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

Comparison of Inhalational and Intravenous Anesthesia Induction on Electroencephalogram and Cerebral Perfusion in Children With Congenital Heart Disease: A Secondary Analysis of a Randomized Controlled Trial

OBJECTIVES

The effects of anesthetics on electroencephalograms and cerebral perfusion remain understudied in children with congenital heart disease. With regard to this, we compared inhalational anesthesia induction and intravenous anesthesia induction.

DESIGN

A randomized controlled trial.

SETTING

Operating room in 2 tertiary hospitals.

PARTICIPANTS

A cohort of 116 pediatrics patients undergoing cardiac surgery.

MEASUREMENTS AND MAIN RESULTS

The patients were randomly assigned to either the intravenous group (n = 58) or the inhalational group (n = 58). The inhalational group received anesthesia induction with 4% to 6% sevoflurane and a bolus of pipecuronium 0.2 mg/kg, whereas the intravenous group received anesthesia induction with intravenous midazolam 0.2 mg/kg, pipecuronium 0.2 mg/kg, and sufentanil 1 μg/kg. Ten minutes after tracheal intubation, the following parameters were measured: spectral edge frequency, burst suppression event, patient state index, middle cerebral artery blood flow velocity, cerebral oxygen saturation, and hemodynamic parameters. In comparison with the intravenous group, the inhalational group exhibited significant increases in 95% spectral edge frequency, ratio of burst suppression event, blood flow velocity in the middle cerebral artery, and cerebral oxygen saturation (p < 0.05 for all), as well as decreases in systolic pressure, diastolic pressure, cardiac index, and the maximal slope of systolic upstroke (p < 0.05 for all).

CONCLUSIONS

The administration of sevoflurane for anesthesia induction results in more burst suppression, while also demonstrating superior cerebral perfusion when compared with the use of intravenous medications for anesthesia induction.

TRIAL REGISTRATION

Chinese Clinical Trial Registry (ChiCTR1800015946).

Optimizing anesthesia management based on early identification of electroencephalogram burst suppression risk in non-cardiac surgery patients: a visualized dynamic nomogram

BACKGROUND

Burst suppression (BS) is a specific electroencephalogram (EEG) pattern that may contribute to postoperative delirium and negative outcomes. Few prediction models of BS are available and some factors such as frailty and intraoperative hypotension (IOH) which have been reported to promote the occurrence of BS were not included. Therefore, we look forward to creating a straightforward, precise, and clinically useful prediction model by incorporating new factors, such as frailty and IOH.

MATERIALS AND METHODS

We retrospectively collected 540 patients and analyzed the data from 418 patients. Univariate analysis and backward stepwise logistic regression were used to select risk factors to develop a dynamic nomogram model, and then we developed a web calculator to visualize the process of prediction. The performance of the nomogram was evaluated in terms of discrimination, calibration, and clinical utility.

RESULTS

According to the receiver operating characteristic (ROC) analysis, the nomogram showed good discriminative ability (AUC = 0.933) and the Hosmer-Lemeshow goodness-of-fit test demonstrated the nomogram had good calibration (p = 0.0718). Age, Clinical Frailty Scale (CFS) score, midazolam dose, propofol induction dose, total area under the hypotensive threshold of mean arterial pressure (MAP_AUT), and cerebrovascular diseases were the independent risk predictors of BS and used to construct nomogram. The web-based dynamic nomogram calculator was accessible by clicking on the URL: https://eegbsnomogram.shinyapps.io/dynnomapp/ or scanning a converted Quick Response (QR) code.

CONCLUSIONS

Incorporating two distinctive new risk factors, frailty and IOH, we firstly developed a visualized nomogram for accurately predicting BS in non-cardiac surgery patients. The model is expected to guide clinical decision-making and optimize anesthesia management.

Correlation between burst suppression and postoperative delirium in elderly patients: a prospective study

OBJECTIVE

To explore the correlation between intraoperative burst suppression (BS) and postoperative delirium (POD) in elderly patients, and provide more ideas for reducing POD in clinical.

METHODS

Ninety patients, aged over 60 years, who underwent lumbar internal fixation surgery in our hospital were selected. General information of patients was obtained and informed consent was signed during preoperative visits. Patients were divided into burst suppression (BS) group and non-burst suppression (NBS) group by intraoperative electroencephalogram monitoring. Intraoperative systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and heart rate (HR) were recorded, and the variation and minimum value were obtained by calculating. Hemoglobin (HGB), C-reactive protein (CRP), system immune inflammatory index (SII) at 24 and 72 h after surgery, the incidence of postoperative adverse reactions, postoperative hospital stay, and total cost were recorded after operation. POD assessment was performed using CAM within 7 days after surgery or until discharge. SPSS25.0 was used for statistical analysis.

RESULTS

Compared with the NBS group, the number of elderly patients with high frailty level in BS group was more (P = 0.048). There is correlation between BS and POD (OR: 4.954, 95%CI 1.034-23.736, P = 0.045), and most of the POD patients in BS group behave as hyperactive type.

CONCLUSION

The occurrence of intraoperative BS is associated with POD, and elderly patients with frailty are more likely to have intraoperative BS. BS can be used as a predictor of POD.

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.

Unveiling age-independent spectral markers of propofol-induced loss of consciousness by decomposing the electroencephalographic spectrum into its periodic and aperiodic components

Background

Induction of general anesthesia with propofol induces radical changes in cortical network organization, leading to unconsciousness. While perioperative frontal electroencephalography (EEG) has been widely implemented in the past decades, validated and age-independent EEG markers for the timepoint of loss of consciousness (LOC) are lacking. Especially the appearance of spatially coherent frontal alpha oscillations (8-12 Hz) marks the transition to unconsciousness.Here we explored whether decomposing the EEG spectrum into its periodic and aperiodic components unveiled markers of LOC and investigated their age-dependency. We further characterized the LOC-associated alpha oscillations by parametrizing the adjusted power over the aperiodic component, the center frequency, and the bandwidth of the peak in the alpha range.

Methods

In this prospective observational trial, EEG were recorded in a young (18-30 years) and an elderly age-cohort (≥ 70 years) over the transition to propofol-induced unconsciousness. An event marker was set in the EEG recordings at the timepoint of LOC, defined with the suppression of the lid closure reflex. Spectral analysis was conducted with the multitaper method. Aperiodic and periodic components were parametrized with the FOOOF toolbox. Aperiodic parametrization comprised the exponent and the offset. The periodic parametrization consisted in the characterization of the peak in the alpha range with its adjusted power, center frequency and bandwidth. Three time-segments were defined: preLOC (105 - 75 s before LOC), LOC (15 s before to 15 s after LOC), postLOC (190 - 220 s after LOC). Statistical significance was determined with a repeated-measures ANOVA.

Results

Loss of consciousness was associated with an increase in the aperiodic exponent (young: p = 0.004, elderly: p = 0.007) and offset (young: p = 0.020, elderly: p = 0.004) as well as an increase in the adjusted power (young: p < 0.001, elderly p = 0.011) and center frequency (young: p = 0.008, elderly: p < 0.001) of the periodic alpha peak. We saw age-related differences in the aperiodic exponent and offset after LOC as well as in the power and bandwidth of the periodic alpha peak during LOC.

Conclusion

Decomposing the EEG spectrum over induction of anesthesia into its periodic and aperiodic components unveiled novel age-independent EEG markers of propofol-induced LOC: the aperiodic exponent and offset as well as the center frequency and adjusted power of the power peak in the alpha range.