Anaesthetic awareness is a complication of general anaesthesia where the patient is able to recall - implicitly or explicitly - intraoperative events. There is a spectrum of severity, with only a third of cases experiencing pain, more commonly awareness of sounds, conversations, or the siting of an endotracheal tube. The experience is potentially distressing and has medico-legal implications. Severe cases may cause post-traumatic-stress disorder, and there is public awareness of these with campaigning and support groups. (eg http://www.anesthesiaawareness.com )
Several studies have demonstrated an incidence of 1-2/1000 GAs. There are 3 recognised higher-risk groups: emergency trauma surgery (1:20), the first stage of emergency obstetric GAs (1:250), and cardiac surgery (1:100). These share a common cause, lighter anaesthesia being employed to reduce the dangerous depressant effects of anaesthesia on the patient or unborn infant. Individual sensitivities to anaesthetic agents (MAC) are normally distributed, so under very light anaesthesia the outliers will be at risk.
Although EEG-based monitors have been shown to reduce the incidence of awareness, standards setting bodies have not to date included these in essential monitoring. The EEG signal is < 50 microvolts, and therefore susceptible to interference from the EMG, movement, less-than-perfect electrode performance, ocular and glossal artefacts, and monopolar diathermy.
In 2007, a US study [1] reported a much lower incidence of awareness. 1:14,560 overall risk, dropping to 1:42,000 if the three recognised at-risk groups were excluded. No brain monitors were used. It is accepted that 95% of awareness incidents are due to human error, faulty anaesthetic technique, or apparatus failure, 2.5% no cause identified, and the residual 2.5% are spurious. It is tempting to speculate that evolutionary changes to the anaesthetic environment and mindset are driving this risk down. These would include: documented, standardised, anaesthetic machine checks; improving anaesthetic machine features (eg the removal of locking oxygen bypass, disconnection alarms); universal use of end-tidal agent monitoring; standards for record-keeping; a climate of systematic anaesthetic incident review programs; and perhaps also an increasing awareness of the risk of litigation.
[1] Pollard et al,
Intra-operative awareness in a regional medical system, a review of 3 years data.
Anesthesiology 2007;106:269-274
4 tag(s) Complications GA Monitoring Critical Incidents
If you were not the anaesthetist involved with the case:
1) Take the patient seriously (the distress in some cases was compounded by staff making light of the patient's statements).
2) Remember this may have medicolegal implications. Carefully document the patient's statements, and your responses. Be careful not to introduce leading words or concepts into the conversation - be a passive and sympathetic listener. Do not try to take a detailed history at this point, nor draw any conclusions. The anaesthetist is the appropriate person to do this.
3) Offer the patient an anxiolytic.
4) Contact the anaesthetist at the earliiest opportunity - there may be equipment or monitoring issues to investigate, and the anaesthetist will want to deal with the matter in detail - and with the full knowledge of peri-operative events.
Depth of anaesthesia can be assessed clinically, and a number of monitors also provide useful information. This is an important concept as it forms the basis of the prevention of awareness as well as the avoidance of unnecessarily deep anaesthesia.
Entropy Rate Estimation (usually referred to as Entropy) is a complexity measure which has been assessed as a depth of anaesthesia measure when applied to the unstimulated EEG.
The term Entropy was borrowed from thermodynamics by a branch of mathematics: non-linear dynamics. Kolmogorov-Sinai Entropy (KSE) is a theoretical measure of the predictability of a time-series. Entropy Rates are a measure of how unpredictable a single value is based on the values which preceded it. Truly random processes have high entropy rates conversely periodic processes have low entropy rates.
A number of techniques for estimating entropy rates have been published. Some of these specifically apply corrections for limited or noisy data ... the sort of EEG data available in a clinical setting.
These techniques include Conditional Entropy (CEn); Corrected Conditional Entropy (CCEn); Approximate Entropy (ApEn); Coarse-grained Entropy rates (CGEn); and Gaussian Process Entropy rates (GPEn). There is, as you would expect, considerable correllation between these, differences being mainly due to the way they handle imperfect data.
A related entropic measure, Spectral Entropy (SpEn) is defined as the Shannon entropy computed over the normalized power spectral density function. Shannon entropy is computed from a number of values, rather than from a time-series. SpEn is an entropic measure which can be used as a measure of system complexity and is therefore included here. However, the complexity of the system in SpEn is understood as the number of different processes making up the time series rather than the sense of regularity as in the case of the above listed entropy rates measures.
Of these methods (Spectral Entropy) has been introduced as the basis of a commercial depth-of-anaesthesia monitor (Datex-Ohmeda).
Comparison with Bispectral Index Scale (BIS). BIS is an established proprietary measure of depth of anaesthesia which is a development of earlier measures of EEG slowing. It incorporates a measure of phase alignment, and of burst suppression, and has been refined mathematically against a large clinical database. Entropy Rate Estimation, unrefined, correlates well with BIS in the clinical setting, and it will be interesting to see how this newer technology fares as different algorithms are trialled and the results refined.
