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Anesth Analg 1999;89:1311
© 1999 International Anesthesia Research Society

GENERAL ARTICLES

The Performance of Electroencephalogram Bispectral Index and Auditory Evoked Potential Index to Predict Loss of Consciousness During Propofol Infusion

Stefan Schraag, MD*, Ulrich Bothner, MD{dagger}, Roger Gajraj, MD{ddagger}, Gavin N. C. Kenny, MD, BSc, FRCA§, and Michael Georgieff, MD, PhD*

*Department of Anesthesiology, University of Ulm, Ulm, Germany; {dagger}Department of Medical Informatics, University of Utah, Salt Lake City, Utah; {ddagger}Department of Anaesthesia, The General Infirmary, Leeds, UK; and §Department of Anaesthesia, Glasgow Royal Infirmary, Glasgow, UK

Address correspondence and reprint requests to Stefan Schraag, MD, Department of Anesthesiology, University of Ulm, Stein- hövelstraße 9, D-89075 Ulm, Germany. Address e-mail to stefanschraag{at}compuserve.com


   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 References
 
The bispectral index (BIS) of the electroencephalogram and middle latency auditory evoked potentials are likely candidates to measure the level of unconsciousness and, thus, may improve the early recovery profile. We prospectively investigated the predictive performance of both measures to distinguish between the conscious and unconscious state. Twelve patients undergoing lower limb orthopedic surgery during regional anesthesia additionally received propofol by target-controlled infusion for sedation. The electroencephalogram BIS and the auditory evoked potential index (AEPi), a mathematical derivative of the morphology of the auditory evoked potential waveform, were recorded simultaneously in all patients during repeated transitions from consciousness to unconsciousness. Logistic regression procedures, receiver operating characteristic analysis, and sensitivity and specificity were used to compare predictive ability of both indices. In the logistic regression models, both the BIS and AEPi were significant predictors of unconsciousness (P < 0.0001). The area under the receiver operating characteristic curve for discrete descending index threshold values was apparently, but not significantly (P > 0.05), larger for the AEPi (0.968) than for the BIS (0.922), indicating a trend of better discriminatory performance. We conclude that both the BIS and AEPi are reliable means for monitoring the level of unconsciousness during propofol infusion. However, AEPi proved to offer more discriminatory power in the individual patient.

Implications: Both the bispectral index of the electroencephalogram and the auditory evoked potentials index are good predictors of the level of sedation and unconsciousness during propofol infusion. However, the auditory evoked potentials index offers better discriminatory power in describing the transition from the conscious to the unconscious state in the individual patient.


   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 References
 
An increasing concern in today’s anesthetic practice for both the patient and the anesthesiologist is the problem of possible awareness and recall during general anesthesia. However, the means of monitoring the appropriate level of unconsciousness remains controversial. The bispectral index (BIS) of the electroencephalogram (EEG) may be one of the most reliable methods for assessing the level of sedation (1,2). However, measures based on the auditory evoked response reliably differentiate the conscious from the unconscious patient during anesthesia (3,4). Although these techniques have been studied for various aspects of anesthetic depth and anesthetic drug modeling, there are few data available on the performance of both measures obtained simultaneously in the same individual. Therefore, our aim in this study was to quantify the performance of the EEG BIS and auditory evoked potential index (AEPi), with respect to the probability of correctly differentiating the conscious from the unconscious state in the same patient during propofol infusion.


   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 References
 
After obtaining hospital ethics committee approval and written, informed consent, we studied 12 patients scheduled for lower limb orthopedic surgery under spinal anesthesia who required additional intraoperative sedation. Patients with psychiatric disorders or hearing abnormalities were excluded. All patients were premedicated with temazepam 30 mg, given 2 h before surgery. Spinal anesthesia was established with either 3.0–3.5 mL of 0.5% or 3.0 mL of 0.75% plain bupivacaine, administered via a 26-gauge needle at the L2/3 interspace. An epidural catheter was also inserted for the administration of top-ups during surgery in prolonged cases.

After ensuring an adequate level of the local block, a target-controlled infusion of propofol was commenced (5), and patients were given supplementary oxygen via a nasal sponge, while breathing spontaneously. The propofol target concentration was adjusted according to the sedation requirements of each patient.

At intervals of 30 s, the presence or absence of an eyelash reflex and the patient’s response to a verbal command to squeeze the investigator’s hand were recorded. The investigator was blinded to both the propofol target concentration and the obtained EEG signals. The transition from consciousness to unconsciousness was defined as the point at which there was no response to the verbal command, and the return of this response was considered the transition from unconsciousness to consciousness. All patients were interviewed the day after surgery for memory of intraoperative events.

Auditory Evoked Potential (AEP) Monitoring
The EEG was obtained from three disposable silver-silver chloride electrodes (Zipprep; Aspect Medical Systems, Natick, MA) placed on the right mastoid (+), middle forehead (-), and Fp2 as reference. The custom-built amplifier had a 5-kV medical grade isolation, common mode rejection ratio of 170 dB with balanced source impedance, input voltage-noise of 0.3 µV, and current input noise of 4 pA (0.05 Hz–1 kHz rms). A third-order Butterworth analog band-pass filter with a bandwidth of 1–220 Hz was used. The auditory clicks were of 1-ms duration and 70 dB above hearing threshold. They were presented to both ears at a rate of 6.9 Hz. The amplified EEG was sampled at a frequency of 1778 Hz by a high accuracy, low distortion, 12-bit analog to digital converter (PCM-DAS08; Computer Boards, Mansfield, MA) and processed in real time on a microcomputer (T1950CT; Toshiba Corp., Tokyo, Japan). AEPs were produced by averaging 256 sweeps of 144-ms duration. The time required to update a full signal was 36.9 s, but a moving time average technique allowed a faster response time to any change in the signal every 3 s.

The AEPi, which reflects the morphology of the AEP waveform, is a mathematical derivative and is calculated as the sum of the square root of the absolute difference between every two successive 0.56-ms segments (6).

EEG BIS
The EEG was obtained from four Zipprep electrodes placed on both sides of the outer malar bone (At1 and At2) with Fpz as reference and Fp1 as ground. The EEG bispectrum was monitored using a commercially available EEG monitor (A-1000, BIS 3.0 algorithm, rev. 0.40 software version; Aspect Medical Systems). Data from the EEG monitor were downloaded and stored every 5 s.

Both the AEPi and BIS were recorded simultaneously and stored on the computer’s hard disk for enabling offline analysis.

Assessment of the nonlinear association between AEPi or BIS values and probability of unconsciousness was accomplished with the logistic regression procedure of the SPSS software, version 8.0 (SPSS Inc., Chicago, IL). This method is able to estimate the probability of a binary yes/no response, depending on a continuous variable according to the following equation (7): Go


where P = probability of consciousness, ß0 = intercept (constant), x1 = value of the independent variable (AEPi or BIS value), and ß1 = estimate of the coefficient of the independent variable. Significance of the coefficient estimate was calculated using the Wald test (8), and model fit to the observed data was performed by the Hosmer and Lemeshow goodness-of-fit-test (9). Probability values < 0.05 were considered significant. Solving the equation for 0.5 (0.05, 0.95), gives values of the BIS and AEPi, where 50% (5%, 95%) unresponsiveness can be expected for the given population.

The area under the receiver operating characteristic (ROC) curve for discrete index threshold values was used to summarize the accuracy of both the AEPi and BIS. The ROC curve for each index plots sensitivity (fraction of unresponsive patients who are correctly predicted to be unresponsive) against 1-specificity (fraction of responsive patients correctly identified) and reflects the discriminating power of the index (10). The area under the ROC curve was determined together with SE and 95% confidence interval by maximum-likelihood estimation as described by Metz et al. (11).

The prediction probability (Pk) was also calculated as described by Smith et al. (12). Pk was introduced for assessing prediction accuracy when indicator value and patient state are polytomous ordinal, and both variables are measured experimentally. Pk has a value of 1 when the indicator predicts observed anesthetic depth perfectly and a value of 0.5 when the indicator predicts no better than a 50:50 chance (13).


   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 References
 
Data for this statistical investigation were obtained during a study of electrophysiologic measurements during the transition phase from consciousness to unconsciousness (14).

Two male patients and 10 female patients, mean age 74 (range 62–82) yr and mean weight 71 (range 55–84) kg completed the study. Surgery had a mean duration of 74 (range 58–121) min, and there was a mean of 10 (range 6–20) transition periods from consciousness to unconsciousness. No patient had recall of any event in the operating theater, including application of earphones and auditory clicks.

ROC curves simultaneously show sensitivity and specificity for several discrete threshold values of the indices and the observed data (Fig. 1). The area under the ROC curve for AEP was 0.968 (SE 0.014, 95% CI 0.929–0.987) and for BIS 0.9217 (SE 0.023, 95% CI 0.868–0.961). Whereas the area under the ROC curve for the AEPi is larger than that of BIS, indicating better discriminatory performance; this is not a statistically significant difference (P > 0.05).



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  		Figure 1. Receiver operating characteristics curves for discrete threshold values of the auditory evoked potentials (AEP) index and the bispectral (BIS) index.

 
When building logistic regression models both the AEPi and BIS were significant predictors (P < 0.0001) of probability of unconsciousness. The coefficient estimates ß were 0.34 (SE 0.07) for the AEPi and 0.20 (SE 0.03) for the BIS. If the anti-logarithm of the coefficient [exp(ß)] is the factor by which the odds Pconscious/Punconscious = P/(1-P) changes when the index increases by one unit, there will be a 1.4-fold change for the AEPi and a 1.2-fold change for the BIS. In the goodness-of-fit-test, actual observed data versus predicted data from the model were not significantly different (P = 0.93 for the AEPi and P = 0.13 for the BIS), indicating excellent performance of both models. Figure 2 shows the logistic probability response curve of the proposed models for the AEPi and BIS, respectively. Looking at the observed data in the upper and lower part of the figures allows comparisons of how well the models distinguish the clinical state of consciousness. Choosing the optimal threshold probability yields a minimal rate of misclassification considering the clinical consequence of an incorrectly predicted case. Generally, the more the two groups of cases (conscious or unconscious) cluster at their respective ends of the probability plot, the better the discrimination.



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  		Figure 2. Logistic probability response curves for the models of auditory evoked potential (AEP) index (dashed line, y = 1/1 + exp [16.3639–0.3404 · x]) and bispectral (BIS) index (solid line, y = 1/1 + exp [15.3455–0.1989 · x]). Actually observed cases are indicated in histograms above and below the curve. Error bars indicate p5%, p50%, and p95% of unconsciousness.

 
Given a threshold probability of 50% unconsciousness, the classification table reveals values for sensitivity, specificity, and overall correct classification of 93.1%, 85.7%, and 89.4% for the AEPi model and 86.1%, 83.1%, and 84.9% for the BIS model, respectively.

The summary of probability values is given in Table 1. It shows that a 95% probability of unconsciousness can be assumed with a BIS value of 62 and an AEPi value of 39 during propofol infusion. The Pk values of 0.92 for the BIS and 0.97 for the AEPi indicate that both measures bear a high potential of reliability and validity.


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  		Table 1. Probabilities of Correct Prediction of Unconsciousness
 

   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
References
 
The absence of a universally accepted standard by which to compare monitors designed to quantify the level of hypnosis remains an unsolved problem. While the anesthetic dose-clinical response relationship curve of propofol is steep, the curves relating most EEG variables, such as 95% (50%) spectral edge frequency to clinical responses are shallow with large inter-patient variability at a given level of consciousness, which makes it difficult to predict the response in the individual patient (15). A value with a sensitivity of 100% is likely to have low specificity, and, if used to control anesthetic depth, an overdose will be administered to many patients. The available variables derived from the EEG seem primarily related to drug concentrations, although recent advances in EEG measurements by including the BIS show a much better correlation with the level of sedation (16).

Liu et al. (17) evaluated the effectiveness of the EEG BIS for assessing the level of propofol induced sedation and amnesia during regional anesthesia. The BIS correlated well with the level of sedation, and decreasing BIS values were associated with progressively reduced incidents of recall. These results were confirmed by a recent study in volunteers, in which the BIS accurately predicted response to verbal commands during sedation with propofol (1).

A significant relationship was found between middle latency AEPs and the ability to respond to verbal commands in volunteers (18). The middle latency AEP recording seems to be a reliable method to monitor the level of anesthesia as defined by spontaneous movements during anesthesia (19). A threshold value of 60 ms of Nb wave proved to be most predictive with a 100% sensitivity and a 99.51% specificity in patients undergoing laparotomy under epidural analgesia and propofol anesthesia, above which explicit and implicit memory is unlikely to occur. This was supported by a study published one year earlier by Davies et al. (3), who found a mean threshold value for Nb wave latency of 55 ms during repeated transitions from consciousness to unconsciousness. In fact, awake latencies were slightly higher than those in baseline awake, whereas anesthetized latencies were similar to the ones obtained during the first period of unconsciousness.

But how do the threshold values obtained for loss of consciousness help in avoiding awareness in patients undergoing general anesthesia? If intraoperative awareness has to include subsequent memory (implicit or explicit), then the situation becomes more complicated, because thresholds for recall may not necessarily be identical with those obtained for loss of consciousness and may vary with the administration of additional amnesic drugs. All of our patients received temazepam for premedication, so it seems not surprising that none had recall of any events.

A recent study by Iselin-Chaves et al. (20) investigating the effects of propofol and alfentanil on sedation and the BIS reported a BIS50 (50% probability) for lack of recall of 89 (85–93, 95% CI) for propofol alone. Of all their volunteers who had no recall, the lowest propofol plasma concentration was 0.7 µg/mL. This is not surprising, as the BIS correlates well with propofol plasma concentrations but not as well with the overall level of consciousness (16). Therefore, propofol blood concentrations were not measured in our study.

However, comparing the BIS values for a 50% probability of loss of consciousness (77 in our study) with the BIS50 for lack of recall [89 in the study by Iselin-Chaves et al. (20)] suggests that subsequent memory will be obtunded reliably, when the level of anesthesia is titrated below the loss of consciousness threshold, either by the BIS or AEPi, rather than above a certain propofol blood concentration. But when transferring these results to patients receiving general anesthesia for surgical procedures, we must be cautious and aware that our data were obtained in individuals with a local block. It has been shown that, for both the BIS (21) and AEP (22), the intensity of noxious stimulation input may alter the resulting values, representing a balance of arousal on one side and anesthetic suppression on the other.

Logistic regression analysis, as we used, has the advantage of describing nonlinear associations between continuous explanatory variables and binary outcomes. As such, it is a meaningful alternative to standard confirmation statistics, which are based on linear models, especially when the probability of an event has to be described. Compared with a strict unpaired model design, we used a pooled data approach, which is likely to diminish the overall difference that may be present when different individuals respond more heterogeneously. Also there is the phenomenon of regression-to-the-mean in repeated measurements, which might lead to an underestimation of real effects (23). However, as we compared and tested both monitors within the same individuals and settings, this theoretical shortcoming statistically becomes less important.

In summary, we undertook a comparison of the ability of the EEG BIS and AEPi to distinguish consciousness from unconsciousness in patients receiving propofol as a target-controlled infusion. It was shown, that both measures are characterized by a high level of predictive accuracy, expressed by significant predictor values in the logistic regression model and by comparable high Pk values. However, the larger area under the ROC curve suggests a trend that AEPi better discriminates consciousness from unconsciousness in the individual patient. These results may be considered when discussing the appropriate means of monitoring depth of hypnosis.


   Acknowledgments
 
We thank Warren D. Smith, Sacramento, for his statistical support to compute the prediction probability Pk with PKMACRO.


   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 References
 

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Accepted for publication June 29, 1999.




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Lippincott, Williams & Wilkins Anesthesia & Analgesia® is published for the International Anesthesia Research Society® by Lippincott Williams & Wilkins with the assistance of Stanford University Libraries' HighWire Press®. Copyright 2006 by the International Anesthesia Research Society. Online ISSN: 1526-7598   Print ISSN: 0003-2999 HighWire Press