Brain monitoring revisited: What is it all about?

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.

A study of the relationship between Bispectral index and age-adjusted minimum alveolar concentration during the maintenance phase of general anesthesia in elective surgery

Background and Aims

Bispectral index (BIS) and minimum alveolar concentration (MAC) are commonly used to monitor the depth of anesthesia. The objective was to study the correlation between BIS and age-adjusted minimum alveolar concentration (aaMAC) during the maintenance phase of anesthesia. The influence of variables affecting BIS and or aaMAC was studied to determine an equation between BIS and aaMAC.

Material and Methods

This prospective observational study was carried out after institutional ethical approval in adult patients 18-60 years of either sex, ASA I and II posted for elective surgery under general anesthesia. Five minutes after airway management, BIS values and aaMAC equivalents were noted during the maintenance phase of anesthesia. aaMAC and corresponding BIS values were recorded every minute for periods, where the anesthetic agent concentration had remained the same during preceding 5 minutes till the switching off of the anesthetic agent. Age, sex, ASA status, use of nitrous oxide, inhalational agent, dose of midazolam, and opioid used were also recorded.

Results

BIS/aaMAC showed an inverse correlation. Increasing age, ASA II status, morphine equivalent >5, and use of nitrous oxide, sevoflurane, or isoflurane were associated with a higher BIS at equivalent aaMAC. Using the exchangeable correlation structure, a generalized estimation equation was obtained as the best predictor.

Conclusion

Factors affecting both aaMAC and BIS affect the relationship between the two, and although there are wide variations, BIS and aaMAC can be equated and values of either can be calculated if one is known using a generalized estimates equation.

Neuromonitoring during general anesthesia in non-neurologic surgery

Cerebral complications are common in perioperative settings even in non-neurosurgical procedures. These include postoperative cognitive dysfunction or delirium as well as cerebrovascular accidents. During surgery, it is essential to ensure an adequate degree of sedation and analgesia, and at the same time, to provide hemodynamic and respiratory stability in order to minimize neurological complications. In this context, the role of neuromonitoring in the operating room is gaining interest, even in the non-neurolosurgical population. The use of multimodal neuromonitoring can potentially reduce the occurrence of adverse effects during and after surgery, and optimize the administration of anesthetic drugs. In addition to the traditional focus on monitoring hemodynamic and respiratory systems during general anesthesia, the ability to constantly monitor the activity and maintenance of brain homeostasis, creating evidence-based protocols, should also become part of the standard of care: in this challenge, neuromonitoring comes to our aid. In this review, we aim to describe the role of the main types of noninvasive neuromonitoring such as those based on electroencephalography (EEG) waves (EEG, Entropy module, Bispectral Index, Narcotrend Monitor), near-infrared spectroscopy (NIRS) based on noninvasive measurement of cerebral regional oxygenation, and Transcranial Doppler used in the perioperative settings in non-neurosurgical intervention. We also describe the advantages, disadvantage, and limitation of each monitoring technique.

Data Driven Investigation of Bispectral Index Algorithm

Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters. However, only a portion of the proprietary algorithm has been identified. We investigated the BIS algorithm using clinical big data and machine learning techniques. Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training datasets and test datasets, respectively. A histogram of data points was plotted to define five BIS ranges representing the depth of anaesthesia. Decision tree analysis was performed to determine the electroencephalography subparameters and their thresholds for classifying five BIS ranges. Random sample consensus regression analyses were performed using the subparameters to derive multiple linear regression models of BIS calculation in five BIS ranges. The performance of the decision tree and regression models was externally validated with positive predictive value and median absolute error, respectively. A four-level depth decision tree was built with four subparameters such as burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio. Positive predictive values were 100%, 80%, 80%, 85% and 89% in the order of increasing BIS in the five BIS ranges. The average of median absolute errors of regression models was 4.1 as BIS value. A data driven BIS calculation algorithm using multiple electroencephalography subparameters with different weights depending on BIS ranges has been proposed. The results may help the anaesthesiologists interpret the erroneous BIS values observed during clinical practice.

The Feasibility and Utility of Continuous Sleep Monitoring in Critically Ill Patients Using a Portable Electroencephalography Monitor

BACKGROUND

Sleep disruption in critically ill adults can result in acute decrements in cognitive function, including delirium, but it is underdiagnosed in the setting of the intensive care unit (ICU). Although sleep stages can be assessed by polysomnography (PSG), acquisition and interpretation of PSG is costly, is labor intensive, is difficult to do over an extended period of time with critically ill patients (multiple days of continuous recording), and may interfere with patient care. In this pilot study, we investigated the feasibility and utility of monitoring sleep in the ICU setting using a portable electroencephalography (EEG) monitor, the SedLine brain monitor.

METHODS

We first performed a baseline comparison study of the SedLine brain monitor by comparing its recordings to PSG recorded in a sleep laboratory (n = 3). In a separate patient cohort, we enrolled patients in the ICU who were monitored continuously with the SedLine monitor for sleep disruption (n = 23). In all enrolled patients, we continuously monitored their EEG. The raw EEG was retrieved and sleep stages and arousals were analyzed by a board-certified technologist. Delirium was measured by a trained research nurse using the Confusion Assessment Method developed for the ICU.

RESULTS

For all enrolled patients, we continuously monitored their EEGs and were able to retrieve the raw EEGs for analysis of sleep stages. Overall, the SedLine brain monitor was able to differentiate sleep stages, as well as capture arousals and transitions between sleep stages compared with the PSG performed in the sleep laboratory. The percentage agreement was 67% for the wake stage, 77% for the non-rapid eye movement (REM) stage (N1 = 29%, N2 = 88%, and N3 = 6%), and 89% for the REM stage. The overall agreement was measured with the use of weighted kappa, which was 0.61, 95% confidence interval, 0.58 to 0.64. In the ICU study, the mean recording time for the 23 enrolled patients was 19.10 hours. There were several signs indicative of poor-quality sleep, where sleep was distributed throughout the day, with reduced time spent in REM (1.38% ± 2.74% of total sleep time), and stage N3 (2.17% ± 5.53% of total sleep time) coupled with a high arousal index (34.63 ± 19.04 arousals per hour). The occurrence of ICU delirium was not significantly different between patients with and without sleep disruption.

CONCLUSIONS

Our results suggest the utility of a portable EEG monitor to measure different sleep stages, transitions, and arousals; however, the accuracy in measuring different sleep stages by the SedLine monitor varies compared with PSG. Our results also support previous findings that sleep is fragmented in critically ill patients. Further research is necessary to develop portable EEG monitors that have higher agreement with PSG.

Spectral entropy in monitoring anesthetic depth

Monitoring the brain response to hypnotics in general anesthesia, with the nociceptive and hemodynamic stimulus interaction, has been a subject of intense investigation for many years. Nowadays, monitors of depth of anesthesia are based in processed electroencephalogram by different algorithms, some of them unknown, to obtain a simplified numeric parameter approximate to brain activity state in each moment. In this review we evaluate if spectral entropy suitably reflects the brain electric behavior in response to hypnotics and the different intensity nociceptive stimulus effect during a surgical procedure.

The median effective concentration (EC50) of propofol with different doses of fentanyl during colonoscopy in elderly patients

BACKGROUND

Propofol and fentanyl are the most widely administered anesthesia maintaining drugs during colonoscopy. In this study, we determined the median effective concentration (EC50) of propofol required for colonoscopy in elderly patients, and the purpose of this study was to describe the pharmacodynamic interaction between fentanyl and propofol when used in combination for colonoscopy in elderly patients.

METHODS

Ninety elderly patients scheduled for colonoscopy were allocated into three groups in a randomized, double-blinded manner as below, F0.5 group (0.5 μg.kg(-1) fentanyl), F1.0 group (1.0 μg.kg(-1) fentanyl) and saline control group. Anaesthesia was achieved by target-controlled infusion of propofol (Marsh model, with an initial plasma concentration of 2.0 μg.ml(-1)) and fentanyl. Colonoscopy was started 3 min after the injection of fentanyl. The EC50 of propofol for colonoscopy with different doses of fentanyl was measured by using an up-and-down sequential method with an adjacent concentration gradient at 0.5 μg.ml(-1) to inhibit purposeful movements. Anaesthesia associated adverse events and recovery characters were also recorded.

RESULTS

The EC50 of propofol for colonoscopy in elderly patients were 2.75 μg.ml(-1) (95% CI, 2.50-3.02 μg.ml(-1)) in F0.5 group, 2.05 μg.ml(-1) (95% CI, 1.98-2.13 μg.ml(-1)) in F1.0 group and 3.08 μg.ml(-1) (95% CI, 2.78-3.42 μg.ml(-1)) in control group respectively (P < 0.05). Patients in the F1.0 group had a significantly longer awake time and length of hospital stay than those in control group (P < 0.05).

CONCLUSION

Increasing doses of fentanyl up to 1.0 μg.kg(-1) reduces the propofol EC50 required for elderly patients undergoing colonoscopy, and there was no significant difference in anaesthesia associated adverse events but prolonged awake and discharge time.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR15006368. Date of registration: May 3, 2015.

Two different approaches for pharmacokinetic modeling of exhaled drug concentrations

Online measurement of drug concentrations in patient's breath is a promising approach for individualized dosage. A direct transfer from breath- to blood-concentrations is not possible. Measured exhaled concentrations are following the blood-concentration with a delay in non-steady-state situations. Therefore, it is necessary to integrate the breath-concentration into a pharmacological model. Two different approaches for pharmacokinetic modelling are presented. Usually a 3-compartment model is used for pharmacokinetic calculations of blood concentrations. This 3-compartment model is extended with a 2-compartment model based on the first compartment of the 3-compartment model and a new lung compartment. The second approach is to calculate a time delay of changes in the concentration of the first compartment to describe the lung-concentration. Exemplarily both approaches are used for modelling of exhaled propofol. Based on time series of exhaled propofol measurements using an ion-mobility-spectrometer every minute for 346 min a correlation of calculated plasma and the breath concentration was used for modelling to deliver R(2) = 0.99 interdependencies. Including the time delay modelling approach the new compartment coefficient k(e0lung) was calculated to k(e0lung) = 0.27 min(-1) with R(2) = 0.96. The described models are not limited to propofol. They could be used for any kind of drugs, which are measurable in patient's breath.