Tracheal extubation under Narcotrend EEG monitoring at different depths of anesthesia after tonsillectomy in children: a prospective randomized controlled study.

Objective: This study aims to investigate whether tracheal extubation at different depths of anesthesia using Narcotrend EEG (NT value) can influence the recovery quality from anesthesia and cognitive function of children who underwent tonsillotomy. Methods: The study enrolled 152 children who underwent tonsillotomy and were anesthetized with endotracheal intubation in our hospital from September 2019 to March 2022. These patients were divided into Group A (conscious group, NT range of 95-100), Group B (light sedation group, NT range of 80-94), and Group C (conventional sedation group, NT range of 65-79). A neonatal pain assessment tool, namely, face, legs, activity, cry, and consolability (FLACC), was used to compare the pain scores of the three groups as the primary end point. The Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scales were used to evaluate the cognitive function of children in the three groups before and after surgery as the secondary end points. Results: Differences were observed in the awakening time and FLACC scores after awakening among the three groups (P < 0.05). Among them, Group A exhibited a significantly shorter awakening time and higher FLACC score after awakening than those in Groups B and C (both P < 0.05). The total incidence of adverse reactions in Group B was significantly lower than that in Groups A and C (P < 0.05). No significant difference was observed in MMSE and MoCA scores before the operation and at 7 days after the operation among the three groups (P > 0.05), but a significant difference was found in MMSE and MoCA scores at 1 day and 3 days after the operation among the three groups (P < 0.05). In addition, MMSE and MoCA scores of the three groups decreased significantly at 1 day and 3 days after the operation than those at 1 day before the operation (P < 0.05). Conclusion: When the NT value of tonsillectomy is between 80 and 94, tracheal catheter removal can effectively improve the recovery quality and postoperative cognitive dysfunction of children.

Alternative sensor montage for Index based EEG monitoring. A systematic review.

The main objective of this systematic review is to assess the reliability of alternative positions of processed electroencephalogram sensors for depth of anesthesia monitoring and its applicability in clinical practice. A systematic search was conducted in PubMed, Embase, Cochrane Library, Clinical trial.gov in accordance with reporting guidelines of PRISMA statement together with the following sources: Google and Google Scholar. We considered eligible prospective studies, written in the English language. The last search was run on the August 2023. Risk of bias and quality assessment were performed. Data extraction was performed by two authors and results were synthesized narratively owing to the heterogeneity of the included studies. Thirteen prospective observational studies (438 patients) were included in the systematic review after the final assessment, with significant diversity in study design. Most studies had a low risk of bias but due to lack of information in one key domain of bias (Bias due to missing data) the overall judgement would be No Information. However, there is no clear indication that the studies are at serious or critical risk of bias. Bearing in mind, the heterogeneity and small sample size of the included studies, current evidence suggests that the alternative infraorbital sensor position is the most comparable for clinical use when the standard sensor position in the forehead is not possible.

How Is the Nociceptive Withdrawal Reflex Influenced by Increasing Doses of Propofol in Pigs?

The nociceptive withdrawal reflex (NWR) is a physiological, polysynaptic spinal reflex occurring in response to noxious stimulations. Continuous NWR threshold (NWRt) tracking has been shown to be possibly useful in the depth of anesthesia assessment. The primary aim of this study was to describe how propofol modulates the NWRt over time in pigs. Five juvenile pigs (anesthetized three times) were included. An intravenous (IV) infusion of propofol (20 mg/kg/h) was started, and boli were administered to effect until intubation. Afterwards, the infusion was increased every ten minutes by 6 mg/kg/h, together with an IV bolus of 0.5 mg/kg, until reaching an electroencephalographic suppression ratio (SR) of between 10% and 30%. The NWRt was continuously monitored. For data analysis, the time span between 15 min following intubation and the end of propofol infusion was considered. Individual durations of propofol administration were divided into five equal time intervals for each pig (TI1-TI5). A linear regression between NWRt and TI was performed for each pig. Moreover, the baseline NWRt and slopes of the linear regression (b1) were compared among days using a Friedman Repeated Measures Analysis of Variance on Ranks. The NWRt always increased with the propofol dose (b1 = 4.71 ± 3.23; mean ± standard deviation). No significant differences were found between the baseline NWRt and the b1 values. Our results suggest that the NWRt may complement the depth of anesthesia assessment in pigs receiving propofol.

Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology.

Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.

Assessment of seizure duration and utility of using SedLine® EEG tracing in veterans undergoing electroconvulsive therapy: a retrospective analysis.

BACKGROUND: Electroconvulsive therapy (ECT) endures as a definitive treatment for refractory depression and catatonia and is also considered an effective treatment for a number of other severe psychiatric disorders (Lisanby, N Engl J Med 357:1939-1945, 2007)(Weiner and Prudic, Biol Psychiatry 73:105-106, 2013). GA is an essential component of the ECT procedure for various reasons (Lee, Jenkins and Sparkle, Life 11, 2021). Monitoring anesthetic effects on the brain is desirable as anesthetic agents affect seizure duration and recovery (Rasulo, Hopkins, Lobo, et al,  Neurocrit Care 38:296-311, 2023) (Jones , Nittur , Fleming and Applegate,  BMC Anesthesiol 21:105, 2021) (Soehle , Kayser , Ellerkmann and Schlaepfer,  BJA 112:695-702, 2013). Perioperative anesthetic effects on consciousness can be assessed with brain function monitoring using raw electroencephalogram (EEG) traces and processed EEG indices. OBJECTIVE: We examined the usefulness and utility of the SedLine® anesthetic effect monitor during ECT procedures. We hypothesized that the seizure duration as measured by the EEG tracing of the ECT machine is equivalent to the duration assessed by the SedLine® EEG tracing. A secondary objective was to describe the SedLine® patient state indices (PSI) at different phases of treatment. METHODS: Following IRB approval, we analyzed the data of the electronic medical records of 45 ECT treatments of 23 patients in an urban VA medical center between July 01, 2021, and March 30, 2022. We compared the seizure duration in minutes and seconds as measured either by the ECT machine EEG tracing or the SedLine® EEG tracing. We then collected SedLine® processed EEG indices at four different stages during the treatment. Appropriate comparative and observational statistical analyses were applied. RESULTS: There was no significant difference in measured seizure duration between the two methods examined (p < 0.05). We observed a lag of the SedLine PSI value at the time before stimulus delivery and limited PSI utility during the course of ECT. CONCLUSION: The SedLine® EEG tracing can be an alternative to the machine EEG tracing for the determination of seizure duration. The SedLine® processed EEG indices are not consistently useful before and after ECT delivery. Anesthetic effect monitoring during ECT is feasible.

Comparison of post-auricular and frontal bispectral index values obtained during renal surgeries.

BACKGROUND: The bispectral index (BIS) monitor is one of the EEG-derived monitoring techniques and well-established devices used to measure the depth of anesthesia. This study aimed to assess the agreement of BIS values based on the positions of either post-auricular or frontal sensors in individual patients undergoing renal surgeries while lateral positions at various stages of anesthesia. PATIENTS AND METHODS: 12 patients older than 18 years, ASA I-III patients scheduled for elective renal operations, two BIS were placed on each patient, one on each side of the post-auricular region and one across the forehead, and each sensor was connected to a different BIS monitor. We gathered three pieces of data at each of the six-time points: BIS score, signal quality index (SQI) score calculating the signal's strength and electromyography (EMG) score: before the onset of anesthesia (awake) when the eyelash reflex is lost (LOC), after intubation (intubation), following the initial surgical incision, each 30 min throughout the procedure (maintenance), and at the moment the patient's eyes open naturally after waking up from anesthesia (emergence). RESULTS: The overall BIS value at the frontal position was significantly higher than the post-auricular position (52.5 ± 22.2 and 52.1 ± 22.1, respectively, P = 0.010). On the other hand, the BIS value was comparable between the frontal and post-auricular positions at LOC, intubation, 60, 120, and 80 min and at emergence. A strong link between the two sensor positions, as indicated by the correlation coefficient (r = 0.607, P < 0.001), and the Bland-Altman analysis revealed a small mean difference (-1.8) and a low (9.0/- 12.5) limit of agreement, with just 4.3% of the readings falling outside of it during the anesthetic maintenance period. CONCLUSION: Acceptable variation in BIS data was observed when obtained from the two different sensor positions for clinical usage. The post-auricular BIS sensor system may be a suitable substitute for an impractical frontal setup. PROTOCOL REGISTRATION: The study was registered in clinicaltrials.gov on 11/07/2022 (trial registration number: NCT05451823).

Monitoring and evaluation of anesthesia depth status data based on neuroscience.

Monitoring and analysis of anesthesia depth status data refers to evaluating the anesthesia depth status of patients during the surgical process by monitoring their physiological index data, and conducting analysis and judgment. The depth of anesthesia is crucial for the safety and success of the surgical process. By monitoring the state of anesthesia depth, abnormal conditions of patients can be detected in a timely manner and corresponding measures can be taken to prevent accidents from occurring. Traditional anesthesia monitoring methods currently include computer tomography, electrocardiogram, respiratory monitoring, etc. In this regard, traditional physiological indicator monitoring methods have certain limitations and cannot directly reflect the patient's neural activity status. The monitoring and analysis methods based on neuroscience can obtain more information from the level of brain neural activity. PURPOSE: In this article, the monitoring and analysis of anesthesia depth status data would be studied through neuroscience. METHODS: Through a controlled experiment, the monitoring accuracy of traditional anesthesia status monitoring algorithm and neuroscience-based anesthesia status monitoring algorithm was studied, and the information entropy and oxygen saturation of electroencephalogram signals in patients with different anesthesia depth were explored. RESULTS: The experiment proved that the average monitoring accuracy of the traditional anesthesia state monitoring algorithm in patients' blood drug concentration and oxygen saturation reached 95.55 and 95.00%, respectively. In contrast, the anesthesia state monitoring algorithm based on neuroscience performs better, with the average monitoring accuracy of drug concentration and oxygen saturation reaching 98.00 and 97.09%, respectively. This experimental result fully proved that the monitoring performance of anesthesia state monitoring algorithms based on neuroscience is better. CONCLUSION: The experiment proved the powerful monitoring ability of the anesthesia state monitoring algorithm based on neuroscience used in this article, and explained the changing trend of brain nerve signals and oxygen saturation of patients with different anesthesia depth states, which provided a new research method for the monitoring and analysis technology of anesthesia depth state data.

A Deep Learning Framework for Anesthesia Depth Prediction from Drug Infusion History.

In the target-controlled infusion (TCI) of propofol and remifentanil intravenous anesthesia, accurate prediction of the depth of anesthesia (DOA) is very challenging. Patients with different physiological characteristics have inconsistent pharmacodynamic responses during different stages of anesthesia. For example, in TCI, older adults transition smoothly from the induction period to the maintenance period, while younger adults are more prone to anesthetic awareness, resulting in different DOA data distributions among patients. To address these problems, a deep learning framework that incorporates domain adaptation and knowledge distillation and uses propofol and remifentanil doses at historical moments to continuously predict the bispectral index (BIS) is proposed in this paper. Specifically, a modified adaptive recurrent neural network (AdaRNN) is adopted to address data distribution differences among patients. Moreover, a knowledge distillation pipeline is developed to train the prediction network by enabling it to learn intermediate feature representations of the teacher network. The experimental results show that our method exhibits better performance than existing approaches during all anesthetic phases in the TCI of propofol and remifentanil intravenous anesthesia. In particular, our method outperforms some state-of-the-art methods in terms of root mean square error and mean absolute error by 1 and 0.8, respectively, in the internal dataset as well as in the publicly available dataset.

Efficacy of intraoperative thoracoscopic intercostal nerve blocks in nonintubated and intubated video-assisted thoracic surgery: A randomized study.

BACKGROUND: The efficacy of thoracoscopic intercostal nerve blocks (TINBs) for noxious stimulation from video-assisted thoracic surgery (VATS) remains unclear. The efficacy of TINBs may also be different between nonintubated VATS (NIVATS) and intubated VATS (IVATS). We aim to compare the efficacy of TINBs on analgesia and sedation for NIVATS and IVATs intraoperatively. METHODS: Sixty patients randomized to the NIVATS or IVATS group (30 each) received target-controlled propofol and remifentanil infusions, with bispectral index (BIS) maintained at 40-60, and multilevel (T3-T8) TINBs before surgical manipulations. Intraoperative monitoring data, including pulse oximetry, mean arterial pressure (MAP), heart rate, BIS, density spectral arrays (DSAs), and propofol and remifentanil effect-site concentration (Ce) at different time points. A two way ANOVA with post hoc analysis was applied to analyze the differences and interactions of groups and time points. RESULTS: In both groups, DSA monitoring revealed burst suppression and α dropout immediately after the TINBs. The Ce of the propofol infusion had to be reduced within 5 min post-TINBs in both NIVATS (p < 0.001) and IVATS (p = 0.252) groups. The Ce of remifentanil infusion was significantly reduced after TINBs in both groups (p < 0.001), and was significantly lower in NIVATS (p < 0.001) without group interactions. CONCLUSION: The surgeon-performed intraoperative multilevel TINBs allow reduced anesthetic and analgesic requirement for VATS. With lower requirement of remifentanil infusion, NIVATS presents a significantly higher risk of hypotension after TINBs. DSA is beneficial for providing real-time data that facilitate the preemptive management, especially for NIVATS.

Depth of anesthesia, temperature, and postoperative delirium in children and adolescents undergoing cardiac surgery.

BACKGROUND: After pediatric cardiosurgical interventions, postoperative delirium can occur, which can be associated with undesirable consequences during and after the hospital stay. It is therefore important to avoid any factors causing delirium as far as possible. Electroencephalogram (EEG) monitoring can be used during anesthesia to individually adjust dosages of hypnotically acting drugs. It is necessary to gain knowledge about the relationship between intraoperative EEG and postoperative delirium in children. METHODS: In a dataset comprising 89 children (53 male, 36 female; median age: 0.99 (interquartile range: 0.51, 4.89) years) undergoing cardiac surgery involving use of a heart-lung machine, relationships between depth of anesthesia as measured by EEG (EEG index: Narcotrend Index (NI)), sevoflurane dosage, and body temperature were analyzed. A Cornell Assessment of Pediatric Delirium (CAP-D) score ≥ 9 indicated delirium. RESULTS: The EEG could be used in patients of all age groups for patient monitoring during anesthesia. In the context of induced hypothermia, EEG monitoring supported individually adjusted sevoflurane dosing. The NI was significantly correlated with the body temperature; decreasing temperature was accompanied by a decreasing NI. A CAP-D score ≥ 9 was documented in 61 patients (68.5%); 28 patients (31.5%) had a CAP-D < 9. Delirious patients with an intubation time ≤ 24 h showed a moderate negative correlation between minimum NI (NImin) and CAP-D (rho = -0.41, 95% CI: -0.70 - -0.01, p = 0.046), i.e., CAP-D decreased with increasing NImin. In the analysis of all patients' data, NImin and CAP-D showed a weak negative correlation (rho = -0.21, 95% CI: -0.40 - 0.01, p = 0.064). On average, the youngest patients had the highest CAP-D scores (p = 0.002). Patients with burst suppression / suppression EEG had a longer median intubation time in the intensive care unit than patients without such EEG (p = 0.023). There was no relationship between minimum temperature and CAP-D score. CONCLUSIONS: The EEG can be used to individually adjust sevoflurane dosing during hypothermia. Of the patients extubated within 24 h and classified as delirious, patients with deeper levels of anesthesia had more severe delirium symptoms than patients with lighter levels of anesthesia.

Association of volatile anesthesia exposure and depth with emergence agitation and delirium in children: Prospective observational cohort study.

Background: Sevoflurane anesthesia is widely used in pediatric ambulatory surgery. However, emergency agitation (EA) and emergency delirium (ED), as major complications following sevoflurane anesthesia in children, pose risks to surgery and prognosis. Identifying the high risk of EA/ED, especially anesthesia exposure and the depth of anesthesia, may allow preemptive treatment. Methods: A total of 137 patients were prospectively enrolled in this single-center observational cohort study to assess the incidence of EA or ED. Multivariable logistic regression analyses were used to test the association between volatile anesthesia exposure and depth with EA or ED. The Richmond Agitation and Sedation Scale (RASS), Pediatric Anesthesia Emergence Delirium Scale (PAED) and Face, Legs, Activity, Cry, and Consolability (FLACC) behavioural pain scale was used to assess the severity of EA or ED severity and pain. Bispectral index (BIS) to monitor the depth of anesthesia, as well as TimeLOW-BIS/TimeANES %, EtSevo (%) and EtSevo-time AUC were included in the multivariate logistic regression model as independent variables to analyze their association with EA or ED. Results: The overall prevalence of EA and ED was 73/137 (53.3%) and 75/137 (54.7%) respectively, where 48/137 (35.0%), 19/137 (13.9%), and 6/137 (4.4%) had mild, moderate, and severe EA. When the recovery period was lengthened, the prevalence of ED and extent of FLACC decreased and finally normalized within 30 min in recovered period. Multivariable logistic regression demonstrated that intraoperative agitation [2.84 (1.08, 7.47) p = 0.034], peak FLACC [2.56 (1.70, 3.85) p < 0.001] and adverse event (respiratory complications) [0.03 (0.00, 0.29) p = 0.003] were independently associated with higher odds of EA. Taking EtSevo-time AUC ≤ 2,000 as a reference, the incidence of EA were [15.84 (2.15, 116.98) p = 0.002] times and 16.59 (2.42, 113.83) p = 0.009] times for EtSevo-time AUC 2,500-3,000 and EtSevo-time AUC > 3,000, respectively. Peak FLACC [3.46 (2.13, 5.62) p < 0.001] and intraoperative agitation [5.61 (1.99, 15.86) p = 0.001] were independently associated with higher odds of developing ED. EtSevo (%), intraoperative BIS value and the percentage of the duration of anesthesia at different depths of anesthesia (BIS ≤ 40, BIS ≤ 30, BIS ≤ 20) were not associated with EA and ED. Conclusions: For pediatrics undergoing ambulatory surgery where sevoflurane anesthesia was administered, EA was associated with surgical time, peak FLACC, respiratory complications, and "EtSevo-time AUC" with a dose-response relationship; ED was associated with peak FLACC and intraoperative agitation.

Time-trend analysis of the center frequency of the intrinsic mode function from the Hilbert-Huang transform of electroencephalography during general anesthesia: a retrospective observational study.

BACKGROUND: Anesthesiologists are required to maintain an optimal depth of anesthesia during general anesthesia, and several electroencephalogram (EEG) processing methods have been developed and approved for clinical use to evaluate anesthesia depth. Recently, the Hilbert-Huang transform (HHT) was introduced to analyze nonlinear and nonstationary data. In this study, we assessed whether the changes in EEG characteristics during general anesthesia that are analyzed by the HHT are useful for monitoring the depth of anesthesia. METHODS: This retrospective observational study enrolled patients who underwent propofol anesthesia. Raw EEG signals were obtained from a monitor through a previously developed software application. We developed an HHT analyzer to decompose the EEG signal into six intrinsic mode functions (IMFs) and estimated the instantaneous frequencies (HHT_IF) for each IMF. Changes over time in the raw EEG waves and parameters such as HHT_IF, BIS, spectral edge frequency 95 (SEF95), and electromyogram parameter (EMGlow) were assessed, and a Gaussian process regression model was created to assess the association between BIS and HHT_IF. RESULTS: We analyzed EEG signals from 30 patients. The beta oscillation frequency range (13-25 Hz) was detected in IMF1 and IMF2 during the awake state, then after loss of consciousness, the frequency decreased and alpha oscillation (8-12 Hz) was detected in IMF2. At the emergence phase, the frequency increased and beta oscillations were detected in IMF1, IMF2, and IMF3. BIS and EMGlow changed significantly during the induction and emergence phases, whereas SEF95 showed a wide variability and no significant changes during the induction phase. The root mean square error between the observed BIS values and the values predicted by a Gaussian process regression model ranged from 4.69 to 9.68. CONCLUSIONS: We applied the HHT to EEG analyses during propofol anesthesia. The instantaneous frequency in IMF1 and IMF2 identified changes in EEG characteristics during induction and emergence from general anesthesia. Moreover, the HHT_IF in IMF2 showed strong associations with BIS and was suitable for depicting the alpha oscillation. Our study suggests that the HHT is useful for monitoring the depth of anesthesia.

Consistency of the anesthesia consciousness index versus the bispectral index during laparoscopic gastrointestinal surgery with sevoflurane anesthesia: A prospective multi-center randomized controlled clinical study.

Background: This study aimed to compare the consistency of anesthesia consciousness index (Ai) with that of bispectral index (BIS) in monitoring the depth of anesthesia (DOA) during sevoflurane anesthesia, to reveal the optimal cutoff values in different states of consciousness, and explore the stability of DOA monitoring during intraoperative injurious stimulation. Methods: We enrolled 145 patients (97 men and 48 women) from 10 medical centers. General anesthesia was induced using intravenous anesthetics and maintained with sevoflurane. Ai and BIS values were recorded. Results: The mean difference between the Ai and BIS was-0.1747 (95% confidence interval, -0.6660 to 0.3166; p = 0.4857). The regression equation of Ai and BIS from the Deming regression analysis was y = 5.6387 + 0.9067x (y is BIS, x is Ai), and the slope and intercept were statistically significant. Meanwhile, the receiver operating characteristic curve analysis of anesthesia-induced unconsciousness, loss of consciousness, and recovery of consciousness revealed that the accuracy of Ai and BIS were similar. In addition, the optimal cutoff values of the different states of consciousness were not sensitive to age, and both Ai and BIS had no correlation with hemodynamics. Conclusion: We conclude that Ai and BIS show no systematic deviation in readings with high consistency, similar accuracy, and good stability; these insights provide more data for clinical application.

The Management of Postoperative Cognitive Dysfunction in Cirrhotic Patients: An Overview of the Literature.

Background and objectives: Postoperative cognitive dysfunction (POCD) represents a decreased cognitive performance in patients undergoing general anesthesia for major surgery. Since liver cirrhosis is associated with high mortality and morbidity rates, cirrhotic patients also assemble many risk factors for POCD. Therefore, preserving cognition after major surgery is a priority, especially in this group of patients. The purpose of this review is to summarize the current knowledge regarding the effectiveness of perioperative therapeutic strategies in terms of cognitive dysfunction reduction. Data Collection: Using medical search engines such as PubMed, Google Scholar, and Cochrane library, we analyzed articles on topics such as: POCD, perioperative management in patients with cirrhosis, hepatic encephalopathy, general anesthesia in patients with liver cirrhosis, depth of anesthesia, virtual reality in perioperative settings. We included 115 relevant original articles, reviews and meta-analyses, and other article types such as case reports, guidelines, editorials, and medical books. Results: According to the reviewed literature, the predictive capacity of the common clinical tools used to quantify cognitive dysfunction in cirrhotic settings is reduced in perioperative settings; however, novel neuropsychological tools could manage to better identify the subclinical forms of perioperative cognitive impairments in cirrhotic patients. Moreover, patients with preoperative hepatic encephalopathy could benefit from specific preventive strategies aimed to reduce the risk of further neurocognitive deterioration. Intraoperatively, the adequate monitoring of the anesthesia depth, appropriate anesthetics use, and an opioid-sparing technique have shown favorable results in terms of POCD. Early recovery after surgery (ERAS) protocols should be implemented in the postoperative setting. Other pharmacological strategies provided conflicting results in reducing POCD in cirrhotic patients. Conclusions: The perioperative management of the cognitive function of cirrhotic patients is challenging for anesthesia providers, with specific and targeted therapies for POCD still sparse. Therefore, the implementation of preventive strategies appears to remain the optimal attitude. Further research is needed for a better understanding of POCD, especially in cirrhotic patients.

Estimating the Depth of Anesthesia from EEG Signals Based on a Deep Residual Shrinkage Network.

The reliable monitoring of the depth of anesthesia (DoA) is essential to control the anesthesia procedure. Electroencephalography (EEG) has been widely used to estimate DoA since EEG could reflect the effect of anesthetic drugs on the central nervous system (CNS). In this study, we propose that a deep learning model consisting mainly of a deep residual shrinkage network (DRSN) and a 1 × 1 convolution network could estimate DoA in terms of patient state index (PSI) values. First, we preprocessed the four raw channels of EEG signals to remove electrical noise and other physiological signals. The proposed model then takes the preprocessed EEG signals as inputs to predict PSI values. Then we extracted 14 features from the preprocessed EEG signals and implemented three conventional feature-based models as comparisons. A dataset of 18 patients was used to evaluate the models' performances. The results of the five-fold cross-validation show that there is a relatively high similarity between the ground-truth PSI values and the predicted PSI values of our proposed model, which outperforms the conventional models, and further, that the Spearman's rank correlation coefficient is 0.9344. In addition, an ablation experiment was conducted to demonstrate the effectiveness of the soft-thresholding module for EEG-signal processing, and a cross-subject validation was implemented to illustrate the robustness of the proposed method. In summary, the procedure is not merely feasible for estimating DoA by mimicking PSI values but also inspired us to develop a precise DoA-estimation system with more convincing assessments of anesthetization levels.

Performance of the bispectral index and electroencephalograph derived parameters of anesthetic depth during emergence from xenon and sevoflurane anesthesia.

Many processed EEG monitors (pEEG) are unreliable when non-GABAergic anesthetic agents are used. The primary aim of the study was to compare the response of the Bispectral Index (BIS) during emergence from anesthesia maintained by xenon and sevoflurane. To better understand the variation in response of pEEG to these agents, we also compared several EEG derived parameters relevant to pEEG monitoring during emergence. Twenty-four participants scheduled for lithotripsy were randomized to receive xenon or sevoflurane anesthesia. Participants were monitored with the BIS and had simultaneous raw EEG collected. BIS index values were compared at three key emergence timepoints: first response, eyes open and removal of airway. Two sets of EEG derived parameters, three related to the BIS: relative beta ratio, SynchFastSlow and SynchFastSlow biocoherence, and two unrelated to the BIS: spectral edge frequency and the composite cortical state, were calculated for comparison. BIS index values were significantly lower in the xenon group than the sevoflurane group at each emergence timepoint. The relative beta ratio parameter increased significantly during emergence in the sevoflurane group but not in the xenon group. The spectral edge frequency and composite cortical state parameters increased significantly in both groups during emergence. The BIS index is lower at equivalent stages of behavioural response during emergence from xenon anesthesia when compared to sevoflurane anesthesia, most likely due to differences in how these two agents influence the relative beta ratio. The spectral edge frequency and composite cortical state might better reflect emergence from xenon anaesthesia.Clinical trial number and registry Australia New Zealand Clinical Trials Registry Number: ACTRN12618000916246.

Changes of anesthetic drug concentration in plasma during isolation of autologous blood with acute isovolumic hemodilution and its influence on anesthetic effect after reinfusion / 中国临床药理学与治疗学

To investigate the changes of anesthetic drug concentration in plasma during isolation of autologous blood with acute normovolemic hemodiluti-on and its influence on the depth of anesthesia, muscle relaxant effect and blood drug concentration after reinfusion. METHODS: Forty patients of both sexes, aged 20-60 yr, American Society of Anesthesiologists physical status or Ⅱ, hemoglobin (Hb) >120 g / L, hematocrit (Hct) >35%, undergoing eletive multilevel spinal surgery were included, were divided into 2 groups (n=20 each) using a random number table. ANH group (group A): ANH was performed after stable induction of anesthesia, the target Hct value was 28%-30%, and autologous blood was reinfused after the main operation steps. Control group (group C): routine transfusion and infusion treatment. The bispectral index (BIS) and Train-of-Four stimulation (TOF) were observed and recorded at the stable induction of anesthesia (T1), 30 minutes of stable induction (T2), the end of operation (T3), 30 minutes after the end of the operation (T4), 1 hour after the end of the operation (T5) and 2 hours after the end of the operation (T6). The concentrations of propofol and cisatracurium besylate in plasma at T1-T6, stored blood at 1 h (TS1), 2 h (TS2), and before reinfusion (TS3) were detected by Liquid Chromatography-tandem Mass Spectrometry. The extubation time and recovery score at T4-6 hours were recorded. RESULTS: There was no significant difference in propofol between the two groups at each time point (P > 0.05). The plasma concentration of cisatracurium besylate in group A was higher than that in group C at T3 (P0.05). The BIS value at T4 and TOF value at T3 in group A were significantly lower than those in group C. The recovery score of group A was lower than that of group C at T4 (P0.05). CONCLUSION: The plasma concentrations of propofol and cisatracurium besylate were basically unchanged during the in vitro isolation of ANH autologous blood. The plasma concentrations of cisatracurium besylate were only temporarily affected after the main operation steps, but the postoperative muscle relaxation recovery and recovery quality were not significantly affected.

Fractal Properties of Heart Rate Dynamics: A New Biomarker for Anesthesia-Biphasic Changes in General Anesthesia and Decrease in Spinal Anesthesia.

Processed electroencephalogram (EEG) has been considered a useful tool for measuring the depth of anesthesia (DOA). However, because of its inability to detect the activities of the brain stem and spinal cord responsible for most of the vital signs, a new biomarker for measuring the multidimensional activities of the central nervous system under anesthesia is required. Detrended fluctuation analysis (DFA) is a new technique for detecting the scaling properties of nonstationary heart rate (HR) behavior. This study investigated the changes in fractal properties of heart rate variability (HRV), a nonlinear analysis, under intravenous propofol, inhalational desflurane, and spinal anesthesia. We compared the DFA method with traditional spectral analysis to evaluate its potential as an alternative biomarker under different levels of anesthesia. Eighty patients receiving elective procedures were randomly allocated different anesthesia. HRV was measured with spectral analysis and DFA short-term (4-11 beats) scaling exponent (DFAα1). An increase in DFAα1 followed by a decrease at higher concentrations during propofol or desflurane anesthesia is observed. Spinal anesthesia decreased the DFAα1 and low-/high-frequency ratio (LF/HF ratio). DFAα1 of HRV is a sensitive and specific method for distinguishing changes from baseline to anesthesia state. The DFAα1 provides a potential real-time biomarker to measure HRV as one of the multiple dimensions of the DOA.

Age-dependent Electroencephalogram Characteristics During Different Levels of Anesthetic Depth.

Objective The monitoring of anesthetic depth based on electroencephalogram derivation is not currently adjusted for age. Here we analyze the influence of age factors on electroencephalogram characteristics. Methods Frontal electroencephalogram recordings were obtained from 80 adults during routine clinical anesthesia. The characteristics of electroencephalogram with age and anesthesia were observed during four kinds of anesthesia. Results The slow wave power, δ power, Bispectral Index (BIS) and approximate entropy can be used to distinguish different states of anesthesia (P < 0.05). In the deep and very deep anesthesia states, δ power decreased with age (P < 0.0001). In the very deep anesthesia state, θ power decreased with age (P < 0.05). In the deep and very deep anesthesia states, α power decreased with age (P = 0.0002). In the light and deep anesthesia states, ß power decreased with age (P = 0.003). In the deep anesthesia state, γ power decreased with age (P = 0.002). In the very deep anesthesia state, permutation entropy increased significantly with age (P = 0.0001). In the very deep anesthesia state, BIS value increased with age (P = 0.006). The slow wave power, approximate entropy, and sample entropy did not show age-dependent changes. Conclusions The influence of age should be considered when using BIS and δ power to monitor the depth of anesthesia, while the influence of age should not be considered when using slow wave power and approximate entropy to monitor the depth of anesthesia.