Preoperative Midazolam and Patient-Centered Outcomes of Older Patients: The I-PROMOTE Randomized Clinical Trial.

Importance: The effect of oral midazolam premedication on patient satisfaction in older patients undergoing surgery is unclear, despite its widespread use. Objective: To determine the differences in global perioperative satisfaction in patients with preoperative administration of oral midazolam compared with placebo. Design, Setting, and Participants: This double-blind, parallel-group, placebo-controlled randomized clinical trial was conducted in 9 German hospitals between October 2017 and May 2019 (last follow-up, June 24, 2019). Eligible patients aged 65 to 80 years who were scheduled for elective inpatient surgery for at least 30 minutes under general anesthesia and with planned extubation were enrolled. Data were analyzed from November 2019 to December 2020. Interventions: Patients were randomized to receive oral midazolam, 3.75 mg (n = 309), or placebo (n = 307) 30 to 45 minutes prior to anesthesia induction. Main Outcomes and Measures: The primary outcome was global patient satisfaction evaluated using the self-reported Evaluation du Vécu de l'Anesthésie Generale (EVAN-G) questionnaire on the first postoperative day. Key secondary outcomes included sensitivity and subgroup analyses of the primary outcome, perioperative patient vital data, adverse events, serious complications, and cognitive and functional recovery up to 30 days postoperatively. Results: Among 616 randomized patients, 607 were included in the primary analysis. Of these, 377 (62.1%) were male, and the mean (SD) age was 71.9 (4.4) years. The mean (SD) global index of patient satisfaction did not differ between the midazolam and placebo groups (69.5 [10.7] vs 69.6 [10.8], respectively; mean difference, -0.2; 95% CI, -1.9 to 1.6; P = .85). Sensitivity (per-protocol population, multiple imputation) and subgroup analyses (anxiety, frailty, sex, and previous surgical experience) did not alter the primary results. Secondary outcomes did not differ, except for a higher proportion of patients with hypertension (systolic blood pressure ≥160 mm Hg) at anesthesia induction in the placebo group. Conclusion and Relevance: A single low dose of oral midazolam premedication did not alter the global perioperative patient satisfaction of older patients undergoing surgery or that of patients with anxiety. These results may be affected by the low dose of oral midazolam. Further trials-including a wider population with commonplace low-dose intravenous midazolam and plasma level measurements-are needed. Trial Registration: ClinicalTrials.gov Identifier: NCT03052660.

Substance-dependent EEG during recovery from anesthesia and optimization of monitoring.

The electroencephalographic (EEG) activity during anesthesia emergence contains information about the risk for a patient to experience postoperative delirium, but the EEG dynamics during emergence challenge monitoring approaches. Substance-specific emergence characteristics may additionally limit the reliability of commonly used processed EEG indices during emergence. This study aims to analyze the dynamics of different EEG indices during anesthesia emergence that was maintained with different anesthetic regimens. We used the EEG of 45 patients under general anesthesia from the emergence period. Fifteen patients per group received sevoflurane, isoflurane (+ sufentanil) or propofol (+ remifentanil) anesthesia. One channel EEG and the bispectral index (BIS A-1000) were recorded during the study. We replayed the EEG back to the Conox, Entropy Module, and the BIS Vista to evaluate and compare the index behavior. The volatile anesthetics induced significantly higher EEG frequencies, causing higher indices (AUC > 0.7) over most parts of emergence compared to propofol. The median duration of "awake" indices (i.e., > 80) before the return of responsiveness (RoR) was significantly longer for the volatile anesthetics (p < 0.001). The different indices correlated well under volatile anesthesia (rs > 0.6), with SE having the weakest correlation. For propofol, the correlation was lower (rs < 0.6). SE was significantly higher than BIS and, under propofol anesthesia, qCON. Systematic differences of EEG-based indices depend on the drugs and devices used. Thus, to avoid early awareness or anesthesia overdose using an EEG-based index during emergence, the anesthetic regimen, the monitor used, and the raw EEG trace should be considered for interpretation before making clinical decisions.

Preoperative characterization of baseline EEG recordings for risk stratification of post-anesthesia care unit delirium.

STUDY OBJECTIVE: Delirium in the post-anesthesia care unit (PACU-D) presents a serious condition with a high medical and socioeconomic impact. In particular, PACU-D is among common postoperative complications of elderly patients. As PACU-D may be associated with postoperative delirium, early detection of at-risk patients and strategies to prevent PACU-D are important. We characterized EEG baseline signatures of patients who developed PACU-D following surgery and general anesthesia and patients who did not. DESIGN AND SETTING: We conducted a post-hoc analysis of preoperative EEG recordings between patients with and without PACU-D, as indicated by positive bCAM scores post general anesthesia and surgery. PATIENTS AND MEASUREMENTS: Preoperative baseline EEG recordings from 89 patients were recorded at controlled eyes-open (focused wakefulness) and eyes-closed (relaxed wakefulness) conditions. We computed power spectral densities, permutation entropy, spectral entropy and spectral edge frequency to see if these parameters can reflect potential baseline EEG differences between PACU-D (31.5%) and noPACU-D (68.5%) patients. Wilcoxon's Rank Sum Test as well as AUC values were used to determine statistical significance. MAIN RESULTS: Baseline EEG recordings showed significant differences between PACU-D and noPACU-D patients preoperatively. Compared to the noPACU-D group, PACU-D patients presented with lower power in higher frequencies during relaxed and focused wakefulness alike. These differences in power led to AUC values of 0.73 [0.59;0.85] (permutation entropy) and 0.72 [0.61;0.83] (spectral edge frequency) indicative of a "fair" performance to separate patients with and without PACU-D. CONCLUSIONS: The baseline EEG of relaxed wakefulness as well as focused wakefulness may be used to assess the risk of developing PACU-D following surgery under general anesthesia. Moreover, routinely used monitoring parameters capture these differences as well, potentially allowing an easy transfer to clinical settings. CLINICAL TRIAL NUMBER: NCT03775356.

Connected consciousness after tracheal intubation in young adults: an international multicentre cohort study.

BACKGROUND: Connected consciousness, assessed by response to command, occurs in at least 5% of general anaesthetic procedures and perhaps more often in young people. Our primary objective was to establish the incidence of connected consciousness after tracheal intubation in young people aged 18-40 yr. The secondary objectives were to assess the nature of these responses, identify relevant risk factors, and determine their relationship to postoperative outcomes. METHODS: This was an international, multicentre prospective cohort study using the isolated forearm technique to assess connected consciousness shortly after tracheal intubation. RESULTS: Of 344 enrolled subjects, 338 completed the study (mean age, 30 [standard deviation, 6.3] yr; 232 [69%] female). Responses after intubation occurred in 37/338 subjects (11%). Females (13%, 31/232) responded more often than males (6%, 6/106). In logistic regression, the risk of responsiveness was increased with female sex (odds ratio [ORadjusted]=2.7; 95% confidence interval [CI], 1.1-7.6; P=0.022) and was decreased with continuous anaesthesia before laryngoscopy (ORadjusted=0.43; 95% CI, 0.20-0.96; P=0.041). Responses were more likely to occur after a command to respond (and not to nonsense, 13 subjects) than after a nonsense statement (and not to command, four subjects, P=0.049). CONCLUSIONS: Connected consciousness occured after intubation in 11% of young adults, with females at increased risk. Continuous exposure to anaesthesia between induction of anaesthesia and tracheal intubation should be considered to reduce the incidence of connected consciousness. Further research is required to understand sex-related differences in the risk of connected consciousness.

Always Assess the Raw Electroencephalogram: Why Automated Burst Suppression Detection May Not Detect All Episodes.

BACKGROUND: Electroencephalogram (EEG)-based monitors of anesthesia are used to assess patients' level of sedation and hypnosis as well as to detect burst suppression during surgery. One of these monitors, the Entropy module, uses an algorithm to calculate the burst suppression ratio (BSR) that reflects the percentage of suppressed EEG. Automated burst suppression detection monitors may not reliably detect this EEG pattern. Hence, we evaluated the detection accuracy of BSR and investigated the EEG features leading to errors in the identification of burst suppression. METHODS: With our study, we were able to compare the performance of the BSR to the visual burst suppression detection in the raw EEG and obtain insights on the architecture of the unrecognized burst suppression phases. RESULTS: We showed that the BSR did not detect burst suppression in 13 of 90 (14%) patients. Furthermore, the time comparison between the visually identified burst suppression duration and elevated BSR values strongly depended on the BSR value being used as a cutoff. A possible factor for unrecognized burst suppression by the BSR may be a significantly higher suppression amplitude ( P = .002). Six of the 13 patients with undetected burst suppression by BSR showed intraoperative state entropy values >80, indicating a risk of awareness while being in burst suppression. CONCLUSIONS: Our results complement previous results regarding the underestimation of burst suppression by other automated detection modules and highlight the importance of not relying solely on the processed index, but to assess the native EEG during anesthesia.

Targeted Interventions to Increase Blood Pressure and Decrease Anaesthetic Concentrations Reduce Intraoperative Burst Suppression: A Randomised, Interventional Clinical Trial.

Background: It has been suggested that intraoperative electroencephalographic (EEG) burst suppression (BSupp) may be associated with post-operative neurocognitive disorders in the elderly, and EEG-guided anaesthesia may help to reduce BSupp. Despite of this suggestion, a standard treatment does not exist, as we have yet to fully understand the phenomenon and its underlying pathomechanism. This study was designed to address two underlying phenomena-cerebral hypoperfusion and individual anaesthetic overdose. Objectives: We aimed to demonstrate that targeted anaesthetic interventions-treating intraoperative hypotension and/or reducing the anaesthetic concentration-reduce BSupp. Methods: We randomly assigned patients to receive EEG-based interventions during anaesthesia or EEG-blinded standard anaesthesia. If BSupp was detected, defined as burst suppression ratio (BSR) > 0, the primary intervention aimed to adjust the mean arterial blood pressure to patient baseline (MAP intervention) followed by reduction of anaesthetic concentration (MAC intervention). Results: EEG-based intervention significantly reduced total cumulative BSR, BSR duration, and maximum BSR. MAP intervention caused a significant MAP increase at the end of a BSR > 0 episode compared to the control group. Coincidentally, the maximum BSR decreased significantly; in 55% of all MAP interventions, the BSR decreased to 0% without any further action. In the remaining events, additional MAC intervention was required. Conclusion: Our results show that targeted interventions (MAC/MAP) reduce total cumulative amount, duration, and maximum BSR > 0 in the elderly undergoing general anaesthesia. Haemodynamic intervention already interrupted or reduced BSupp, strengthening the current reflections that hypotension-induced cerebral hypoperfusion may be seen as potential pathomechanism of intraoperative BSupp. Clinical Trial Registration: NCT03775356 [ClinicalTrials.gov], DRKS00015839 [German Clinical Trials Register (Deutsches Register klinischer Studien, DRKS)].

Evaluation of Anesthetic Specific EEG Dynamics during State Transitions between Loss and Return of Responsiveness.

PURPOSE: electroencephalographic (EEG) information is used to monitor the level of cortical depression of a patient undergoing surgical intervention under general anesthesia. The dynamic state transitions into and out of anesthetic-induced loss and return of responsiveness (LOR, ROR) present a possibility to evaluate the dynamics of the EEG induced by different substances. We evaluated changes in the EEG power spectrum during anesthesia emergence for three different anesthetic regimens. We also assessed the possible impact of these changes on processed EEG parameters such as the permutation entropy (PeEn) and the cerebral state index (CSI). METHODS: we analyzed the EEG from 45 patients, equally assigned to three groups. All patients were induced with propofol and the groups differed by the maintenance anesthetic regimen, i.e., sevoflurane, isoflurane, or propofol. We evaluated the EEG and parameter dynamics during LOR and ROR. For the emergence period, we focused on possible differences in the EEG dynamics in the different groups. RESULTS: depending on the substance, the EEG emergence patterns showed significant differences that led to a substance-specific early activation of higher frequencies as indicated by the "wake" CSI values that occurred minutes before ROR in the inhalational anesthetic groups. CONCLUSION: our results highlight substance-specific differences in the emergence from anesthesia that can influence the EEG-based monitoring that probably have to be considered in order to improve neuromonitoring during general anesthesia.

State entropy and burst suppression ratio can show contradictory information: A retrospective study.

BACKGROUND: Burst suppression is a characteristic electroencephalographic (EEG) pattern that reflects very deep levels of general anaesthesia and may correlate with increased risk of adverse outcomes such as postoperative delirium. EEG-based monitors such as the Entropy Module estimate the level of anaesthesia (state entropy) and provide another index reflecting the occurrence of burst suppression, that is the ratio of burst and suppression (BSR). In the Entropy Module, state entropy and BSR are not interconnected, as they are in the bispectral index (BIS). Hence, state entropy and BSR may provide contradicting information regarding the level of anaesthesia. OBJECTIVES: We aimed to describe the frequency and characteristics of contradicting state entropy and BSR and to present possible strategies of how to act in these situations. METHODS: We based our analyses on state entropy and BSR trend recordings from 2551 patients older than 59 years that showed BSR was > 0 throughout their intervention under general anaesthesia. We determined the maximum state entropy when BSR was > 0, the minimum state entropy with BSR = 0 and the duration of high state entropy with BSR > 0. Further, we selected four exemplar patients to present details of how state entropy and BSR can contradict each other during anaesthesia. RESULTS: We observed a wide range of state entropy values with BSR > 0. The median [IQR] of the maximum state entropy with BSR > 0 was 53 [45 to 61] and the median of the minimum state entropy without BSR was 21 [15 to 26]. Contradictory BSR and state entropy could persist over several minutes. The presented cases highlight these contradictory BSR and state entropy situations. CONCLUSIONS: Our results illustrate contradictory state entropy and BSR indices that may be relevant for anaesthesia navigation. Longer-lasting episodes may lead to incorrect titration of the depth of the hypnotic component of anaesthesia. Hence, our results demonstrate the necessity to monitor and check the raw EEG or EEG parameters that are less processed than the commercially available indices to safely navigate anaesthesia.

Substance-Specific Differences in Human Electroencephalographic Burst Suppression Patterns.

Different anesthetic agents induce burst suppression in the electroencephalogram (EEG) at very deep levels of general anesthesia. EEG burst suppression has been identified to be a risk factor for postoperative delirium (POD). EEG based automated detection algorithms are used to detect burst suppression patterns during general anesthesia and a burst suppression ratio (BSR) is calculated. Unfortunately, applied algorithms do not give information as precisely as suggested, often resulting in an underestimation of the patients' burst suppression level. Additional knowledge of substance-specific burst suppression patterns could be of great importance to improve the ability of EEG based monitors to detect burst suppression. In a re-analysis of EEG recordings obtained from a previous study, we analyzed EEG data of 45 patients undergoing elective surgery under general anesthesia. The patients were anesthetized with sevoflurane, isoflurane or propofol (n = 15, for each group). After skin incision, the used agent was titrated to a level when burst suppression occurred. In a visual analysis of the EEG, blinded to the used anesthetic agent, we included the first distinct burst in our analysis. To avoid bias through changing EEG dynamics throughout the burst, we only focused on the first 2 s of the burst. These episodes were analyzed using the power spectral density (PSD) and normalized PSD, the absolute burst amplitude and absolute burst slope, as well as permutation entropy (PeEn). Our results show significant substance-specific differences in the architecture of the burst. Volatile-induced bursts showed higher burst amplitudes and higher burst power. Propofol-induced bursts had significantly higher relative power in the EEG alpha-range. Further, isoflurane-induced bursts had the steepest burst slopes. We can present the first systematic comparison of substance-specific burst characteristics during anesthesia. Previous observations, mostly derived from animal studies, pointing out the substance-specific differences in bursting behavior, concur with our findings. Our findings of substance-specific EEG characteristics can provide information to help improve automated burst suppression detection in monitoring devices. More specific detection of burst suppression may be helpful to reduce excessive EEG effects of anesthesia and therefore the incidence of adverse outcomes such as POD.

Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.

BACKGROUND: It is claimed that bispectral index (BIS) and state entropy reflect an identical clinical spectrum, the hypnotic component of anaesthesia. So far, it is not known to what extent different devices display similar index values while processing identical electroencephalogram (EEG) signals. OBJECTIVE: To compare BIS and state entropy during analysis of identical EEG data. Inspection of raw EEG input to detect potential causes of erroneous index calculation. DESIGN: Offline re-analysis of EEG data from a randomised, single-centre controlled trial using the Entropy Module and an Aspect A-2000 monitor. SETTING: Klinikum rechts der Isar, Technische Universität München, Munich. PATIENTS: Forty adult patients undergoing elective surgery under general anaesthesia. INTERVENTIONS: Blocked randomisation of 20 patients per anaesthetic group (sevoflurane/remifentanil or propofol/remifentanil). Isolated forearm technique for differentiation between consciousness and unconsciousness. MAIN OUTCOME MEASURES: Prediction probability (PK) of state entropy to discriminate consciousness from unconsciousness. Correlation and agreement between state entropy and BIS from deep to light hypnosis. Analysis of raw EEG compared with index values that are in conflict with clinical examination, with frequency measures (frequency bands/Spectral Edge Frequency 95) and visual inspection for physiological EEG patterns (e.g. beta or delta arousal), pathophysiological features such as high-frequency signals (electromyogram/high-frequency EEG or eye fluttering/saccades), different types of electro-oculogram or epileptiform EEG and technical artefacts. RESULTS: PK of state entropy was 0.80 and of BIS 0.84; correlation coefficient of state entropy with BIS 0.78. Nine percent BIS and 14% state entropy values disagreed with clinical examination. Highest incidence of disagreement occurred after state transitions, in particular for state entropy after loss of consciousness during sevoflurane anaesthesia. EEG sequences which led to false 'conscious' index values often showed high-frequency signals and eye blinks. High-frequency EEG/electromyogram signals were pooled because a separation into EEG and fast electro-oculogram, for example eye fluttering or saccades, on the basis of a single EEG channel may not be very reliable. These signals led to higher Spectral Edge Frequency 95 and ratio of relative beta and gamma band power than EEG signals, indicating adequate unconscious classification. The frequency of other artefacts that were assignable, for example technical artefacts, movement artefacts, was negligible and they were excluded from analysis. CONCLUSION: High-frequency signals and eye blinks may account for index values that falsely indicate consciousness. Compared with BIS, state entropy showed more false classifications of the clinical state at transition between consciousness and unconsciousness.

Intraoperative multimodal evoked potential monitoring during carotid endarterectomy: a retrospective study of 264 patients.

BACKGROUND: Methods for detecting intraoperative cerebral ischemia arising from internal carotid artery (ICA) cross-clamping during carotid endarterectomy (CEA) should be sensitive, specific, and rapid to prevent intraoperative stroke. We had 3 objectives pertaining to this: (1) investigation of the rates of success of multimodal evoked potential (mEP) monitoring using a combination of median nerve (m) somatosensory (SS) EPs, tibial nerve SSEPs (tSSEPs), and transcranial electrical stimulated motor EPs (tcMEPs); (2) evaluation of the rates of false-negative mEP results; and (3) analysis of the relationship between different time periods associated with ICA cross-clamping and the postoperative outcome of motor function in patients with significant changes in mEP monitoring. METHODS: Two hundred sixty-four patients undergoing CEA using general anesthesia with monitoring of bilateral mSSEPs, tSSEPs, and tcMEPs were retrospectively reviewed between 2009 and 2012. The rates of successful assessment of mEPs were investigated, and the rate of false-negative mEP results was analyzed. Different time periods (T1--time of clamping, T2--clamping to significant mEP changes, T3--significant mEP change to intervention, and T4--intervention to recovery of EP) were tested using Welch t test for significant association with postoperative motor deficit. RESULTS: (1) Multimodal EP monitoring was achieved in 241 patients (91.3%, point estimate [PE] 0.91, confidence interval [CI] 0.87 to 0.94), whereas none of the modalities were recordable in one case (PE 0.0038, CI 0.0002 to 0.019). Additionally, tSSEP was not recordable in 21 patients (PE 0.08, CI 0.05 to 0.12), and we found one case of isolated failure of tcMEP recording (PE 0.0038, CI 0.0002 to 0.019). (2) False-negative mEP results were found in 1 patient (0.4%; PE 0.0038, CI 0.0002 to 0.019). Significant mEP changes occurred in 32 patients (12.1%), and thus, arterioarterial shunt was performed in 17 (6.4%) patients. Eleven patients (4.2%) showed transient and 1 showed permanent postoperative motor deficit. (3) There was no significant difference regarding any of the time periods associated with ICA cross-clamping and postoperative alteration of motor function (T1: P = 0.19, CI -30.1 to 6.8 minutes; T2: P = 0.38, CI -23 to 9.5 minutes; T3: P = 0.25, -9.7 to 2.8 minutes; T4: P = 0.42, CI to -15.5 to 7.0 minutes). CONCLUSIONS: Multimodal EP monitoring is applicable during CEA. The 0.4% false-negative rate suggests an advantage of mEP monitoring when compared with isolated mSSEP monitoring. Our data suggest that periods of time during cross-clamping were not significantly associated with postoperative motor deficit. However, the small number of patients limits the conclusiveness of these findings. mEP monitoring could not prevent a postoperative motor deficit in all patients, but our results suggest that it is a useful adjunct to mSSEP monitoring.

A novel technique to treat Grisel's syndrome: results of a simplified, therapeutical algorithm.

PURPOSE: Grisel's syndrome is a rare entity and usually develops in paediatric patients after otolaryngologic surgery or infection. It is defined as a fixed torticollis combined with a rotatory atlanto-axial subluxation. The success rate of physiotherapy is low. Conventional therapy concepts imply stage-related recommendations based on the Fielding classification (type I-IV). This classification was introduced in 1977 to assess the degree of subluxation between atlas (C1) and axis (C2). Thus, instability increases from type I to IV. Higher stages may require surgical intervention. The purpose of this study was to evaluate the value of an alternative, less invasive treatment protocol in Grisel's syndrome. METHODS: Irrespective of the underlying Fielding type we treated five children (Fielding type I-III) by manual repositioning under general anaesthesia. Consecutively, the cervical spine was immobilized with a Minerva cast for four to eight weeks. Additional surgical treatment or immobilization in a Halo-Fixateur was not necessary. RESULTS: Overall period of treatment was reduced, even in patients with delayed diagnosis. No case of recurrence was observed within a follow-up of six months. CONCLUSIONS: Current conventional recommendations suggest invasive treatment with Halo-Fixateur in patients with higher degrees of subluxation (e.g., Fielding type III) or after delayed diagnosis. Even in those patients, this novel therapy concept enables us to achieve excellent clinical results without surgical intervention.

[Awareness - clinical relevance]. / Awareness - Klinische Relevanz.

Awareness is a rare but typical complication during general anesthesia, with a reported incidence of 0.1- 0.2% to 1% (high risk patients) in adults and probably much higher in children with 0.2 -1.2%. Awareness is defined as consciousness during general anesthesia with explicit (conscious) recall of memories.Wakefulness during anesthesia can meet DSM-IV criteria of trauma. Significant long-term psychological sequelae (e.g. post-traumatic stress disorder) may occur.Recommendations of the German Society of Anesthesiologists have been developed according to the ASA Task Force for intraoperative awareness and brain function monitoring.The article focuses on risk factors and sequelae of awareness, explains the options for detection of intraoperative wakefulness and postoperative memories and makes recommendations for prevention and handling of awareness.

Time delay of monitors of the hypnotic component of anesthesia: analysis of state entropy and index of consciousness.

Monitors evaluating the hypnotic component of anesthesia by analyzing the electroencephalogram (EEG) may help to decrease the incidence of intraoperative awareness with recall. To calculate an index representing the anesthetic level, these monitors have different time delays until the correct index is displayed. In previous studies, intraoperatively recorded real and simulated EEG signals were used to determine time delays of cerebral state and Narcotrend and Bispectral indices. In the present study, we determined time delays of state entropy and index of consciousness. For this purpose, recorded real and simulated EEG sequences representing different anesthetic levels were played back to the tested monitors. Simulated and real perioperatively recorded EEG signals indicating stable states "awake," "general anesthesia," and "cortical suppression" were used to evaluate the time delays. Time delays were measured when switching from one state to another and were defined as the required time span of the monitor to reach the stable target index. Comparable results were obtained using simulated and real EEG sequences. Time delays were not constant and ranged from 18 to 152 seconds. They were also different for increasing and decreasing values. Time delays were dependent on starting and target index values. Time delays of index calculation may limit the investigated monitor's ability to prevent interoperative awareness with recall. Different time delays for increasing and decreasing transitions could be a problem if the monitors are used for pharmacodynamic studies.

Does the cerebral state index separate consciousness from unconsciousness?

BACKGROUND: The Cerebral State Monitor™ (CSM) is an electroencephalogram (EEG)-based monitor that is claimed to measure the depth of hypnosis during general anesthesia. We calculated the prediction probability (P(K)) for its ability to separate consciousness from unconsciousness in surgical patients with different anesthetic regimens. METHODS: Digitized EEG recordings of a previous study of 40 nonpremedicated, adult patients undergoing elective surgery under general anesthesia were replayed using an EEG player and reanalyzed using the CSM. Patients were randomly assigned to receive either sevoflurane-remifentanil or propofol-remifentanil. The study design included a slow induction of anesthesia and an episode of intended wakefulness. CSM values at loss and return of consciousness were compared. P(K) was calculated from values 30 seconds before and 30 seconds after loss and return of consciousness. RESULTS: The P(K) for the differentiation between consciousness and unconsciousness was 0.75 ± 0.03 (mean ± SE). For sevoflurane-remifentanil, P(K) was 0.71 ± 0.04. For propofol-remifentanil, P(K) was 0.81 ± 0.03. CONCLUSIONS: The ability of CSM for separation of consciousness and unconsciousness was comparable to other commercially available EEG-based indices.

A combination of electroencephalogram and auditory evoked potentials separates different levels of anesthesia in volunteers.

BACKGROUND: It has been shown that the combination of electroencephalogram (EEG) and auditory evoked potentials (AEP) allows a good separation of consciousness from unconsciousness. In the present study, we sought a combined EEG/AEP indicator that allows both separation of consciousness from unconsciousness and discrimination among different levels of sedation and hypnosis over a wider range of anesthesia. METHODS: Fifteen unpremedicated volunteers received mono-anesthesia with sevoflurane or propofol in a randomized crossover design in two consecutive sessions. Loss of consciousness (LOC) and EEG burst suppression (BSP) defined end-points from the upper and lower range of general anesthesia. In addition to those two extremes, the difference between anesthetic concentration at BSP and LOC was divided into three equal intervals, resulting in two intermediate levels which divided the concentration from LOC (minimum) to BSP (maximum) into three equal steps. This data set was used to test whether a previously described combined EEG/AEP indicator "detector of consciousness" can also discriminate among degrees of anesthetic effects from the awake state to BSP. Furthermore, a new improved combined EEG/AEP indicator was developed on the basis of the data from the current study, and subsequently this new indicator was tested for its ability to separate consciousness from unconsciousness with the patient data set. RESULTS: The former "detector of consciousness" showed a prediction probability (P(K)) of 0.77 to separate different levels of anesthesia from the current study, whereas for the new combined EEG/AEP indicator, P(K) was 0.94. The new indicator was further applied to the previous study and achieved a P(K) of 0.89. CONCLUSIONS: These results show that with the new indicator presented here, a combination of EEG and AEP parameters can be used to differentiate degrees of anesthetic effects over a wide range of hypnosis, from the conscious state to deep anesthesia (i.e., BSP).

Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness: an analysis of approximate entropy, permutation entropy, recurrence rate, and phase coupling of order recurrence plots.

BACKGROUND: Nonlinear electroencephalographic parameters, e.g., approximate entropy, have been suggested as measures of the hypnotic component of anesthesia. Compared with linear methods, they may detect additional information and quantify the irregularity of a dynamical system. High dimensionality of a signal and disturbances may affect these parameters and change their ability to distinguish consciousness from unconsciousness. Methods of order pattern analysis, in this investigation represented by permutation entropy, recurrence rate, and phase coupling of order recurrence plots, are suitable for any type of time series, whether deterministic or noisy. They may provide a better estimation of the hypnotic component of anesthesia than other nonlinear parameters. METHODS: The current analysis is based on electroencephalographic data from two similar clinical studies in adult patients undergoing general anesthesia with sevoflurane or propofol. The study period was from induction until patients followed command after surgery, including a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the parameter's ability to separate consciousness from unconsciousness at the transition between both states. RESULTS: Parameters of order pattern analysis provide a prediction probability of maximal 0.85 (training study) and 0.78 (evaluation study) with frequencies from 0 to 30 Hz, and maximal 0.87 (training study) and 0.83 (evaluation study) including frequencies up to 70 Hz, both higher than 0.77 (approximate entropy). CONCLUSIONS: Parameters of the nonlinear method order pattern analysis separate consciousness from unconsciousness and are grossly independent of high-frequency components of the electroencephalogram.

Construction of the electroencephalogram player: a device to present electroencephalogram data to electroencephalogram-based anesthesia monitors.

BACKGROUND: Recently, an increasing number of electroencephalogram (EEG)-based monitors of the hypnotic component of anesthesia has become available. Most of these monitors calculate a numerical index reflecting the hypnotic component of anesthesia. Most of the underlying algorithms are proprietary. Therefore, a quality check or comparison of different indices is very complex. METHODS: Because there is limited information about the algorithms used for index calculation of the different monitors, a reliable comparison or test of the monitors is possible only if the same set of EEG data are presented to each monitor. RESULTS: Parallel EEG monitoring during surgery is limited to two or three monitors because the space for electrode placement on the head is limited. This problem can be solved by using the EEG player to play back recorded EEG data to different monitors. CONCLUSIONS: The output of the player corresponds to the original EEG signal. A comparison of different indices based on identical EEGs is therefore possible. The index reproducibility can also be checked, if the same signal is presented to different monitors.

Time delay of index calculation: analysis of cerebral state, bispectral, and narcotrend indices.

BACKGROUND: On the basis of electroencephalographic analysis, several parameters have been proposed as a measure of the hypnotic component of anesthesia. All currently available indices have different time lags to react to a change in the level of anesthesia. The aim of this study was to determine the latency of three frequently used indices: the Cerebral State Index (Danmeter, Odense, Denmark), the Bispectral Index (Aspect Medical Systems Inc., Newton, MA), and the Narcotrend Index (MonitorTechnik, Bad Bramstedt, Germany). METHODS: Artificially generated signals were used to produce up to 14 constant index values per monitor that indicate "awake state," "general anesthesia," and "deep anesthesia" and smaller steps in between. The authors simulated loss of and return to consciousness by changing between the artificial electroencephalographic signals in a full-step and two stepwise approaches and measured the time necessary to adapt the indices to the particular input signal. RESULTS: Time delays between 14 and 155 s were found for all indices. These delays were not constant. Results were different for decreasing and increasing values and between the full-step and the stepwise approaches. Calculation time depended on the particular starting and target index value. CONCLUSIONS: The time delays of the tested indices may limit their value in prevention of recall of intraoperative events. Furthermore, different latencies for decreasing and increasing values may indicate a limitation of these monitors for pharmacodynamic studies.