Search

Background: Closed-loop control of the hypnotic component of anesthesia has been proposed in an attempt to optimize drug delivery. Here, we introduce a newly developed Bayesian-based, patient-individualized, model-based, adaptive control method for bispectral index (BIS) guided propofol infusion into clinical practice and compare its accuracy and clinical feasibility under direct observation of an anesthesiologist versus BIS guided, effect compartment controlled propofol administration titrated by the anesthesiologist during ambulatory gynecological procedures., Methods: Forty ASA patients were randomly allocated to the closed-loop or manual control group. All patients received midazolam 1 mg IV and alfentanil 0.5 mg IV before induction. In the closed-loop control group, propofol was administered using the previously described closed-loop control system to reach and maintain a target BIS of 50. In the manual control group, the propofol effect-site concentration was adapted at the discretion of the anesthesiologist to reach and maintain a BIS as close as possible to 50. Induction characteristics, performance, and robustness during maintenance and recovery times were compared. Hemodynamic and respiratory stability were calculated as clinical feasibility parameters., Results: The closed-loop control system titrated propofol administration accurately resulting in BIS values close to the set point. The closed-loop control system was able to induce the patients within clinically accepted time limits and with less overshoot than the manual control group. Automated control resulted in beneficial recovery times. Our closed-loop control group showed similar acceptable clinical performance specified by similar hemodynamic, respiratory stability, comparable movement rates, and quality scores than the manual control group., Conclusions: The Bayesian-based closed-loop control system for propofol administration using the BIS as a controlled variable performed accurate during anesthesia for ambulatory gynecological procedures. This control system is clinical feasibility and can be further validated in clinical practice.

Background: Implementing Bayesian methods in a model-based closed-loop system requires the integration of a standard response model with a patient-specific response model. This process makes use of specific modeling weights, called Bayesian variances, which determine how the specific model can deviate from the standard model. In this study we applied simulations to select the Bayesian variances yielding the optimal controller for a Bayesian-based closed-loop system for propofol administration using the Bispectral Index (BIS) as a controlled variable., Methods: The relevant Bayesian variances determining the modeling process were identified. Each set of such Bayesian variances represents a potential controller. The set, which will result in optimal control, was estimated using calculations on a simulated population. We selected 625 candidate sets. Similar to our previous closed-loop performance study, we applied a simulation protocol to evaluate controller performance. Our population consisted of 416 virtual patients, generated using population characteristics from previous work. A BIS offset trajectory similar to a surgical case was used., Results: We were able to develop, describe, and optimize the parameter setting for a patient-individualized model-based closed-loop controller using Bayesian optimization. Selection of the optimal set yields a controller performing with the following median absolute prediction errors at BIS targets 30, 50, and 70: 12.9 +/- 2.87, 7.59 +/- 0.74, and 5.76 +/- 1.03 respectively., Conclusions: We believe this system can be introduced safely into clinical testing for both induction and maintenance of anesthesia under direct observation of an anesthesiologist.

Background: Although automated closed-loop control systems may improve quality of care, their safety must be proved under extreme control conditions. This study describes a simulation methodology to test automated controllers and its application in a comparison of two published controllers for Bispectral Index (BIS)-guided propofol administration., Methods: A patient simulator was developed to compare controllers. Using input scripts to dictate patient characteristics, target BIS values, and the time course of surgical events, the simulator continuously monitors the infusion pump under control and generates BIS values as a composite of modeled response to drug, perceived stimulation, and random noise. The simulator formats the output stream of BIS data as input to the controller under test to emulate the serial output of the actual BIS monitor. A published model-based controller and a classic proportional integral derivative controller were compared when using the BIS value as a controlled variable. Each controller was tested using a set of 10 virtual patients undergoing a fixed surgical profile that was repeated with BIS targets set at 30, 50, and 70. Controller performance was assessed using median (absolute) prediction error, divergence, wobble, and percentage time within BIS target range metrics., Results: The median prediction error was significantly smaller for the proportional integral derivative controller than for the model-based controller. The median absolute prediction error was smaller for the model-based controller than for the proportional integral derivative controller for each BIS target, reaching statistical significance for targets 30 and 50., Conclusions: When simulating closed-loop control of BIS using propofol, the use of a patient-individualized, model-based adaptive closed-loop system with effect site control resulted in better control of BIS compared with a standard proportional integral derivative controller with plasma site control. Even under extreme conditions, the modeled-based controller exhibited no behavioral problems.