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Dose-dependent effects of ketamine on processed electroencephalographic depth-of-hypnosis indices have been reported. Limited data are available for the NeuroSENSE WAVCNS index. Our aim was to establish the feasibility of closed-loop propofol-remifentanil anesthesia guided by the WAVCNS index in the presence of an analgesic dose of ketamine. Thirty ASA I-II adults, 18-54 years, requiring general anesthesia for anterior cruciate ligament surgery were randomized to receive: full-dose [ketamine, 0.5 mg kg-1 initial bolus, 10 mcg kg-1 min-1 infusion] (recommended dose for postoperative pain management); half-dose [ketamine, 0.25 mg kg-1 bolus, 5 mcg kg-1 min-1 infusion]; or control [no ketamine]. After the ketamine bolus, patients received 1.0 mcg kg-1 remifentanil over 30 s, then 1.5 mg kg-1 propofol over 30 s, followed by manually-adjusted propofol-remifentanil anesthesia. The WAVCNS was > 60 for 7/9 patients in the full-dose group at 7 min after starting the propofol infusion. This was inconsistent with clinical observations of depth-of-hypnosis and significantly higher than control (median difference [MD] 17.0, 95% confidence interval [CI] 11.4-26.8). WAVCNS was median [interquartile range] 49.3 [42.2-62.6] in the half-dose group, and not different to control (MD 5.1, 95% CI - 4.9 to 17.9). During maintenance of anesthesia, the WAVCNS was higher in the full-dose group compared to control (MD 14.7, 95% CI 10.2-19.2) and in the half-dose group compared to control (MD 11.4, 95% CI 4.7-20.4). The full-dose of ketamine recommended for postoperative pain management had a significant effect on the WAVCNS. This effect should be considered when using the WAVCNS to guide propofol-remifentanil dosing.Trial Registration ClinicalTrails.gov No. NCT02908945. [ABSTRACT FROM AUTHOR]

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]

Objective: The goal of this paper was to optimize robust PID control for propofol anesthesia in children aged 5–10 years to improve performance, particularly to decrease the time of induction of anesthesia while maintaining robustness. Methods: We analyzed results of a previous study conducted by our group to identify opportunities for system improvement. Allometric scaling was introduced to reduce the interpatient variability and a new robust PID controller was designed using an optimization-based method. We evaluated this optimized design in a clinical study involving 16 new cases. Results: The optimized controller design achieved the performance predicted in simulation studies in the design stage. Time of induction of anesthesia was median [Q1, Q3] 3.7 [2.3, 4.1] min and the achieved global score was 13.4 [9.9, 16.8]. Conclusion: Allometric scaling reduces the interpatient variability in this age group and allows for improved closed-loop performance. The uncertainty described by the model set, the predicted closed-loop responses, and the predicted robustness margins are realistic. The system meets the design objectives of improved speed of induction of anesthesia while maintaining robustness and improving clinically relevant system behavior. Significance: Control system optimization and ongoing system improvements are essential to the development of a clinically relevant commercial device. This paper demonstrates the validity of our approach, including system modeling, controller optimization, and pre-clinical testing in simulation. [ABSTRACT FROM AUTHOR]

Sedation in the intensive care unit (ICU) is challenging, as both over- and under-sedation are detrimental. Current methods of assessment, such as the Richmond Agitation Sedation Scale (RASS), are measured intermittently and rely on patients' behavioral response to stimulation, which may interrupt sleep/rest. A non-stimulating method for continuous sedation monitoring may be beneficial and allow more frequent assessment. Processed electroencephalography (EEG) monitors have not been routinely adopted in the ICU. The aim of this observational study was to assess the feasibility of using the NeuroSENSE™ monitor for EEG-based continuous sedation assessment. With ethical approval, ICU patients on continuous propofol sedation were recruited. Depth-of-hypnosis index (WAVCNS) values were obtained from the NeuroSENSE. Bedside nurses, blinded to the NeuroSENSE, performed regular RASS assessments and maintained the sedation regimen as per standard of care. Participants were monitored throughout the duration of their propofol infusion, up to 24 h. Fifteen patients, with median [interquartile range] age of 57 [52-62.5] years were each monitored for a duration of 9.0 [5.7-20.1] h. Valid WAVCNS values were obtained for 89% [66-99] of monitoring time and were widely distributed within and between individuals, with 6% [1-31] spent < 40 (very deep), and 3% [1-15] spent > 90 (awake). Significant EEG suppression was detected in 3/15 (20%) participants. Observed RASS matched RASS goals in 36/89 (40%) assessments. The WAVCNS variability, and incidence of EEG suppression, highlight the limitations of using RASS as a standalone sedation measure, and suggests potential benefit of adjunct continuous brain monitoring. [ABSTRACT FROM AUTHOR]

This paper describes the design and evaluation of a controller for multi-input single-output (MISO) propofol-remifentanil anesthesia, guided by feedback from a measure of depth-of-hypnosis (DOH). DOH monitors are commonly used in clinical practice to guide anesthetic dosing, however, there is currently no widely accepted nociception monitor to guide remifentanil (analgesic) infusion. Variability in the DOH measure has been associated with insufficient analgesia, and feasibility of closed-loop control of both propofol and remifentanil infusion using DOH feedback has been demonstrated. However, DOH variability does not provide a measure of analgesia in the absence of stimulation. Consequently, control of the opioid-hypnotic balance is lost in control systems relying on DOH feedback alone. The proposed design overcomes this limitation by introducing a second, indirect control objective. This paper defines clinical design specifications to achieve adequate anesthesia in a wide range of clinical cases, proposes a modification of the habituating control framework, and presents methods to translate the clinical objectives into control objectives within this framework. The developed design methodology provides a controller that: 1) meets the clinical objectives; 2) is robust to interpatient variability, both in single-input single-output and MISO operation; 3) is robust to nonlinear drug interactions; 4) gives the user control of the opioid-hypnotic balance in the absence of stimulation and in the presence of input saturation; and 5) improves disturbance rejection following nociceptive stimulation. The MISO system performed as designed in 80 clinical cases. [ABSTRACT FROM AUTHOR]

This paper describes the design of a robust proportional-integral derivative (PID) controller for propofol infusion in children and presents the results of clinical evaluation of this closed-loop system during endoscopic investigations in children age 6–17. The controller design is based on a set of models that describes the interpatient variability in the response to propofol infusion in the study population. The PID controller is tuned to achieve sufficient robustness margins for the identified uncertainty. 108 children were enrolled in this study, and anesthesia was closed-loop controlled in 102 of these cases. Clinical evaluation of the system shows that closed-loop control of both induction and maintenance of anesthesia in children based on the WAVCNS index as a measure of clinical effect is feasible. A robustly tuned PID controller can accommodate the interpatient variability in children and spontaneous breathing can be maintained in most subjects. [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]

One of the difficulties in the development of a reliable artificial pancreas for people with type 1 diabetes mellitus (T1DM) is the lack of accurate models of an individual’s response to insulin. Most control algorithms proposed to control the glucose level in subjects with T1DM are model-based. Avoiding postprandial hypoglycemia (<60 mg/dl) while minimizing prandial hyperglycemia (>180 mg/dl) has shown to be difficult in a closed-loop setting due to the patient–model mismatch. In this paper, control-relevant models are developed for T1DM, as opposed to models that minimize a prediction error. The parameters of these models are chosen conservatively to minimize the likelihood of hypoglycemia events. To limit the conservatism due to large intersubject variability, the models are personalized using a priori patient characteristics. The models are implemented in a zone model predictive control algorithm. The robustness of these controllers is evaluated in silico, where hypoglycemia is completely avoided even after large meal disturbances. The proposed control approach is simple and the controller can be set up by a physician without the need for control expertise. [ABSTRACT FROM PUBLISHER]

This paper studies application of different constrained control concepts for the control of Depth of Hypnosis (DOH) in closed-loop anesthesia to guarantee patient safety, while ensuring acceptable tracking performance. The core idea is to formulate Overdosing (OD) prevention and Blood Pressure Decrease (BPD) prevention as operational constraints, and then use a constrained control scheme to enforce the constraints satisfaction at all times. In this paper, three methods are studied: (1) Explicit Reference Governor (ERG), (2) Safety Preserving Control (SPC), and (3) Model Predictive Control (MPC). The performance of the methods is assessed with respect to a simulated surgical procedure for 44 patients whose models have been identified using clinical data. In particular, three realistic clinical scenarios are studied in this paper: (1) normal mode, (2) stimulation, and (3) low clearance. The results demonstrate cons and pros of each method in closed-loop anesthesia according to clinically relevant assessments. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had an enormous toll on human health and well-being and led to major social and economic disruptions. Public health interventions in response to burgeoning case numbers and hospitalizations have repeatedly bent down the epidemic curve, effectively creating a feedback control system. Worst case scenarios have been avoided in many places through this responsive feedback. We aim to formalize and illustrate how to incorporate principles of feedback control into pandemic projections and decision-making, and ultimately shift the focus from prediction to the design of interventions. Starting with an epidemiological model for COVID-19, we illustrate how feedback control can be incorporated into pandemic management using a simple design that couples recent changes in case numbers or hospital occupancy with explicit policy restrictions. We demonstrate robust ability to control a pandemic using a design that responds to hospital cases, despite simulating large uncertainty in reproduction number R0 (range: 1.04-5.18) and average time to hospital admission (range: 4-28 days). We show that shorter delays, responding to case counts versus hospital measured infections, reduce both the cumulative case count and the average level of interventions. Finally, we show that feedback is robust to changing compliance to public health directives and to systemic changes associated with variants of concern and with the introduction of a vaccination program. The negative impact of a pandemic on human health and societal disruption can be reduced by coupling models of disease propagation with models of the decision-making process. In contrast to highly varying open-loop projections, incorporating feedback explicitly in the decision-making process is more reflective of the real-world challenge facing public health decision makers. Using feedback principles, effective control strategies can be designed even if the pandemic characteristics are highly uncertain, encouraging earlier and smaller actions that reduce both case counts and the extent of interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 van Heusden et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, inter-patient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic-pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.Clinical relevance-The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing.

In safety-critical control systems, such as closed-loop control of anesthesia, it is of utmost importance to guarantee the ability of a control approach to maintain states of the systems within a safe region. In this paper, we address the problem by applying a safety-preserving control technique to anesthesia control. The approach relies on a conservative approximation of the viability set. The set specifies initial states for which there exists an input that keeps the trajectory emanating from those states within the safe region. This approach can be applied to any type of controller which satisfies the performance criteria. Furthermore, it prevents the performance controller from taking the states out of the safe region.