Closed loop control of anaesthesia: an assessment of the bispectral index as the target of control

Artificial intelligence and anesthesia: A narrative review

Rapid advances in Artificial Intelligence (AI) have led to diagnostic, therapeutic, and intervention-based applications in the field of medicine. Today, there is a deep chasm between AI-based research articles and their translation to clinical anesthesia, which needs to be addressed. Machine learning (ML), the most widely applied arm of AI in medicine, confers the ability to analyze large volumes of data, find associations, and predict outcomes with ongoing learning by the computer. It involves algorithm creation, testing and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes. AI-supported closed loops have been designed for pharmacological maintenance of anesthesia and hemodynamic management. Mechanical robots can perform dexterity and skill-based tasks such as intubation and regional blocks with precision, whereas clinical-decision support systems in crisis situations may augment the role of the clinician. The possibilities are boundless, yet widespread adoption of AI is still far from the ground reality. Patient-related “Big Data” collection, validation, transfer, and testing are under ethical scrutiny. For this narrative review, we conducted a PubMed search in 2020-21 and retrieved articles related to AI and anesthesia. After careful consideration of the content, we prepared the review to highlight the growing importance of AI in anesthesia. Awareness and understanding of the basics of AI are the first steps to be undertaken by clinicians. In this narrative review, we have discussed salient features of ongoing AI research related to anesthesia and perioperative care.

Do we have today a reliable method to detect the moment of loss of consciousness during induction of general anaesthesia?

This review aims to give an overview of the current state of monitoring depth of anaesthesia and detecting the moment of loss of consciousness, from the first clinical signs involved in anaesthesia to the latest technologies used in this area. Such techniques are extremely important for the development of automatic systems for anaesthesia control, including preventing intraoperative awareness episodes and overdoses. A search in the databases Pubmed and IEEE Xplore was performed using terms such anaesthetic monitoring, depth of anaesthesia, loss of consciousness, as well as anaesthesia indexes, namely BIS. Despite the several methods capable of monitoring the hypnotic state of anaesthesia, there is still no methodology to accurate detect the moment of loss of consciousness during induction of general anaesthesia.

Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control.

An Adaptive Monitoring Scheme for Automatic Control of Anaesthesia in dynamic surgical environments based on Bispectral Index and Blood Pressure

During surgical procedures, bispectral index (BIS) is a well-known measure used to determine the patient's depth of anesthesia (DOA). However, BIS readings can be subject to interference from many factors during surgery, and other parameters such as blood pressure (BP) and heart rate (HR) can provide more stable indicators. However, anesthesiologist still consider BIS as a primary measure to determine if the patient is correctly anaesthetized while relaying on the other physiological parameters to monitor and ensure the patient's status is maintained. The automatic control of administering anesthesia using intelligent control systems has been the subject of recent research in order to alleviate the burden on the anesthetist to manually adjust drug dosage in response physiological changes for sustaining DOA. A system proposed for the automatic control of anesthesia based on type-2 Self Organizing Fuzzy Logic Controllers (T2-SOFLCs) has been shown to be effective in the control of DOA under simulated scenarios while contending with uncertainties due to signal noise and dynamic changes in pharmacodynamics (PD) and pharmacokinetic (PK) effects of the drug on the body. This study considers both BIS and BP as part of an adaptive automatic control scheme, which can adjust to the monitoring of either parameter in response to changes in the availability and reliability of BIS signals during surgery. The simulation of different control schemes using BIS data obtained during real surgical procedures to emulate noise and interference factors have been conducted. The use of either or both combined parameters for controlling the delivery Propofol to maintain safe target set points for DOA are evaluated. The results show that combing BIS and BP based on the proposed adaptive control scheme can ensure the target set points and the correct amount of drug in the body is maintained even with the intermittent loss of BIS signal that could otherwise disrupt an automated control system.

Fully automated life support: an implementation and feasibility pilot study in healthy pigs

Background

Automated systems are available in various application areas all over the world for the purpose of reducing workload and increasing safety. However, such support systems that would aid caregivers are still lacking in the medical sector. With respect to workload and safety, especially, the intensive care unit appears to be an important and challenging application field. Whereas many closed-loop subsystems for single applications already exist, no comprehensive system covering multiple therapeutic aspects and interactions is available yet. This paper describes a fully closed-loop intensive care therapy and presents a feasibility analysis performed in three healthy pigs over a period of 72 h each to demonstrate the technical and practical implementation of automated intensive care therapy.

Methods

The study was performed in three healthy, female German Landrace pigs under general anesthesia with endotracheal intubation. An arterial and a central venous line were implemented, and a suprapubic urinary catheter was inserted. Electrolytes, glucose levels, acid-base balance, and respiratory management were completely controlled by an automated fuzzy logic system based on individual targets. Fluid management by adaption of the respective infusion rates for the individual parameters was included.

Results

During the study, no manual modification of the device settings was allowed or required. Homoeostasis in all animals was kept stable during the entire observation period. All remote-controlled parameters were maintained within physiological ranges for most of the time (free arterial calcium 73%, glucose 98%, arterial base excess 89%, and etCO₂ 98%). Subsystem interaction was analyzed.

Conclusions

In the presented study, we demonstrate the feasibility of a fully closed-loop system, for which we collected high-resolution data on the interaction and response of the different subsystems. Further studies should use big data approaches to analyze and investigate the interactions between the subsystems in more detail.

Closed-Loop Delivery Systems Versus Manually Controlled Administration of Total IV Anesthesia: A Meta-analysis of Randomized Clinical Trials

Bispectral Index Scale (BIS)-guided closed-loop delivery of anesthetics has been extensively studied. We performed a meta-analysis of all the randomized clinical trials comparing efficacy and performance between BIS-guided closed-loop delivery and manually controlled administration of total IV anesthesia. Scopus, PubMed, EMBASE, and the Cochrane Central Register of clinical trials were searched for pertinent studies. Inclusion criteria were random allocation to treatment and closed-loop delivery systems versus manually controlled administration of total IV anesthesia in any surgical setting. Exclusion criteria were duplicate publications and nonadult studies. Twelve studies were included, randomly allocating 1284 patients. Use of closed-loop anesthetic delivery systems was associated with a significant reduction in the dose of propofol administered for induction of anesthesia (mean difference [MD] = 0.37 [0.17-0.57], P for effect <0.00001, P for heterogeneity = 0.001, I = 74%) and a significant reduction in recovery time (MD = 1.62 [0.60-2.64], P for effect <0.0001, P for heterogeneity = 0.06, I = 47%). The target depth of anesthesia was preserved more frequently with closed-loop anesthetic delivery than with manual control (MD = -15.17 [-23.11 to -7.24], P for effect <0.00001, P for heterogeneity <0.00001, I = 83%). There were no differences in the time required to induce anesthesia and the total propofol dose. Closed-loop anesthetic delivery performed better than manual-control delivery. Both median absolute performance error and wobble index were significantly lower in closed-loop anesthetic delivery systems group (MD = 5.82 [3.17-8.46], P for effect <0.00001, P for heterogeneity <0.00001, I = 90% and MD = 0.92 [0.13-1.72], P for effect = 0.003, P for heterogeneity = 0.07, I = 45%). When compared with manual control, BIS-guided anesthetic delivery of total IV anesthesia reduces propofol requirements during induction, better maintains a target depth of anesthesia, and reduces recovery time.

Optimized PID control of depth of hypnosis in anesthesia

BACKGROUND AND OBJECTIVE

This paper addresses the use of proportional-integral-derivative controllers for regulating the depth of hypnosis in anesthesia by using propofol administration and the bispectral index as a controlled variable. In fact, introducing an automatic control system might provide significant benefits for the patient in reducing the risk for under- and over-dosing.

METHODS

In this study, the controller parameters are obtained through genetic algorithms by solving a min-max optimization problem. A set of 12 patient models representative of a large population variance is used to test controller robustness. The worst-case performance in the considered population is minimized considering two different scenarios: the induction case and the maintenance case.

RESULTS

Our results indicate that including a gain scheduling strategy enables optimal performance for induction and maintenance phases, separately. Using a single tuning to address both tasks may results in a loss of performance up to 102% in the induction phase and up to 31% in the maintenance phase. Further on, it is shown that a suitably designed low-pass filter on the controller output can handle the trade-off between the performance and the noise effect in the control variable.

CONCLUSIONS

Optimally tuned PID controllers provide a fast induction time with an acceptable overshoot and a satisfactory disturbance rejection performance during maintenance. These features make them a very good tool for comparison when other control algorithms are developed.

Fully Automated Anesthesia and Fluid Management Using Multiple Physiologic Closed-Loop Systems in a Patient Undergoing High-Risk Surgery

Automated delivery of anesthesia guided by processed electroencephalogram monitoring using a closed-loop system is no longer a novel concept. However, combining multiple independent physiologic closed-loop systems together has never been documented before. The purpose of this case report was to evaluate the feasibility of automated anesthesia and fluid management based on a combination of physiological variables (bispectral index, stroke volume, and stroke volume variations) using 2 independent closed-loop systems.

A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring

OBJECTIVE

Multiscale permutation entropy (MSPE) is becoming an interesting tool to explore neurophysiological mechanisms in recent years. In this study, six MSPE measures were proposed for on-line depth of anesthesia (DoA) monitoring to quantify the anesthetic effect on the real-time EEG recordings. The performance of these measures in describing the transient characters of simulated neural populations and clinical anesthesia EEG were evaluated and compared.

METHODS

Six MSPE algorithms-derived from Shannon permutation entropy (SPE), Renyi permutation entropy (RPE) and Tsallis permutation entropy (TPE) combined with the decomposition procedures of coarse-graining (CG) method and moving average (MA) analysis-were studied. A thalamo-cortical neural mass model (TCNMM) was used to generate noise-free EEG under anesthesia to quantitatively assess the robustness of each MSPE measure against noise. Then, the clinical anesthesia EEG recordings from 20 patients were analyzed with these measures. To validate their effectiveness, the ability of six measures were compared in terms of tracking the dynamical changes in EEG data and the performance in state discrimination. The Pearson correlation coefficient (R) was used to assess the relationship among MSPE measures.

RESULTS

CG-based MSPEs failed in on-line DoA monitoring at multiscale analysis. In on-line EEG analysis, the MA-based MSPE measures at 5 decomposed scales could track the transient changes of EEG recordings and statistically distinguish the awake state, unconsciousness and recovery of consciousness (RoC) state significantly. Compared to single-scale SPE and RPE, MSPEs had better anti-noise ability and MA-RPE at scale 5 performed best in this aspect. MA-TPE outperformed other measures with faster tracking speed of the loss of unconsciousness.

CONCLUSIONS

MA-based multiscale permutation entropies have the potential for on-line anesthesia EEG analysis with its simple computation and sensitivity to drug effect changes. CG-based multiscale permutation entropies may fail to describe the characteristics of EEG at high decomposition scales.

Neuroelectronics and Biooptics: Closed-Loop Technologies in Neurological Disorders

Brain-implanted devices are no longer a futuristic idea. Traditionally, therapies for most neurological disorders are adjusted based on changes in clinical symptoms and diagnostic measures observed over time. These therapies are commonly pharmacological or surgical, requiring continuous or irreversible treatment regimens that cannot respond rapidly to fluctuations of symptoms or isolated episodes of dysfunction. In contrast, closed-loop systems provide intervention only when needed by detecting abnormal neurological signals and modulating them with instantaneous feedback. Closed-loop systems have been applied to several neurological conditions (most notably epilepsy and movement disorders), but widespread use is limited by conceptual and technical challenges. Herein, we discuss how advances in experimental closed-loop systems hold promise for improved clinical benefit in patients with neurological disorders.

Feasibility of Closed-loop Titration of Propofol and Remifentanil Guided by the Bispectral Monitor in Pediatric and Adolescent Patients

Background:

This study was designed to assess the feasibility of dual closed-loop titration of propofol and remifentanil guided solely by the Bispectral Index (BIS) monitor in pediatric and adolescent patients during anesthesia.

Methods:

Children undergoing elective surgery in this single-blind randomized study were allocated into the closed-loop (auto) or manual (manual) group. Primary outcome was the percentage of time with the BIS in the range 40 to 60 (BIS40–60). Secondary outcomes were the percentage of deep (BIS&lt;40) anesthesia and drug consumption. Data are presented as median (interquartile range) or number (%).

Results:

Twenty-three patients (12 [10 to 14] yr) were assigned to the auto group and 19 (14 [7 to 14] yr) to the manual group. The closed-loop controller was able to provide induction and maintenance for all patients. The percentage of time with BIS40–60 was greater in the auto group (87% [75 to 96] vs. 72% [48 to 79]; P = 0.002), with a decrease in the percentage of BIS&lt;40 (7% [2 to 17] vs. 21% [11 to 38]; P = 0.002). Propofol (2.4 [1.9 to 3.3] vs. 1.7 [1.2 to 2.8] mg/kg) and remifentanil (2.3 [2.0 to 3.0] vs. 2.5 [1.2 to 4.3] μg/kg) consumptions were similar in auto versus manual groups during induction, respectively. During maintenance, propofol consumption (8.2 [6.0 to 10.2] vs. 7.9 [7.2 to 9.1] mg kg−1 h−1; P = 0.89) was similar between the two groups, but remifentanil consumption was greater in the auto group (0.39 [0.22 to 0.60] vs. 0.22 [0.17 to 0.32] μg kg−1 min−1; P = 0.003). Perioperative adverse events and length of stay in the postanesthesia care unit were similar.

Conclusion:

Intraoperative automated control of hypnosis and analgesia guided by the BIS is clinically feasible in pediatric and adolescent patients and outperformed skilled manual control.

Total intravenous anesthesia will supercede inhalational anesthesia in pediatric anesthetic practice

Inhalational anesthesia has dominated the practice of pediatric anesthesia. However, as the introduction of agents such as propofol, short-acting opioids, midazolam, and dexmedetomidine a monumental change has occurred. With increasing use, the overwhelming advantages of total intravenous anesthesia (TIVA) have emerged and driven change in practice. These advantages, outlined in this review, will justify why TIVA will supercede inhalational anesthesia in future pediatric anesthetic practice.

Lessons learned from closed loops in engineering: towards a multivariable approach regulating depth of anaesthesia

In this paper is presented a brief state of art regarding the multivariable formulation for controlling the depth of anaesthesia by means of two intravenously administrated drugs, i.e. propofol and remifentanil. In a feasibility study of determining a suitable variable to quantify analgesia levels in patients undergoing cardiac surgery, the bispectral index and an electromyogram-based surrogate variable are proposed as the controlled variables. The study is carried on in the context of implementing a multivariable predictive control algorithm. The simulation results show that such a paradigm is feasible, although it does not guarantee perfect knowledge of the analgesia level-in other words, the variable is not validated against typical evaluations of the pain levels (e.g. clinical scores).

A brain-machine interface for control of medically-induced coma

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.

Brain-machine interfaces (BMI) for closed-loop control of anesthesia have the potential to enable fully automated and precise control of brain states in patients requiring anesthesia care. Medically-induced coma is one such drug-induced state in which the brain is profoundly inactivated and unconscious and the electroencephalogram (EEG) pattern consists of bursts of electrical activity alternating with periods of suppression, termed burst suppression. Medical coma is induced to treat refractory intracranial hypertension and uncontrollable seizures. The state of coma is often required for days, making accurate manual control infeasible. We develop a BMI that can automatically and precisely control the level of burst suppression in real time in individual rodents. The BMI consists of novel estimation and control algorithms that take as input the EEG activity, estimate the burst suppression level based on this activity, and use this estimate as feedback to control the drug infusion rate in real time. The BMI maintains precise control and promptly changes the level of burst suppression while avoiding overshoot or undershoot. Our work demonstrates the feasibility of automatic reliable and accurate control of medical coma that can provide considerable therapeutic benefits.

Hypnosis control based on the minimum concentration of anesthetic drug for maintaining appropriate hypnosis

This paper proposes a novel hypnosis control method using Auditory Evoked Potential Index (aepEX) as a hypnosis index. In order to avoid side effects of an anesthetic drug, it is desirable to reduce the amount of an anesthetic drug during surgery. For this purpose many studies of hypnosis control systems have been done. Most of them use Bispectral Index (BIS), another hypnosis index, but it has problems of dependence on anesthetic drugs and nonsmooth change near some particular values. On the other hand, aepEX has an ability of clear distinction between patient consciousness and unconsciousness and independence of anesthetic drugs. The control method proposed in this paper consists of two elements: estimating the minimum effect-site concentration for maintaining appropriate hypnosis and adjusting infusion rate of an anesthetic drug, propofol, using model predictive control. The minimum effect-site concentration is estimated utilizing the property of aepEX pharmacodynamics. The infusion rate of propofol is adjusted so that effect-site concentration of propofol may be kept near and always above the minimum effect-site concentration. Simulation results of hypnosis control using the proposed method show that the minimum concentration can be estimated appropriately and that the proposed control method can maintain hypnosis adequately and reduce the total infusion amount of propofol.

Real-time closed-loop control in a rodent model of medically induced coma using burst suppression

BACKGROUND

A medically induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection after traumatic brain injuries. The authors 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, the authors 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 online 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 5-10 min. The posterior probability that the closed-loop anesthetic delivery system was reliable across all levels was 0.94 (95% CI, 0.77-1.00; n = 18) and that the system was accurate across all levels was 1.00 (95% CI, 0.84-1.00; n = 18).

CONCLUSION

The findings of this study 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.

Robust closed-loop control of induction and maintenance of propofol anesthesia in children

BACKGROUND

During closed-loop control, a drug infusion is continually adjusted according to a measure of clinical effect (e.g., an electroencephalographic depth of hypnosis (DoH) index). Inconsistency in population-derived pediatric pharmacokinetic/pharmacodynamic models and the large interpatient variability observed in children suggest a role for closed-loop control in optimizing the administration of intravenous anesthesia.

OBJECTIVE

To clinically evaluate a robustly tuned system for closed-loop control of the induction and maintenance of propofol anesthesia in children undergoing gastrointestinal endoscopy.

METHODS

One hundred and eight children, aged 6-17, ASA I-II, were enrolled. Prior to induction of anesthesia, NeuroSENSE™ sensors were applied to obtain the WAVCNS DoH index. An intravenous cannula was inserted and lidocaine (0.5 mg·kg(-1) ) administered. Remifentanil was administered as a bolus (0.5 μg·kg(-1) ), followed by continuous infusion (0.03 μg·kg(-1) ·min(-1) ). The propofol infusion was closed-loop controlled throughout induction and maintenance of anesthesia, using WAVCNS as feedback.

RESULTS

Anesthesia was closed-loop controlled in 102 cases. The system achieved and maintained an adequate DoH without manual adjustment in 87/102 (85%) cases. Induction of anesthesia (to WAVCNS  ≤ 60) was completed in median 3.8 min (interquartile range (IQR) 3.1-5.0), culminating in a propofol effect-site concentration (Ce ) of median 3.5 μg·ml(-1) (IQR 2.7-4.5). During maintenance of anesthesia, WAVCNS was measured within 10 units of the target for median 89% (IQR 79-96) of the time. Spontaneous breathing required no manual intervention in 91/102 (89%) cases.

CONCLUSIONS

A robust closed-loop system can provide effective propofol administration during induction and maintenance of anesthesia in children. Wide variation in the calculated Ce highlights the limitation of open-loop regimes based on pharmacokinetic/pharmacodynamic models.

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

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.

A brain-machine interface for control of burst suppression in medical coma

Burst suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of bursts of electrical activity alternating with periods of isoelectricity called suppression. Burst suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of burst suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of burst suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the burst suppression level in real time in rodents. We quantify the burst suppression level using the burst suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of burst suppression without overshoot or undershoot and maintains precise control of time-varying target levels of burst suppression in individual rodents in real time.

Evaluation of the clinical efficacy of two partial intravenous anesthetic protocols, compared with isoflurane alone, to maintain general anesthesia in horses

OBJECTIVE

To compare the ability of 2 partial IV anesthesia (PIVA) techniques to maintain anesthesia, compared with isoflurane alone, in horses.

ANIMALS

45 horses.

PROCEDURES

Client-owned horses requiring general anesthesia for a variety of procedures of at least 1 hour's duration were randomly allocated to 3 groups (n = 15/group) that differed for the maintenance protocol. Anesthesia was maintained with isoflurane with a starting end-tidal isoflurane concentration of 1.3% (isoflurane group) or a concentration of 1% supplemented with an adjustable continuous infusion of guaifenesin-ketamine (IGK group) or romifidine-ketamine (IRK group). A predefined scoring system was used to assess anesthetic depth and to adjust anesthetic delivery. The need for rescue anesthetics and recovery quality were compared.

RESULTS

A mean ± SD end-tidal isoflurane concentration of 1.36 ± 0.16% was necessary to maintain a surgical plane of anesthesia in the isoflurane group. Mean infusion rates of 5.0 ± 1.3 μL/kg/min and 5.1 ± 0.8 μL/kg/min were necessary to maintain a surgical plane of anesthesia in the IRK and IGK groups, respectively. A lower need for ketamine as a rescue anesthetic was observed in the IGK group, compared with the isoflurane group. Higher blood pressure and lower heart rates were found at selected time points for the IRK group, compared with the IGK and isoflurane groups.

CONCLUSIONS AND CLINICAL RELEVANCE

Both PIVA protocols were satisfactory to maintain smooth and stable surgical anesthesia in horses. The present study supports previous findings in which PIVA has isoflurane-sparing effects. Furthermore, PIVA did not impair recovery quality.

Design and implementation of a closed-loop control system for infusion of propofol guided by bispectral index (BIS)

BACKGROUND

This study describes the design of a hypnosis closed-loop control system with propofol. The controller used a proportional-integral (PI) algorithm with the bispectral index (BIS) as the feedback signal. Our hypothesis was that a PI closed-loop control could be applied in clinical practice safely keeping the BIS within a pre-determined target range.

METHODS

The adjustment of the PI parameters was based on simulation. The procedure had three steps: obtaining a patient model using data from 12 patients, designing and adjusting the controller in simulation, and fine tuning the PI parameters in a pilot study (10 patients). The resulting controller was tested in 24 American Society of Anesthesiology (ASA) I-II patients. The controller directly decides the infusion rate of propofol, and no model is necessary in its online operation. The BIS target was set to 50. Remifentanil was used for analgesia.

RESULTS

We evaluated the efficiency and safety of the automatic feedback system. It worked properly in all the patients. The median performance error was -1.62, and the median absolute performance error was 11.03. Average propofol-normalized consumption was 5.3 ± 1.8 mg/kg/h. Mean percentage of BIS in the range 40-60 was 83%. Mean time to open eyes was 8 ± 4 min. Time to extubation was 9 ± 5 min. Hemodynamic adverse event or intraoperative awareness were not recorded.

CONCLUSIONS

The closed-loop system was able to maintain the BIS within an acceptable range of levels. The control of a propofol infusion guided by the BIS is feasible without hemodynamic instability in ASA I/II patients.

Review article: closed-loop systems in anesthesia: is there a potential for closed-loop fluid management and hemodynamic optimization?

Closed-loop (automated) controllers are encountered in all aspects of modern life in applications ranging from air-conditioning to spaceflight. Although these systems are virtually ubiquitous, they are infrequently used in anesthesiology because of the complexity of physiologic systems and the difficulty in obtaining reliable and valid feedback data from the patient. Despite these challenges, closed-loop systems are being increasingly studied and improved for medical use. Two recent developments have made fluid administration a candidate for closed-loop control. First, the further description and development of dynamic predictors of fluid responsiveness provides a strong parameter for use as a control variable to guide fluid administration. Second, rapid advances in noninvasive monitoring of cardiac output and other hemodynamic variables make goal-directed therapy applicable for a wide range of patients in a variety of clinical care settings. In this article, we review the history of closed-loop controllers in clinical care, discuss the current understanding and limitations of the dynamic predictors of fluid responsiveness, and examine how these variables might be incorporated into a closed-loop fluid administration system.

Closed-loop coadministration of propofol and remifentanil guided by bispectral index: a randomized multicenter study

BACKGROUND

We have developed a proportional-integral-derivative controller allowing the closed-loop coadministration of propofol and remifentanil, guided by a Bispectral Index (BIS) monitor, during induction and maintenance of general anesthesia. The controller was compared with manual target-controlled infusion.

METHODS

In a multicenter study, 196 surgical patients were randomly assigned to dual closed-loop or manual administration of propofol and remifentanil. Comparison between groups was evaluated by calculating a global score that characterized the overall performance of the controller including the percentage of adequate anesthesia, defined as BIS between 40 and 60, the median absolute performance error, and wobble. Secondary outcomes included occurrence of burst suppression ratio, time to tracheal extubation, and drug consumption.

RESULTS

Eighty-three patients assigned to dual-loop control and 84 patients assigned to manual control completed the study. The global score and the percentage of time with BIS between 40 and 60 were better in the dual-loop group (26 ± 11 vs 43 ± 40, P < 0.0001; 82% ± 12% vs 71% ± 19%, P < 0.0001). Overshoot (BIS <40), undershoot (BIS >60), and burst suppression ratio were all significantly less common in the dual-loop group. Modifications to the propofol and remifentanil infusions were more frequent, and adjustments smaller in the dual-loop group. Remifentanil consumption was greater (0.22 ± 0.07 vs 0.16 ± 0.07 μg · kg(-1) · min(-1); P < 0.0001) and the speed to tracheal extubation was shorter (10 ± 4 vs 11 ± 5 minutes; P = 0.02) in the dual-loop group.

CONCLUSION

The controller allows the automated delivery of propofol and remifentanil and maintains BIS values in predetermined boundaries during general anesthesia better than manual administration.

[Propofol administration systems. Handling, hemodynamics and propofol consumption]

BACKGROUND

During anaesthesia propofol is administered either by manual controlled infusion (MCI) or by target controlled infusion (TCI) techniques. In this study two different TCI systems for propofol administration were evaluated with regard to handling, patient safety, and costs and compared to administration of propofol by the MCI technique.

METHODS

In a prospective study, 90 patients scheduled for elective surgery of the nose or nasal sinuses were randomly enrolled in three groups. The two TCI systems were examined in two groups of 30 patients: one group received propofol following the pharmacokinetic TCI model of Schnider (TCI-Schnider) and the other group received propofol following the TCI model of Marsh (TCI-Marsh). A manual perfusion technique (MCI, n=30) was used in the control group. Depth of anesthesia was controlled using the bispectral index (BSI) which was adjusted to fall within the range of 40-55. Hemodynamics, extubation times and time of awaking, rate and quality of propofol dose adjustment, total drug requirements, costs, and quality of recovery were documented. The incidence of postoperative nausea and vomiting (PONV) as well as shivering and patient satisfaction were also documented.

RESULTS

Demographics, hemodynamics and perioperative data did not differ between the groups. Propofol consumption within the first 60 min also showed no significant differences. In the course of extended anaesthesia, propofol consumption was significantly less in both TCI groups compared to the control group (MCI) and the TCI-Schnider group also showed less episodes of bradycardia. The necessity of propofol dose adjustment did not differ significantly between the TCI groups. Administration and consumption of anaesthesia co-medication (fentanyl, remifentanil, cisatracurium) did not differ between the groups.

CONCLUSION

The investigated propofol administration procedures using the MCI or TCI techniques were safe and easy to handle under BIS monitoring. No differences were found concerning extubation times and time of awaking. During extended anaesthesia procedures (>60 min), propofol consumption was lower with both TCI techniques and thus costs could be saved.

Comparison of closed loop vs. manual administration of propofol using the Bispectral index in cardiac surgery

BACKGROUND

In recent years, electroencephalographic indices of anaesthetic depth have facilitated automated anaesthesia delivery systems. Such closed-loop control of anaesthesia has been described in various surgical settings in ASA I-II patients (1-4), but not in open heart surgery characterized by haemodynamic instability and higher risk of intra-operative awareness. Therefore, a newly developed closed-loop anaesthesia delivery system (CLADS) to regulate propofol infusion by the Bispectral index (BIS) was compared with manual control during open heart surgery.

METHODS

Forty-four adult ASA II-III patients undergoing elective cardiac surgery under cardiopulmonary bypass were enrolled. The study participants were randomized to two groups: the CLADS group received propofol delivered by the CLADS, while in the manual group, propofol delivery was adjusted manually. The depth of anaesthesia was titrated to a target BIS of 50 in both the groups.

RESULTS

During induction, the CLADS group required lower doses of propofol (P<0.001), resulting in lesser overshoots of BIS (P<0.001) and mean arterial blood pressure (P=0.004). Subsequently, BIS was maintained within +/- 10 of the target for a significantly longer time in the CLADS group (P=0.01). The parameters of performance assessment, median absolute performance error (P=0.01), wobble (P=0.04) and divergence (P<0.001), were all significantly better in the CLADS group. Haemodynamic stability was better in the CLADS group and the requirement of phenylephrine in the pre-cardiopulmonary bypass period as well as the cumulative dose of phenylephrine used were significantly higher in the manual group.

CONCLUSION

The automated delivery of propofol using CLADS was safe, efficient and performed better than manual administration in open heart surgery.

Titration automatisée du propofol guidée par l'index bispectral

This review analyzes the clinical studies concerning the automated perfusion, or closed-loop, of propofol guided by the bispectral index (BIS). To carry out the maintenance of general anaesthesia by a closed loop propofol-BIS is feasible as shown by studies comprising few low risk patients. We showed that induction of anaesthesia is feasible with a closed loop, haemodynamic stability being similar to a manual titration. A second study, bearing on the whole of the anaesthesia of patients ASA I to III undergoing very diverse surgical acts, showed that the closed loop propofol-BIS was more precise than a manual perfusion. This confirms that the closed loop propofol-BIS is not an esoteric research and that it represents a tool with a future for the clinician.

Feasibility of closed-loop titration of propofol guided by the Bispectral Index for general anaesthesia induction

BACKGROUND

This study was designed to evaluate the feasibility of propofol infusion by a closed-loop system for the titration of anaesthetic induction guided by Bispectral Index.

METHODS

Forty patients were prospectively and randomly allocated into two groups: the target control infusion (TCI) group, where propofol titration was performed manually guided by the Bispectral Index using a commercial pharmacokinetic model (Diprifusor device) and the closed-loop group where titration was performed using a proportional differential algorithm. For both groups, the objective was to achieve a Bispectral Index of 50. Remifentanil TCI was infused at a target of 2 ng mL-1 and was maintained constant throughout the study. Feasibility of automatic induction was evaluated with performance error and haemodynamic data.

RESULTS

Bispectral Index overshoot (-9 +/- 13% vs. -16 +/- 20%, P = 0.035) and mean duration of induction (381 +/- 106 s vs. 490 +/- 131 s, P = 0.004) were lower in the closed-loop group than in the TCI group. Haemodynamic data were similar between groups with a similar use of ephedrine bolus.

CONCLUSION

The system was able to allow induction clinically for all patients. Automated titration guided by Bispectral Index for propofol infusion was feasible without increase in haemodynamic adverse effects.

Closed loops in anaesthesia

Closed-loop systems are able to make their own decisions and to try to reach and maintain a preset target. As a result, they might help the anaesthetist to optimise the titration of drug administration without any overshoot, controlling physiological functions and guiding monitoring variables. Thanks to the development of fast computer technology and more reliable pharmacological effect measures, the study of automation in anaesthesia has regained popularity. This short review focuses on the most recently developed and tested feedback systems in anaesthesia. Various new approaches for controlling the administration of intravenous and inhaled hypnotic-anaesthetic drugs have recently been published. For analgesics, a framework for further research has been presented in the literature. For other drugs, such as muscle relaxants and haemodynamic agents, only short reviews can be found. Until now, most of these systems have had to be under development. The challenge is now fully to establish the safety, efficacy, reliability and utility of closed-loop anaesthesia so that it can be adopted in the clinical setting. Besides, their role in optimising the controlled variables and control models, these systems have to be tested in extreme circumstances in order to test their robustness.