Robust control of burst suppression for medical coma

Abstract

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.

Design, implementation, and evaluation of a physiological closed-loop control device for medically-induced coma

“…The algorithms used in our PCLC device are based on established frameworks for closed-loop control of medically-induced coma[10]–[13]. We use a threshold-based mechanism for EEG artifact reduction[19].…”

“…A previously validated recursive algorithm is used to identify periods of suppression based on EEG variance in real-time[14]. Finally, an algorithm infers BSPs from the binary observations of bursts and suppressions, based on a state-space model of propofol pharmacokinetics and pharmacodynamics (PK-PD)[13]. For control, the inferred BSP is compared with a target BSP provided by the operator of the system.…”

“…The difference between the inferred and target BSP is fed into a robust proportional-integral (PI) controller, which adjusts the infusion rate of propofol. The robust PI controller is tuned for individual patients to optimize performance and minimize sensitivity to disturbances[13]. Patient-specific parameters used for controller design and BSP inference are obtained from the Schnider model of propofol PK and a ramp-drop procedure designed to capture PD characteristics of individuals[13].…”

“…The robust PI controller is tuned for individual patients to optimize performance and minimize sensitivity to disturbances[13]. Patient-specific parameters used for controller design and BSP inference are obtained from the Schnider model of propofol PK and a ramp-drop procedure designed to capture PD characteristics of individuals[13]. Specifically, the Schnider model produces customized PK parameters based on patients’ mass, age, sex and height; and PD modeling uses an iterative least-squares algorithm to find the parameters of a sigmoidal PD curve that describes the BSPs as a function of the steady state propofol concentration in the brain[20].…”

Model-based drug administration

“…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].…”