Nonlinear model-based predictive control of non-depolarizing muscle relaxants using neural networks

Neuromuscular blockade can be relatively easily measured in the clinical se tting. Consequently, closed-loop control can be exercised by measuring the neuromuscular activity, calculating the dose of drug necessary to achieve a predefined degree of neuromuscular blockade and finally directing an infus ion pump. Recently introduced short-acting blocking agents like mivacurium provide benefits for the clinical routine due to a small onset time and hal f life. In order to provide a stable blockade for different groups of patie nts a fast and highly adaptable control unit is needed. Furthermore its dev elopment should not imply costly investigations for determining a pharmacol ogical model. The fulfilling of these requirements yield a self-adapting mo del-based predictive control system. The application of artificial neural n etworks allows an appropriate adjustment of specific parameters without the knowledge of inner pharmacodynamic processes. In a clinical study the EMG module within a Datex AS/3 monitor was used to measure the blockade and a G rasepy 3500 infusion pump for i.v. administration of mivacurium to 35 patie nts (ASA I-III). The performance of the novel system (mean of the T-1 error : -0.32 +/- 1.7) compares favourably with closed-loop controllers demonstra ted in the past. These promising results and the easy adaption to other blo cking agents encourage to apply this technology even for delivering hypnoti c drugs.