Modelling propofol pharmacodynamics using BIS-guided anaesthesia

Hierarchical Deep Reinforcement Learning-Based Propofol Infusion Assistant Framework in Anesthesia

This article aims to provide a hierarchical reinforcement learning (RL)-based solution to the automated drug infusion field. The learning policy is divided into the tasks of: 1) learning trajectory generative model and 2) planning policy model. The proposed deep infusion assistant policy gradient (DIAPG) model draws inspiration from adversarial autoencoders (AAEs) and learns latent representations of hypnotic depth trajectories. Given the trajectories drawn from the generative model, the planning policy infers a dose of propofol for stable sedation of a patient under total intravenous anesthesia (TIVA) using propofol and remifentanil. Through extensive evaluation, the DIAPG model can effectively stabilize bispectral index (BIS) and effect site concentration given a potentially time-varying target sequence. The proposed DIAPG shows an increased performance of 530% and 15% when a human expert and a standard reinforcement algorithm are used to infuse drugs, respectively.

Improved Individualized Patient-Oriented Depth-of-Hypnosis Measurement Based on Bispectral Index

Total intravenous anesthesia is an anesthesiologic technique where all substances are injected intravenously. The main task of the anesthesiologist is to assess the depth of anesthesia, or, more specifically, the depth of hypnosis (DoH), and accordingly adjust the dose of intravenous anesthetic agents. However, it is not possible to directly measure the anesthetic agent concentrations or the DoH, so the anesthesiologist must rely on various vital signs and EEG-based measurements, such as the bispectral (BIS) index. The ability to better measure DoH is directly applicable in clinical practice-it improves the anesthesiologist's assessment of the patient state regarding anesthetic agent concentrations and, consequently, the effects, as well as provides the basis for closed-loop control algorithms. This article introduces a novel structure for modeling DoH, which employs a residual dynamic model. The improved model can take into account the patient's individual sensitivity to the anesthetic agent, which is not the case when using the available population-data-based models. The improved model was tested using real clinical data. The results show that the predictions of the BIS-index trajectory were improved considerably. The proposed model thus seems to provide a good basis for a more patient-oriented individualized assessment of DoH, which should lead to better administration methods that will relieve the anesthesiologist's workload and will benefit the patient by providing improved safety, individualized treatment, and, thus, alleviation of possible adverse effects during and after surgery.

Correlation of Sedline-generated variables and clinical signs with anaesthetic depth in experimental pigs receiving propofol

Most of currently available electroencephalographic (EEG)-based tools to assess depth of anaesthesia have not been studied or have been judged unreliable in pigs. Our primary aim was to investigate the dose-effect relationship between increasing propofol dose and variables generated by the EEG-based depth of anaesthesia monitor Sedline in pigs. A secondary aim was to compare the anaesthetic doses with clinical outcomes commonly used to assess depth of anaesthesia in this species. Sixteen juvenile pigs were included. Propofol infusion was administered at 10 mg kg-1 h-1, increased by 10 mg kg-1 h-1 every 15 minutes, and stopped when an EEG Suppression ratio >80% was reached. Patient state index, suppression ratio, left and right spectral edge frequency 95%, and outcomes from commonly used clinical methods to assess depth of anaesthesia in pigs were recorded. The best pharmacodynamic model was assessed for Patient state index, suppression ratio, left and right spectral edge frequency 95% in response to propofol administration. The decrease of Patient state index best fitted to an inhibitory double-sigmoid model (including a plateau phase). The increase of suppression ratio fitted a typical sigmoid Emax model. No relevant relationship could be identified between spectral edge frequency 95% values and propofol administration. A large variability in clinical outcomes was observed among pigs, such that they did not provide a reliable evaluation of propofol dose. The relationship between propofol dose and Patient state index/suppression ratio described in the present study can be used for prediction in future investigations. The evaluation of depth of anaesthesia based on common clinical outcomes was not reliable.

Multi-model generalised predictive control for intravenous anaesthesia under inter-individual variability

Inter-individual variability possesses a major challenge in the regulation of hypnosis in anesthesia. Understanding the variability towards anesthesia effect is expected to assist the design of controller for anesthesia regulation. However, such studies are still very scarce in the literature. This study aims to analyze the inter-individual variability in propofol pharmacokinetics/pharmacodynamics (PK/PD) model and proposed a suitable controller to tackle the variability. This study employed Sobol' sensitivity analysis to identify significance parameters in propofol PK/PD model that affects the model output Bispectral Index (BIS). Parameters' range is obtained from reported clinical data. Based on the finding, a multi-model generalized predictive controller was proposed to regulate propofol in tackling patient variability. [Formula: see text] (concentration that produces 50% of the maximum effect) was found to have a highly-determining role on the uncertainty of BIS. In addition, the Hill coefficient, [Formula: see text], was found to be significant when there is a drastic input, especially during the induction phase. Both of these parameters only affect the process gain upon model linearization. Therefore, a predictive controller based on switching of model with different process gain is proposed. Simulation result shows that it is able to give a satisfactory performance across a wide population. Both the parameters [Formula: see text] and [Formula: see text], which are unknown before anesthesia procedure, were found to be highly significant in contributing the uncertainty of BIS. Their range of variability must be considered during the design and evaluation of controller. A linear controller may be sufficient to tackle most of the variability since both [Formula: see text] and [Formula: see text] would be translated into process gain upon linearization.

Modelling the PSI response in general anesthesia

In anesthesia automation, one of the main important issues is the availability of a reliable measurement of the depth of consciousness level (hypnosis) of the patient. According to this value, the hypnotic drug dosage can be adequately calculated. One of the most studied hypnosis indexes is the bispectral index (BIS). In this article we analyzed an alternative called patient state index (PSI). The objectives of this study are, first, to validate the accuracy of the PSI describing the hypnosis level during the maintenance phase of general anesthesia, by comparing with the BIS and, second, to model the relationship between propofol infusion rate and PSI values, obtained from a SEDLine monitor. For this, real data from patients undergoing general anesthesia simultaneously monitored with both BIS and PSI signals was used. Results obtained are interesting for a correct interpretation of PSI signal in clinical practice.

Protective effect of dexmedetomidine infusion combined with epidural blockade on postoperative complications after surgery: A prospective randomized controlled clinical trial

Objectives

This prospective, randomized, controlled study aimed to explore the efficacy of dexmedetomidine combined with epidural blockade on postoperative recovery of elderly patients after radical resection for colorectal cancer.

Methods

Ninety-six elderly patients who underwent radical resection for colorectal cancer were randomly divided into the following four groups: dexmedetomidine, epidural blockade (ropivacaine), combined (dexmedetomidine + epidural blockade), and control (0.9% saline). The Mini-Mental State Examination (MMSE), Visual Analog Scale (VAS), and Ramsay scores at 48 hours, and time to first activity, length of hospital stay, and postoperative complication rates at 3 months were assessed.

Results

Twelve hours after surgery, Ramsay scores were higher in the combined compared with the control and epidural blockade groups. Twenty-four hours after surgery, MMSE scores were higher in the combined compared with the other groups. The combined group showed the lowest VAS scores except at 48 hours. Time to first activity and length of hospital stay were significantly shorter in the combined compared with the other groups. There was no difference in total postoperative complication rates among the groups.

Conclusions

A combination of intraoperative dexmedetomidine infusion and epidural blockade could mitigate pain after surgery, improve cognitive dysfunction in early surgery, and facilitate recovery.

Adaptive smith predictor controller for total intravenous anesthesia automation

Anesthetic agent propofol needs to be administered at an appropriate rate to prevent hypotension and postoperative adverse reactions. To comprehend more suitable anesthetic drug rate during surgery is a crucial aspect. The main objective of this proposal is to design robust automated control system that work efficiently in most of the patients with smooth BIS and minimum variations of propofol during surgery to avoid adverse post reactions and instability of anesthetic parameters. And also, to design advanced computer control system that improves the health of patient with short recovery time and less clinical expenditures. Unlike existing research work, this system administrates propofol as a hypnotic drug to regulate BIS, with fast bolus infusion in induction phase and slow continuous infusion in maintenance phase of anesthesia. The novelty of the paper lies in possibility to simplify the drug sensitivity-based adaption with infusion delay approach to achieve closed-loop control of hypnosis during surgery. Proposed work uses a brain concentration as a feedback signal in place of the BIS signal. Regression model based estimated sensitivity parameters are used for adaption to avoid BIS signal based frequent adaption procedure and large offset error. Adaptive smith predictor with lead-lag filter approach is applied on 22 different patients' model identified by actual clinical data. The actual BIS and propofol infusion signals recorded during clinical trials were used to estimate patient's sensitivity parameters EC ₅₀ and λ. Simulation results indicate that patient's drug sensitivity parameters based adaptive strategy facilitates optimal controller performance in most of the patients. Results are obtained with proposed scheme having less settling time, BIS oscillations and small offset error leads to adequate depth of anesthesia. A comparison with manual control mode and previously reported system shows that proposed system achieves reduction in the total variations of the propofol dose. Proposed adaptive scheme provides better performance with less oscillation in spite of computation delay, surgical stimulations and patient variability. Proposed scheme also provides improvement in robustness and may be suitable for clinical practices.

Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries

This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BIS^TM) monitor to estimate the patient’s unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician—or the automatic controller—will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method’s effectiveness.

Adaptive pharmacokinetic and pharmacodynamic modelling to predict propofol effect using BIS-guided anesthesia

BACKGROUND AND OBJECTIVE

Propofol is widely used for hypnosis induction and maintenance of general anesthesia. Its effect can be assessed using the bispectral index (BIS). Many automatic infusion systems are based in pharmacokinetics (PK) and pharmacodynamics (PD) models to predict the response of the patient to the drug. However, all these models do not take into account intra and inter-patient variability. An adjusted intraoperative drug administration allows faster recovery and provides post-operative side-effect mitigation

METHODS

BIS evolution and surgery-recorded propofol infusion data of a group of 60 adult patients (30 males/30 females) with ASA I/II physical status were used to test a real time PK/PD compartmental model. This new algorithm tunes three model parameters (ce50, γ and ke0), minimizing a performance function online.

RESULTS

The error in the BIS signal predicted by the real time PK/PD model was smaller than the error measured with fixed parameter equations. This model shows that ce50, γ and ke0 change with time and patients, given a mean (95% confidence interval) of 3.89 (3.52-4.26)mg/l, 4.63 (4.13-5.13) and 0.36 (0.31-0.4)min(-1), respectively.

CONCLUSIONS

The real time PK/PD model proposed provides a closer description of the patient real state at each sample time. This allows for greater control of the drug infusion, and thus the quantity of drug administered can be titrated to achieve the desired effect for the desired duration, and reduce unnecessary waste or post-operative effects.

Adaptive fuzzy modeling of the hypnotic process in anesthesia

This paper addresses the problem of patient model synthesis in anesthesia. Recent advanced drug infusion mechanisms use a patient model to establish the proper drug dose. However, due to the inherent complexity and variability of the patient dynamics, difficulty obtaining a good model is high. In this paper, a method based on fuzzy logic and genetic algorithms is proposed as an alternative to standard compartmental models. The model uses a Mamdani type fuzzy inference system developed in a two-step procedure. First, an offline model is obtained using information from real patients. Then, an adaptive strategy that uses genetic algorithms is implemented. The validation of the modeling technique was done using real data obtained from real patients in the operating room. Results show that the proposed method based on artificial intelligence appears to be an improved alternative to existing compartmental methodologies.

Data-driven modeling of pharmacological systems using endpoint information fusion

This study investigated the feasibility of deriving data-driven model of a class of pharmacological systems using the information fusion of endpoint responses. For a class of pharmacological systems subsuming conventional steady-state dose-response models, compartmental pharmacokinetic-pharmacodynamic models and indirect response models, a relation between multiple endpoint responses was formalized and analyzed to elucidate if this class of systems is identifiable, i.e., if the data-driven model of this class of systems can be derived from the endpoint responses alone. It was shown that this class of systems is fully identifiable in case all the responses involve effect compartments. However, it was also observed that persistently exciting dose profiles may be required in accurately deriving reliable data-driven model with low variance. The findings from the identifiability analysis were demonstrated using benchmark pharmacological system examples.