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.

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.

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.

Application of subhairline EEG montage in intensive care unit: comparison with full montage.

PURPOSE: Problems with the availability of standard EEG monitoring in the intensive care unit have led to the use of recordings that have limited spatial coverage. We studied the performance of limited coverage EEG compared with more traditional full-montage EEG. METHODS: Continuous EEG recordings were performed on 170 patients using the full-montage 10-20 placement of electrodes as a reference recording and an abbreviated montage of electrodes applied below the hairline (subhairline). Recordings were reviewed independently, with the identity of the patients concealed. RESULTS: Seizures were found in 8% of patients. Sensitivity for detecting patients with seizures using the subhairline system was 0.54 [95% confidence interval (95% CI), 0.29-0.77] with specificity of 1.00 (95% CI, 0.97-1.00) and positive predictive value of 1.00 (95% CI, 0.65-1.00). For detecting interictal epileptiform activity, we found sensitivity to be 0.60 (95% CI, 0.46-0.74), specificity to be 0.94 (95% CI, 0.88-0.97), and positive predictive value to be 0.81 (95% CI, 0.65-0.91). Performance was poor for triphasic waves, alpha/theta/spindle coma, and suppression. CONCLUSIONS: The subhairline montage shows excellent specificity for detecting patients with seizure activity but has limited sensitivity. It has relatively poor performance for other EEG phenomena, but further applications in trending and assessing reactivity should be assessed in further studies.

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 degrees 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.

An assessment of the validity of spectral entropy as a measure of sedation state in mechanically ventilated critically ill patients.

OBJECTIVE: To assess whether the Entropy Module (GE Healthcare, Helsinki, Finland), a device to measure hypnosis in anesthesia, is a valid measure of sedation state in critically ill patients by comparing clinically assessed sedation state with Spectral Entropy DESIGN: Prospective observational study. SETTING: Teaching hospital general ICU. PATIENTS AND PARTICIPANTS: 30 intubated, mechanically ventilated patients without primary neurological diagnoses or drug overdose receiving continuous sedation. INTERVENTIONS: Monitoring of EEG and fEMG activity via forehead electrodes for up to 72h and assessments of conscious level using a modified Ramsay Sedation Scale. MEASUREMENTS AND RESULTS: 475 trained observer assessments were made and compared with concurrent Entropy numbers. Median State (SE) and Response (RE) Entropy values decreased as Ramsay score increased, but wide variation occurred, especially in Ramsay 4-6 categories. Discrimination between different sedation scores [mean (SEM) P(K) value: RE 0.713 (0.019); SE 0.710 (0.019)] and between lighter (Ramsay 1-3) vs.deeper (Ramsay 4-6) sedation ranges was inadequate [P(K): RE 0.750 (0.025); SE 0.748 (0.025)]. fEMG power decreased with increasing Ramsay score but was often significant even at Ramsay 4-6 states. Frequent "on-off" effects occurred for both RE and SE, which were associated with fEMG activity. Values switched from low to high values even in deeply sedated patients. High Entropy values during deeper sedation were strongly associated with simultaneous high relative fEMG powers. CONCLUSIONS: Entropy of the frontal EEG does not discriminate sedation state adequately for clinical use in ICU patients. Facial EMG is a major confounder in clinical sedation ranges.

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.