Hanna E. Viertio-Oja

Hypothermia-treated cardiac arrest patients with good neurological outcome differ early in quantitative variables of EEG suppression and epileptiform activity.

Objective:To evaluate electroencephalogram-derived quantitative variables after out-of-hospital cardiac arrest. Design:Prospective study. Setting:University hospital intensive care unit. Patients:Thirty comatose adult patients resuscitated from a witnessed out-of-hospital ventricular fibrillation cardiac arrest and treated with induced hypothermia (33°C) for 24 hrs. Interventions:None. Measurements and Main Results:Electroencephalography was registered from the arrival at the intensive care unit until the patient was extubated or transferred to the ward, or 5 days had elapsed from cardiac arrest. Burst-suppression ratio, response entropy, state entropy, and wavelet subband entropy were derived. Serum neuron-specific enolase and protein 100B were measured. The Pulsatility Index of Transcranial Doppler Ultrasonography was used to estimate cerebral blood flow velocity. The Glasgow-Pittsburgh Cerebral Performance Categories was used to assess the neurologic outcome during 6 mos after cardiac arrest. Twenty patients had Cerebral Performance Categories of 1 to 2, one patient had a Cerebral Performance Categories of 3, and nine patients had died (Cerebral Performance Categories of 5). Burst-suppression ratio, response entropy, and state entropy already differed between good (Cerebral Performance Categories 1–2) and poor (Cerebral Performance Categories 3–5) outcome groups (p = .011, p = .011, p = .008) during the first 24 hrs after cardiac arrest. Wavelet subband entropy was higher in the good outcome group between 24 and 48 hrs after cardiac arrest (p = .050). All patients with status epilepticus died, and their wavelet subband entropy values were lower (p = .022). Protein 100B was lower in the good outcome group on arrival at ICU (p = .010). After hypothermia treatment, neuron-specific enolase and protein 100B values were lower (p = .002 for both) in the good outcome group. The Pulsatility Index was also lower in the good outcome group (p = .004). Conclusions:Quantitative electroencephalographic variables may be used to differentiate patients with good neurologic outcomes from those with poor outcomes after out-of-hospital cardiac arrest. The predictive values need to be determined in a larger, separate group of patients.

Spectral entropy monitoring is associated with reduced propofol use and faster emergence in propofol-nitrous oxide-alfentanil anesthesia.

Background:This multicenter study evaluated the effect of a new depth of anesthesia–monitoring device based on time-frequency–balanced spectral entropy of electroencephalogram monitoring (GE Healthcare Finland, Helsinki, Finland) on consumption of anesthetic drugs and recovery times after anesthesia. Methods:The study was a prospective, randomized, single-blind study performed in six hospitals in Finland, Sweden, and Norway. After institutional review board approval and written informed consent from each patient, the patients were randomly allocated to anesthesia with entropy values either shown (entropy group) or not shown (control group). Anesthesia was maintained with propofol, nitrous oxide, and alfentanil. In the entropy group, propofol was given to keep the state entropy value between 45 and 65, and alfentanil was given to keep the state entropy–response entropy difference below 10 units and heart rate and blood pressure within ±20% of the baseline values. The control group patients were anesthetized to keep heart rate and blood pressure within ±20% of the baseline values. Statistical methods included Mann–Whitney U test and unpaired t tests. Results:A total of 368 patients were studied. In the entropy group, entropy values were higher during the whole operation and especially during the last 15 min (P < 0.001). Consequently, propofol consumption was smaller in the entropy group during the whole anesthesia period (P < 0.001) and especially during the last 15 min (P < 0.001). This shortened the time delay in the early recovery parameters in the entropy group. Conclusion:Entropy monitoring assisted titration of propofol, especially during the last part of the procedures, as indicated by higher entropy values, decreased consumption of propofol, and shorter recovery times in the entropy group.

Frontal electroencephalogram variables are associated with the outcome and stage of hepatic encephalopathy in acute liver failure

Acute liver failure (ALF) and hepatic encephalopathy (HE) can lead to an elevated intracranial pressure (ICP) and death within days. The impaired liver function increases the risks of invasive ICP monitoring, whereas noninvasive methods remain inadequate. The purpose of our study was to explore reliable noninvasive methods of neuromonitoring for patients with ALF in the intensive care unit (ICU) setting; more specifically, we wanted to track changes in HE and predict the outcomes of ALF patients treated with albumin dialysis. The study included 20 patients with severe ALF at admission who had been referred to the ICU of the liver transplantation (LT) center for albumin dialysis treatment and evaluation for transplantation. Data were collected from all study patients in the form of continuous frontal electroencephalography (EEG) recordings and transcranial Doppler (TCD) measurements of cerebral blood flow. Among the studied EEG variables, the 50% spectral edge frequency decreased and the delta power increased as the HE stage increased. Both variables were predictive of the stage of HE [prediction probability (PK) of 50% spectral edge frequency = 0.23, standard error (SE) = 0.03; PK of delta power = 0.76, SE = 0.03]. The total wavelet subband entropy, a novel variable that we used for tracking abnormal EEG activity, predicted the outcome of ALF patients treated with albumin dialysis (PK = 0.88, SE = 0.09). With a threshold value of 1.6, the TCD pulsatility index had an odds ratio of 1.1 (95% confidence interval = 0.1‐9.3) for a poor outcome (LT or death). In conclusion, EEG variables are useful for the monitoring of HE and can be used to predict outcomes of ALF. TCD measurements do not predict patient outcomes. Liver Transpl 20:1256–1265, 2014.

Algorithm for Quantifying Frontal EMG Responsiveness for Sedation Monitoring.

INTRODUCTION To study stimulation-related facial electromyographic (FEMG) activity in intensive care unit (ICU) patients, develop an algorithm for quantifying the FEMG activity, and to optimize the algorithm for monitoring the sedation state of ICU patients. METHODS First, the characteristics of FEMG response patterns related to vocal stimulation of 17 ICU patients were studied. Second, we collected continuous FEMG data from 30 ICU patients. Based on these data, we developed the Responsiveness Index (RI) algorithm that quantifies FEMG responses. Third, we compared the RI values with clinical sedation level assessments and adjusted algorithm parameters for best performance. RESULTS In patients who produced a clinically observed response to the vocal stimulus, the poststimulus FEMG power was 0.33 µV higher than the prestimulus power. In nonresponding patients, there was no difference. The sensitivity and specificity of the developed RI for detecting deep sedation in the subgroup with low probability of encephalopathy were 0.90 and 0.79, respectively. CONCLUSION Consistent FEMG patterns were found related to standard stimulation of ICU patients. A simple and robust algorithm was developed and good correlation with clinical sedation scores achieved in the development data.

An evaluation of the validity and potential utility of facial electromyelogram Responsiveness Index for sedation monitoring in critically ill patients.

PURPOSE The purpose of this study is to explore the validity of a novel sedation monitoring technology based on facial electromyelography (EMG) in sedated critically ill patients. MATERIALS AND METHODS The Responsiveness Index (RI) integrates the preceding 60 minutes of facial EMG data. An existing data set was used to derive traffic light cut-offs for low (red), intermediate (amber), and higher (green) states of patient arousal. The validity of these was prospectively evaluated in 30 sedated critically ill patients against hourly Richmond Agitation Sedation Scale (RASS) assessments with concealment of RI data from clinical staff. RESULTS With derivation data, an RI less than or equal to 35 had best discrimination for a Ramsay score of 5/6 (sensitivity, 90%; specificity, 79%). For traffic lights, we chose RI less than or equal to 20 as red, 20 to 40 as amber, and more than 40 as green. In the prospective study, RI values were red/amber for 76% of RASS -5/-4 assessments, but RI varied dynamically over time in many patients, and discordance with RASS may have resulted from the use of 1 hour of data for RI calculations. We also noted that red/amber values resulted from sleep, encephalopathy, and low levels of stimulation. CONCLUSIONS Responsiveness Index is not directly comparable with clinical sedation scores but is a potential continuous alert to possible deep sedation in critically ill patients.

P36.22 EEG entropy in assessment of the depth of natural sleep in healthy volunteers

Background: It was hypothesized (Ivanitsky et al., Zh Vyssh Nerv Deiat Im I P Pavlova 2003;53:541–51) that when words are delivered in two concurrent channels (auditory and visual), the irrelevant stimuli are not memorized due to the inhibition of information transfer to the hippocampal structures. The components of event-related potentials (ERP) may reflect the processes of relevant information fixation in semantic memory (about 450 ms) and inhibition of irrelevant information storage in episodic memory (in 500–700 ms). Aims: We aimed at studying brain structures involved in crossmodal selective attention and memory using dipole modeling. Methods: In each of four experimental conditions, verbal stimuli were delivered synchronously in both auditory and visual modalities. In the control condition the instruction was only to attend the stimuli. In the other three conditions participants had to attend and memorize words delivered (1) in auditory, (2) in visual, and (3) in both modalities. Modeling window was chosen in 450–850 ms. Dipole models were developed independently for six difference waves obtained by subtracting different conditions, and also for some components of original ERPs. Results: The majority of equivalent sources for difference wave components were located in temporal and frontal areas, implying that those areas mainly change their activity depending on the attentional and memory demands. Sources in the hippocampus and adjacent areas showed higher activity in almost all models in 450–850 ms. In this window, sources for the earlier components were mostly in parahippocampal and fusiform gyri, and may reflect the activity related to semantic memory. The source location of later components mostly in the hippocampus possibly reflected the processes of active inhibition of irrelevant information. Conclusion: The results of dipole source analysis strongly support our hypothesis: the selection of relevant verbal information is executed via selective on/off switching of the memory systems located in medial temporal area.