Nonlinear model-based predictive control of non-depolarizing muscle relaxants using XXXX .

@entity1 @entity4094 @entity24478 @entity1274 @entity616
@entity4094 can be relatively easily measured in the clinical setting. Consequently, closed-loop control can be exercised by measuring the neuromuscular activity, calculating the dose of drug necessary to achieve a predefined degree of @entity4094 and finally directing an infusion pump. Recently introduced short-acting @entity616 agents like @entity24478 provide benefits for the clinical routine due to a small onset time and half life. In order to provide a stable blockade for different groups of @entity1 a fast and highly adaptable control unit is needed. Furthermore its development should not imply costly investigations for determining a pharmacological model. The fulfilling of these requirements yield a self-adapting model-based predictive control system. The application of artificial @entity1274 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 Grasepy 3500 infusion pump for i.v. administration of mivacurium to 35 @entity1 (ASA I-III). The performance of the novel system (mean of the T1 error: -0.32 +/- 1.7) compares favourably with closed-loop controllers demonstrated in the past. These promising results and the easy adaption to other @entity616 agents encourage to apply this technology even for delivering hypnotic drugs.
