Comparative Evaluation of a New Depth of Anesthesia Index in ConView® System and the Bispectral Index during Total Intravenous Anesthesia: A Multicenter Clinical Trial

Differences in the EEG Power Spectrum and Cross-Frequency Coupling Patterns between Young and Elderly Patients during Sevoflurane Anesthesia

Electroencephalography (EEG) is widely used for monitoring the depth of anesthesia in surgical patients. Distinguishing age-related EEG features under general anesthesia will help to optimize anesthetic depth monitoring during surgery for elderly patients. This retrospective cohort study included 41 patients aged from 18 to 79 years undergoing noncardiac surgery under general anesthesia. We compared the power spectral signatures and phase-amplitude coupling patterns of the young and elderly groups under baseline and surgical anesthetic depth. General anesthesia by sevoflurane significantly increased the spectral power of delta, theta, alpha, and beta bands and strengthened the cross-frequency coupling both in young and elderly patients. However, the variation in EEG power spectral density and the modulation of alpha amplitudes on delta phases was relatively weaker in elderly patients. In conclusion, the EEG under general anesthesia using sevoflurane exhibited similar dynamic features between young and elderly patients, and the weakened alteration of spectral power and cross-frequency coupling patterns could be utilized to precisely quantify the depth of anesthesia in elderly patients.

Effects of intra-operative administration of subanesthetic s-ketamine on emergence from sevoflurane anesthesia: a randomized double-blind placebo-controlled study

BACKGROUND

Ketamine is administered in the perioperative period for its benefits in analgesia, anti-agitation and anti-depression when administered at a small dose. However, it is not clear whether the intra-operative administration of ketamine would affect emergence under sevoflurane anesthesia. To investigate this effect, we designed this trial.

METHODS

In this randomized, double-blind, placebo-controlled study, we enrolled 44 female patients aged 18-60 who were scheduled to elective laparoscopic gynecological surgeries. All patients were randomly assigned to saline or s-ketamine group. In s-ketamine group, patients received 0.125 mg/kg s-ketamine 30 min after the start of surgery. In saline group, patients were administered the same volume of saline. Sevoflurane and remifentanil were used to maintain general anesthesia. The primary outcome was emergence time. We also assessed postoperative agitation, cognitive function, and delirium. In addition, we collected and analyzed prefrontal electroencephalogram (EEG) during and after general anesthesia.

RESULTS

There were no significant differences in emergence time between s-ketamine group and saline group (10.80 ± 3.77 min vs. 10.00 ± 2.78 min, P = 0.457). Neither postoperative agitation (4 [3, 4] vs. 4 [3, 4], P = 0.835) nor cognitive function (25.84 ± 2.69 vs. 25.55 ± 2.19, P = 0.412) differed between groups. No postoperative delirium was observed in either group. Subanesthetic s-ketamine resulted in active EEG with decreased power of slow (-0.35 ± 1.13 dB vs. -1.63 ± 1.03 dB, P = 0.003), delta (-0.22 ± 1.11 dB vs. -1.32 ± 1.09 dB, P = 0.011) and alpha (-0.31 ± 0.71 dB vs. -1.71 ± 1.34 dB, P = 0.0003) waves and increased power of beta-gamma bands (-0.30 ± 0.89 dB vs. 4.20 ± 2.08 dB, P < 0.0001) during sevoflurane anesthesia, as well as an increased alpha peak frequency (-0.16 ± 0.48 Hz vs. 0.31 ± 0.73 Hz, P = 0.026). EEG patterns did not differ during the recovery period after emergence between groups.

CONCLUSION

Ketamine administered during sevoflurane anesthesia had no apparent influence on emergence time in young and middle-aged female patients undergoing laparoscopic surgery. Subanesthetic s-ketamine induced an active prefrontal EEG pattern during sevoflurane anesthesia but did not raise neurological side effects after surgery.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2100046479 (date: 16/05/2021).

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.

Study of the Association of Single Nucleotide Polymorphisms in Candidate Genes With Sevoflurane

The susceptibility of different individuals to anesthetics varies widely, and sevoflurane is no exception. We hypothesized that polymorphisms in genes involved in pharmacokinetics and pharmacodynamics may explain this variation. A total of 151 individuals undergoing otorhinolaryngology surgery were included. The influence of genetic polymorphisms on sevoflurane sensitivity were investigated through SNaPshot technology. Individuals carrying KCNK2 rs6686529 G > C, MTRR rs3733784 TT, rs2307116 GG, or rs1801394 AA polymorphisms had a higher sensitivity to the sedative effect of sevoflurane than those without those polymorphisms. The univariate linear regression analysis indicated that MTRR rs3733784 TT, rs2307116 GG, and rs1801394 AA were potentially significant predictors of higher sensitivity to the sedative effect of sevoflurane. Moreover, CYP2E1 rs3813867 G > C and rs2031920 C > T, GABRG1 rs279858 T > C, KCNK3 rs1275988 CC, GRIN2B rs1806201 GG, MTRR rs2307116 G > A, and rs1801394 A > G were associated with a higher sensitivity to the cardiovascular effect of sevoflurane. Our results suggested that 9 single nucleotide polymorphisms in genes involved in metabolizing enzymes, transport proteins, target proteins of sevoflurane and folate metabolism may help to explain individual differences in the susceptibility to the sedative or cardiovascular effect of sevoflurane.

Electroencephalogram Signatures of Agitation Induced by Sevoflurane and Its Association With Genetic Polymorphisms

Background: Volatile anesthetic-induced agitation, also called paradoxical excitation, is not uncommon during anesthesia induction. Clinically, patients with agitation may lead to self-injury or disrupt the operative position, increasing the incidence of perioperative adverse events. The study was designed to investigate clinical features of sevoflurane-induced agitation and examined whether any gene polymorphisms can potentially be used to predict agitation.

Methods: One hundred seventy-six patients underwent anesthesia induction with sevoflurane were included in this study. Frontal electroencephalogram (EEG), electromyography (EMG), and hemodynamics were recorded continuously during anesthesia induction. DNA samples were genotyped using the Illumina Infinium Asian Screening Array and the SNaPshot technology. Genetic association was analyzed by genome-wide association study. Logistic regression analysis was used to determine the role of variables in the prediction of agitation.

Results: Twenty-five (14.2%) patients experienced agitation. The depth of anesthesia index (Ai index) (p < 0.001), EMG (p < 0.001), heart rate (HR) (p < 0.001), and mean arterial pressure (MAP) (p < 0.001) rapidly increased during the agitation. EEG exhibited a shift toward high frequencies with spikes during agitation. The fast waves (alpha and beta) were more pronounced and the slow rhythms (delta) were less prominent during the occurrence of agitation. Moreover, three SNPs in the methionine synthase reductase (MTRR) gene were correlated to the susceptibility to agitation (p < 5.0 × 10⁻⁶). Carrying rs1801394 A > G (odds ratio 3.50, 95% CI 1.43–9.45) and/or rs2307116 G > A (3.31, 1.36–8.95) predicted a higher risk of agitation.

Discussion: This study suggests that the agitation/paradoxical excitation induced by sevoflurane is characterized as increases in Ai index, EMG, HR and MAP, and the high frequency with spikes in EEG. Moreover, our results provide preliminary evidence for MTRR genetic polymorphisms, involving folate metabolism function, may be related to the susceptibility to agitation.

Clinical Trial Number and Registry URL: ChiCTR1900026218; http://www.chictr.org.cn/showproj.aspx?proj=40655.

The Investigation of Behavior Change in EEG Signals During Induction of Anesthesia

Anesthesiology aims to make anesthesia safer and increase the precision of prognoses. Correct assessment of the anesthesia depth is crucial to its safety. At present, intraoperative electroencephalogram (EEG) monitoring is the primary mode of anesthesia depth monitoring and judgment. However, most clinical anesthesiologists rely on commercial anesthesia depth monitors to judge anesthesia depth, such as bispectral index (BIS) and patient state index (PSI). This may lack an understanding of associated changes in brain wave quantization. Therefore, this study conducts quantitative analyses of EEG signals during anesthesia induction. EEG signals are processed within specific time windows and extracted brainpower density spectrum arrays with different frequency bands, brain electrical signal spectra, source frequencies and other key indicators. Analysis and comparison of these indicators clarifies patterns of variation in EEG signals during early anesthesia induction. The spectral edge frequencies (SEFs) of EEG signals within different time windows can be modeled accurately, from which the specific time points of EEG signal changes are derived. Furthermore, the relationship between patient age and the effect of anesthetic drugs is preliminarily investigated by analyzing the SEF variations of different age groups. This study quantifies changes in the EEG signals of patients at the initial stage of anesthesia induction and drug-related effects are observed, which opens a way for further exploration of EEG changes in patients under general anesthesia.

A "Smart" Biosensor-Enabled Intravascular Catheter and Platform for Dynamic Delivery of Propofol to "Close the Loop" for Total Intravenous Anesthesia

BACKGROUND

Target-controlled infusion anesthesia is used worldwide to provide user-defined, stable, blood concentrations of propofol for sedation and anesthesia. The drug infusion is controlled by a microprocessor that uses population-based pharmacokinetic data and patient biometrics to estimate the required infusion rate to replace losses from the blood compartment due to drug distribution and metabolism. The objective of the research was to develop and validate a method to detect and quantify propofol levels in the blood, to improve the safety of propofol use, and to demonstrate a pathway for regulatory approval for its use in the USA.

METHODS

We conceptualized and prototyped a novel "smart" biosensor-enabled intravenous catheter capable of quantifying propofol at physiologic levels in the blood, in real time. The clinical embodiment of the platform is comprised of a "smart" biosensor-enabled catheter prototype, a signal generation/detection readout display, and a driving electronics software. The biosensor was validated in vitro using a variety of electrochemical methods in both static and flow systems with biofluids, including blood.

RESULTS

We present data demonstrating the experimental detection and quantification of propofol at sub-micromolar concentrations using this biosensor and method. Detection of the drug is rapid and stable with negligible biofouling due to the sensor coating. It shows a linear correlation with mass spectroscopy methods. An intuitive graphical user interface was developed to: (1) detect and quantify the propofol sensor signal, (2) determine the difference between targeted and actual propofol concentration, (3) communicate the variance in real time, and (4) use the output of the controller to drive drug delivery from an in-line syringe pump. The automated delivery and maintenance of propofol levels was demonstrated in a modeled benchtop "patient" applying the known pharmacokinetics of the drug using published algorithms.

CONCLUSIONS

We present a proof-of-concept and in vitro validation of accurate electrochemical quantification of propofol directly from the blood and the design and prototyping of a "smart," indwelling, biosensor-enabled catheter and demonstrate feedback hardware and software architecture permitting accurate measurement of propofol in blood in real time. The controller platform is shown to permit autonomous, "closed-loop" delivery of the drug and maintenance of user-defined propofol levels in a dynamic flow model.

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

Background

Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment.

Methods

A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which utilized the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.

Results

The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.

Conclusions

The incorporation of four HRV-derived features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method—with other evaluation methods, such as EEG—is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01285-x.