Feasibility of Closed-loop Titration of Propofol and Remifentanil Guided by the Spectral M-Entropy Monitor

Liu, Ngai; Guen, Morgan Le; Benabbes-Lambert, F; Chazot, Thierry; Trillat, Bernard; Sessler, Daniel I.; Fischler, Marc

doi:10.1097/aln.0b013e318242ad4f

Cited by 84 publications

(53 citation statements)

References 47 publications

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“…The most commonly used control signal is the Bispectral Index (BIS) [26]–[40]. Other control signals include an auditory evoked potential index [41], the spectrogram median frequency [24], [42], [43], a wavelet-based index [44] and an entropy measure [45]. Both standard and non-standard control paradigms [24], [27], [35], [41], [45] have been used in these systems with the principal objective being control of unconsciousness [24], [26]–[32], .…”

Section: Discussionmentioning

confidence: 99%

A Brain-Machine Interface for Control of Medically-Induced Coma

et al. 2013

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Medically-induced coma is a drug-induced state of profound brain inactivation and unconsciousness used to treat refractory intracranial hypertension and to manage treatment-resistant epilepsy. The state of coma is achieved by continually monitoring the patient's brain activity with an electroencephalogram (EEG) and manually titrating the anesthetic infusion rate to maintain a specified level of burst suppression, an EEG marker of profound brain inactivation in which bursts of electrical activity alternate with periods of quiescence or suppression. The medical coma is often required for several days. A more rational approach would be to implement a brain-machine interface (BMI) that monitors the EEG and adjusts the anesthetic infusion rate in real time to maintain the specified target level of burst suppression. We used a stochastic control framework to develop a BMI to control medically-induced coma in a rodent model. The BMI controlled an EEG-guided closed-loop infusion of the anesthetic propofol to maintain precisely specified dynamic target levels of burst suppression. We used as the control signal the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state. We characterized the EEG response to propofol using a two-dimensional linear compartment model and estimated the model parameters specific to each animal prior to initiating control. We derived a recursive Bayesian binary filter algorithm to compute the BSP from the EEG and controllers using a linear-quadratic-regulator and a model-predictive control strategy. Both controllers used the estimated BSP as feedback. The BMI accurately controlled burst suppression in individual rodents across dynamic target trajectories, and enabled prompt transitions between target levels while avoiding both undershoot and overshoot. The median performance error for the BMI was 3.6%, the median bias was -1.4% and the overall posterior probability of reliable control was 1 (95% Bayesian credibility interval of [0.87, 1.0]). A BMI can maintain reliable and accurate real-time control of medically-induced coma in a rodent model suggesting this strategy could be applied in patient care.

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Section: Discussionmentioning

confidence: 99%

A Brain-Machine Interface for Control of Medically-Induced Coma

et al. 2013

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“…Several prototype closed-loop systems for general anesthesia have been tested in pilot studies [27–31, 66, 67, 70–73]. Systems developed to date have relied on a variety of computed EEG features, including wavelet coefficients [72], median spectral frequency [74], BIS [27, 28, 75–77], and signal entropy [78]. Some of these derived EEG signals have recently come under criticism for being unphysiological and for being unreliable indicators of depth of anesthesia [36, 79–81].…”

Section: Discussionmentioning

confidence: 99%

Robust control of burst suppression for medical coma

et al. 2015

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Objective Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by burst suppression. Feedback control can be used to regulate burst suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma. Approach We developed a robust CLAD system to control the burst suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment. Main results In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]%. Under steady state conditions the CLAD system exhibited a median error of 0.1 [−0.5, 0.9]%; inaccuracy of 1.8 [0.9, 3.4]%; oscillation index of 1.8 [0.9, 3.4]%; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg−1. The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg−1 h−1. Performance fell within clinically acceptable limits for all measures. Significance A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.

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“…Chen and colleagues [ 4 ] used the SSI for guidance of remifentanil administration, which resulted in lower remifentanil consumption, more stable hemodynamics, and a lower incidence of unwanted events. Liu et al [ 18 ] demonstrated that an intraoperative automated control of hypnosis and analgesia guided by M-Entropy analysis with differential sensitivities to hypnosis and analgesia is clinically feasible and more precise than skilled manual control.…”

Section: Discussionmentioning

confidence: 99%