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Critical Care Medicine  |   January 2017
Pilot Study of Propofol-induced Slow Waves as a Pharmacologic Test for Brain Dysfunction after Brain Injury
Author Notes
  • From the Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, Medical Research Center Oulu (J.Kortelainen, E.V., T.S.), Department of Clinical Neurophysiology, Medical Research Center Oulu (J.Kortelainen, U.H.), Unit of Surgery, Anaesthesia and Intensive Care, Medical Faculty (J.L., J.Koskenkari, S.A., T.A.-K.), Division of Intensive Care Medicine, Medical Research Center Oulu (J.L., J.Koskenkari, T.A.-K.), and Department of Anaesthesiology, Medical Research Center Oulu (S.A.), University of Oulu and Oulu University Hospital, Oulu, Finland; and Department of Clinical Pharmacology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland (J.T.B.).
  • Submitted for publication October 10, 2015. Accepted for publication September 8, 2016.
    Submitted for publication October 10, 2015. Accepted for publication September 8, 2016.×
  • Address correspondence to Dr. Kortelainen: Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, University of Oulu, P.O. Box 4500, Oulu FIN-90014, Finland. jukortel@ee.oulu.fi.Information on purchasing reprints may be found at www.anesthesiology.org or on the masthead page at the beginning of this issue. Anesthesiology’s articles are made freely accessible to all readers, for personal use only, 6 months from the cover date of the issue.
Article Information
Critical Care Medicine / Clinical Science / Central and Peripheral Nervous Systems / Critical Care / Pharmacology
Critical Care Medicine   |   January 2017
Pilot Study of Propofol-induced Slow Waves as a Pharmacologic Test for Brain Dysfunction after Brain Injury
Anesthesiology 1 2017, Vol.126, 94-103. doi:10.1097/ALN.0000000000001385
Anesthesiology 1 2017, Vol.126, 94-103. doi:10.1097/ALN.0000000000001385
Abstract

Background: Slow waves (less than 1 Hz) are the most important electroencephalogram signatures of nonrapid eye movement sleep. While considered to have a substantial importance in, for example, providing conditions for single-cell rest and preventing long-term neural damage, a disturbance in this neurophysiologic phenomenon is a potential indicator of brain dysfunction.

Methods: Since, in healthy individuals, slow waves can be induced with anesthetics, the authors tested the possible association between hypoxic brain injury and slow-wave activity in comatose postcardiac arrest patients (n = 10) using controlled propofol exposure. The slow-wave activity was determined by calculating the low-frequency (less than 1 Hz) power of the electroencephalograms recorded approximately 48 h after cardiac arrest. To define the association between the slow waves and the potential brain injury, the patients’ neurologic recovery was then followed up for 6 months.

Results: In the patients with good neurologic outcome (n = 6), the low-frequency power of electroencephalogram representing the slow-wave activity was found to substantially increase (mean ± SD, 190 ± 83%) due to the administration of propofol. By contrast, the patients with poor neurologic outcome (n = 4) were unable to generate propofol-induced slow waves.

Conclusions: In this experimental pilot study, the comatose postcardiac arrest patients with poor neurologic outcome were unable to generate normal propofol-induced electroencephalographic slow-wave activity 48 h after cardiac arrest. The finding might offer potential for developing a pharmacologic test for prognostication of brain injury by measuring the electroencephalographic response to propofol.

What We Already Know about This Topic
  • There are currently no reliable methods for assessing brain function in the early stages of recovery from hypoxic, ischemic, or traumatic insults that might inform neurologic prognosis and therapy

  • Slow electroencephalographic waves induced by general anesthetics might provide a neurophysiologic signature of healthy brain function

What This Article Tells Us That Is New
  • In a pilot study of comatose cardiac arrest survivors, propofol-induced slow-wave activity was present in a greater proportion of patients with good neurologic outcome than in those with poor outcome

  • Further studies are necessary to test propofol-induced slow-wave activity as an early predictor of neurologic outcome after brain injury

SLOW waves (less than 1 Hz) are the most important electroencephalogram signatures of nonrapid eye movement (NREM) sleep.1,2  This neurophysiologic phenomenon originates from the neurons in the neocortex and thalamus, which have been shown to exhibit slow (less than 1 Hz) oscillations that correlate with the slow-wave activity of electroencephalogram.3  Even though slow oscillations are commonly still considered to be generated exclusively in the neocortex before spreading to the other brain areas,4,5  compelling evidence highlights the thalamic contribution to their full electroencephalographic expression.6–8  The physiologic importance of the slow waves in higher cognitive function has convincingly been shown,9–11  and the lack of this electrophysiologic phenomenon has been associated with disorders of consciousness.12,13  In addition to the natural sleep, slow waves are seen in healthy individuals during general anesthesia. Originating from the same cellular and network level mechanisms as those found during NREM sleep,14,15  anesthetic-induced slow waves are considered to be a product of an unconscious brain occurring only in deep sedation/anesthesia.16  Recently, by using simultaneously recorded electroencephalogram and functional magnetic resonance imaging, the slow-wave activity during anesthesia was shown to be related to the isolation of the thalamocortical system from sensory stimulation and the retention of the internal thalamocortical exchange.17 
Motivated by the physiologic importance of the slow waves and the possibility to test their generation with anesthetics in a controlled manner, we hypothesized this electrophysiologic phenomenon to be disrupted in an injured brain. The synchronized activity of large neuronal populations and the delicate interaction between the cortical and subcortical areas required in the normal formation of the waves was expected to be sensitive to brain dysfunction. To test our hypothesis, we carried out an experiment with 10 comatose patients treated in an intensive care unit (ICU) after cardiopulmonary resuscitation from out-of-hospital cardiac arrest. Because of the reduced oxygen supply during cardiac arrest, the patients potentially suffered from hypoxic–ischemic brain injury due to which they had received therapeutic hypothermia treatment as a neuroprotective measure before the experiment. These patients generally represent a substantial diagnostic challenge, as detecting the potential diffuse brain injury in the early phase of recovery is highly demanding.18  In the experiment, the patients’ ability to generate anesthetic-induced slow waves was assessed 36 to 48 h after the insult with a pharmacologic test in which they were exposed to varying amounts of anesthetic drug propofol in a controlled manner.
Materials and Methods
Experimental Design
The experimental protocol was approved by The Regional Ethics Committee of the Northern Ostrobothnia Hospital District (decision 34/2012; March 28, 2012), Oulu, Finland, which follows the Declaration of Helsinki guidelines. The patients’ closest relative was asked for an informed written consent for participation.
The study was carried out with 10 comatose patients resuscitated from out-of-hospital cardiac arrest between May 2012 and April 2013 (table 1). The sample size was selected to be appropriate for an experimental pilot study, and no a priori statistical power analysis was conducted. We included patients with initial cardiac rhythm of ventricular fibrillation and persistent coma after the return of spontaneous circulation. The exclusion criteria were age younger than 18 yr, cardiogenic shock, possible causes of coma other than cardiac arrest (e.g., drug overdose, head trauma, or cerebrovascular accident), and previous disease affecting the central nervous system. The subjects represented 10 consecutive patients filling the inclusion criteria, and no other selection was carried out. Before the experiment, the patients had received hypothermia treatment (33° to 34°C for 24 h) according to the European Resuscitation Council guideline19  as a neuroprotective measure. The experiment was carried out 36 to 48 h after the cardiac arrest when the hypothermia treatment had ended (body temperature greater than 35°C), but the patients were still sedated and intubated. For sedation, we used a continuous intravenous infusion of propofol. In addition, eight of the patients received a low-dose infusion of analgesic fentanyl whose dosage was kept fixed during the experiment. The blood pressure was maintained with norepinephrine. Benzodiazepines such as midazolam were not used.
Table 1.
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest×
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest
Table 1.
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest×
×
In the experiment, we recorded 19-channel electroencephalogram according to the 10 of 20 international systems using an electrode cap with Ag/AgCl electrodes (impedance less than 5 kΩ). For the recording, we used the Nicolet nEEG Modular Neurodiagnostic System with a v32 amplifier (CareFusion, USA). The amplifier had a sampling frequency of 500 Hz and a bandwidth of 0.053 to 125 Hz. For reference, we used the common average. Before the recording, we maintained the patient’s sedation with propofol given by an infusion pump following the ICU’s common practice. During the experiment, we incrementally decreased the propofol infusion rate following a predefined protocol (fig. 1) to determine the drug-induced changes in the slow-wave activity at different anesthetic levels. We started the step-wise decrease from the highest acceptable infusion rate during the intensive care (4 mg · kg–1 · h–1) and continued every 30 min until the drug administration was finally switched off. We used the same infusion rates (4, 3, 2, 1, 0.5, and 0 mg · kg–1 · h–1) for all patients. For one patient, we excluded the highest infusion rate as the burst suppression pattern was already seen at the infusion rate 3 mg · kg–1 · h–1 and higher drug administration was thus not justified. We took a blood sample before each change in the infusion rate and at the end of the experiment, i.e., 30 min after turning propofol off, for the determination of plasma propofol concentration (fig. 1A). Plasma was separated within 30 min and stored at −70°C until analysis. The concentration of propofol in plasma was determined by an approach utilizing liquid chromatography with fluorescence detection.20  The day-to-day coefficient of variation of the method was 3.7% at 9.9 mg/l, 3.5% at 0.96 mg/l, and 4.5% at 0.19 mg/l (n = 4).
Fig. 1.
Effect of propofol on electroencephalogram (EEG) slow-wave activity in a single patient with good neurologic outcome. (A) Propofol infusion rate (blue line) and corresponding measures of plasma propofol concentration (red line) during the experiment. The infusion rate was decreased step-wise every 30 min, and the drug concentration was determined at every step just before the next decrease and at the end of the experiment (red circles). (B) Raw EEG of a single channel (F7) during the experiment. Four 10-s signal samples from different phases of the experiment are presented above the whole signal. (C) Power spectral density as a function of time for the signal given in B. The frequency axis is given in the logarithmic scale. (D) Low-frequency (less than 1 Hz) EEG power representing a patient’s slow-wave activity during the experiment. The average low-frequency power (red curve) is calculated from all 19 single-channel powers (gray curves). The topographic distribution of the low-frequency EEG power at different phases of the experiment is given above the curves.
Effect of propofol on electroencephalogram (EEG) slow-wave activity in a single patient with good neurologic outcome. (A) Propofol infusion rate (blue line) and corresponding measures of plasma propofol concentration (red line) during the experiment. The infusion rate was decreased step-wise every 30 min, and the drug concentration was determined at every step just before the next decrease and at the end of the experiment (red circles). (B) Raw EEG of a single channel (F7) during the experiment. Four 10-s signal samples from different phases of the experiment are presented above the whole signal. (C) Power spectral density as a function of time for the signal given in B. The frequency axis is given in the logarithmic scale. (D) Low-frequency (less than 1 Hz) EEG power representing a patient’s slow-wave activity during the experiment. The average low-frequency power (red curve) is calculated from all 19 single-channel powers (gray curves). The topographic distribution of the low-frequency EEG power at different phases of the experiment is given above the curves.
Fig. 1.
Effect of propofol on electroencephalogram (EEG) slow-wave activity in a single patient with good neurologic outcome. (A) Propofol infusion rate (blue line) and corresponding measures of plasma propofol concentration (red line) during the experiment. The infusion rate was decreased step-wise every 30 min, and the drug concentration was determined at every step just before the next decrease and at the end of the experiment (red circles). (B) Raw EEG of a single channel (F7) during the experiment. Four 10-s signal samples from different phases of the experiment are presented above the whole signal. (C) Power spectral density as a function of time for the signal given in B. The frequency axis is given in the logarithmic scale. (D) Low-frequency (less than 1 Hz) EEG power representing a patient’s slow-wave activity during the experiment. The average low-frequency power (red curve) is calculated from all 19 single-channel powers (gray curves). The topographic distribution of the low-frequency EEG power at different phases of the experiment is given above the curves.
×
In addition to the experiment, we carried out several measures routinely used for the evaluation of brain injury and neurologic prognostication. As an electrophysiologic test, we performed median nerve N20 somatosensory evoked potential measurement approximately 48 h after the cardiac arrest. We measured the neuron-specific enolase and S100B protein immediately after the experiment and in the two consecutive days to improve the evaluation of possible neural injury. We also assessed the patients’ neurologic status throughout the ICU admission with clinical tests (corneal reflex, eye opening and movement, pupillary light reflex, and verbal and motor behavior). The treating physicians were unaware of the result of the experiment, and the treating decisions were thus not affected by the study.
We determined the severity of the hypoxic–ischemic brain injury by evaluating the neurologic recovery 6 months after the cardiac arrest using the Cerebral Performance Category (CPC; table 2) as recommended by the American Heart Association.21  We assigned the patients to either good (CPC 1 to 2) or poor (CPC 3 to 5) outcome groups based on if they were independent in activities of daily living after the follow-up period. If not clearly expressed in the patient files, we determined the status of recovery by telephone interview with the patient or his/her relative.
Table 2.
CPC
CPC×
CPC
Table 2.
CPC
CPC×
×
Analysis of Electroencephalogram
The signal processing of the electroencephalogram (fig. 2) was carried out as follows. First, we extracted 5-min signal samples at each step of the decrease in the drug infusion rate.The samples were taken at the end of the 30-min period just before the change in the infusion rate and at the end of the experiment corresponding to the collection of the drug concentration blood samples. The electroencephalogram samples were evaluated for abnormalities such as epileptic activity or suppression and artifacts by a clinical neurophysiology specialist who was blinded to the experiment and the outcome of the patients. From each 5-min signal sample, one to four 30-s representative sequences with minimal artifact were selected for further analysis. Some of these contained static electromyographic artifact that was, however, concentrated on the higher frequencies and thus did not disturb the analysis of slow-wave activity. We filtered the selected 30-s signal sequences using a low-pass finite impulse response filter with a cutoff frequency of 48 Hz before the calculation of a power spectral density (PSD) estimate using the Welch averaged periodogram method.22  The estimates were created using a 5-s Hamming window and 4.9-s overlap. The window size was selected to be appropriate in capturing the slow waves while excluding the really low-frequency electrode potentials (less than 2 Hz). We then calculated an average for the one to four PSD estimates, representing the same infusion rate to improve the robustness of the estimate. From the average PSD estimate, we summed the components less than 1 Hz to represent the low-frequency electroencephalogram power. Finally, we calculated the average low-frequency power quantifying the patient’s slow-wave activity at a certain infusion rate for all the 19 channels of electroencephalogram. The computational electroencephalogram analysis was carried out with Matlab technical computing language, version 2011b (The MathWorks Inc., USA), and the topographic plots were made with EEGLAB v13.23 
Fig. 2.
Electroencephalogram (EEG) signal processing steps. (A) First, 5-min signal samples at each step of the drug infusion rate decrease were extracted. From each 5-min signal sample, one to four 30-s representative artifact-free sequences were selected for further analysis. (B) Next, the power spectral density (PSD) estimates were determined for each 30-s sequences. (C) Finally, an average for the one to four PSD estimates was calculated, from which the components less than 1 Hz were summed to represent the low-frequency EEG power.
Electroencephalogram (EEG) signal processing steps. (A) First, 5-min signal samples at each step of the drug infusion rate decrease were extracted. From each 5-min signal sample, one to four 30-s representative artifact-free sequences were selected for further analysis. (B) Next, the power spectral density (PSD) estimates were determined for each 30-s sequences. (C) Finally, an average for the one to four PSD estimates was calculated, from which the components less than 1 Hz were summed to represent the low-frequency EEG power.
Fig. 2.
Electroencephalogram (EEG) signal processing steps. (A) First, 5-min signal samples at each step of the drug infusion rate decrease were extracted. From each 5-min signal sample, one to four 30-s representative artifact-free sequences were selected for further analysis. (B) Next, the power spectral density (PSD) estimates were determined for each 30-s sequences. (C) Finally, an average for the one to four PSD estimates was calculated, from which the components less than 1 Hz were summed to represent the low-frequency EEG power.
×
Statistical Analysis
The effect of the infusion rate and group (independent variables) on the average low-frequency power of electroencephalogram was statistically compared. In addition, the effect of these independent variables on plasma propofol concentration and blood pressure was analyzed. Repeatedly, measured data were analyzed using linear mixed model with random intercept for subjects. The infusion rate-wise comparisons between groups were performed only if the infusion rate × the group interaction was significant (P < 0.05). For the statistical analyses, we used SAS (version 9.3; SAS Institute Inc., Cary, North Carolina).
Results
Patient Outcome
Of the 10 patients included in the study, 6 had good neurologic outcomes being, after the 6-month follow-up period, independent in activities of daily living without any subjective neurologic or psychologic deficit due to the event (table 1). Four of the ten patients, on the other hand, were eventually diagnosed with severe anoxic brain injury that led to permanent coma and finally death during the follow-up period.
Control Measures for Brain Injury
With the small amount of patients included in this experimental study, the routine measures for brain injury were inconsistent with the outcomes (table 1). For example, absent N20 somatosensory evoked potential was detected only in half of the patients with poor outcomes. Epileptic electroencephalogram and neuron-specific enolase analysis produced, on the other hand, both false-positives and false-negatives in poor outcome classification points of view. These findings emphasize the diagnostic challenge related to the early-phase detection of the potential diffuse brain injury due to cardiac arrest.
Plasma Propofol Concentration
During the experiment, we decreased the amount of propofol administered by infusion stepwise from 4 to 0 mg · kg–1 · h–1 (fig. 1A). We measured the plasma drug concentration before every decrease of the infusion rate and at the end of the experiment (fig. 1A). The highest (infusion rate 4 mg · kg–1 · h–1) and the lowest (infusion rate 0 mg · kg–1 · h–1) individual drug concentrations during the experiment varied between 2.4 ± 0.4 (mean ± SD) and 0.98 ± 0.26 mg/l, respectively. The individual concentration values at different propofol infusion rates are given in fig. 3. There was a significant effect of infusion rate (P < 0.0001) on the plasma propofol concentration. The effect of group (P = 0.14) and the infusion rate times the group interaction (P = 0.72) were not statistically significant.
Fig. 3.
Plasma propofol concentrations at different infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Plasma propofol concentrations at different infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Fig. 3.
Plasma propofol concentrations at different infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
×
Slow-wave Activity
In the patients with good neurologic outcome, the low-frequency (less than 1 Hz) electroencephalogram power representing the slow-wave activity was found to substantially decrease when the amount of propofol was reduced (figs. 1, B–D and 4). While the absolute effect was most pronounced in the prefrontal and temporal areas in which the slow-wave activity was strongest at high propofol infusion rates (figs. 1D and 5), a clear relative change was observed in all channels regardless of the brain region (figs. 1D and 6A). The findings are in line with previous studies, showing the propofol-induced low-frequency activity to occur widely across the whole scalp in healthy individuals.16  Compared to the individual values at infusion rate 0 mg · kg–1 · h–1, the propofol-induced increase in the low-frequency power at the maximum infusion rate (4 mg · kg–1 · h–1) was 190 ± 83% (fig. 6B).
Unlike those who recovered well, the patients with poor outcomes were unable to generate propofol-induced slow waves (figs. 4–6). Decreasing the propofol infusion rate during the experiment did not markedly reduce the low-frequency electroencephalogram power. The power at the maximum infusion rate was 78 ± 78% compared to the individual values at infusion rate 0 mg · kg–1 · h–1 (fig. 6B). The average low-frequency powers at different infusion rates are given in fig. 4B. The effect of infusion rate (P = 0.81) and group (P = 0.48) on the average low-frequency power was not statistically significant. However, a significant infusion rate times the group interaction (P < 0.0001) was observed, leading to a statistically significant difference between groups at infusion rate 4 mg · kg–1 · h–1 (P < 0.01) in the infusion rate-wise comparisons.
Fig. 4.
The effect of propofol on the low-frequency electroencephalogram (EEG) power. (A) Plasma propofol concentration and the low-frequency EEG power for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are individual average powers calculated for all the 19 EEG channels. The samples of each individual are connected with lines. For one patient with a poor outcome, periodic epileptiform discharges, emphasized at low concentrations, strongly affected the low-frequency electroencephalogram power. (B) Average low-frequency EEG power at different propofol infusion rates.
The effect of propofol on the low-frequency electroencephalogram (EEG) power. (A) Plasma propofol concentration and the low-frequency EEG power for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are individual average powers calculated for all the 19 EEG channels. The samples of each individual are connected with lines. For one patient with a poor outcome, periodic epileptiform discharges, emphasized at low concentrations, strongly affected the low-frequency electroencephalogram power. (B) Average low-frequency EEG power at different propofol infusion rates.
Fig. 4.
The effect of propofol on the low-frequency electroencephalogram (EEG) power. (A) Plasma propofol concentration and the low-frequency EEG power for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are individual average powers calculated for all the 19 EEG channels. The samples of each individual are connected with lines. For one patient with a poor outcome, periodic epileptiform discharges, emphasized at low concentrations, strongly affected the low-frequency electroencephalogram power. (B) Average low-frequency EEG power at different propofol infusion rates.
×
Fig. 5.
Individual topographic distributions of the low-frequency activity at different infusion rates. Rows represent different patients and columns different infusion rates. The patients with good neurologic outcome (green border) are shown above those with a poor outcome (red border). The values are absolute low-frequency (less than 1 Hz) powers.
Individual topographic distributions of the low-frequency activity at different infusion rates. Rows represent different patients and columns different infusion rates. The patients with good neurologic outcome (green border) are shown above those with a poor outcome (red border). The values are absolute low-frequency (less than 1 Hz) powers.
Fig. 5.
Individual topographic distributions of the low-frequency activity at different infusion rates. Rows represent different patients and columns different infusion rates. The patients with good neurologic outcome (green border) are shown above those with a poor outcome (red border). The values are absolute low-frequency (less than 1 Hz) powers.
×
Fig. 6.
Propofol-induced slow-wave activity in patients with good and poor neurologic outcomes. (A) Topographic distribution of the low-frequency (less than 1 Hz) electroencephalogram (EEG) power representing slow-wave activity at different propofol infusion rates. The distributions are averages calculated separately for the patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are given in percentages relative to the individual channel-wise powers at the propofol infusion rate 0 mg · kg–1 · h–1. (B) The low-frequency EEG power at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes is shown. The values represent individual average powers calculated from all 19 channels given relative to the individual average power at propofol infusion rate 0 mg · kg–1 · h–1. The samples of each individual are connected with lines.
Propofol-induced slow-wave activity in patients with good and poor neurologic outcomes. (A) Topographic distribution of the low-frequency (less than 1 Hz) electroencephalogram (EEG) power representing slow-wave activity at different propofol infusion rates. The distributions are averages calculated separately for the patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are given in percentages relative to the individual channel-wise powers at the propofol infusion rate 0 mg · kg–1 · h–1. (B) The low-frequency EEG power at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes is shown. The values represent individual average powers calculated from all 19 channels given relative to the individual average power at propofol infusion rate 0 mg · kg–1 · h–1. The samples of each individual are connected with lines.
Fig. 6.
Propofol-induced slow-wave activity in patients with good and poor neurologic outcomes. (A) Topographic distribution of the low-frequency (less than 1 Hz) electroencephalogram (EEG) power representing slow-wave activity at different propofol infusion rates. The distributions are averages calculated separately for the patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are given in percentages relative to the individual channel-wise powers at the propofol infusion rate 0 mg · kg–1 · h–1. (B) The low-frequency EEG power at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes is shown. The values represent individual average powers calculated from all 19 channels given relative to the individual average power at propofol infusion rate 0 mg · kg–1 · h–1. The samples of each individual are connected with lines.
×
In the analysis, we also considered other factors possibly affecting the electroencephalographic recordings during the experiment and causing a difference between the groups. The patients with poor and good neurologic outcomes did not differ in age (64 ± 8 and 65 ± 7 yr, respectively). Furthermore, the effect of propofol infusion rate and group on blood pressures was analyzed (fig. 7). The infusion rate times group interaction on the systolic (P = 0.32), mean (P = 0.39), and diastolic (P = 0.43) blood pressures was not significant.
Fig. 7.
Blood pressure at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Blood pressure at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Fig. 7.
Blood pressure at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
×
Visual Evaluation of Electroencephalograms
In addition to the computational analysis, the electroencephalograms were visually evaluated by a clinical neurophysiology specialist. Based on this evaluation, none of the patients with a good or poor outcome had fully suppressed isoelectric electroencephalogram at any phase of the experiment. A burst suppression pattern was observed in nine of the ten patients, focusing on the samples representing high propofol infusion rates, whereas, in one patient with a poor outcome, the signal was continuous during the entire recording. Clear epileptic activity was observed in three of the ten patients (table 1), from which two (one with a good and one with a poor outcome) developed periodic epileptiform discharges as the propofol infusion was decreased. In addition, at low propofol infusion rates, one patient with a poor outcome had sharp frontal θ transients indicating possible epileptic activity. For the patient with periodic epileptiform discharges and a poor outcome, epileptic activity strongly affected the low-frequency electroencephalogram power measured at low infusion rates (figs. 4 and 5). However, due to the antiepileptic properties of propofol, the activity was suppressed in deeper anesthetic levels revealing the absence of background slow-wave activity.
Discussion
In this experimental pilot study, we tested the ability of comatose postcardiac arrest patients to generate normal propofol-induced electroencephalographic slow-wave activity 48 h after cardiac arrest. Unlike those who recovered well, the patients with poor neurologic outcomes after a 6-month follow-up period were unable to generate such activity.
In the current study, the changes in the low-frequency power of electroencephalogram were analyzed during a protocol in which the propofol infusion rate was incrementally decreased from the highest acceptable infusion rate in the intensive care to zero. The protocol was designed to bring out propofol-induced changes in the electroencephalogram as good as possible while taking into account the limitations related to the clinical setup. The conclusions of the study are based on the observation of the difference between groups during the protocol, more specifically, in high infusion rates. However, since the protocol could not be designed to achieve steady-state anesthesia at different infusion rates, the findings should be interpreted while taking into account the plasma propofol concentrations. This is especially important as the data set only contained a few samples with high propofol concentrations in the poor outcome group (fig. 4A). In healthy individuals, propofol-induced slow waves occur after loss of consciousness and increase to a saturation point as the anesthesia deepens.17  After this, increasing the anesthetic effect starts to decrease the activity while the signal turns to the burst suppression pattern and finally isoelectric. With our protocol, nine of the ten patients (three of the four in the poor outcome group) had suppression periods in their signal at high infusion rates, meaning that these patients had already reached their saturation point in terms of slow-wave activity. In other words, for these patients, increasing the anesthetic effect would not increase the slow-wave activity. In one patient with a poor outcome, the electroencephalogram was continuous throughout the recording. However, for this patient, the plasma propofol concentration at the highest infusion rate was 1.63 mg/l, representing a value at which nearly all of the patients with good outcomes had already produced substantial slow-wave activity (fig. 4A). Consequently, it is unlikely that increasing the anesthetic effect in the patients with poor outcomes would have increased their slow-wave activity supporting the conclusion of the study.
Assessing brain function after an insult with potentially injurious effect to the brain, such as cardiac arrest, cerebrovascular accident, or traumatic brain injury, remains a substantial medical challenge. Appropriate treatment and development of new therapeutic interventions would benefit from reliable detection of brain dysfunction in the early phase of recovery.24–26  On the other hand, an objective measure to avoid misdiagnosis of the vegetative state, for example, might later be needed.27  It has been suggested that, instead of the laborious and expensive modern brain imaging techniques, the most practical screening tool for estimating brain function could be the century-old electroencephalogram.28  Compared to the functional magnetic resonance imaging, for example, a clear benefit of electroencephalogram is, in addition to its price and availability and the possibility for bedside monitoring, an essential property especially for ICU patients.
If hypoxic brain injury fundamentally disturbs the neural system responsible for the generation of slow waves, as suggested by this experimental pilot study, this disturbance might be possible to reveal and use as a prognostic tool for irreversible damage by measuring the electroencephalographic response to propofol. Resulting from the rhythmic alternation of neocortical up and down states, slow waves are generated by periods of persistent, widespread network activity and collective neuronal silence, respectively.29  Their full manifestation requires the sensory deafferentation achieved during anesthesia and NREM sleep,30–32  as well as retention of input from intrinsically oscillating thalamocortical neurons.1,30,33  Whereas the exact mechanisms underlying the initiation and maintenance of the up/down state bistability have remained elusive,29  the importance of this neurophysiologic phenomenon for normal brain function has convincingly been shown.9–13  Affecting virtually all neocortical neurons,34  the slow oscillations are considered to have an essential role in providing conditions for single-cell rest and preventing long-term neural damage.35  Consequently, in addition to revealing an already existing injury, the lack of slow-wave activity may also be a sign of an ongoing process having a potential injurious effect itself.
Anesthetics are generally considered a disturbance to the interpretation of electroencephalogram. In addition to epileptiform activity, the recordings of patients with potential brain injury are assessed for unreactivity, burst suppression, and low voltage/flatness of the signal,36  all of which may also partly or entirely be induced by general anesthetics like propofol. Consequently, for reliable analysis of electroencephalogram, minimization of the anesthetic delivery before signal interpretation is required, causing difficulties especially in the early phase of the treatment. If the results of this pilot study can be confirmed with a larger data set, this problem is turned upside down as it may indeed be a product of the anesthetized and unconscious brain that helps in detecting the dysfunction and possible injury. The finding is particularly intriguing as, historically, anesthetic-induced coma has been applied to the patients at high risk for brain injury due to its potential neuroprotective effect.
Compared to the traditional electroencephalographic reactivity test, in which a response to an auditory or painful stimulation is observed, pharmacologic testing could provide an objective and reproducible approach. Delivering the stimulus, i.e., administrating the anesthetic, does not involve subjective human activity, such as calling the patient by name or squeezing the trapezius muscle. Furthermore, the pharmacologically induced activity could be a more easily quantifiable measure and therefore independent of the investigator than the varying changes in the background frequency and amplitude seen in the electroencephalogram after an auditory or painful stimulus. Due to its conceptual similarity with the traditional electroencephalographic reactivity test and to be in line with the previous electroencephalogram terminology, we suggest this kind of a pharmacologic approach be called an electroencephalogram anesthetic reactivity test.
Several steps need to be taken before the findings presented in this article can be applied in clinical practice. First, and most importantly, only a small number of patients were included in this experimental pilot study requiring the validation of the results with larger data sets. These data were emphasized at both ends of the CPC scoring system, meaning that the patients either recovered perfectly (CPC = 1) or died (CPC = 5) during the follow-up period. The applicability of the methodology in patients with CPC 2 to 4 should thus be validated as well. Furthermore, the study does not answer the question how early from the cardiac arrest the approach could be used to predict the outcome. Background electroencephalogram has been shown to substantially change during the first hours and days after the cardiac arrest. How does the reactivity of electroencephalogram to propofol change over time, and at what point, if any, does it reliably predict the outcome is yet to be defined. To simplify its usage in the clinical environment, the method should also be further developed in terms of the anesthetic exposure. In the current study, the changes in the slow-wave activity were assessed at decreasing amounts of propofol requiring a lot of time due to the slow wearing-off of the drug. However, our results suggest that it might be indeed the lack of slow-wave activity at deep levels of propofol anesthesia that indicates brain dysfunction, suggesting that the test could alternatively be carried out by exposing lightly sedated patients to higher amounts of propofol. While this would make the testing much quicker and consequently easier to be applied in the clinic, further investigations are required to confirm its validity. The generalizability of the results to different anesthetics should be examined as well. While one can hypothesize all of the general anesthetics with similar pharmacodynamics, i.e., γ-aminobutyric acid-mediated inhibitory tone in the central nervous system, to produce similar results, these should be tested in separate clinical studies. Finally, reducing the number of electrodes to minimum would be highly appreciated when carrying out the measurement in the clinical environment. Based on the results presented, the propofol-induced relative increase in the low-frequency power is a topographically independent phenomenon (fig. 6A), suggesting that its absence could potentially be detected with significantly lower numbers of recording sites. This issue should be addressed in future studies.
Acknowledgments
The authors thank Per Rosenberg, M.D., Ph.D., Department of Anaesthesiology and Intensive Care Medicine, University of Helsinki, Helsinki, Finland, and Seppo Mustola, M.D., Ph.D., Department of Anesthesiology, South Carelia Central Hospital, Lappeenranta, Finland, for the arrangements related to the drug concentration measurements. The expert help of critical care study nurse Sinikka Sälkiö, Oulu University Hospital, Oulu, Finland, to carry out the experiments is highly appreciated. We also thank Pasi Ohtonen, M.Sc., Oulu University Hospital, Oulu, Finland, for the advice related to the statistical analysis.
Research Support
Supported by grant No. 40273/14 from Tekes: Finnish Funding Agency for Innovation, Helsinki, Finland, Medical Research Center Oulu, Oulu, Finland, Orion Research Foundation, Espoo, Finland, Instrumentarium Science Foundation, Helsinki, Finland, Emil Aaltonen Foundation, Helsinki, Finland, Oulu University Scholarship Foundation, Oulu, Finland, Finnish Foundation for Cardiovascular Research, Helsinki, Finland, and Finnish Science Foundation for Economics and Technology, Helsinki, Finland.
Competing Interests
A patent application has been filed by Dr. Kortelainen, Dr. Väyrynen, and Dr. Seppänen for an apparatus and a method for electroencephalographic examination (US14/674,318). The other authors declare no competing interests.
References
Crunelli, V, Hughes, SW The slow (<1 Hz) rhythm of non-REM sleep: A dialogue between three cardinal oscillators.. Nat Neurosci. (2010). 13 9–17 [Article] [PubMed]
Steriade, M, McCormick, DA, Sejnowski, TJ Thalamocortical oscillations in the sleeping and aroused brain.. Science. (1993). 262 679–85 [Article] [PubMed]
Crunelli, V, Errington, AC, Hughes, SW, Tóth, TI The thalamic low-threshold Ca2+ potential: A key determinant of the local and global dynamics of the slow (<1 Hz) sleep oscillation in thalamocortical networks.. Philos Trans A Math Phys Eng Sci. (2011). 369 3820–39 [Article] [PubMed]
Hoffman, KL, Battaglia, FP, Harris, K, MacLean, JN, Marshall, L, Mehta, MR The upshot of up states in the neocortex: From slow oscillations to memory formation.. J Neurosci. (2007). 27 11838–41 [Article] [PubMed]
Yuste, R, MacLean, JN, Smith, J, Lansner, A The cortex as a central pattern generator.. Nat Rev Neurosci. (2005). 6 477–83 [Article] [PubMed]
Hughes, SW, Cope, DW, Blethyn, KL, Crunelli, V Cellular mechanisms of the slow (<1 Hz) oscillation in thalamocortical neurons in vitro.. Neuron. (2002). 33 947–58 [Article] [PubMed]
Blethyn, KL, Hughes, SW, Tóth, TI, Cope, DW, Crunelli, V Neuronal basis of the slow (<1 Hz) oscillation in neurons of the nucleus reticularis thalami in vitro.. J Neurosci. (2006). 26 2474–86 [Article] [PubMed]
Rigas, P, Castro-Alamancos, MA Thalamocortical Up states: Differential effects of intrinsic and extrinsic cortical inputs on persistent activity.. J Neurosci. (2007). 27 4261–72 [Article] [PubMed]
Stickgold, R Sleep-dependent memory consolidation.. Nature. (2005). 437 1272–8 [Article] [PubMed]
Marshall, L, Helgadóttir, H, Mölle, M, Born, J Boosting slow oscillations during sleep potentiates memory.. Nature. (2006). 444 610–3 [Article] [PubMed]
Rasch, B, Born, J About sleep’s role in memory.. Physiol Rev. (2013). 93 681–766 [Article] [PubMed]
Forgacs, PB, Conte, MM, Fridman, EA, Voss, HU, Victor, JD, Schiff, ND Preservation of electroencephalographic organization in patients with impaired consciousness and imaging-based evidence of command-following.. Ann Neurol. (2014). 76 869–79 [Article] [PubMed]
Cologan, V, Drouot, X, Parapatics, S, Delorme, A, Gruber, G, Moonen, G, Laureys, S Sleep in the unresponsive wakefulness syndrome and minimally conscious state.. J Neurotrauma. (2013). 30 339–46 [Article] [PubMed]
Crunelli, V, David, F, Lőrincz, ML, Hughes, SW The thalamocortical network as a single slow wave-generating unit.. Curr Opin Neurobiol. (2015). 31 72–80 [Article] [PubMed]
Brown, EN, Lydic, R, Schiff, ND General anesthesia, sleep, and coma.. N Engl J Med. (2010). 363 2638–50 [Article] [PubMed]
Purdon, PL, Pierce, ET, Mukamel, EA, Prerau, MJ, Walsh, JL, Wong, KF, Salazar-Gomez, AF, Harrell, PG, Sampson, AL, Cimenser, A, Ching, S, Kopell, NJ, Tavares-Stoeckel, C, Habeeb, K, Merhar, R, Brown, EN Electroencephalogram signatures of loss and recovery of consciousness from propofol.. Proc Natl Acad Sci USA. (2013). 110 E1142–51 [Article] [PubMed]
Ní Mhuircheartaigh, R, Warnaby, C, Rogers, R, Jbabdi, S, Tracey, I Slow-wave activity saturation and thalamocortical isolation during propofol anesthesia in humans.. Sci Transl Med. (2013). 5 208ra148 [Article] [PubMed]
Rossetti, AO, Oddo, M, Logroscino, G, Kaplan, PW Prognostication after cardiac arrest and hypothermia: A prospective study.. Ann Neurol. (2010). 67 301–7 [PubMed]
Deakin, CD, Nolan, JP, Soar, J, Sunde, K, Koster, RW, Smith, GB, Perkins, GD European Resuscitation Council Guidelines for Resuscitation 2010 Section 4: Adult advanced life support.. Resuscitation. (2010). 81 1305–52 [Article] [PubMed]
Plummer, GF Improved method for the determination of propofol in blood by high-performance liquid chromatography with fluorescence detection.. J Chromatogr. (1987). 421 171–6 [Article] [PubMed]
Jacobs, I, Nadkarni, V, Bahr, J, Berg, RA, Billi, JE, Bossaert, L, Cassan, P, Coovadia, A, D’Este, K, Finn, J, Halperin, H, Handley, A, Herlitz, J, Hickey, R, Idris, A, Kloeck, W, Larkin, GL, Mancini, ME, Mason, P, Mears, G, Monsieurs, K, Montgomery, W, Morley, P, Nichol, G, Nolan, J, Okada, K, Perlman, J, Shuster, M, Steen, PA, Sterz, F, Tibballs, J, Timerman, S, Truitt, T, Zideman, D International Liaison Committee on Resuscitation; American Heart Association; European Resuscitation Council; Australian Resuscitation Council; New Zealand Resuscitation Council; Heart and Stroke Foundation of Canada; InterAmerican Heart Foundation; Resuscitation Councils of Southern Africa; ILCOR Task Force on Cardiac Arrest and Cardiopulmonary Resuscitation Outcomes, Cardiac arrest and cardiopulmonary resuscitation outcome reports: Update and simplification of the Utstein templates for resuscitation registries: A statement for healthcare professionals from a task force of the International Liaison Committee on Resuscitation (American Heart Association, European Resuscitation Council, Australian Resuscitation Council, New Zealand Resuscitation Council, Heart and Stroke Foundation of Canada, InterAmerican Heart Foundation, Resuscitation Councils of Southern Africa).. Circulation. (2004). 110 3385–97 [Article] [PubMed]
Welch, PD The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms.. Audio and Electroacoustics, IEEE Transactions on 1967. 15 70–3 [Article]
Delorme, A, Makeig, S EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.. J Neurosci Methods. (2004). 134 9–21 [Article] [PubMed]
Nielsen, N, Wetterslev, J, Cronberg, T, Erlinge, D, Gasche, Y, Hassager, C, Horn, J, Hovdenes, J, Kjaergaard, J, Kuiper, M, Pellis, T, Stammet, P, Wanscher, M, Wise, MP, Åneman, A, Al-Subaie, N, Boesgaard, S, Bro-Jeppesen, J, Brunetti, I, Bugge, JF, Hingston, CD, Juffermans, NP, Koopmans, M, Køber, L, Langørgen, J, Lilja, G, Møller, JE, Rundgren, M, Rylander, C, Smid, O, Werer, C, Winkel, P, Friberg, H TTM Trial Investigators, Targeted temperature management at 33°C versus 36°C after cardiac arrest.. N Engl J Med. (2013). 369 2197–206 [Article] [PubMed]
Myles, PS, Daly, D, Silvers, A, Cairo, S Prediction of neurological outcome using bispectral index monitoring in patients with severe ischemic-hypoxic brain injury undergoing emergency surgery.. Anesthesiology. (2009). 110 1106–15 [Article] [PubMed]
Zandbergen, EG, de Haan, RJ, Stoutenbeek, CP, Koelman, JH, Hijdra, A Systematic review of early prediction of poor outcome in anoxic-ischaemic coma.. Lancet. (1998). 352 1808–12 [Article] [PubMed]
Cruse, D, Chennu, S, Chatelle, C, Bekinschtein, TA, Fernández-Espejo, D, Pickard, JD, Laureys, S, Owen, AM Bedside detection of awareness in the vegetative state: A cohort study.. Lancet. (2011). 378 2088–94 [Article] [PubMed]
Underwood, E Neuroscience. An easy consciousness test?. Science. (2014). 346 531–2 [Article] [PubMed]
Lőrincz, ML, Gunner, D, Bao, Y, Connelly, WM, Isaac, JT, Hughes, SW, Crunelli, V A distinct class of slow (~0.2-2 Hz) intrinsically bursting layer 5 pyramidal neurons determines UP/DOWN state dynamics in the neocortex.. J Neurosci. (2015). 35 5442–58 [Article] [PubMed]
David, F, Schmiedt, JT, Taylor, HL, Orban, G, Di Giovanni, G, Uebele, VN, Renger, JJ, Lambert, RC, Leresche, N, Crunelli, V Essential thalamic contribution to slow waves of natural sleep.. J Neurosci. (2013). 33 19599–610 [Article] [PubMed]
Steriade, M, Nuñez, A, Amzica, F A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: Depolarizing and hyperpolarizing components.. J Neurosci. (1993). 13 3252–65 [PubMed]
Steriade, M, Timofeev, I, Grenier, F Natural waking and sleep states: A view from inside neocortical neurons.. J Neurophysiol. (2001). 85 1969–85 [PubMed]
Lemieux, M, Chen, JY, Lonjers, P, Bazhenov, M, Timofeev, I The impact of cortical deafferentation on the neocortical slow oscillation.. J Neurosci. (2014). 34 5689–703 [Article] [PubMed]
Chauvette, S, Volgushev, M, Timofeev, I Origin of active states in local neocortical networks during slow sleep oscillation.. Cereb Cortex. (2010). 20 2660–74 [Article] [PubMed]
Vyazovskiy, VV, Harris, KD Sleep and the single neuron: The role of global slow oscillations in individual cell rest.. Nat Rev Neurosci. (2013). 14 443–51 [Article] [PubMed]
Sandroni, C, Cariou, A, Cavallaro, F, Cronberg, T, Friberg, H, Hoedemaekers, C, Horn, J, Nolan, JP, Rossetti, AO, Soar, J Prognostication in comatose survivors of cardiac arrest: An advisory statement from the European Resuscitation Council and the European Society of Intensive Care Medicine.. Intensive Care Med. (2014). 40 1816–31 [Article] [PubMed]
Fig. 1.
Effect of propofol on electroencephalogram (EEG) slow-wave activity in a single patient with good neurologic outcome. (A) Propofol infusion rate (blue line) and corresponding measures of plasma propofol concentration (red line) during the experiment. The infusion rate was decreased step-wise every 30 min, and the drug concentration was determined at every step just before the next decrease and at the end of the experiment (red circles). (B) Raw EEG of a single channel (F7) during the experiment. Four 10-s signal samples from different phases of the experiment are presented above the whole signal. (C) Power spectral density as a function of time for the signal given in B. The frequency axis is given in the logarithmic scale. (D) Low-frequency (less than 1 Hz) EEG power representing a patient’s slow-wave activity during the experiment. The average low-frequency power (red curve) is calculated from all 19 single-channel powers (gray curves). The topographic distribution of the low-frequency EEG power at different phases of the experiment is given above the curves.
Effect of propofol on electroencephalogram (EEG) slow-wave activity in a single patient with good neurologic outcome. (A) Propofol infusion rate (blue line) and corresponding measures of plasma propofol concentration (red line) during the experiment. The infusion rate was decreased step-wise every 30 min, and the drug concentration was determined at every step just before the next decrease and at the end of the experiment (red circles). (B) Raw EEG of a single channel (F7) during the experiment. Four 10-s signal samples from different phases of the experiment are presented above the whole signal. (C) Power spectral density as a function of time for the signal given in B. The frequency axis is given in the logarithmic scale. (D) Low-frequency (less than 1 Hz) EEG power representing a patient’s slow-wave activity during the experiment. The average low-frequency power (red curve) is calculated from all 19 single-channel powers (gray curves). The topographic distribution of the low-frequency EEG power at different phases of the experiment is given above the curves.
Fig. 1.
Effect of propofol on electroencephalogram (EEG) slow-wave activity in a single patient with good neurologic outcome. (A) Propofol infusion rate (blue line) and corresponding measures of plasma propofol concentration (red line) during the experiment. The infusion rate was decreased step-wise every 30 min, and the drug concentration was determined at every step just before the next decrease and at the end of the experiment (red circles). (B) Raw EEG of a single channel (F7) during the experiment. Four 10-s signal samples from different phases of the experiment are presented above the whole signal. (C) Power spectral density as a function of time for the signal given in B. The frequency axis is given in the logarithmic scale. (D) Low-frequency (less than 1 Hz) EEG power representing a patient’s slow-wave activity during the experiment. The average low-frequency power (red curve) is calculated from all 19 single-channel powers (gray curves). The topographic distribution of the low-frequency EEG power at different phases of the experiment is given above the curves.
×
Fig. 2.
Electroencephalogram (EEG) signal processing steps. (A) First, 5-min signal samples at each step of the drug infusion rate decrease were extracted. From each 5-min signal sample, one to four 30-s representative artifact-free sequences were selected for further analysis. (B) Next, the power spectral density (PSD) estimates were determined for each 30-s sequences. (C) Finally, an average for the one to four PSD estimates was calculated, from which the components less than 1 Hz were summed to represent the low-frequency EEG power.
Electroencephalogram (EEG) signal processing steps. (A) First, 5-min signal samples at each step of the drug infusion rate decrease were extracted. From each 5-min signal sample, one to four 30-s representative artifact-free sequences were selected for further analysis. (B) Next, the power spectral density (PSD) estimates were determined for each 30-s sequences. (C) Finally, an average for the one to four PSD estimates was calculated, from which the components less than 1 Hz were summed to represent the low-frequency EEG power.
Fig. 2.
Electroencephalogram (EEG) signal processing steps. (A) First, 5-min signal samples at each step of the drug infusion rate decrease were extracted. From each 5-min signal sample, one to four 30-s representative artifact-free sequences were selected for further analysis. (B) Next, the power spectral density (PSD) estimates were determined for each 30-s sequences. (C) Finally, an average for the one to four PSD estimates was calculated, from which the components less than 1 Hz were summed to represent the low-frequency EEG power.
×
Fig. 3.
Plasma propofol concentrations at different infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Plasma propofol concentrations at different infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Fig. 3.
Plasma propofol concentrations at different infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
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Fig. 4.
The effect of propofol on the low-frequency electroencephalogram (EEG) power. (A) Plasma propofol concentration and the low-frequency EEG power for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are individual average powers calculated for all the 19 EEG channels. The samples of each individual are connected with lines. For one patient with a poor outcome, periodic epileptiform discharges, emphasized at low concentrations, strongly affected the low-frequency electroencephalogram power. (B) Average low-frequency EEG power at different propofol infusion rates.
The effect of propofol on the low-frequency electroencephalogram (EEG) power. (A) Plasma propofol concentration and the low-frequency EEG power for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are individual average powers calculated for all the 19 EEG channels. The samples of each individual are connected with lines. For one patient with a poor outcome, periodic epileptiform discharges, emphasized at low concentrations, strongly affected the low-frequency electroencephalogram power. (B) Average low-frequency EEG power at different propofol infusion rates.
Fig. 4.
The effect of propofol on the low-frequency electroencephalogram (EEG) power. (A) Plasma propofol concentration and the low-frequency EEG power for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are individual average powers calculated for all the 19 EEG channels. The samples of each individual are connected with lines. For one patient with a poor outcome, periodic epileptiform discharges, emphasized at low concentrations, strongly affected the low-frequency electroencephalogram power. (B) Average low-frequency EEG power at different propofol infusion rates.
×
Fig. 5.
Individual topographic distributions of the low-frequency activity at different infusion rates. Rows represent different patients and columns different infusion rates. The patients with good neurologic outcome (green border) are shown above those with a poor outcome (red border). The values are absolute low-frequency (less than 1 Hz) powers.
Individual topographic distributions of the low-frequency activity at different infusion rates. Rows represent different patients and columns different infusion rates. The patients with good neurologic outcome (green border) are shown above those with a poor outcome (red border). The values are absolute low-frequency (less than 1 Hz) powers.
Fig. 5.
Individual topographic distributions of the low-frequency activity at different infusion rates. Rows represent different patients and columns different infusion rates. The patients with good neurologic outcome (green border) are shown above those with a poor outcome (red border). The values are absolute low-frequency (less than 1 Hz) powers.
×
Fig. 6.
Propofol-induced slow-wave activity in patients with good and poor neurologic outcomes. (A) Topographic distribution of the low-frequency (less than 1 Hz) electroencephalogram (EEG) power representing slow-wave activity at different propofol infusion rates. The distributions are averages calculated separately for the patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are given in percentages relative to the individual channel-wise powers at the propofol infusion rate 0 mg · kg–1 · h–1. (B) The low-frequency EEG power at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes is shown. The values represent individual average powers calculated from all 19 channels given relative to the individual average power at propofol infusion rate 0 mg · kg–1 · h–1. The samples of each individual are connected with lines.
Propofol-induced slow-wave activity in patients with good and poor neurologic outcomes. (A) Topographic distribution of the low-frequency (less than 1 Hz) electroencephalogram (EEG) power representing slow-wave activity at different propofol infusion rates. The distributions are averages calculated separately for the patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are given in percentages relative to the individual channel-wise powers at the propofol infusion rate 0 mg · kg–1 · h–1. (B) The low-frequency EEG power at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes is shown. The values represent individual average powers calculated from all 19 channels given relative to the individual average power at propofol infusion rate 0 mg · kg–1 · h–1. The samples of each individual are connected with lines.
Fig. 6.
Propofol-induced slow-wave activity in patients with good and poor neurologic outcomes. (A) Topographic distribution of the low-frequency (less than 1 Hz) electroencephalogram (EEG) power representing slow-wave activity at different propofol infusion rates. The distributions are averages calculated separately for the patients with good (n = 6) and poor (n = 4) neurologic outcomes. The values are given in percentages relative to the individual channel-wise powers at the propofol infusion rate 0 mg · kg–1 · h–1. (B) The low-frequency EEG power at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes is shown. The values represent individual average powers calculated from all 19 channels given relative to the individual average power at propofol infusion rate 0 mg · kg–1 · h–1. The samples of each individual are connected with lines.
×
Fig. 7.
Blood pressure at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Blood pressure at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
Fig. 7.
Blood pressure at different propofol infusion rates for patients with good (n = 6) and poor (n = 4) neurologic outcomes. The samples of each individual are connected with lines.
×
Table 1.
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest×
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest
Table 1.
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest
Patient Characteristics and Measures for Brain Injury 48 h after Cardiac Arrest×
×
Table 2.
CPC
CPC×
CPC
Table 2.
CPC
CPC×
×