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

Ching, ShiNung; Liberman, Max; Chemali, Jessica J.; Westover, M. Brandon; Kenny, Jonathan D.; Solt, Ken; Purdon, Patrick L.; Brown, Emery N.

doi:10.1097/aln.0b013e31829d4ab4

Cited by 56 publications

(84 citation statements)

References 44 publications

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“…Our burst suppression segmentation algorithm identifies bursts and suppressions in human EEG recordings with a performance comparable to human experts [20]. We have also successfully used this algorithm previously in two sets of burst suppression CLAD control experiments in animals [37, 48]. In the remainder of this work we therefore treat the binary observations as given.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…Instead, we use the BSP, a statistically rigorous measure of burst suppression depth [18]. We have used the BSP in our prior CLAD work for medical coma [37, 48]. …”

Section: Theory and Methodsmentioning

confidence: 99%

“…Several CLAD system designs for managing medical coma have previously been proposed and tested in computer simulations and animal models [17, 37, 47]. Early controllers were based on ad hoc designs, and, reported average rather than individualized results, and considered only constant target levels of anesthesia without investigating controller behavior in response to changes in target burst suppression ratio (BSR) values [17].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Theory and Methodsmentioning

confidence: 99%

“…Instead, we use the BSP, a statistically rigorous measure of burst suppression depth [18]. We have used the BSP in our prior CLAD work for medical coma [37, 48]. …”

Section: Theory and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust control of burst suppression for medical coma

et al. 2015

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“…The BSP was calculated using previously published methods. (Chemali et al 2011, 2013; Ching et al 2013)…”

Section: Methodsmentioning

confidence: 99%

The human burst suppression electroencephalogram of deep hypothermia

Westover

Ching

Kumaraswamy

et al. 2015

Clinical Neurophysiology

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Objective Deep hypothermia induces ‘burst suppression’ (BS), an electroencephalogram pattern with low-voltage ‘suppressions’ alternating with high-voltage ‘bursts’. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. Methods We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Results Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, burst lengths increase, and burst amplitudes and lengths decrease, while the spectral content of bursts remains constant. Conclusions These findings support an existing theoretical model in which the common mechanism of burst suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing burst amplitudes at lower temperatures. Significance These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying burst suppression.

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“…Additionally, automated titration of hypnotics and analgesics might decrease the clinician's workload during anaesthesia [54]. Closed-loop administration of propofol was also tested in a rodent animal model to induce coma using burst suppression of the EEG as a controlled variable [55,56].…”

Section: Closed-loop Controlmentioning

confidence: 99%