Closed-loop Anesthesia Delivery Research Articles

Analyzing the States of Brain under Anesthesia Using ANES-metanet based on Deep Learning Models

Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time .Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA), local anesthesia (LA) and regional anesthesia (RA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. In this process we propose a deep learning algorithm to analysis the anesthesia from EEG signal. So the deep learning algorithms are Convolution Neural Network, Long Short Term Neural Network and Artificial Neural Network will applied and analyze EEG signal and it generates the results in the form of metrics like accuracy, precision, recall and f1-score.

Role of artificial intelligence in perioperative monitoring in anaesthesia.

Artificial intelligence (AI) is making giant strides in the medical domain, and the field of anaesthesia is not untouched. Enhancement in technology, especially AI, in many fields, including medicine, has proven to be far superior, safer and less erratic than human decision-making. The intersection of anaesthesia and AI holds the potential for augmenting constructive advances in anaesthesia care. AI can improve anaesthesiologists' efficiency, reduce costs and improve patient outcomes. Anaesthesiologists are well placed to harness the advantages of AI in various areas like perioperative monitoring, anaesthesia care, drug delivery, post-anaesthesia care unit, pain management and intensive care unit. Perioperative monitoring of the depth of anaesthesia, clinical decision support systems and closed-loop anaesthesia delivery aid in efficient and safer anaesthesia delivery. The effect of various AI interventions in clinical practice will need further research and validation, as well as the ethical implications of privacy and data handling. This paper aims to provide an overview of AI in perioperative monitoring in anaesthesia.

Closed-loop control of anesthetic state in nonhuman primates.

Research in human volunteers and surgical patients has shown that unconsciousness under general anesthesia can be reliably tracked using real-time electroencephalogram processing. Hence, a closed-loop anesthesia delivery (CLAD) system that maintains precisely specified levels of unconsciousness is feasible and would greatly aid intraoperative patient management. The US Federal Drug Administration has approved no CLAD system for human use due partly to a lack of testing in appropriate animal models. To address this key roadblock, we implement a nonhuman primate (NHP) CLAD system that controls the level of unconsciousness using the anesthetic propofol. The key system components are a local field potential (LFP) recording system; propofol pharmacokinetics and pharmacodynamic models; the control variable (LFP power between 20 and 30 Hz), a programmable infusion system and a linear quadratic integral controller. Our CLAD system accurately controlled the level of unconsciousness along two different 125-min dynamic target trajectories for 18 h and 45 min in nine experiments in two NHPs. System performance measures were comparable or superior to those in previous CLAD reports. We demonstrate that an NHP CLAD system can reliably and accurately control in real-time unconsciousness maintained by anesthesia. Our findings establish critical steps for CLAD systems' design and testing prior to human testing.

Automated Closed-Loop Propofol Anesthesia Versus Desflurane Inhalation Anesthesia in Obese Patients Undergoing Bariatric Surgery: A Comparative Randomized Analysis of Recovery Profile.

Precision general anesthesia (GA) techniques that minimize the presence of residual anesthetic and facilitate recovery, are desirable in patients with morbid obesity. Automated administration of propofol total intravenous anesthesia (TIVA), which facilitates precision propofol delivery by factoring in continuous patient input variable (bispectral index) to establish a closed feedback loop system, may help mitigate concerns related to propofol's lipid solubility and adverse accumulation kinetics in patients with morbid obesity. This randomized study evaluated the recovery of patients with morbid obesity undergoing bariatric surgery under propofol TIVA automated by a closed-loop anesthesia delivery system (CLADS) versus desflurane GA. Forty patients, randomly allocated to receive propofol TIVA (CLADS group) or desflurane GA (desflurane group), were evaluated for postoperative recovery (early and intermediate) (primary objective); they were evaluated for intraoperative hemodynamics, anesthesia depth consistency, anesthesia delivery performance characteristics, patient satisfaction, and incidence of adverse events (sedation, pain, postoperative nausea, and vomiting) (secondary objective). No difference was found for the time-to-eye-opening (CLADS group: 4.7 [3.0, 6.7] min vs. desflurane group: 5.6 [4.0, 6.9] min, P = 0.576), time-to-tracheal-extubation (CLADS group: 6.7 [4.7, 9.3] min vs. desflurane group: 7.0 [5.8, 9.2] min, P = 0.528), ability-to-shift score from operating room table to the transport bed (CLADS group: 3 [3.0, 3.5] vs. desflurane group: 3 [3.0, 4.0], P = 0.703), and time to achieve a modified Aldrete score 9/10 (CLADS group: 15 [15.0, 37.5] min vs. desflurane group: 15 [15.0, 43.7] min, P = 0.867). Automated propofol TIVA as administered by CLADS, which matched desflurane GA with respect to depth of anesthesia consistency and postanesthesia recovery profile, can be explored further as an alternative anesthesia technique in patients with morbid obesity.

A novel recognition of artificial intelligence in regional anaesthesia

Mechanised analysis of algorithms for problem solving, object recognition and decision-making assistance defines the general concept underlining artificial intelligence (AI).[1] The former presents notable applications in perioperative medicine across diverse situations like depth of anaesthesia monitoring, closed-loop anaesthesia delivery, risk-predictive models, pain-management through objective analgo-scoring, operating-room planning logistics and, guidance of perioperative ultrasonography. The recent Food and Drug Administration (FDA) approval[2] of the Scan Nav™ Anatomy Peripheral Nerve Block (PNB) system (Intelligent Ultrasound Group, Cardiff, United Kingdom)[3] only emerges as a novel recognition of the potential of AI in regional anaesthesia. Worthwhile to mention, Hemmerling et al.[4] described an encouraging seminal use of the robotic ultrasound-guided nerve block system or the Magellan system, almost a decade ago. Employing the same system, Morse et al.[5] went on to compare the performance parameters (learning curve and success rates) and subjective performance variability of manual versus robot-assisted ultrasonographic nerve blocks in a phantom model. The authors found that assistance of nerve blocks, enabled significant faster needle guidance (1.8 [1.6] seconds vs. 0.3 [0.3] seconds; P = 0.007), than for manual blocks. Inter-subject performance variability was also found to be considerably attenuated. Specific to the context of the recently FDA approved software medical device, ScanNav™ Anatomy PNB system uses deep-learning AI technology. During performance of a peripheral nerve block the ScanNav™ system identifies and grades a live ultrasound image using this technology. A distinctive colour overlay of the key sono-anatomical structures during a live ultrasound scan is a characteristic valuable attribute of the system.[3] This enhances standardisation and accuracy of the ultrasound image interpretation. This device colour highlights anatomy of 10 vital regions relevant to ultrasound-guided regional anaesthesia namely Interscalene, Supraclavicular, Axillary and Superior Trunk Brachial plexus of the upper limb; Erector Spinae Plane, Rectus Sheath and Supra-inguinal Fascia Iliaca sheath of truncal structures; and Femoral Nerve, Adductor canal and Popliteal nerve sheath of the lower limb. Furthermore, a 3-dimension inbuilt reference material provides a review of the relevant anatomy, probe-positioning technique; and demonstrates a virtual scan of the selected anatomical region. The system enables adjustments in the intensity of coloured highlighting according to user preference and allows for viewing the unmodified and highlighted images side-by-side for better comprehension of the sono-anatomy. The use of ScanNav™ system thereby facilitates as a labelling device alongside real-time anatomy visualisation during regional anaesthesia. The system also has inbuilt virtual scans that assist the clinician functioning as a 3D reference material of the relevant PNB anatomy scanned. Notably so, the software is compatible with the general-purpose ultrasound systems in addition to being a stand-alone device. Talking of the literature relevant to the ScanNav™ Anatomy PNB system, Bowness et al.[6] compared 240 ultrasound scans performed across 15 expert versus non-expert regional anaesthesia providers on evaluating its' role in promoting the uptake and generalisability of ultrasound-guided regional anaesthesia. The authors outlined that non-experts were more likely to submit positive feedback and less likely to submit negative reviews than the experts (P = 0.001). Positive feedbacks were obtained on the role of the device in training (37/60, 61.7%) and utility for teaching (30/60, 50.0%). Meanwhile the non-experts appreciated the anatomic structure identification and learning, as the contextual benefits, some nonexperts and experts thought the device could decrease the scanning confidence. Additionally, expert providers reported a potentially increased, yet insignificant, risk in 12/254 (4.7%) vs. 8/254 (3.1%, P = 0.362) scans for needle trauma to “safety” structures. The discussion elaborates the tremendous opportunity AI has to offer in guiding regional anaesthesia, awaiting prospective validation outside the core 10 block areas. The utility of this device can be further extended to sympathetic blocks like the stellate-ganglion, celiac-plexus and other blocks for treatment of chronic pain syndromes like complex regional pain syndrome. The times might not be distant when perioperative physicians have routine access to ultrasound machines integrated with AI-assisted colour reframing for anatomical landmark “coding” side by side with the present B-mode, M-mode and colour/pulse Doppler profiling.

Automated Closed-Loop Propofol Anesthesia Versus Desflurane Inhalation Anesthesia in Obese Patients Undergoing Bariatric Surgery: A Comparative Randomized Analysis of Recovery Profile.

Precision general anesthesia (GA) techniques that minimize the presence of residual anesthetic and facilitate recovery, are desirable in patients with morbid obesity. Automated administration of propofol total intravenous anesthesia (TIVA), which facilitates precision propofol delivery by factoring in continuous patient input variable (bispectral index) to establish a closed feedback loop system, may help mitigate concerns related to propofol's lipid solubility and adverse accumulation kinetics in patients with morbid obesity. This randomized study evaluated the recovery of patients with morbid obesity undergoing bariatric surgery under propofol TIVA automated by a closed-loop anesthesia delivery system (CLADS) versus desflurane GA. Forty patients, randomly allocated to receive propofol TIVA (CLADS group) or desflurane GA (desflurane group), were evaluated for postoperative recovery (early and intermediate) (primary objective); they were evaluated for intraoperative hemodynamics, anesthesia depth consistency, anesthesia delivery performance characteristics, patient satisfaction, and incidence of adverse events (sedation, pain, postoperative nausea, and vomiting) (secondary objective). No difference was found for the time-to-eye-opening (CLADS group: 4.7 [3.0, 6.7] min vs. desflurane group: 5.6 [4.0, 6.9] min, P = 0.576), time-to-tracheal-extubation (CLADS group: 6.7 [4.7, 9.3] min vs. desflurane group: 7.0 [5.8, 9.2] min, P = 0.528), ability-to-shift score from operating room table to the transport bed (CLADS group: 3 [3.0, 3.5] vs. desflurane group: 3 [3.0, 4.0], P = 0.703), and time to achieve a modified Aldrete score 9/10 (CLADS group: 15 [15.0, 37.5] min vs. desflurane group: 15 [15.0, 43.7] min, P = 0.867). Automated propofol TIVA as administered by CLADS, which matched desflurane GA with respect to depth of anesthesia consistency and postanesthesia recovery profile, can be explored further as an alternative anesthesia technique in patients with morbid obesity.

Newer Perspectives in Anesthesiology: A Philatelic Based Review

Over the past decade, the practice of anesthesiology had become inextricably dependent upon technology. Anesthetics were first discovered, then made increasingly safe, and now, more precise and efficient, all due to tremendous advances in monitoring and delivery technology. Telemedicine has revolutionized medicine and preoperative evaluations and teaching are being done via teleconference and zoom meetings. Patient monitoring has become noninvasive, and closed-loop anesthesia delivery systems are making anesthetic administration very precise and safe. Next-generation technology and innovations will eliminate human errors entirely.

Automatic Regulation of Anesthesia via Ultra-Local Model Control

Abstract As a part of the BMS2021 Benchmark Challenge, this paper deals with the design and testing of a closed-loop anesthesia delivery regulation system by exploiting the open-source Matlab-based patient simulator. Because of system inherent complexity together with intra-and inter-patient parameters variability and partially unknown disturbances, traditional model-based approaches may suffer. To overcome these limitations, we opt for a data-driven approach using real-time ultra-local models coupled with the corresponding so-called intelligent controllers. In this way, one maintains the hemodynamic variables while regulating the levels of hypnosis, analgesia, and neuromuscular blockade in anesthesia by automatic delivery of drugs. The performance of the proposed approach has been evaluated in silico by considering a representative dataset composed of 24 patients, the presence of disturbances mimicking both surgical stimulations and actions of “anesthesiologist in the loop”, including also noise effects and time-varying system delays.

Evaluation of Automated Delivery of Propofol Using a Closed-Loop Anesthesia Delivery System in Patients Undergoing Thoracic Surgery: A Randomized Controlled Study

Automated propofol total intravenous anesthesia (TIVA) administered by a closed-loop anesthesia delivery system (CLADS) exhibits greater efficiency than conventional manual methods, but its use in major thoracic surgery is limited. Prospective, single-blind, randomized controlled study. Single-center tertiary care hospital. Patients undergoing thoracic surgery. Patients were randomly allocated to receive CLADS-driven (CLADS group) or manually controlled (manual group) propofol TIVA. Anesthesia depth consistency (primary objective) and anesthesia delivery performance, propofol usage, work ergonomics, intraoperative hemodynamics, and recovery profile (secondary objectives) were analyzed. No differences were found for anesthesia depth consistency (percentage of time the bispectral index was within ± 10 of target) (CLADS group: 82.5% [78.5%-87.2%] v manual group: 86.5% [74.2%-92.5%]; p = 0.581) and delivery performance, including median performance error (CLADS group: 3 [-4 to 6] v manual group: 1 [-2.5 to 6]); median absolute performance error (CLADS group: 10 [10-12] v manual group:10 [8-12]); wobble (CLADS group: 10 [8-12] v manual group: 9 [6-10.5]); and global score (CLADS group: 24.2 [21.2-29.3] v manual group: 22.1 [17.3-32.3]) (p > 0.05). However, propofol requirements were significantly lower in the CLADS group for induction (CLADS group: 1.27 ± 0.21] mg/kg v manual group: 1.78 ± 0.51 mg/kg; p = 0.014) and maintenance (CLADS group: 4.02 ± 0.99 mg/kg/h v manual group: 5.11 ± 1.40 mg/kg/h; p = 0.025) of TIVA. Ergonomically, CLADS-driven TIVA was found to be significantly superior to manual control (infusion adjustment frequency/h) (manual infusion: 9.6 [7.8-14.9] v CLADS delivery [none]). In thoracic surgery patients, CLADS-automated propofol TIVA confers significant ergonomic advantage along with lower propofol usage.

Recent advances in the technology of anesthesia.

The practice of anesthesiology is inextricably dependent upon technology. Anesthetics were first made possible, then increasingly safe, and now more scalable and efficient in part due to advances in monitoring and delivery technology. Herein, we discuss salient advances of the last three years in the technology of anesthesiology. Consumer technology and telemedicine have exploded onto the scene of outpatient medicine, and perioperative management is no exception. Preoperative evaluations have been done via teleconference, and copious consumer-generated health data is available. Regulators have acknowledged the vast potential found in the transfer of consumer technology to medical practice, but issues of privacy, data ownership/security, and validity remain. Inside the operating suite, monitoring has become less invasive, and clinical decision support systems are common. These technologies are susceptible to the “garbage in, garbage out” conundrum plaguing artificial intelligence, but they will improve as network latency decreases. Automation looms large in the future of anesthesiology as closed-loop anesthesia delivery systems are being tested in combination (moving toward a comprehensive system). Moving forward, consumer health companies will search for applications of their technology, and loosely regulated health markets will see earlier adoption of next-generation technology. Innovations coming to anesthesia will need to account for human factors as the anesthesia provider is increasingly considered a component of the patient care apparatus.

A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery

Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists efficiently achieve and maintain desired anesthetic depth over an extended period of time. A typical CLAD system would use an anesthetic marker, calculated from physiological signals, as real-time feedback to adjust anesthetic dosage towards achieving a desired set-point of the marker. Since control strategies for CLAD vary across the systems reported in recent literature, a comparative analysis of common control strategies can be useful. For a nonlinear plant model based on well-established models of compartmental pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the set-point tracking performance of three output-feedback linear control strategies: proportional-integral-derivative (PID) control, linear quadratic Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically, we numerically simulate multiple CLAD sessions for the scenario where the plant model parameters are unavailable for a patient and the controller is designed based on a nominal model and controller gains are held constant throughout a session. Based on the numerical analyses performed here, conditioned on our choice of model and controllers, we infer that in terms of accuracy and bias PID control performs better than ILQG which in turn performs better than LQG. In the case of noisy observations, ILQG can be tuned to provide a smoother infusion rate while achieving comparable steady state response with respect to PID. The numerical analysis framework and findings reported here can help CLAD developers in their choice of control strategies. This paper may also serve as a tutorial paper for teaching control theory for CLAD.

Constructing a control-ready model of EEG signal during general anesthesia in humans

Significant effort toward the automation of general anesthesia has been made in the past decade. One open challenge is in the development of control-ready patient models for closed-loop anesthesia delivery. Standard depth-of-anesthesia tracking does not readily capture inter-individual differences in response to anesthetics, especially those due to age, and does not aim to predict a relationship between a control input (infused anesthetic dose) and system state (commonly, a function of electroencephalography (EEG) signal). In this work, we developed a control-ready patient model for closed-loop propofol-induced anesthesia using data recorded during a clinical study of EEG during general anesthesia in ten healthy volunteers. We used principal component analysis to identify the low-dimensional state-space in which EEG signal evolves during anesthesia delivery. We parameterized the response of the EEG signal to changes in propofol target-site concentration using logistic models. We note that inter-individual differences in anesthetic sensitivity may be captured by varying a constant cofactor of the predicted effect-site concentration. We linked the EEG dose-response with the control input using a pharmacokinetic model. Finally, we present a simple nonlinear model predictive control in silico demonstration of how such a closed-loop system would work.

Closed-Loop Control of Anesthesia: Survey on Actual Trends, Challenges and Perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy.

Hemodynamic stability of closed-loop anesthesia systems: a systematic review.

This systematic review investigates the effect of closed-loop anesthesia delivery on the maintenance of cardiovascular parameters. The specific challenges arise from the fact that many physiological variables used for the control of anesthetic delivery and maintenance of hemodynamic stability are regulated by the autonomic nervous system, which is subject to high inter-individual variability. A systematic database search (MEDLINE, EMBASE and Web of Science) was conducted following the PRISMA guidelines and the principles of the Cochrane Handbook for Systematic Reviews of Interventions. Identified articles were screened and studies that fulfilled the eligibility criteria using the PICO approach (Patient, Intervention, Comparison, Outcome) were included in a random effects model to calculate weighted mean and 95% confidence intervals. Twenty studies (1402 subjects: 706 intervention and 696 control) were included in this review. Meta-analysis showed that closed-loop systems achieved longer duration of heart rate and MAP control, at 90.9% (95% CI: 90.0-91.8%) and 88.2% (95% CI: 87.4-89.0%) respectively, compared to the respective manual control group at 86.6% (95% CI: 85.1-88.0%) and 85.1% (95% CI: 84.3-86.0%). Subgroup analysis demonstrated better performance and faster recovery compared to the control group. The findings support the use of closed-loop systems for anesthetic delivery. Interpretation should take into account limitations, such as the large variations in the selected studies in the type of parameters used to measure outcomes. In summary, this review provides evidence supporting the importance of considering cardiovascular variables in the design of automated anesthetic delivery systems.

The Effect of Dexmedetomidine on Propofol Requirements During Anesthesia Administered by Bispectral Index-Guided Closed-Loop Anesthesia Delivery System: A Randomized Controlled Study.

Dexmedetomidine, a selective α2-adrenergic agonist currently approved for continuous intensive care unit sedation, is being widely evaluated for its role as a potential anesthetic. The closed-loop anesthesia delivery system (CLADS) is a method to automatically administer propofol total intravenous anesthesia using bi-spectral index (BIS) feedback and attain general anesthesia (GA) steady state with greater consistency. This study assessed whether dexmedetomidine is effective in further lowering the propofol requirements for total intravenous anesthesia facilitated by CLADS. After ethics committee approval and written informed consent, 80 patients undergoing elective major laparoscopic/robotic surgery were randomly allocated to receive GA with propofol CLADS with or without the addition of dexmedetomidine. Quantitative reduction of propofol and quality of depth-of-anesthesia (primary objectives), intraoperative hemodynamics, incidence of postoperative adverse events (sedation, analgesia, nausea, and vomiting), and intraoperative awareness recall (secondary objectives) were analyzed. There was a statistically significant lowering of propofol requirement (by 15%) in the dexmedetomidine group for induction of anesthesia (dexmedetomidine group: mean ± standard deviation 0.91 ± 0.26 mg/kg; nondexmedetomidine group: 1.07 ± 0.23 mg/kg, mean difference: 0.163, 95% CI, 0.04-0.28; P = .01) and maintenance of GA (dexmedetomidine group: 3.25 ± 0.97 mg/kg/h; nondexmedetomidine group: 4.57 ± 1.21 mg/kg/h, mean difference: 1.32, 95% CI, 0.78-1.85; P < .001). The median performance error of BIS control, a measure of bias, was significantly lower in dexmedetomidine group (1% [-5.8%, 8%]) versus nondexmedetomidine group (8% [2%, 12%]; P = .002). No difference was found for anesthesia depth consistency parameters, including percentage of time BIS within ±10 of target (dexmedetomidine group: 79.5 [72.5, 85.3]; nondexmedetomidine group: 81 [68, 88]; P = .534), median absolute performance error (dexmedetomidine group: 12% [10%, 14%]; nondexmedetomidine group: 12% [10%, 14%]; P = .777), wobble (dexmedetomidine group: 10% [8%, 10%]; nondexmedetomidine group: 8% [6%, 10%]; P = .080), and global score (dexmedetomidine group: 25.2 [23.1, 35.8]; nondexmedetomidine group: 24.7 [20, 38.1]; P = .387). Similarly, there was no difference between the groups for percentage of time intraoperative heart rate and mean arterial pressure remained within 20% of baseline. However, addition of dexmedetomidine to CLADS propofol increased the incidence of significant bradycardia (dexmedetomidine group: 14 [41.1%]; nondexmedetomidine group: 3 [9.1%]; P = .004), hypotension (dexmedetomidine group: 9 [26.5%]; nondexmedetomidine group: 2 [6.1%]; P = .045), and early postoperative sedation. The addition of dexmedetomidine to propofol administered by CLADS was associated with a consistent depth of anesthesia along with a significant decrease in propofol requirements, albeit with an incidence of hemodynamic depression and early postoperative sedation.

Developing a personalized closed-loop controller of medically-induced coma in a rodent model

Objective. Personalized automatic control of medically-induced coma, a critical multi-day therapy in the intensive care unit, could greatly benefit clinical care and further provide a novel scientific tool for investigating how the brain response to anesthetic infusion rate changes during therapy. Personalized control would require real-time tracking of inter- and intra-subject variabilities in the brain response to anesthetic infusion rate while simultaneously delivering the therapy, which has not been achieved. Current control systems for medically-induced coma require a separate offline model fitting experiment to deal with inter-subject variabilities, which would lead to therapy interruption. Removing the need for these offline interruptions could help facilitate clinical feasbility. In addition, current systems do not track intra-subject variabilities. Tracking intra-subject variabilities is essential for studying whether or how the brain response to anesthetic infusion rate changes during therapy. Further, such tracking could enhance control precison and thus help facilitate clinical feasibility. Approach. Here we develop a personalized closed-loop anesthetic delivery (CLAD) system in a rodent model that tracks both inter- and intra-subject variabilities in real time while simultaneously controlling the anesthetic in closed loop. We tested the CLAD in rats by administrating propofol to control the electroencephalogram (EEG) burst suppression. We first examined whether the CLAD can remove the need for offline model fitting interruption. We then used the CLAD as a tool to study whether and how the brain response to anesthetic infusion rate changes as a function of changes in the depth of medically-induced coma. Finally, we studied whether the CLAD can enhance control compared with prior systems by tracking intra-subject variabilities. Main results. The CLAD precisely controlled the EEG burst suppression in each rat without performing offline model fitting experiments. Further, using the CLAD, we discovered that the brain response to anesthetic infusion rate varied during control, and that these variations correlated with the depth of medically-induced coma in a consistent manner across individual rats. Finally, tracking these variations reduced control bias and error by more than 70% compared with prior systems. Significance. This personalized CLAD provides a new tool to study the dynamics of brain response to anesthetic infusion rate and has significant implications for enabling clinically-feasible automatic control of medically-induced coma.

Ant Colony Optimization PID Control of Hypnosis With Propofol Using Renyi Permutation Entropy as Controlled Variable

General anesthesia is a critical procedure in clinical surgery. To offer a closed control of depth of hypnosis, we establish a closed-loop anesthetic delivery system for propofol anesthesia. The system consists of three components: 1) three-compartment pharmacokinetic model; 2) a pharmacodynamics model obtained by identifying the relationship of effect–site concentration and Renyi permutation entropy via particle swarm optimization; and 3) ant colony optimization proportion integration differentiation controller. The performance of Renyi permutation entropy and bispectral index in tracking the effect–site concentration is evaluated by the prediction probability. The assessments of the performance of the controller are using: 1) the rising time, percent overshoot, and settling time and 2) median performance error, median absolute performance error, wobble, divergence, and integral absolute error. The results show that the prediction probability of Renyi permutation entropy (0.79±0.13 and mean±standard deviation) is higher than the bispectral index (0.74±0.15). The ant colony optimization proportion integration differentiation controller is quick to respond to sudden changes and maintains the stabilty at the desired depth of hypnosis (rise time and overshoot are 4.53±1.96 min and 3.48±1.49 (%), respectively). In conclusion, the proposed closed-loop anesthetic delivery system has potential value for accurate anesthetic administration.

Anaesthetic depth control using closed loop anaesthesia delivery system vs. target controlled infusion in patients with moderate to severe left ventricular systolic dysfunction

ObjectivesTo compare the efficacy of anaesthetic depth control using Closed Loop Anaesthesia Delivery System (CLADS) and Target Controlled Infusion (TCI) in patients with moderate to severe left ventricular dysfunction (LVSD). DesignRandomized control trial. PatientsForty ASA III/IV adult patients with moderate to severe LVSD scheduled for open heart surgery. InterventionsPropofol was administered using CLADS or TCI for maintaining BIS of 50. Induction and maintenance doses were controlled automatically in CLADS. Dixon's up and down method was used to estimate the plasma concentration needed for induction in TCI. MeasurementPercentage of total anaesthesia time (“valid CLADS time”) for which BIS remained within ±10 of target (BIS=50). Main resultsBIS remained within ±10 of the target for a significantly longer duration of time in CLADS group (p=0.001). Performance parameters like Median Performance Error (MDPE), p=0.024; Median Absolute Performance Error (MDAPE), p=0.0212; and global score p=0.017 were significantly better in CLADS group. Total propofol consumption was significantly less in CLADS group (p=0.014). Mean value (95% CI) of EC50 and EC95 for target plasma propofol concentration for induction was 1.62 (1.45–1.79) μgml−1 and 1.87 (1.73–2.96) μgml−1 respectively using probit analysis. ConclusionsClosed loop delivery of propofol using CLADS performed significantly better than TCI in this subset of patients. Clinical trials registration no.www.ClinicalTrials.gov-NCT02645994

Efficacy of closed-loop infusion of propofol for surgery on body surface in preschool and school-age pediatric patients

Objective To evaluate the efficacy of closed-loop infusion of propofol for surgery on the body surface in preschool and school-age pediatric patients. Methods American Society of Anesthesiologists physical status Ⅰ pediatric patients of both sexes, undergoing elective surgery on the body surface, were included in the study.One hundred twenty-four preschool pediatric patients (aged 3-6 yr) were divided into 2 groups (n=62 each) using a random number table: closed-loop group (CPRE group) and open-loop group (OPRE group). Thirty-eight school-age pediatric patients (aged 7-12 yr) were divided into 2 groups (n=19 each) using a random number table: closed-loop group (CSTU group) and open-loop group (OSTU group). The target bispectral index (BIS) value was set at 50 during maintenance of anesthesia.Propofol was delivered using closed-loop anesthesia delivery system, and the target plasma concentration of propofol was automatically regulated in CPRE and CSTU groups.Propofol was given using open-loop anesthesia delivery system, and the target plasma concentration of propofol was manually regulated in OPRE and OSTU groups.The adequate anesthesia time ratio (BIS40-60%) and global score (GS) of the delivery system were recorded during maintenance of anesthesia.The consumption of propofol, frequency of regulation and mean BIS value were recorded.The delivery system-related complications were recorded during the perioperative period. Results Compared with group OPRE, the consumption of propofol, GS and mean BIS value were significantly decreased, BIS40-60% was increased (P 0.05). Compared with group OSTU, GS was significantly decreased, BIS40-60% was increased (P 0.05). Compared with group CPRE, the consumption of propofol and GS were significantly decreased, BIS40-60% was increased (P 0.05). One pediatric patient in group CSTU developed airway spasm, and no severe complications were found in the other pediatric patients. Conclusion For surgery on the body surface in preschool and school-age pediatric patients, closed-loop infusion of propofol is safe and effective, the stability and precision of anesthesia is superior to that of open-loop anesthesia delivery system, and it provides better efficacy in school-age pediatric patients than in preschool pediatric patients. Key words: Propofol; Drug delivery systems; Child

The effect of sevoflurane on propofol with closed-loop target controlled infusion anesthesia system

Objective The primary goal was to determine the influence of sevoflurane on propofol with closed-loop target controlled infusion(TCI) delivery system in combined intravenous-inhalation anesthesia. Methods Sixty patients with ASA Ⅰ or Ⅱ were enrolled in this double-blinded study and randomly devided into three groups using the random number table method: group A, in which patients were only administrated propofol, group B and C, in which patients were given propofol for 20 min, followed with 0.6 MAC or 0.8 MAC sevoflurane for 40 min, respectively and the inhalation concentration was adjusted according to MAC. Serum propofol concentration was monitored continuouslyin. Results There was no difference in BIS among three groups (P>0.05). Serum concentration of propofol in group B and group C decreased constantly after the inhalation of sevoflurane but increased slowly when the inhalation was terminated(P<0.05), while it remained unchanged in group A. Compared with group A[(2.53±0.55), (2.54±0.61) mg/L], the decline of serum concentration was more evident in group C[(1.81±0.63), (1.24±0.4) mg/L] than group B[(1.92±0.58), (1.67±0.64) mg/L] at 30 min and 40 min during the surgery. The changing rate in group C was higher than that of group B and became stable 40 min after the stop point of propofol. Conclusions The administration of propofol with closed-loop anesthesia delivery system could adjust the anesthesia depth automatically and effectively, when the sevoflurane was inhaled with the MAC under 0.8. Key words: Sevoflurane; Propofol; Blood drug level; Closed-loop anesthesia delivery system