Bayesian Probabilistic Network Modeling of Remifentanil and Propofol Interaction on Wakeup Time after Closed-Loop Controlled Anesthesia

Objective.Until now, the knowledge of combining anestheticsto obtain an adequate level of anesthesia and to economize wakeup timehas been empirical and difficult to represent in quantitative models.Since there is no reason to expect that the effect of non-opioid andopioid anesthetics can be modeled in a simple linear manner, the use ofa new computational approach with Bayesian belief network software isdemonstrated. Methods.A data set from a pharmacodynamic studywas used where remifentanil was randomly given in three fixed targetconcentrations (2, 4, and 8 ng/ml) to 62 subjects. Target concentrationsof propofol were controlled according to the closed-loop system feedbackof the auditory evoked potential index to render modeling unbiased bythe level of anesthesia. Time to open eyes was measured to representwakeup time after surgery. The NETICA version 1.37 software was used ona personal computer for network building, validation, and prediction.Results.After the learning phase, the network was used togenerate a series of random cases whose probability distribution matchesthat of the compiled network. The sampling algorithms used are precise,so that the frequencies of the simulated cases will exactly approach theprobabilities of the network and that of the data learned. Thegraphical display of the predicted wakeup time shows less variabilitybut a more complex interaction pattern than with the unadjusted originaldata. Conclusions.Model building and evaluation with Bayesiannetworks does not depend on underlying linear relationships. Bayesianrelationships represent true features of the represented data sample.Data may be sparse, uncertain, stochastic, or imprecise. Multipleplatform 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 thecomplex interaction of anesthetics.