Bayesian probabilistic network modeling of remifentanil and propofol interaction on wakeup time after closed-loop controlled anesthesia.

Publisher: Springer Country of Publication: Netherlands NLM ID: 9806357 Publication Model: Print Cited Medium: Print ISSN: 1387-1307 (Print) Linking ISSN: 13871307 NLM ISO Abbreviation: J Clin Monit Comput Subsets: MEDLINE

Publication: Amsterdam : Springer
Original Publication: Dordrecht, The Netherlands ; Boston : Kluwer Academic Publishers, c1998-

Anesthesia Recovery Period* ; Anesthesia, Closed-Circuit* ; Anesthetics, Intravenous* ; Bayes Theorem* ; Piperidines* ; Propofol*; Humans ; Remifentanil

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.

Anesth Analg. 1999 Nov;89(5):1311-5. (PMID: 10553858)
J Clin Epidemiol. 1996 Nov;49(11):1225-31. (PMID: 8892489)
Clin Pharmacokinet. 2000 Feb;38(2):181-90. (PMID: 10709777)
BMJ. 1999 Aug 21;319(7208):508-12. (PMID: 10454409)
Anaesthesia. 1984 Dec;39(12):1168-71. (PMID: 6335003)
Stat Med. 1996 Feb 28;15(4):361-87. (PMID: 8668867)
Anaesthesia. 1998 Apr;53(4):320-5. (PMID: 9613295)
Br J Anaesth. 1999 Aug;83(2):223-8. (PMID: 10618933)
Anesthesiology. 1997 Jan;86(1):10-23. (PMID: 9009935)
Anesthesiology. 1998 Sep;89(3):562-73. (PMID: 9743391)
Anesthesiology. 1997 Dec;87(6):1549-62. (PMID: 9416739)
Br J Anaesth. 1997 Feb;78(2):180-4. (PMID: 9068338)
Anaesthesia. 1997 Nov;52(11):1030-6. (PMID: 9404162)
Br J Anaesth. 1995 Nov;75(5):562-6. (PMID: 7577281)
Clin Pharmacokinet. 1995 Aug;29(2):80-94. (PMID: 7586903)
BMJ. 1999 Aug 28;319(7209):557-60. (PMID: 10463900)
Br J Anaesth. 1984 Jun;56(6):613-6. (PMID: 6609708)

0 (Anesthetics, Intravenous)
0 (Piperidines)
P10582JYYK (Remifentanil)
YI7VU623SF (Propofol)

Date Created: 20020710 Date Completed: 20021219 Latest Revision: 20191106

20231215

10.1023/a:1015492919566

12102247