Novel insights on association and reactivity of Bispectral Index, frontal electromyogram, and autonomic responses in nociception-sedation monitoring of critical care patients.

BACKGROUND: Assessing nociception and sedation in mechanically ventilated patients in the ICU is challenging, with few reliable methods available for continuous monitoring. Measurable cardiovascular and neurophysiological signals, such as frontal EEG, frontal EMG, heart rate, and blood pressure, have potential in sedation and nociception monitoring. The hypothesis of this explorative study is that derived variables from the aforementioned signals predict the level of sedation, as described by the Richmond Agitation-Sedation score (RASS), and respond to painful stimuli during critical care. METHODS: Thirty adult postoperative ICU patients on mechanical ventilation and receiving intravenous sedation, excluding patients with primary neurological disorders, head injury, or need for continuous neuromuscular blockage. Bispectral Index (BIS), EMG power (EMG), EMG-derived Responsiveness Index (RI), and averaged blood pressure variability (ARV) were tested against RASS measurements. The aforementioned variables together with blood pressure and Surgical Pleth Index (SPI) were explored before and after painful stimuli (for example bronchoscopy, or pleural puncture) at varying RASS levels, to test variable responsiveness. RESULTS: BIS, EMG, and RI predicted RASS levels with a prediction probability (PK) of 0.776 for BIS, 0.761 for EMG, and 0.763 for RI. In addition, BIS, EMG, and ARV demonstrated responsiveness to painful stimuli during deep sedation (RASS score ≤ -3). CONCLUSION: Variables derived from EEG and EMG are associated with sedation levels, as described by the RASS score. Furthermore, these variables, along with ARV, react with consistency to painful stimuli during deep sedation (RASS -5 to -3), offering novel tools for nociception-sedation monitoring of mechanically ventilated ICU patients requiring deep sedation.

Responsiveness Index versus the RASS-Based Method for Adjusting Sedation in Critically Ill Patients.

BACKGROUND: Sedation of intensive care patients is needed for patient safety, but deep sedation is associated with adverse outcomes. Frontal electromyogram-based Responsiveness Index (RI) aims to quantify the level of sedation and is scaled 0-100 (low index indicates deep sedation). We compared RI-based sedation to Richmond Agitation-Sedation Scale- (RASS-) based sedation. Our hypothesis was that RI-controlled sedation would be associated with increased total time alive without mechanical ventilation at 30 days without an increased number of adverse events. METHODS: 32 critically ill adult patients with mechanical ventilation and administration of sedation were randomized to either RI- or RASS-guided sedation. Patients received propofol and oxycodone, if possible. The following standardized sedation protocol was utilized in both groups to achieve the predetermined target sedation level: either RI 40-80 (RI group) or RASS -3 to 0 (RASS group). RI measurement was blinded in the RASS group, and the RI group was blinded to RASS assessments. State Entropy (SE) values were registered in both groups. RESULTS: RI and RASS groups did not differ in total time alive in 30 days without mechanical ventilation (p=0.72). The incidence of at least one sedation-related adverse event did not differ between the groups. Hypertension was more common in the RI group (p=0.01). RI group patients were in the target RI level 22% of the time and RASS group patients had 57% of scores within the target RASS level. The RI group spent significantly more time in their target sedation level than the RASS group spent in the corresponding RI level (p=0.03). No difference was observed between the groups (p=0.13) in the corresponding analysis for RASS. Propofol and oxycodone were administered at higher RI and SE values and lower RASS values in the RI group than in the RASS group. CONCLUSION: Further studies with a larger sample size are warranted to scrutinize the optimal RI level during different phases of critical illness.

Quantitative EEG Parameters for Prediction of Outcome in Severe Traumatic Brain Injury: Development Study.

Monitoring of quantitative EEG (QEEG) parameters in the intensive care unit (ICU) can aid in the treatment of traumatic brain injury (TBI) patients by complementing visual EEG review done by an expert. We performed an explorative study investigating the prognostic value of 59 QEEG parameters in predicting the outcome of patients with severe TBI. Continuous EEG recordings were done on 28 patients with severe TBI in the ICU of Turku University Hospital. We computed a set of QEEG parameters for each patient, and correlated these to patient outcome, measured by dichotomized Glasgow Outcome Scale (GOS) at a follow-up visit between 6 and 12 months, using area under receiver operating characteristic curve (AUC) as a nonlinear correlation measure. For 17 of the 59 QEEG parameters (28.8%), the AUC differed significantly from 0.5, most of these parameters measured EEG power or variability. The best QEEG parameters for outcome prediction were alpha power (AUC = 0.87, P < .01) and variability of the relative fast theta power (AUC = 0.84, P < .01). The results of this study indicate that QEEG parameters provide useful information for predicting outcome in severe TBI. Novel QEEG parameters with potential in outcome prediction were found, the prognostic value of these parameters should be confirmed in later studies. The results also provide further evidence of the usefulness of parameters studied in preexisting studies.

Diagnostic accuracy between readers for identifying electrographic seizures in critically ill adults.

OBJECTIVE: Electrographic seizures in critically ill patients are often equivocal. In this study, we sought to determine the diagnostic accuracy of electrographic seizure annotation in adult intensive care units (ICUs) and to identify affecting factors. METHODS: To investigate diagnostic accuracy, interreader agreement (IRA) measures were derived from 5,769 unequivocal and 6,263 equivocal seizure annotations by five experienced electroencephalogram (EEG) readers after reviewing 74 days of EEGs from 50 adult ICU patients. Factors including seizure equivocality (unequivocal vs. equivocal) and laterality (generalized, partial, or bilaterally independent), cyclicity (cyclic vs. noncyclic), persistency (occurrence of status epilepticus), and patient consciousness level (coma vs. noncoma) were further investigated for their influence on IRA measures. RESULTS: On average, 70% of seizures marked by a reference reader overlapped, at least in part, with those marked by a test reader (any-overlap sensitivity, AO-Sn). Agreed seizure duration between reader pairs (overlap-integral sensitivity, OI-Sn) was 62%, while agreed nonseizure duration (overlap-integral specificity, OI-Sp) was 99%. A test reader would annotate one additional seizure not overlapping with a reference reader's annotation in every 11.7 h of EEG, that is, the false-positive rate (FPR) was 0.0854/h. Classifying seizure patterns into unequivocal and equivocal improved specificity and FPR (unequivocal patterns) but compromised sensitivity only for equivocal patterns. Sensitivity of all and unequivocal annotations was higher for patients with status epilepticus. Specificity was higher for partial than for bilaterally independent unequivocal seizure patterns, and lower for cyclic all seizure patterns. SIGNIFICANCE: Diagnosing electrographic seizures in critically ill adults is highly specific and moderately sensitive. Improved criteria for diagnosing electrographic seizures in the ICU are needed.

A randomized controlled proof-of-concept trial of early sedation management using Responsiveness Index monitoring in mechanically ventilated critically ill patients.

INTRODUCTION: Deep sedation is associated with adverse patient outcomes. We recently described a novel sedation-monitoring technology, the Responsiveness Index (RI), which quantifies patient arousal using processed frontal facial EMG data. We explored the potential effectiveness and safety of continuous RI monitoring during early intensive care unit (ICU) care as a nurse decision-support tool. METHODS: In a parallel-group controlled single centre proof of concept trial, patients requiring mechanical ventilation and sedation were randomized via sequential sealed envelopes following ICU admission. Control group patients received hourly clinical sedation assessment and daily sedation holds; the RI monitor was connected but data were concealed from clinical staff. The intervention group received control group care, but RI monitoring was visible and nurses were asked to adjust sedation to maintain patients with an RI>20 whenever possible. Traffic-light colour coding (RI<20, Red; 20-40, Amber; >40, Green) simplified decision-making. The intervention lasted up to 48 hours. Sixteen nurses were interviewed to explore their views of the novel technology. RESULTS: We analysed 74 patients treated per protocol (36 intervention; 38 control). The proportion of patients with RI<20 was identical at the start of monitoring (54% both groups). Overall, the proportion of time with RI<20 trended to lower values for the intervention group (median 16% (1-3rd quartile 8-30%) versus 33% (10-54%); P = 0.08); sedation and analgesic use was similar. A post hoc analysis restricted to patients with RI<20 when monitoring started, found intervention patients spent less time with low RI value (16% (11-45%) versus 51% (33-72%); P = 0.02), cumulative propofol use trended to lower values (median 1090 mg versus 2390 mg; P = 0.14), and cumulative alfentanil use was lower (21.2 mg versus 32.3 mg; P = 0.01). RASS scores were similar for both groups. Sedation related adverse event rates were similar (7/36 versus 5/38). Similar proportions of patients had sedation holds (83% versus 87 %) and were extubated (47% versus 44%) during the intervention period. Nurses valued the objective visible data trends and simple colour prompts, and found RI monitoring a useful adjunct to existing practice. CONCLUSIONS: RI monitoring was safe and acceptable. Data suggested potential to modify sedation decision-making. Larger trials are justified to explore effects on patient-centred outcomes. TRIAL REGISTRATION: NCT01361230 (registered April 19, 2010).

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.

Bispectral index, entropy, and quantitative electroencephalogram during single-agent xenon anesthesia.

BACKGROUND: The aim was to evaluate the performance of anesthesia depth monitors, Bispectral Index (BIS) and Entropy, during single-agent xenon anesthesia in 17 healthy subjects. METHODS: After mask induction with xenon and intubation, anesthesia was continued with xenon only. BIS, State Entropy and Response Entropy, and electroencephalogram were monitored throughout induction, steady-state anesthesia, and emergence. The performance of BIS, State Entropy, and Response Entropy were evaluated with prediction probability, sensitivity, and specificity analyses. The power spectrum of the raw electroencephalogram signal was calculated. RESULTS: The mean (SD) xenon concentration during anesthesia was 66.4% (2.4%). BIS, State Entropy, and Response Entropy demonstrated low prediction probability values at loss of response (0.455, 0.656, and 0.619) but 1 min after that the values were high (0.804, 0.941, and 0.929). Thereafter, equally good performance was demonstrated for all indices. At emergence, the prediction probability values to distinguish between steady-state anesthesia and return of response for BIS, State Entropy, and Response Entropy were 0.988, 0.892, and 0.992. No statistical differences between the performances of the monitors were observed. Quantitative electroencephalogram analyses showed generalized increase in total power (P < 0.001), delta (P < 0.001) and theta activity (P < 0.001), and increased alpha activity (P = 0.003) in the frontal brain regions. CONCLUSIONS: Electroencephalogram-derived depth of sedation indices BIS and Entropy showed a delay to detect loss of response during induction of xenon anesthesia. Both monitors performed well in distinguishing between conscious and unconscious states during steady-state anesthesia. Xenon-induced changes in electroencephalogram closely resemble those induced by propofol.

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.

Quantification of epileptiform electroencephalographic activity during sevoflurane mask induction.

BACKGROUND: Sevoflurane may induce epileptiform electroencephalographic activity leading to unstable Bispectral Index numbers, underestimating the hypnotic depth of anesthesia. The authors developed a method for the quantification of epileptiform electroencephalographic activity during sevoflurane anesthesia. METHODS: Electroencephalographic data from 60 patients under sevoflurane mask induction were used in the analysis. Electroencephalographic data were visually classified. A novel electroencephalogram-derived quantity, wavelet subband entropy (WSE), was developed. WSE variables were calculated from different frequency bands. Performance of the WSE in detection and quantification of epileptiform electroencephalographic activity and the ability of the WSE to recognize misleading Bispectral Index readings caused by epileptiform activity were evaluated. RESULTS: Two WSE variables were found to be sufficient for the quantification of epileptiform activity: WSE from the frequency bands 4-16 and 16-32 Hz. The lower frequency band was used for monophasic pattern monitoring, and the higher frequency band was used for spike activity monitoring. WSE values of the lower and higher bands followed the time evolution of epileptiform activity with prediction probabilities of 0.809 (SE, 0.007) and 0.804 (SE, 0.007), respectively. In deep anesthesia with epileptiform activity, WSE detected electroencephalographic patterns causing Bispectral Index readings greater than 60, with event sensitivity of 97.1%. CONCLUSIONS: The developed method proved useful in detection and quantification of epileptiform electroencephalographic activity during sevoflurane anesthesia. In the future, it may improve the understanding of electroencephalogram-derived information by assisting in recognizing misleading readings of depth-of-anesthesia monitors. The method also may assist in minimizing the occurrence of epileptiform activity and seizures during sevoflurane anesthesia.