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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.)

Background: Closed-loop control of propofol-remifentanil anesthesia using the processed electroencephalography depth-of-hypnosis index provided by the NeuroSENSE monitor (WAVCNS) has been previously described. The purpose of this placebo-controlled study was to evaluate the performance (percentage time within ±10 units of the setpoint during the maintenance of anesthesia) of a closed-loop propofol-remifentanil controller during induction and maintenance of anesthesia in the presence of a low dose of ketamine., Methods: Following ethical approval and informed consent, American Society of Anesthesiologist (ASA) physical status I-II patients aged 19-54 years, scheduled for elective orthopedic surgery requiring general anesthesia for >60 minutes duration, were enrolled in a double-blind randomized, placebo-controlled, 2-group equivalence trial. Immediately before induction of anesthesia, participants in the ketamine group received a 0.25 mg·kg-1 bolus of intravenous ketamine over 60 seconds followed by a continuous 5 µg·kg-1·min-1 infusion for up to 45 minutes. Participants in the control group received an equivalent volume of normal saline. After the initial study drug bolus, closed-loop induction of anesthesia was initiated; propofol and remifentanil remained under closed-loop control until the anesthetic was tapered and turned off at the anesthesiologist's discretion. An equivalence range of ±8.99% was assumed for comparing controller performance., Results: Sixty patients participated: 41 males, 54 ASA physical status I, with a median (interquartile range [IQR]) age of 29 [23, 38] years and weight of 82 [71, 93] kg. Complete data were available from 29 cases in the ketamine group and 27 in the control group. Percentage time within ±10 units of the WAVCNS setpoint was median [IQR] 86.6% [79.7, 90.2] in the ketamine group and 86.4% [76.5, 89.8] in the control group (median difference, 1.0%; 95% confidence interval [CI] -3.6 to 5.0). Mean propofol dose during maintenance of anesthesia for the ketamine group was higher than for the control group (median difference, 24.9 µg·kg-1·min-1; 95% CI, 6.5-43.1; P = .005)., Conclusions: Because the 95% CI of the difference in controller performance lies entirely within the a priori equivalence range, we infer that this analgesic dose of ketamine did not alter controller performance. Further study is required to confirm the finding that mean propofol dosing was higher in the ketamine group, and to investigate the implication that this dose of ketamine may have affected the WAVCNS., Competing Interests: Conflicts of Interest: See Disclosures at the end of the article., (Copyright © 2021 International Anesthesia Research Society.)

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.

Background: Closed-loop control of anesthesia involves continual adjustment of drug infusion rates according to measured clinical effect. The NeuroSENSE monitor provides an electroencephalographic measure of depth of hypnosis (wavelet-based anesthetic value for central nervous system monitoring [WAVCNS]). It has previously been used as feedback for closed-loop control of propofol, in a system designed using robust control engineering principles, which implements features specifically designed to ensure patient safety. Closed-loop control of a second drug, remifentanil, may be added to improve WAVCNS stability in the presence of variable surgical stimulation. The objective of this study was to design and evaluate the feasibility of a closed-loop system for robust control of propofol and remifentanil infusions using WAVCNS feedback, with an infusion safety system based on the known pharmacological characteristics of these 2 drugs., Methods: With Health Canada authorization, research ethics board approval, and informed consent, American Society of Anesthesiologists I-III adults, requiring general anesthesia for elective surgery, were enrolled in a 2-phase study. In both phases, infusion of propofol was controlled in closed loop during induction and maintenance of anesthesia, using WAVCNS feedback, but bounded by upper- and lower-estimated effect-site concentration limits. In phase I, remifentanil was administered using an adjustable target-controlled infusion and a controller was designed based on the collected data. In phase II, remifentanil was automatically titrated to counteract rapid increases in WAVCNS., Results: Data were analyzed for 127 patients, of median (range) age 64 (22-86) years, undergoing surgical procedures lasting 105 (9-348) minutes, with 52 participating in phase I and 75 in phase II. The overall control performance indicator, global score, was a median (interquartile range) 18.3 (14.2-27.7) in phase I and 14.6 (11.6-20.7) in phase II (median difference, -3.25; 95% confidence interval, -6.35 to -0.52). The WAVCNS was within ±10 of the setpoint for 84.3% (76.6-90.6) of the maintenance of anesthesia in phase I and 88.2% (83.1-93.4) in phase II (median difference, 3.7; 95% confidence interval, 0.1-6.9). The lower propofol safety bound was activated during 30 of 52 (58%) cases in phase I and 51 of 75 (68%) cases in phase II., Conclusions: Adding closed-loop control of remifentanil improved overall controller performance. This controller design offers a robust method to optimize the control of 2 drugs using a single sensor. The infusion safety system is an important component of a robust automated anesthesia system, but further research is required to determine the optimal constraints for these safe conditions.

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.

A dip in blood pressure (BP) in response to head-up tilt (HUT) or active standing might be due to rapid pooling in the veins below the heart (preload) or muscle activation-induced drop in systemic vascular resistance (afterload). We hypothesized that, in the cardiovascular response to passive HUT, where, in contrast to active standing, little BP dip is observed, features affecting the preload play a key role. We developed a baroreflex model combined with a lumped-parameter model of the circulation, including viscoelastic stress-relaxation of the systemic veins. Cardiac contraction is modeled using the varying-elastance concept. Gravity affects not only the systemic, but also the pulmonary, circulation. In accordance with the experimental results, model simulations do not show a BP dip on HUT; the tilt-back response is also realistic. If it is assumed that venous capacities are steady-state values, the introduction of stress-relaxation initially reduces venous pooling. The resulting time course of venous pooling is comparable to measured impedance changes. When venous pressure-volume dynamics are neglected, rapid (completed within 30 s) venous pooling leads to a drop in BP. The direct effect of gravity on the pulmonary circulation influences the BP response in the first approximately 5 s after HUT and tilt back. In conclusion, the initial BP response to HUT is mainly determined by the response of the venous system. The time course of lower body pooling is essential in understanding the response to passive HUT.