title: Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia.
abstract: OBJECTIVE
  Until now, the knowledge of combining anesthetics to obtain an adequate level of anesthesia and to economize wakeup time has been empirical and difficult to represent in quantitative models. Since there is no reason to expect that the effect of non-opioid and opioid anesthetics can be modeled in a simple linear manner, the use of a new computational approach with Bayesian belief network software is demonstrated.
  
  
  METHODS
  A data set from a pharmacodynamic study was used where remifentanil was randomly given in three fixed target concentrations (2, 4, and 8 ng/ml) to 62 subjects. Target concentrations of propofol were controlled according to the closed-loop system feedback of the auditory evoked potential index to render modeling unbiased by the level of anesthesia. Time to open eyes was measured to represent wakeup time after surgery. The NETICA version 1.37 software was used on a personal computer for network building, validation, and prediction.
  
  
  RESULTS
  After the learning phase, the network was used to generate a series of random cases whose probability distribution matches that of the compiled network. The sampling algorithms used are precise, so that the frequencies of the simulated cases will exactly approach the probabilities of the network and that of the data learned. The graphical display of the predicted wakeup time shows less variability but a more complex interaction pattern than with the unadjusted original data.
  
  
  CONCLUSIONS
  Model building and evaluation with Bayesian networks does not depend on underlying linear relationships. Bayesian relationships represent true features of the represented data sample. Data may be sparse, uncertain, stochastic, or imprecise. Multiple platform software that is easy to use is increasingly available. Bayesian networks promise to be versatile tools for building valid, nonlinear, predictive instruments to further gain insight into the complex interaction of anesthetics.
authors.affiliation: Department of Anesthesiology, University of Ulm, Germany. Ulrich.Bothner@medizin.uni-ulm.de
authors.forename: Ulrich
authors.initials: U
authors.lastname: Bothtner
authors.forename: Stewart E
authors.initials: SE
authors.lastname: Milne
authors.forename: Gavin N C
authors.initials: GN
authors.lastname: Kenny
authors.forename: Michael
authors.initials: M
authors.lastname: Georgieff
authors.forename: Stefan
authors.initials: S
authors.lastname: Schraag
mesh_terms: D000762:Anesthesia Recovery Period; D000764:Anesthesia, Closed-Circuit; D018686:Anesthetics, Intravenous; D001499:Bayes Theorem; D006801:Humans; D010880:Piperidines; D015742:Propofol; D000077208:Remifentanil
chemical_list: D018686:Anesthetics, Intravenous; D010880:Piperidines; D000077208:Remifentanil; D015742:Propofol