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This brief introduces a novel safety-preserving control scheme with minimal conservatism for uncertain systems. We have recently introduced the model-invariant safety verification technique that provides a formal guarantee of safety for systems with multiplicative model uncertainty. This approach requires a multi-model description of model uncertainty. The resulting safety system may be conservative for systems that do not exhibit the worst case dynamical response. In this brief, we employ model falsification to reduce conservatism of the model-invariant safety verification technique. Members of a model set that characterizes model uncertainty are falsified if discrepancy between predictions of those models and measured responses of the uncertain system is established, thereby reducing model uncertainty. To demonstrate the effectiveness of the proposed technique, we formalize a model-invariant safety system for closed-loop propofol anesthesia. The safety system maintains predicted propofol concentration in plasma as well as the patient’s blood pressure within safety bounds despite uncertainty in patient responses to propofol. [ABSTRACT FROM AUTHOR]

Feasibility of closed-loop propofol anesthesia has been demonstrated in clinical trials; however, no closed-loop system is currently routinely used in clinical practice. Data from closed-loop clinical trials provide valuable information for system evaluation prior to market approval, including outlier behavior. To exploit this valuable data, closed-loop identification in the presence of nonzero disturbances is required. Disturbances due to stimulation are not zero mean and will cause bias in models identified from clinical data. In this paper, we use multiple-input single-output (MISO) closed-loop identification to take the response to disturbances during induction of anesthesia into account. It is shown that direct MISO output-error identification introduces limited bias for this specific closed-loop identification problem and the use of more complex closed-loop identification methods does not improve model accuracy. Using this approach, we identify and validate a set of models that describes both the blood pressure and the depth-of-hypnosis response to propofol infusion for patients at risk of cardiovascular suppression. Quantification of both these responses for the same patients provides a valuable basis for the design and evaluation of constrained control systems. [ABSTRACT FROM AUTHOR]

Abstract Closed-loop anesthesia drug delivery systems can reduce the effect of inter-patient variability, minimize variability in desired clinical effects, and ultimately improve patient safety. Feasibility of closed-loop anesthesia has been clinically evaluated in a number of studies. To obtain regulatory approval to use closed-loop anesthesia as a clinical device, patient safety must be demonstrated. Safety systems for closed-loop anesthesia have been proposed to limit anesthetic concentrations in the plasma and effect-site. Such safety systems minimize the risk of drug under/overdosing. Anesthetic drugs have serious side effects and can significantly reduce blood pressure, thereby jeopardizing patient safety. This paper discusses a safety system that includes safety constraints on blood pressure in addition to the plasma and effect-site concentrations. The proposed safety system is formalized using formal model verification techniques. The performance of closed-loop anesthesia in the presence of constraints on the plasma and effect-site concentrations as well as blood pressure is shown using simulation results. The effectiveness of the proposed safety system is also illustrated in simulation using a realistic clinical scenario. This paper shows that maintaining blood pressure within safety constraints increases the time for anesthesia induction, especially for patients whose blood pressure is more sensitive to anesthetics. For this population, clinical intervention such as administration of vasoactive drugs may be required to counter the effect of propofol on blood pressure during closed-loop anesthesia to achieve a safe yet sufficiently fast induction. This paper extends a previously published formalized safety system which guarantees that the plasma and effect-site concentrations remain within safety limits (Yousefi et al., 2017). Highlights • A formal proof of safety for closed-loop propofol anesthesia is provided. • Closed-loop anesthesia can be performed with minimum risk of drug over/under dosing and hypotension. • Limiting blood pressure increases the induction time of anesthesia. • Administration of vasoactive drugs may be required to have a fast induction of anesthesia while avoiding hypotension. [ABSTRACT FROM AUTHOR]

Closed-loop control of anesthesia is expected to decrease drug dosage and wake up time while increasing patient safety and decreasing the work load of the anesthesiologist. The potential of closed-loop control in anesthesia has been demonstrated in several clinical studies. One of the challenges in the development of a closed-loop system that can be widely accepted by clinicians and regulatory authorities is the effect of interpatient variability in drug sensitivity. This system uncertainty may lead to unacceptable performance, or even instability of the closed-loop system for some individuals. The development of reliable models of the effect of anesthetic drugs and characterization of the uncertainty is, therefore, an important step in the development of a closed-loop system. Model identification from clinical data is challenging due to limited excitation and the lack of validation data. In this paper, approximate models are validated for controller design by evaluating the predictive accuracy of the closed-loop behavior. A set of 47 validated models that describe the interpatient variability in the response to propofol in children is presented. This model set can be used for robust linear controller design provided that the experimental conditions are similar to the conditions during data collection. [ABSTRACT FROM PUBLISHER]