Pharmacodynamic Interaction of Remifentanil and Dexmedetomidine on Depth of Sedation and Tolerance of Laryngoscopy

Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram

BACKGROUND

Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep using multichannel electroencephalogram (EEG) recordings.

METHODS

Multichannel EEG datasets from two different sources were used in this study: (1) sedation dataset consisting of 102 healthy volunteers receiving propofol (N = 36), sevoflurane (N = 36), or dexmedetomidine (N = 30), and (2) publicly available sleep EEG dataset (N = 994). Forty-four quantitative time, frequency and entropy features were extracted from EEG recordings and were used to train the machine learning algorithms on sleep dataset to predict sleep stages in the sedation dataset. The predicted sleep states were then compared with the Modified Observer's Assessment of Alertness/ Sedation (MOAA/S) scores.

RESULTS

The performance of the model was poor (AUC = 0.55-0.58) in differentiating sleep stages during propofol and sevoflurane sedation. In the case of dexmedetomidine, the AUC of the model increased in a sedation-dependent manner with NREM stages 2 and 3 highly correlating with deep sedation state reaching an AUC of 0.80.

CONCLUSIONS

We addressed an important clinical question to identify biological sleep promoting sedatives using EEG signals. We demonstrate that propofol and sevoflurane do not promote EEG patterns resembling natural sleep while dexmedetomidine promotes states resembling NREM stages 2 and 3 sleep, based on current sleep staging standards.

A prospective, randomized, single-blinded study comparing the efficacy and safety of dexmedetomidine and propofol for sedation during endoscopic retrograde cholangiopancreatography

BACKGROUND

Balanced propofol sedation is extensively used in endoscopic retrograde cholangiopancreatography (ERCP), but sedation-related adverse events (SRAEs) are common. In various clinical settings, the combination of dexmedetomidine with opioids and benzodiazepines has provided effective sedation with increased safety. The aim of this investigation was to compare the efficacy and safety of dexmedetomidine and propofol for sedation during ERCP.

METHODS

Forty-one patients were randomly divided into two groups: the dexmedetomidine (DEX) group and the propofol (PRO) group. Patients in the DEX group received an additional bolus of 0.6 μg kg-1 dexmedetomidine followed by a dexmedetomidine infusion at 1.2 μg kg-1 h-1, whereas the PRO group received 1-2 mg kg-1 of propofol bolus followed by a propofol infusion at 2-3 mg kg-1 h-1. During ERCP, the primary outcome was the incidence of hypoxemia (SpO2 < 90% for > 10 s). Other intraoperative adverse events were also recorded as secondary outcomes, including respiratory depression (respiratory rate of < 10 bpm min-1), hypotension (MAP < 65 mmHg), and bradycardia (HR < 45 beats min-1).

RESULTS

The incidence of hypoxemia was significantly reduced in the DEX group compared to the PRO group (0% versus 28.6%, respectively; P = 0.032). Patients in the PRO group exhibited respiratory depression more frequently than patients in the DEX group (35% versus 81%, respectively; P = 0.003). There were no significant differences in terms of hypotension and bradycardia episodes between groups. During the procedures, the satisfaction scores of endoscopists and patients, as well as the pain and procedure memory scores of patients were comparable between groups.

CONCLUSION

In comparison with propofol, dexmedetomidine provided adequate sedation safety with no adverse effects on sedation efficacy during ERCP.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2200061468, 25/06/2022.

Multimodal prevention of emergence cough following nasal endoscopic surgery under general anesthesia: a double-blind randomized trial

Purpose

Cough during emergence from anesthesia is a common problem and may cause adverse events. Monotherapy faces uncertainty in preventing emergence cough due to individual differences. We aimed to evaluate the efficacy and safety of multimodal intervention for preventing emergence cough in patients following nasal endoscopic surgery.

Methods

In this double-blind randomized trial, 150 adult patients undergoing nasal endoscopic surgery were randomly allocated into three groups. For the control group (n = 50), anesthesia was performed according to clinical routine, no intervention was provided. For the double intervention group (n = 50), normal saline 3 mL was sprayed endotracheally before intubation, 0.4 μg/kg dexmedetomidine was infused over 10 min after intubation, and target-controlled remifentanil infusion was maintained at an effect-site concentration of 1.5 ng/mL before extubation after surgery. For the multimodal intervention group (n = 50), 0.5% ropivacaine 3 mL was sprayed endotracheally before intubation, dexmedetomidine and remifentanil were administered as those in the double intervention group. The primary endpoint was the incidence of emergence cough, defined as single cough or more from end of surgery to 5 min after extubation.

Results

The incidences of emergence cough were 98% (49/50) in the control group, 90% (45/50) in the double group, and 70% (35/50) in the multimodal group, respectively. The incidence was significantly lower in the multimodal group than those in the control (relative risk 0.71; 95% CI 0.59 to 0.86; p < 0.001) and double (relative risk 0.78; 95% CI 0.63 to 0.95; p = 0.012) groups; the difference between the double and control groups was not statistically significant (relative risk 0.92; 95% CI 0.83 to 1.02; p = 0.20). The severity of sore throat was significantly lower in the multimodal group than that in the control group (median difference-1; 95% CI -2 to 0; p = 0.016). Adverse events did not differ among the three groups.

Conclusion

For adult patients undergoing endonasal surgery, multimodal intervention including ropivacaine topical anesthesia before intubation, dexmedetomidine administration after intubation, and remifentanil infusion before extubation after surgery significantly reduced emergence cough and was safe.

Antinociceptive Agents as General Anesthetic Adjuncts: Supra-additive and Infra-additive Interactions

The hypothesis "General anesthesia consists of producing both loss of consciousness and the inhibition of noxious stimuli reaching the brain and causing arousal" was used as a basis for the review of published data on general anesthetic interactions with antinociceptive agents: opioids, α 2 adrenergic agonists, and systemic sodium channel blockers. This review is focused on a specific type of anesthetic interaction-the transformation of antinociceptive agents into general anesthetic adjuncts. The primary aim is to answer 2 questions. First, how does an antinociceptive agent transform the effect of an anesthetic in providing a certain component of anesthesia-hypnosis, immobility, or hemodynamic response to noxious stimulation? Second, does a combination of an anesthetic with an adjunct result in a simple summation of their respective effects or in a supra-additive or infra-additive interaction? The Medline database was searched for data describing the interactions of antinociceptive agents and general anesthetics. The following classes of antinociceptive agents were considered: opioids, α 2 adrenergic agonists, and systemic sodium channel blockers. Drugs used in combination with antinociceptive agents were general anesthetics and benzodiazepines. The following terms related to drug interactions were used: anesthetic interactions, synergy, antagonism, isobolographic analysis, response surface analysis, and fractional analysis. The interactions of antinociceptive agents with general anesthetics result in a decrease of general anesthetic requirements, which differ for each of the components of general anesthesia: hypnosis, immobility, and hemodynamic response to noxious stimulation. Most studies of the nature of anesthetic interactions are related to opioid-general anesthetic combinations, and their conclusions usually confirm supra-additivity.

A comparative study of esketamine-dexmedetomidine and sufentanil-dexmedetomidine for sedation and analgesia in lung tumor percutaneous radiofrequency ablation (PRFA): a randomized double-blind clinical trial

OBJECTIVE

To observe and evaluate the effectiveness and safety of Esketamine or Sufentanil combined with Dexmedetomidine for sedation and analgesia in lung tumor percutaneous radiofrequency ablation (PRFA) to provide a clinical basis for the optimization of sedation and analgesia in lung tumor PRFA protocols outside the operating room.

METHODS

In this trial, 44 patients aged 37 to 84 undergoing lung tumor PRFA were enrolled and assigned to Group E (n = 22, Esketamine 0.2 mg/kg) or Group S (n = 22,Sufentanil 0.1 μg/kg ). Dexmedetomidine was infused intravenously as a sedative in both groups. The modified observer's assessment of alertness and sedation scale (MOAAS), physical movement pain scale, intraoperative vital signs, anesthesia recovery time, radiologist and patient satisfaction rates, incidence of respiratory depression, and incidence of postoperative nausea and vomiting were recorded.

RESULTS

Although there was no significant difference in the physical movement pain scale, blood oxygen saturation or incidence of perioperative adverse events between the two groups during ablation, the MOAAS, mean arterial pressure (MAP) and heart rate (HR) were higher in Group E than in Group S. The anesthesia recovery time was shorter in Group E than in Group S, and radiologist satisfaction was better in Group E than in Group S, but there was no significant difference between the two groups in terms of patient satisfaction.

CONCLUSION

Esketamine or Sufentanil combined with Dexmedetomidine is safe for lung tumor PRFA. However, in elderly patients with multiple underlying diseases, low-dose Esketamine combined with Dexmedetomidine has fewer hemodynamic effects on patients, milder respiratory depression, shorter recovery time, and better radiologist satisfaction because of its better controllability of sedation depth.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (Registration number#ChiCTR ChiCTR21000500 21); Date of Registration: 16/08/2021.

Pharmacodynamic mechanism-based interaction model for the haemodynamic effects of remifentanil and propofol in healthy volunteers

BACKGROUND

Propofol and remifentanil are frequently combined for the induction and maintenance of general anaesthesia. Both propofol and remifentanil cause vasodilation and potentially reduce arterial BP. We aimed to develop a mechanism-based model that characterises the haemodynamic interactions between remifentanil and propofol.

METHODS

Data from two clinical trials in healthy volunteers were analysed using remifentanil-alone, propofol-alone, and combination groups. We evaluated remifentanil effects on haemodynamics using a previously developed mechanism-based haemodynamic model of propofol. The interaction between propofol and remifentanil was explored using the principles of the general pharmacodynamic interaction (GPDI) model.

RESULTS

Remifentanil alone increased the dissipation rate of total peripheral resistance by 50% at 3.0 ng ml-1. Additionally, the dissipation rates of HR and stroke volume were attenuated by 4.8% and 4.9% per 1 ng ml-1 increase in remifentanil concentration, respectively. The maximal effect of propofol alone in decreasing the production rate of total peripheral resistance was 78%, which decreased to 32% when combined with remifentanil 4 ng ml-1. The effects of remifentanil on HR and stroke volume were attenuated by propofol with maximum decreases of 11.9% and 21.2%, respectively. Goodness-of-fit plots and prediction-corrected visual predictive check plots showed good predictive performance of the models.

CONCLUSIONS

The structure of the previous mechanism-based haemodynamic model for propofol was able to describe the effects of remifentanil alone on haemodynamic variables. The GPDI model provided a good framework for characterising the pharmacodynamic interaction between remifentanil and propofol on haemodynamic properties.

CLINICAL TRIAL REGISTRATION

NCT02043938; NCT03143972.

Evaluation of the electroencephalogram in awake, sedated, and anesthetized dogs

Sedation and anesthesia alter the raw electroencephalogram (EEG). Interpretation of the EEG is facilitated by measuring the patient state index (PSI), visual inspection of density spectral arrays (DSA), and power density analysis of the delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta plus gamma (12-40 Hz) frequency bands. Baseline data were recorded in six male intact Beagles before sedation with intravenous acepromazine (0.03 mg/kg) and hydromorphone (0.1 mg/kg). Anesthesia was induced and maintained for five minutes with intravenous propofol (1.5 mg/kg over five seconds followed by 12 mg/kg/h). Additional propofol (0.5-1.0 mg/kg and up to 16.7 mg/kg/h) was administered within this time frame if the PSI was above 50. The effects of sedation and anesthesia were evaluated with a mixed-effect model followed by Dunnett's test (alpha = 0.05). The average baseline PSI (95% confidence interval) was 93.0 (91.4-94.6) and decreased on sedation [88.7 (86.0-91.3); p = 0.039] and anesthesia [44.5 (40.8-48.2); p < 0.001]. The awake DSA showed dense power in all bands. The power density decreased with sedation. During anesthesia, the power density was reduced in frequencies above 12 Hz. The baseline power density on the delta, theta, alpha, and beta plus gamma bands was higher than sedation (p < 0.007). Compared to baseline, anesthesia had lower power on delta, and beta plus gamma bands (p < 0.002). The interpretation in awake, sedated, and anesthetized dogs of the EEG can be facilitated by processing and generating PSI and DSA.

Efficacy of Intranasal Atomized Dexmedetomidine for Sedation in Surgical Removal of Impacted Mandibular Third Molars: A Prospective Study

AIMS AND OBJECTIVES

To assess the efficacy of dexmedetomidine atomized intranasally for sedation during surgical removal of impacted mandibular third molars.

MATERIALS AND METHODS

A prospective randomized trial was conducted on 25 anxious patients between the ages of 18 and 40 who had impacted the lower third molars. An intranasal atomization device was used to give the medication 30 minutes prior to the surgical procedure. The Ramsay sedation score and Observer's assessment of alertness/sedation score were used to assess intranasal sedation.

RESULTS

The results of our study state that the sedative effect began to take effect between 30 and 45 minutes later and was nearly back to baseline by 105 minutes after the administration of intranasal dexmedetomidine.

CONCLUSION

Intranasal delivery of 1.5mg/kg atomized dexmedetomidine for patients undergoing surgical removal of impacted mandibular third teeth is safe, feasible, and clinically efficient in daycare settings based on the sedation scores, and secondary variables which were assessed.

Comparison Effects of Propofol-Dexmedetomidine versus Propofol-Remifentanil for Endoscopic Ultrasonography: A Prospective Randomized Comparative Trial

Objective. To compare the effects of propofol-dexmedetomidine versus propofol-remifentanil for endoscopic ultrasonography (EUS). Design, Setting, and Participants. A single-center, randomized trial from August 20, 2020 to August 20, 2021, in patients undergoing EUS. Interventions. Propofol-dexmedetomidine (PD) versus propofol-remifentanil (PR). Outcome Measures. The primary outcome was the endoscopist satisfaction level. The secondary outcomes included patient satisfaction, the incidence of adverse events, induction time, and time to achieve postanesthesia discharge score (PADS) ≥9. Methods. Total of 200 patients were enrolled and randomized into PD and PR groups. A bolus dose of 0.5 μg/kg dexmedetomidine was injected intravenously for 5 min. Subsequently, a continuous infusion of 0.5 μg/kg/h for the PD group. Remifentanil was continuously infused at 1.5 μg/kg/h for the PR group. A bolus dose of 1 mg/kg propofol was administered to both groups and then continuously infused. Results. The endoscopist satisfaction level was higher in the PR group than in the PD group ( $P=0.009$ ). Patient satisfaction was not significantly different between the groups ( $P=0.738$ ). No patients required mask ventilation or tracheal intubation in both groups. All patients were relatively hemodynamically stable. The incidence of body movements during the procedure in the PD group was higher than in the PR group ( $P=0.035$ ). The induction time and time taken to achieve PADS ≥9 in the PD group were longer than in the PR group ( $P<0.05$ ). Conclusions. PR sedation can increase the satisfaction level of the endoscopist by providing faster induction time and lower body movement and that of the patient by achieving faster PADS than PD sedation. Trial registration number: http://www.chictr.org.cn (ChiCTR2000034987).

General Purpose Pharmacokinetic-Pharmacodynamic Models for Target-Controlled Infusion of Anaesthetic Drugs: A Narrative Review

Target controlled infusion (TCI) is a clinically-available and widely-used computer-controlled method of drug administration, adjusting the drug titration towards user selected plasma- or effect-site concentrations, calculated according to pharmacokinetic-pharmacodynamic (PKPD) models. Although this technology is clinically available for several anaesthetic drugs, the contemporary commercialised PKPD models suffer from multiple limitations. First, PKPD models for anaesthetic drugs are developed using deliberately selected patient populations, often excluding the more challenging populations, such as children, obese or elderly patients, of whom the body composition or elimination mechanisms may be structurally different compared to the lean adult patient population. Separate PKPD models have been developed for some of these subcategories, but the availability of multiple PKPD models for a single drug increases the risk for invalid model selection by the user. Second, some models are restricted to the prediction of plasma-concentration without enabling effect-site controlled TCI or they identify the effect-site equilibration rate constant using methods other than PKPD modelling. Advances in computing and the emergence of globally collected databases has allowed the development of new “general purpose” PKPD models. These take on the challenging task of identifying the relationships between patient covariates (age, weight, sex, etc) and the volumes and clearances of multi-compartmental pharmacokinetic models applicable across broad populations from neonates to the elderly, from the underweight to the obese. These models address the issues of allometric scaling of body weight and size, body composition, sex differences, changes with advanced age, and for young children, changes with maturation and growth. General purpose models for propofol, remifentanil and dexmedetomidine have appeared and these greatly reduce the risk of invalid model selection. In this narrative review, we discuss the development, characteristics and validation of several described general purpose PKPD models for anaesthetic drugs.

An Update of Neuroanesthesia for Intraoperative Brain Mapping Craniotomy

The perioperative multidisciplinary team approach has probably been best exemplified by the care of awake craniotomy patients. Advancement in anesthesia and meticulous perioperative care has supported the safety and complexity of the surgical and mapping efforts in glioma resection. The discussions in this review will emphasize on anesthetic and perioperative management strategies to prevent complications and minimize their effects if they occur, including current practice guidelines in anesthesia, updates on the applications of anesthetic medications, and emerging devices. Planning the anesthetic and perioperative management is based on understanding the pharmacology of the medications, the goals of different stages of the surgery and mapping, and anticipating potential problems.

Dexmedetomidine Clearance Decreases with Increasing Drug Exposure: Implications for Current Dosing Regimens and Target-controlled Infusion Models Assuming Linear Pharmacokinetics

BACKGROUND

Numerous pharmacokinetic models have been published aiming at more accurate and safer dosing of dexmedetomidine. The vast majority of the developed models underpredict the measured plasma concentrations with respect to the target concentration, especially at plasma concentrations higher than those used in the original studies. The aim of this article was to develop a dexmedetomidine pharmacokinetic model in healthy adults emphasizing linear versus nonlinear kinetics.

METHODS

The data of two previously published clinical trials with stepwise increasing dexmedetomidine target-controlled infusion were pooled to build a pharmacokinetic model using the NONMEM software package (ICON Development Solutions, USA). Data from 48 healthy subjects, included in a stratified manner, were utilized to build the model.

RESULTS

A three-compartment mamillary model with nonlinear elimination from the central compartment was superior to a model assuming linear pharmacokinetics. Covariates included in the final model were age, sex, and total body weight. Cardiac output did not explain between-subject or within-subject variability in dexmedetomidine clearance. The results of a simulation study based on the final model showed that at concentrations up to 2 ng · ml-1, the predicted dexmedetomidine plasma concentrations were similar between the currently available Hannivoort model assuming linear pharmacokinetics and the nonlinear model developed in this study. At higher simulated plasma concentrations, exposure increased nonlinearly with target concentration due to the decreasing dexmedetomidine clearance with increasing plasma concentrations. Simulations also show that currently approved dosing regimens in the intensive care unit may potentially lead to higher-than-expected dexmedetomidine plasma concentrations.

CONCLUSIONS

This study developed a nonlinear three-compartment pharmacokinetic model that accurately described dexmedetomidine plasma concentrations. Dexmedetomidine may be safely administered up to target-controlled infusion targets under 2 ng · ml-1 using the Hannivoort model, which assumed linear pharmacokinetics. Consideration should be taken during long-term administration and during an initial loading dose when following the dosing strategies of the current guidelines.

EDITOR’S PERSPECTIVE

Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learning

Study Objectives

Dexmedetomidine-induced electroencephalogram (EEG) patterns during deep sedation are comparable with natural sleep patterns. Using large-scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine-induced deep sedation indeed mimics natural sleep patterns.

Methods

We used EEG recordings from three sources in this study: 8,707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine-induced sedation levels were assessed using the Modified Observer’s Assessment of Alertness/Sedation (MOAA/S) score. We extracted 22 spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine, and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state.

Results

The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine-induced deep sedation (MOAA/S = 0) with area under the receiver operator characteristics curve >0.8 outperforming other machine learning models. Power in the delta band (0–4 Hz) was selected as an important feature for prediction in addition to power in theta (4–8 Hz) and beta (16–30 Hz) bands.

Conclusions

Using a large-scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine-induced deep sedation state mimics N3 sleep EEG patterns.

Clinical Trials

Name—Pharmacodynamic Interaction of REMI and DMED (PIRAD), URL—https://clinicaltrials.gov/ct2/show/NCT03143972, and registration—NCT03143972.

A systematic review and meta-analysis of the safety and efficacy of remifentanil and dexmedetomidine for awake fiberoptic endoscope intubation

BACKGROUND

Awake fiberoptic endoscope intubation (AFOI) is the primary strategy for managing anticipated difficult airways. Adequate sedation, most commonly being achieved with remifentanil and dexmedetomidine, is integral to this procedure. This meta-analysis aimed to compare the safety and efficacy of these 2 sedatives.

METHODS

We conducted electronic searches in Embase, Web of Science, PubMed, Google Scholar, Medline, Springer, and Web of Science with no language restrictions. Studies comparing safety and efficacy between the sole use of remifentanil and dexmedetomidine among patients who underwent AFOI were included. Eight randomized controlled trials, comprising 412 patients, met the inclusion criteria. The primary outcomes were first attempt intubation success rate and incidence of hypoxia. The secondary outcomes were the Ramsay Sedation Scale score at intubation, memory recall of endoscopy, and unstable hemodynamic parameters during intubation.

RESULTS

Dexmedetomidine significantly reduced the incidence of hypoxemia during AFOI (risk ratio: 2.47; 95% confidence [CI]: 1.32-4.64]) compared with remifentanil; however, the first intubation success rates were equivalent (risk ratio: 1.12; 95% CI: 0.87-1.46]. No significant differences between the 2 sedatives were found for the Ramsay Sedation Scale score at intubation (mean difference: -0.14; 95% CI: -0.66-0.38) or unstable hemodynamic parameters during intubation (risk ratio: 0.83; 95% CI: 0.59-1.17). Dexmedetomidine reduced memory recall of endoscopy (risk ratio: 1.39; 95% CI: 1.13-1.72).

CONCLUSIONS

While both remifentanil and dexmedetomidine are effective for AFOI and well-tolerated, dexmedetomidine may be more effective in reducing the incidence of hypoxemia and memory recall of endoscopy.

PROSPERP REGISTRATION NUMBER

CRD42020169612.

Population Pharmacodynamic Modeling Using the Sigmoid Emax Model: Influence of Inter-individual Variability on the Steepness of the Concentration-Effect Relationship. a Simulation Study

The relationship between the concentration of a drug and its pharmacological effect is often described by empirical mathematical models. We investigated the relationship between the steepness of the concentration–effect relationship and inter-individual variability (IIV) of the parameters of the sigmoid E_max model, using the similarity between the sigmoid E_max model and the cumulative log-normal distribution. In addition, it is investigated whether IIV in the model parameters can be estimated accurately by population modeling. Multiple data sets, consisting of 40 individuals with 4 binary observations in each individual, were simulated with varying values for the model parameters and their IIV. The data sets were analyzed using Excel Solver and NONMEM. An empirical equation (Eq. (11)) was derived describing the steepness of the population-predicted concentration–effect profile (γ*) as a function of γ and IIV in C50 and γ, and was validated for both binary and continuous data. The tested study design is not suited to estimate the IIV in C50 and γ with reasonable precision. Using a naive pooling procedure, the population estimates γ* are significantly lower than the value of γ used for simulation. The steepness of the population-predicted concentration–effect relationship (γ*) is less than that of the individuals (γ). Using γ*, the population-predicted drug effect represents the drug effect, for binary data the probability of drug effect, at a given concentration for an arbitrary individual.

Frontal electroencephalogram based drug, sex, and age independent sedation level prediction using non-linear machine learning algorithms

Brain monitors which track quantitative electroencephalogram (EEG) signatures to monitor sedation levels are drug and patient specific. There is a need for robust sedation level monitoring systems to accurately track sedation levels across all drug classes, sex and age groups. Forty-four quantitative features estimated from a pooled dataset of 204 EEG recordings from 66 healthy adult volunteers who received either propofol, dexmedetomidine, or sevoflurane (all with and without remifentanil) were used in a machine learning based automated system to estimate the depth of sedation. Model training and evaluation were performed using leave-one-out cross validation methodology. We trained four machine learning models to predict sedation levels and evaluated the influence of remifentanil, age, and sex on the prediction performance. The area under the receiver-operator characteristic curve (AUC) was used to assess the performance of the prediction model. The ensemble tree with bagging outperformed other machine learning models and predicted sedation levels with an AUC = 0.88 (0.81–0.90). There were significant differences in the prediction probability of the automated systems when trained and tested across different age groups and sex. The performance of the EEG based sedation level prediction system is drug, sex, and age specific. Nonlinear machine-learning models using quantitative EEG features can accurately predict sedation levels. The results obtained in this study may provide a useful reference for developing next generation EEG based sedation level prediction systems using advanced machine learning algorithms.

Clinical trial registration: NCT 02043938 and NCT 03143972.

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.

Effects of propofol and dexmedetomidine with and without remifentanil on serum cytokine concentrations in healthy volunteers: a post hoc analysis

BACKGROUND

Anaesthetic agents are likely to alter circulating cytokine concentrations. Because preceding studies have not been able to exclude the contribution of surgical trauma, perioperative stress, or both to circulating cytokine concentrations, the effects of anaesthesia remain unclear. The aim of this study was to quantify serum cytokines in healthy volunteers administered i.v. anaesthetic agents in the absence of surgical trauma and perioperative stress.

METHODS

Serum samples obtained during previous standardised studies from healthy volunteers were compared before and 6-8 h after induction of anaesthesia with propofol (n=31), propofol/remifentanil (n=30), dexmedetomidine (n=17) or dexmedetomidine/remifentanil (n=15). Anaesthetic regimens were standardised and volunteers did not undergo any surgical intervention. Serum concentrations of interleukin (IL)2, IL4, IL6, IL10, IL17, IL18, IL21, IL22, IL23, C-X-C motif ligand 8, interferon gamma, E-selectin, L-selectin, major histocompatibility complex class I chain-polypeptide-related sequence (MIC)A, MICB, Granzyme A, and Granzyme B were quantified using a multiplexed antibody-based assay (Luminex).

RESULTS

Samples were obtained from volunteers of either sex aged 18-70 yr. After anaesthesia with propofol alone, concentrations of IL4 (P=0.012), IL6 (P=0.027), IL21 (P=0.035), IL22 (P=0.002), C-X-C motif ligand 8 (P=0.004), MICB (P=0.046), and Granzyme A (P=0.045) increased. After anaesthesia with propofol and remifentanil, IL17 (P=0.013), interferon gamma (P=0.003), and MICA (P=0.001) decreased, but IL6 (P=0.006) and L-selectin (P=0.001) increased. After dexmedetomidine alone, IL18 (P=0.002), L-selectin (P=0.017), E-selectin (P=0.002), and Granzyme B (P=0.023) decreased. After dexmedetomidine with remifentanil no changes were observed.

CONCLUSIONS

In healthy volunteers not undergoing surgery, different i.v. anaesthesia regimens were associated with differential effects on circulating cytokines.

Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach

BACKGROUND:

Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method.

METHODS:

Using deep learning algorithms, we developed a novel data-repurposing framework to predict hypnotic levels from sleep brain rhythms. We used an online large sleep data set (5723 clinical EEGs) for training the deep learning algorithm and a clinical trial hypnotic data set (30 EEGs) for testing during dexmedetomidine infusion. Model performance was evaluated using accuracy and the area under the receiver operator characteristic curve (AUC).

RESULTS:

The deep learning model (a combination of a convolutional neural network and long short-term memory units) trained on sleep EEG predicted deep hypnotic level with an accuracy (95% confidence interval [CI]) = 81 (79.2–88.3)%, AUC (95% CI) = 0.89 (0.82–0.94) using dexmedetomidine as a prototype drug. We also demonstrate that EEG patterns during dexmedetomidine-induced deep hypnotic level are homologous to nonrapid eye movement stage 3 EEG sleep.

CONCLUSIONS:

We propose a novel method to develop hypnotic level monitors using large sleep EEG data, deep learning, and a data-repurposing approach, and for optimizing such a system for monitoring any given individual. We provide a novel data-repurposing framework to predict hypnosis levels using sleep EEG, eliminating the need for new clinical trials to develop hypnosis level monitors.

Intrathecal injection of dexmedetomidine ameliorates chronic neuropathic pain via the modulation of MPK3/ERK1/2 in a mouse model of chronic neuropathic pain

Objective: Despite the application of dexmedetomidine (DEX) as a perioperative adjuvant in local analgesia, the exact analgesic mechanism underpinning chronic neuropathic pain (CNP) awaits our elucidation. Methods: We investigated the molecular mechanisms of the anti-nociceptive effect of DEX on neuropathic pain in a mouse model induced by chronic constriction injury (CCI). Results: DEX administration significantly increased the paw withdrawal latency (PWL) values 0.5 to 2 h post-injection in CCI-induced CNP mice at day 5 to 21 versus dimethyl sulfoxide (DMSO)-treated mice, confirming its analgesic effect. The c-Fos expression was significantly elevated in CCI mice versus the sham-operated group, whereas the elevation was mitigated by DEX injection. Subsequently, the involvement of MKP1 and MKP3 in the pathogenesis of chronic neuropathic pain was evaluated. Western blotting analyses revealed significant decrease in both MKP1 and MKP3 in the spinal cord in CCI group versus the sham group. DEX markedly elevated the MKP3 expression and modestly reduced the MKP1 expression, with insignificant difference in the latter. Co-injection of BCI (an MKP3 inhibitor) and DEX evidently reduced the PWL values in CCI mice. Furthermore, DEX significantly downregulated the phosphorylation of extracellular-signal-regulated kinase (ERK) 1/2, down-stream effector of MKP3 in CCI mice, whereas the downregulation was reversed by BCI. Conclusion: We confirmed that DEX exerts the analgesic effect on chronic neuropathic pain via the regulation of MKP3/ERK1/2 signaling pathway, which may contribute to clarification of the molecular mechanism and novel therapy for chronic neuropathic pain.

Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers

BACKGROUND

Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used.

METHODS

In total, 102 subjects receiving propofol (N=36; 16 male/20 female), sevoflurane (N=36; 16 male/20 female), or dexmedetomidine (N=30; 15 male/15 female) were included in this study of healthy volunteers. Sedation level was assessed using the Modified Observer's Assessment of Alertness/Sedation (MOAA/S) score. We used 44 QEEG features estimated from the EEG data in a logistic regression algorithm, and an elastic-net regularisation method was used for feature selection. The area under the receiver operator characteristic curve (AUC) was used to assess the performance of the logistic regression model.

RESULTS

The performances obtained when the system was trained and tested as drug-dependent mode to distinguish between awake and sedated states (mean AUC [standard deviation]) were propofol=0.97 (0.03), sevoflurane=0.74 (0.25), and dexmedetomidine=0.77 (0.10). The drug-independent system resulted in mean AUC=0.83 (0.17) to discriminate between the awake and sedated states.

CONCLUSIONS

The incorporation of large numbers of QEEG features and machine learning algorithms is feasible for next-generation monitors of sedation level. Different QEEG features were selected for propofol, sevoflurane, and dexmedetomidine groups, but the sedation-level estimator maintained a high performance for predicting MOAA/S independent of the drug used.

CLINICAL TRIAL REGISTRATION

NCT02043938; NCT03143972.