Effect of different anesthesia depths on perioperative heart rate variability and hemodynamics in middle-aged and elderly patients undergoing general anesthesia.

BACKGROUND: To analyze the effects of different anesthesia depths on perioperative heart rate variability and hemodynamics in middle-aged and elderly patients undergoing general anesthesia, and to provide a basis for clinical application. METHODS: A total of 111 patients with gastric cancer who were treated with epidural anesthesia combined with general anesthesia were selected as the study subjects, and the patients were randomly divided into group A, group B and group C. The bispectral index (BIS) was maintained by adjusting the infusion speed of anesthetics, the BIS of group A was maintained at 50 ~ 59, the BIS of group B was maintained at 40 ~ 49, and the BIS of group C was maintained at 30 ~ 39. The high-frequency power (HFP), low-frequency power (LFP), total power (TP), mean arterial pressure (MAP), heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP) were measured before anesthesia induction (T1), immediately after intubation (T2), 3 min after intubation (T3), and 6 min after extubation (T4). The cognitive function of the patients was evaluated before and 48 h after surgery. RESULTS: The HFP, LFP/HFP, TP, HR, DBP and SBP between the three groups at T1 ~ T3 are significantly difference from each other (P < 0.05). There were significant differences in spontaneous breathing recovery time, eye opening time and extubation time among group A, B and C groups, and group B had the lowest spontaneous breathing recovery time, eye opening time and extubation time (P < 0.05). There was no significant difference in the incidence of adverse reactions during anesthesia between the three groups. The cognitive function score of group B was significantly higher than that of group A and group C (P < 0.05). CONCLUSIONS: BIS maintenance of 40 ~ 49 has little effect on perioperative heart rate variability and hemodynamics in middle-aged and elderly patients undergoing general anesthesia, which is helpful for postoperative recovery.

Bispectral Index Monitoring Effect on Delirium Occurrence and Nursing Quality Improvement in Post-anesthesia Care Unit Patients Recovering From General Anesthesia: A Randomized Controlled Trial.

BACKGROUND: The effect of intraoperative anesthesia depth monitoring on delirium occurrence and improvement of nursing quality in the post-anesthesia care unit (PACU) remains unclear. We aimed to explore the effect of intraoperative anesthesia bispectral index (BIS) monitoring on delirium occurrence and improvement of nursing quality in the PACU for patients recovering from general anesthesia. METHODS:  This randomized controlled trial included 120 patients, aged 20-80 years, classified as grades I-III according to the American Society of Anesthesiologists. The BIS-guided group (group B) underwent intraoperative monitoring of BIS anesthesia depth (maintained within the anesthetic range (40-60)). The depth of anesthesia was not monitored in the non-BIS-guided group (group C). The patient's vital signs were recorded at the beginning of the operation (T0), upon entering the PACU (T1), 15 min after extubation (T2), and after leaving the PACU (T3). Delirium score, emergence period (extubation and PACU observation times), and adverse events in the PACU were monitored. The nursing activity score (NAS) was used to evaluate the quality of care. RESULTS: Group B exhibited significantly lower heart rate and mean arterial pressure at T1 and T2, shorter time to extubation and PACU observation time, and a significantly lower incidence of adverse events than group C. Group B had significantly lower Ricker sedation-agitation scores and a lower incidence of delirium than group C. The NAS was significantly lower for group B than for group C. Patients aged 60-80 years in group C experienced agitation, requiring 30% more frequent assistance from one or two nurses than those in group B. CONCLUSION: Intraoperative BIS monitoring can reduce the incidence of adverse events in the PACU, diminish the incidence of delirium during the recovery period in elderly patients, lessen the nursing workload, improve nursing quality, and promote patient rehabilitation, thus meriting clinical application.

Heart rate variability as a predictor of intraoperative autonomic nervous system homeostasis.

The aim of the proof-of-concept study is to investigate the level of concordance between the heart rate variability (HRV), the EEG-based Narcotrend Index as a surrogate marker for the depth of hypnosis, and the minimal alveolar concentration (MAC) of the inhalation anesthetic sevoflurane across the entire course of a surgical procedure. This non-blinded cross-sectional study recorded intraoperative HRV, Narcotrend Index, and MAC in 31 male patients during radical prostatectomy using the Da-Vinci robotic-assisted surgical system at Mannheim University Medical Center. The degree of concordance was calculated using repeated measures correlation with the R package (rmcorr) and presented using the rmcorr coefficient (rrm). The Narcotrend Index correlates significantly across all measures with the time-dependent parameter of HRV, the standard deviation of the means of RR intervals (SDNN) (rrm = 0.2; p < 0.001), the frequency-dependent parameters low frequency (LF) (rrm = 0.09; p = 0.04) and the low frequency/high frequency ratio (LF/HF ratio) (rrm = 0.11; p = 0.002). MAC correlated significantly negatively with the time-dependent parameter of heart rate variability, SDNN (rrm = -0.28; p < 0.001), the frequency-dependent parameter LF (rrm = -0.06; p < 0.001) and the LF/HF ratio (rrm = -0.18; p < 0.001) and the Narcotrend Index (rrm = -0.49; p < 0.001) across all measures. HRV mirrors the trend of the Narcotrend Index used to monitor depth of hypnosis and the inhibitory influence of the anesthetic sevoflurane on the autonomic nervous system. Therefore, HRV can provide essential information about the homeostasis of the autonomic nervous system during general anesthesia. DRKS00024696, March 9th, 2021.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.