Early phase pharmacokinetics but not pharmacodynamics are influenced by propofol infusion rate

Background: Conventional compartmental pharmacokinetic models wrongly assume instantaneous drug mixing in the central compartment, resulting in a flawed prediction of drug disposition for the first minutes, and the flaw affects pharmacodynamic modeling. This study examined the influence of the administration rate and other covariates on early phase kinetics and dynamics of propofol by using the enlarged structural pharmacokinetic model. Methods: Fifty patients were randomly assigned to one of five groups to receive 1.2 mg/kg propofol given with the rate of 10 to 160 mg . kg(-1) . h(-1). Arterial blood samples were taken frequently, especially during the first minute. The authors compared four basic pharmacokinetic models by using presystemic compartments and the time shift of dosing, LAG time. They also examined a sigmoidal maximum possible drug effect pharmacodynamic model. Patient characteristics and dose rate were obtained to test the model structure. Results: Our final pharmacokinetic model includes two conventional compartments enlarged with a LAG time and six presystemic compartments and includes following covariates: dose rate for transit rate constant, age for LAG time, and weight for central distribution volume. However, the equilibration rate constant between central and effect compartments was not influenced by infusion rate. Conclusions: This study found that a combined pharmacokinetic-dynamic model consisting of a two-compartmental model with a LAG time and presystemic compartments and a sigmoidal maximum possible drug effect model accurately described the early phase pharmacology of propofol during infusion rate between 10 and 160 mg . kg(-1) . h(-1). The infusion rate has an influence on kinetics, but not dynamics. Age was a covariate for LAG time

Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been widely used for monitoring depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 ( p < 0.001 ). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA