0.5 to 3.5 vol%) according to a crisscross design (160 concentration pairs). During pseudo–steady-state anesthesia, the pharmacodynamic measures were obtained before and after a series of noxious and nonnoxious stimulations. For the “prestimulation” and “poststimulation” BIS, SE, RE, Composite Variability Index, and Surgical Pleth Index, interaction models were applied to find the best fit, by using NONMEM 7.2.0. (Icon Development Solutions, Hanover, MD). Results: The authors found an additive interaction between sevoflurane and remifentanil on BIS, SE, and RE. For Composite Variability Index, a moderate synergism was found. The comparison of pre- and poststimulation data revealed a shift of C50SEVO for BIS, SE, and RE, with a consistent increase of 0.3 vol%. The Surgical Pleth Index data did not result in plausible parameter estimates, neither before nor after stimulation. Conclusions: By combining pre- and poststimulation data, interaction models for BIS, SE, and RE demonstrate a consistent influence of “stimulation” on the pharmacodynamic relationship between sevoflurane and remifentanil. Significant population variability exists for Composite Variability Index and Surgical Pleth Index