Automated control of anesthesia ten years later: futuristic novelty or present day reality

Glass, Peter S. A.

doi:10.1007/s12630-010-9336-y

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…24 There have been several clinical studies of CLAD systems and versions are now commercially available. 37 The most frequently used control signal has been the Bispectral Index (BIS). 7-9,11,13,14,17,18,21-23,38-40 Other control signals have included a wavelet-based index, 12 entropy measures, 16 an auditory evoked potential index 15 and the spectrogram median frequency.…”

Section: Discussionmentioning

confidence: 99%

Real-time Closed-loop Control in a Rodent Model of Medically Induced Coma Using Burst Suppression

et al. 2013

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Background A medically-induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection following traumatic brain injuries. We hypothesized that a closed-loop anesthetic delivery system could automatically and precisely control the electroencephalogram state of burst suppression and efficiently maintain a medically-induced coma. Methods In six rats, we implemented a closed-loop anesthetic delivery system for propofol consisting of: a computer-controlled pump infusion, a two-compartment pharmacokinetics model defining propofol’s electroencephalogram effects, the burst suppression probability algorithm to compute in real time from the electroencephalogram the brain’s burst suppression state, an on-line parameter estimation procedure and a proportional-integral controller. In the control experiment each rat was randomly assigned to one of the six burst suppression probability target trajectories constructed by permuting the burst suppression probability levels of 0.4, 0.65 and 0.9 with linear transitions between levels. Results In each animal the controller maintained approximately 60 min of tight, real-time control of burst suppression by tracking each burst suppression probability target level for 15 min and two between-level transitions for 5 to 10 min. The posterior probability that the closed-loop anesthetic delivery system was reliable across all levels was 0.94 [95% confidence interval; (0.77 to 1.00) n = 18] and that the system was accurate was 1.00 [95% confidence interval; (0.84 to 1.00) n = 18]. Conclusion Our findings establish the feasibility of using a closed-loop anesthetic delivery systems to achieve in real-time reliable and accurate control of burst suppression in rodents and suggest a paradigm to precisely control medically-induced coma in patients.

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Section: Discussionmentioning

confidence: 99%

Real-time Closed-loop Control in a Rodent Model of Medically Induced Coma Using Burst Suppression

et al. 2013

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“…Finally, to implement the controller as shown in figure 3, x 2,k|k from equation ( 9) provides the estimate of x 2 (t ) in equation (13). We compute the input u k|k by discretizing equation (12) as…”

Section: Design Of a Pi Controllermentioning

confidence: 99%

“…However, system development did not begin in earnest until the 1980s [11]. Since then, there have been many clinical studies on the use of CLAD systems in anesthesiology practice, and systems are now commercially available [12]. Although most CLAD systems have focused on delivering anesthetics to achieve sedation or unconsciousness [1–6,8–11,13–26], a recent report studied control of unconsciousness and antinociception [7].…”

Section: Introductionmentioning

confidence: 99%

A closed-loop anesthetic delivery system for real-time control of burst suppression

et al. 2013

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Objective There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst suppression, in which bursts of electrical activity alternate with periods of quiescence or suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for burst suppression control to automate management of medically-induced coma. Approach We establish a CLAD system to control burst suppression consisting of: a two-dimensional linear system model relating the anesthetic brain level to the EEG dynamics; a new control signal, the burst suppression probability (BSP) defining the instantaneous probability of suppression; the BSP filter, a state-space algorithm to estimate the BSP from EEG recordings; a proportional-integral controller; and a system identification procedure to estimate the model and controller parameters. Main Results We demonstrate reliable performance of our system in simulation studies of burst suppression control using both propofol and etomidate in rodent experiments based on Vijn and Sneyd, and in human experiments based on the Schnider pharmacokinetic model for propofol. Using propofol, we further demonstrate that our control system reliably tracks changing target levels of burst suppression in simulated human subjects across different epidemiological profiles. Significance Our results give new insights into CLAD system design and suggest a control-theory framework to automate second-to-second control of burst suppression for management of medically-induced coma.

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“…Some systems have been developed, but they are not widely available. [7][8][9] In practice, however, depth of anesthesia is not adjusted according to just one variable, but rather to a constellation of factors, such as the hemodynamic state of the patient, the anticipated stimuli, and past response to the same drugs. Users of TCI devices will either increase or decrease the ''target concentration'' in response to perceived under-or over-dosing, respectively.…”

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confidence: 99%

“…Certains systèmes ont été mis au point mais ils sont peu disponibles. [7][8][9] Dans la pratique toutefois, la profondeur de l'anesthésie n'est pas ajustée en fonction d'une seule variable, mais plutôt d'une myriade de facteurs tels que l'état hémodynamique du patient, les stimuli anticipés, et des effets antérieurs des mêmes médicaments. Les utilisateurs de dispositifs d'AIVOC augmenteront ou au contraire réduiront la « concentration cible » en réponse à un sous-dosage ou à un surdosage, respectivement.…”

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Target-controlled infusion devices: are we missing much?

Donati

Miller

Fiset

2011

Can J Anesth/J Can Anesth

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Automated control of anesthesia ten years later: futuristic novelty or present day reality

Cited by 9 publications

References 14 publications

Real-time Closed-loop Control in a Rodent Model of Medically Induced Coma Using Burst Suppression

Real-time Closed-loop Control in a Rodent Model of Medically Induced Coma Using Burst Suppression

A closed-loop anesthetic delivery system for real-time control of burst suppression

Target-controlled infusion devices: are we missing much?

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