Neuro-Monitoring in Intravenous Anaesthesia

Neuro-Monitoring in Intravenous Anaesthesia: Comparison

Please note this is a comparison between Version 2 by Amina Yu and Version 1 by Nicola Disma.

The monitoring of hypnosis depth by means of electroencephalogram-based (EEG-based) systems is emerging in paediatric anaesthesia common practice. This monitor system measures specific EEG signs which derive from anaesthetic-induced changes in neuronal firing.

neuromonitoring
EEG-derived monitor
depth of anaesthesia
near-infrared-spectroscopy
brain oxygenation
anaesthesia
paediatric

1. Introduction

In children, these signs are influenced not only by both the depth of sedation and the mechanism of action of the specific anaesthetic agent, but also by the typical age-related changes of a developing brain ^[1]. Moreover, the current clinical condition and underlying co-morbidities might significantly affect the validation and application of this monitoring [2,3,4]^[2][3][4]. Therefore, although recent studies have shown that they are worth being used in children ^[5], many of the available monitoring methods show limitations when applied to paediatric patients and have to be cautiously used, especially in those aged less than 1 year [6,7,8]^[6][7][8].

2.Available EEG_S Technology

For daily practice, in addition to the unprocessed EEG (the raw cortical EEG), two different groups of EEG-based devices are currently available. These are the EEG-derived indices (processed EEG or p-EEG) and the spectrogram or Density Spectral Array (DSA) ^[9] (Figure 1).

Jcm 10 02639 g001 550

Figure 1. Mechanism of action of the currently available EEG derived anaesthesia monitoring (in red). The sensor is placed on the forehead and records the electrical potentials produced in the cortex resulting in the raw EEG. The majority of the EEG-derived monitors work by disassembling a complex EEG waveform (the raw EEG), into many series of waves of different frequencies (Hertz—Hz). These, together with several of the EEG variables, are converted into a single index through mathematical algorithms by the processed EEG (p-EEG) devices. The obtained index represents the level of hypnosis. However, the index can also be affected by the Electromyography (EMG) and the Signal Quality Index (SQI or ARTF). The spectrogram, is a real time monitoring which portrays all the EEG frequencies and their power over the time in a three-dimensional method (3D spectrogram or Compressed Spectral Array). The latter is then integrated in a two-dimensional plot using colours to represent different powers in the Density Spectral Array (DSA). Derived additional parameters are the Spectral Edge Frequency (SEF95), Median Frequency and the Burst Count (burst/minute) Suppression Rate (SR), Burst Suppression Ratio (BSR), Suppression Ratio (SR) or BS% Indicator (Burst suppression percentage). These can be either represented on the display by a number or visualized on the Spectrogram.

The majority of the EEG-derived monitors works by disassembling a complex EEG waveform (the raw EEG), into many series of waves of different frequencies. These are, respectively, the slow (<1 Hz), delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–25 Hz) and gamma (25–40 Hz) oscillations. The power of each band is defined by quantity and amplitude ^[9].

The processed EEG (p-EEG) devices convert several of the EEG variables into a single index through mathematical algorithms. The obtained index represents the level of hypnosis. Some examples of p-EEG monitors are the Bispectral Index Monitor (BIS, Medtronic Inc.), the Patient State Index (PSI, Masimo Inc.) of the SEDLine brain function monitor or the Narcotrend index (Narcotrend Inc.). Finally, the Response and the State Entropy measures the degree of disorder in the EEG together with the electromyography (EMG) signals ^[10].

Differently from the pEEG, the Density Spectral Array (DSA) or spectrogram is a real time monitoring method which portrays all the EEG frequencies and their power over the time in a three-dimensional method. The latter is then integrated in a two-dimensional plot using colours to represent different powers ^[11].

Table 1lists some of the most widely used EEG-based anaesthesia monitoring devices which are currently available, along with their key features.

Device	EEG-Derived Index (Processed EEG)	Mechanism of Action-Algorhytm Basis	Anaesthesia Range (Total Range)	Delay (s)	Potential Influencing Factors	Other EEG Monitoring Included into the Device	Additional Parameters	Index Age Limits
BISMonitor	(Bispectral Index Monitor Covidien LP, Medtronic Inc.) - 2 channel - 4 channel	BIS index	Analysis of EEG features (degree of high frequency activation, low frequency synchronisation, nearly suppressed periods, fully suppressed periods) which correlate with hypnosis/sedations EEG in adults	40–60 (0–100) 0 = very deep hypnosis; 100 = conscious	Index	5–15	DSA	Real-time	EMGand NMBDs		Medical Devices	(Electocautery, pacemakers, etc.)	Certain anaesthetic agentis or adjuvants	,	Serious clinical conditions	(cerebral ischemia, hypo perfusion, cardiac arrest, hypovolemia, hypotension, hypothermia)	2 channel M:	Raw EEG	4 channel M:	- Raw-EEG		- DSA	(Density Spectral Array) - Asymmetry	(ASYM)	Indicator	EMG	(bar 0–4 −> EMG power 30 to > 55)	Burst Count	(Burst/minute)	SQI	(Signal Quality Indicator) 0–100 Suppression Ratio (	SR	) number	12 year	= safe	1-5 year	to be cautiously used (2 channel sensor age > 4 years recommended;	<1		year	not safe	DSA	>	6 months
SEDLine	(Root Masimo)	PSI	(Patient State Index)	Quantitative EEG analysis of the power within the α, β, δ and θ frequency bands; the temporal and spatial gradients occurring among these frequency bands when changing anaesthetic dept.	25–50 (0–100) 0 = very deep hypnosis; 100 = conscious	Index	25	DSA	Real-time	EMG and NMBDs		Medical Devices	(Pacemakers, etc.)	Certain anaesthetic agentis or adjuvants	,	Serious clinical conditions	(cerebral ischemia, hypo perfusion, cardiac arrest, hypovolemia, hypotension, hypothermia)	Raw EEG	4 channels Power Spectrum and	DSA		SEFL95	and SEFR95 Spectral Edge Frequency left and right (95% quantile)	ASYM	metry Graph	EMG	Suppression Rate (	SR	) Artifact (	ARTF	) Electrical	Impedance	<	1		year	not safe	DSA	>	6 months
NARCOTREND-Compact M	(Medival)	Narcotrend Monitor	Analysis of multivariate EEG-derived parameters to carry out an automatic classification of the EEG on a scale ranging from stage A (conscious) to stage F (very deep hypnosis).	D (general anaesthesia)-E (general anaesthesia with deep hypnosis); [A (conscious) − F (very deep hypnosis)].	Real-time	EMG and NMBDs		Medical Devices	(Electocautery, pacemakers, etc.)	Serious clinical conditions	(cerebral ischemia, hypo perfusion, cardiac arrest, hypovolemia, hypotension, hypothermia)	Raw-EEG	(1 or 2 channels) Cerebrogram Relative Band Activities/Power Power Spectrum and	DSA	Quantiles	SEF50	(median (50% quantile) and	SEF95	spectral edge frequency (95% quantile)	EMG	Burst Suppression Ratio (	BSR	)	STI	(sharp transient intensity)	Impedance	<1 year	not safe	<60 days	: only EEG classifications for stages with implied or clear suppression lines (E2 to F1). If there is an EEG without suppression lines, the output is “Undiff. EEG”	60 day–1 year	: the full A–F scale is displayed. As long as no fully classifiable EEG is detected −> the output “Undiff. EEG” is displayed.	DSA	>	6 months
	Narcotrend Index	20–64 (0–100) 0 = very deep hypnosis; 100 = conscious		Index	28	EMG and NMBDs		Medical Devices	(Electocautery, pacemakers, etc.)	Serious clinical conditions		Raw-EEG		DSA	Quantiles	SEF50	(median (50% quantile) and	SEF95	spectral edge frequency (95% quantile)	EMG	Burst Suppression Ratio (	BSR	)	STI	(sharp transient intensity)	Impedance	<1 year	not safe	<60 days		60 day–1 year		DSA	>	6 months	DSA	Real-time
E-ENTROPYModule	(GE Healthcare, Inc.)	SE	(State of Entropy)	RE	(Response Entropy)	Analysis of the irregularity, complexity, or unpredictability characteristics of the EEG and the frontal electromyograph (FEMG) signals	40–60 (0–91)	15–60	Medical Devices	(Electocautery, pacemakers, etc.)	Neurological Disorders	, traumas, epileptic seizures and psychoactive	medication	–	EMG	(Index component) Burst Suppression Ratio (	BSR	)	<2 year	not safe
CSM	Cerebral State Monitor (Danmeter)	CSI	(Cerebral State Index)	Quantitative EEG analysis in specific frequency bands (α and β), the relationship between these quantities (β-α) and the amount of instantaneous burst suppression (BS) in each thirty-second period of the EEG.	40–60 (0–100) 0 = very deep hypnosis; 100 = conscious	50	Serious clinical conditions	(severe neurological disorders) and psychoactive	medication		Medical Devices	(Pacemakers)	Raw EEG	EMG	% and EMG bar	BS%	Indicator (Burst suppression percentage	SQI	(Signal Quality Indicator) 0–100 Sensor	Impedance	<2 year	not safe

3. Clinical Application

The EEG ranges from a wakefulness pattern characterised by beta (13–25 Hz) and gamma (25–40 Hz) oscillations, to an isoelectric pattern, which represents an anaesthetic-induced coma with profound hypothermia ^[9]. An unconscious brain under surgical anaesthesia usually shows slow (<1 Hz) and alpha (8–13 Hz) oscillation. After induction of anaesthesia or in case of excessive sedation, slow oscillation or occasional burst suppression can occur. However, the EEG pattern also differs in relation to the anaesthetic drug administered. In this way, the DSA allows a trained anaesthetist to easily identify the specific patterns which correspond to certain hypnosis depth and anaesthesia trends [9,12]^[9][12].

Since the EEG-based devices correlate with the amount of both volatile and intravenous anaesthesia, they can predict the patient’s state of consciousness or unconsciousness ^[7]. They should lead to a better control of anaesthesia depth which, in turn, allows avoiding both over-sedation and light-anaesthesia [13,14]^[13][14]. In adults, this has been proven not only to reduce time to emergence, tracheal tube removal, and time to PACU discharge but also in decreasing post-operative nausea and vomiting and delirium as well as in lowering the overall amount of anaesthesia given. [15,16,17]^[15][16][17].

In children, only a few studies have analysed correlations between EEG-based monitors and the post-anaesthesia outcome [18,19,20,21]^{[18][19][20][21]}. Moreover, the currently available devices for the paediatric population are all based on algorithms which have been tested on adults ^[7].

Notwithstanding, some studies demonstrate that BIS and Narcotrend monitors are associated with a reduced emergence time and a decreased discharge-time from PACU after paediatric general anaesthesia [18,19]^[18][19]. However, discordant results have been found in a randomised trial on the same topic in paediatric cardiac patients ^[20]. Although a correlation between the use of pEEG devices and the reduction of awareness episodes has not been clearly demonstrated in children, the anaesthesia monitoring is considered to be also useful in this area ^[21]. An ongoing prospective, observational, multicentre study aims to further investigate the role of EEG monitoring in infants younger than 36 months. The study observes the incidence of isoelectric EEG events (defined as EEG amplitude less than 20 μV for 2 s or more) and its association with neurological outcome and quality of life in children undergoing at least 30 min of inhalational or propofol-based general anaesthesia ^[22].

Despite some studies demonstrated that EEG-based monitoring devices appropriately correlate with specific EEG signs during sevoflurane and propofol anaesthesia in children older than 1 year of age [5^[5][13],13], the paramount evidence of their beneficial effects is mostly related to total intravenous anaesthesia and propofol (TIVA). In fact, a linear slowing of EEG and a lowering of BIS index has been demonstrated concurrently with the increasing in propofol blood concentration in children ^[5].

A survey of European paediatric anaesthetist in 2018 showed that the main application of EEG monitoring occurs during TIVA and in children over 4 years of age, with the aim of avoiding awareness ^[23]. Furthermore, the use of TIVA has recently emerged into the common practice of paediatric anaesthesia. However, despite fairly reliable models, doubts still exist regarding the pharmacokinetic and pharmacodynamic variability of anaesthetic agents among different ages. In this scenario, the use of anaesthesia depth monitoring systems results very useful, especially when the TIVA is associated with Neuromuscular Blockers (NMBs) ^[24].

4. Conclusions

EEG-derived anaesthesia depth monitors perform well in children that should be applied for guiding clinicians in providing the correct dose of general anaesthetics. However, clinicians should be trained on the specific device applied with its own technology, in order to draw the right interpretation. Moreover, age, type of anaesthesia and underlying neurologic conditions should guide clinicians to correctly titrate medications, beyond the monitoring result by itself. NIRS and brain oxygenations monitoring represent a relatively recent technological advancement. It is clear that acute drop in brain oxygenation will have an impact on the neurological outcome, but the specific intervention that should be applied for acute derangements is still to be validated. NIRS can monitor brain oxygenation in the area where the sensor is applied, and it should not be interpreted as a surrogate index of brain autoregulation. In fact, the limits of autoregulation may vary according to the age, but also to the other physiological parameters. Then, NIRS should be clinically interpreted under the light a more comprehensive clinical condition, which might evolve time by time during anaesthesia and surgery. Nevertheless, both monitoring should be implemented in the daily practice, especially when the most fragile population is treated. Understanding the physiology and pathophysiology of brain autoregulation will be the next step of clinical research to define the limits of tolerance. Right now, clinicians should bear in mind that keeping young patients as close as possible to the baseline parameters is mandatory to stay away from the edge of loss of autoregulation.

References

Cornelissen, L.; Kim, S.E.; Lee, J.M.; Brown, E.N.; Purdon, P.L.; Berde, C.B. Electroencephalographic markers of brain development during sevoflurane anaesthesia in children up to 3 years old. Br. J. Anaesth. 2018, 120, 1274–1286.
Valkenburg, A.J.; De Leeuw, T.G.; Tibboel, D.; Weber, F. Lower bispectral index values in children who are intellectually disabled. Anaest. Analg. 2009, 109, 1428–1433.
Brandt, S.P.; Walsh, E.C.; Cornelissen, L.; Lee, J.M.; Berde, C.; Shank, E.S.; Purdon, P.L. Case Studies Using the Electroencephalogram to Monitor Anesthesia-Induced Brain States in Children. Anesth. Analg. 2020, 131, 1043–1056.
Walsh, E.C.; Lee, J.M.; Terzakis, K.; Zhou, D.W.; Burns, S.; Buie, T.M.; Firth, P.G.; Shank, E.S.; Houle, T.T.; Brown, E.N. Age-dependent changes in the propofol-induced electroencephalogram in children with autism spectrum disorder. Front. Syst. Neurosci. 2018, 12, 23.
Rigouzzo, A.; Khoy-Ear, L.; Laude, D.; Louvet, N.; Moutard, M.-L.; Sabourdin, N.; Constant, I. EEG profiles during general anesthesia in children: A comparative study between sevoflurane and propofol. Paediatr. Anaesth. 2019, 29, 250–257.
Davidson, A.; Huang, G.; Rebmann, C.; Ellery, C. Performance of entropy and Bispectral Index as measures of anaesthesia effect in children of different ages. Br. J. Anaesth. 2005, 95, 674–679.
Davidson, A.; Skowno, J. Neuromonitoring in paediatric anaesthesia. Curr. Opin. Anaesthesiol. 2019, 32, 370–376.
Lee, J.M.; Akeju, O.; Terzakis, K.; Pavone, K.J.; Deng, H.; Houle, T.T.; Firth, P.G.; Shank, E.S.; Brown, E.N.; Purdon, P.L. A prospective study of age-dependent changes in propofol-induced electroencephalogram oscillations in children. Anesthesiology 2017, 127, 293–306.
Purdon, P.L.; Sampson, A.; Pavone, K.J.; Brown, E.N. Clinical Electroencephalography for Anesthesiologists: Part I: Background and Basic Signatures. Anesthesiology 2015, 123, 937–960.
Maja, V.; Talja, P.; Tenkanen, N.; Tolvanen-Laakso, H. Description of the Entropy algorithm as applied in the Datex-Ohmeda S/5 Entropy Module. Acta Anaesthesiol. Scand. 2004, 48, 154–161.
De Heer, I.J.; Bouman, S.J.M.; Weber, F. Electroencephalographic (EEG) density spectral array monitoring in children during sevoflurane anaesthesia: A prospective observational study. Anaesthesia 2019, 74, 45–50.
Zanner, R.; Pilge, S.; Kochs, E.F.; Kreuzer, M.; Schneider, G. Time delay of electroencephalogram index calculation: Analysis of cerebral state, bispectral, and Narcotrend indices using perioperatively recorded electroencephalographic signals. Br. J. Anaesth. 2009, 103, 394–399.
Myles, P.S.; Leslie, K.; McNeil, J.; Forbes, A.; Chan, M.T.V.; B-Aware Trial Group. Bispectral index monitoring to prevent awareness during anaesthesia: The B-Aware randomised controlled trial. Lancet 2004, 363, 1757–1763.
Avidan, M.S.; Zhang, L.; Burnside, B.A.; Finkel, K.J.; Searleman, A.; Selvidge, J.A.; Saager, L.; Turner, M.S.; Rao, S.; Bottros, M.; et al. Anesthesia awareness and the bispectral index. N. Engl. J. Med. 2008, 358, 1097–1108.
Gan, T.J.; Glass, P.S.; Windsor, A.; Payne, F.; Rosow, C.; Sebel, P.; Manberg, P. Bispectral index allows faster emergence and improved recovery from propofol, alfentanil, and nitrous oxide anesthesia. BIS Utility Study Group. Anesthesiology. 1997, 87, 808–815.
Liu, S.S. Effects of Bispectral Index monitoring on ambulatory anesthesia: A meta-analysis of randomised controlled trials and a cost analysis. Anesthesiology 2004, 101, 311–315.
Oliveira, C.R.; Bernardo, W.M.; Nunes, V.M. Benefit of general anesthesia monitored by bispectral index compared with monitoring guided only by clinical parameters. Systematic review and meta-analysis. Braz. J. Anesthesiol. 2017, 67, 72–84.
Sargin, M.; Uluer, M.S.; Ozmen, S. The effects of bispectral index monitoring on hemodynamics and recovery profile in developmentally delayed pediatric patients undergoing dental surgery. Paediatr. Anaesth. 2015, 950–955.
Weber, F.; Walhout, L.C.; Escher, J.C. The impact of narcotrend EEG-guided propofol administration on the speed of recovery from pediatric procedural sedation-a randomized controlled trial. Paediatr. Anaesth. 2018, 28, 443–449.
Vance, J.L.; Shanks, A.M.; Woodrum, D.T. Intraoperative bispectral index monitoring and time to extubation after cardiac surgery: Secondary analysis of a randomized controlled trial. BMC Anesthesiol. 2014, 14, 79.
Sury, M.R. Accidental awareness during anesthesia in children. Paediatr. Anaesth. 2016, 26, 468–474.
Yuan, I.; Olbrecht, V.A.; Mensinger, J.L.; Zhang, B.; Davidson, A.J.; Von Ungern-Sternberg, B.S.; Skowno, J.; Lian, Q.; Song, X.; Zhao, P. Statistical Analysis Plan for “An international multicenter study of isoelectric electroencephalography events in infants and young children during anesthesia for surgery”. Paediatr. Anaesth. 2019, 29, 243–249.
Cheung, Y.M.; Scoones, G.; Stolker, R.J.; Weber, F. Use, applicability and reliability of depth of hypnosis monitors in children—a survey among members of the European Society for Paediatric Anaesthesiology. BMC Anesthesiol. 2018, 18, 40.
Anderson, B.J.; Bagshaw, O. Practicalities of Total Intravenous Anesthesia and Target-controlled Infusion in Children. Anesthesiology 2019, 131, 164–185.