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

Peer reviewe

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) ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13054-015-1043-1) contains supplementary material, which is available to authorized users

Anestesian syvyyden monitoroiminen aivosähkökäyrän avulla: Menetelmät sekä menetelmien suorituskyvyn arviointi

In monitoring depth of anesthesia, use of electroencephalogram (EEG) signal data helps to prevent intraoperative awareness and reduces the costs of anesthesia. Modern depth-of-anesthesia monitors use frontal EEG signal to derive an index value, which decreases monotonically with increasing anesthetic drug levels. In this study, electroencephalogram signal processing methods for depth-of-anesthesia monitoring were developed. The first aim was to develop a method for burst suppression detection and integrate it into the anesthetic depth monitor. Accurate detection of burst suppression improves the accuracy of depth-of-anesthesia monitoring at deep levels of anesthesia. The method developed utilizes a nonlinear energy operator and is based on adaptive segmentation. The developed monitor has been proven accurate in several scientific studies. A second aim was to develop a depth-of-anesthesia monitor that utilizes both cortical and subcortical information and is applicable with most commonly used anesthetics. The method developed is based on the spectral entropy of EEG and facial electromyogram (EMG) signals. In the method, two spectral entropy variables are derived, aiming to differentiate the cortical state of the patient and subcortical responses during surgery. The concept has been confirmed in the scientific studies conducted during surgery. Another aim was to develop a method for monitoring epileptiform activity during anesthesia. The method developed is based on a novel EEG-derived quantity, wavelet subband entropy (WSE), which followed the time evolution of epileptiform activity in anesthesia with prediction probability of 0.8 and recognized misleading readings of the depth-of-anesthesia monitor during epileptiform activity with event-sensitivity of 97%. The fourth aim was to investigate the monitoring technique developed, called Entropy, in S-ketamine anesthesia and in dexmedetomidine sedation. In S-ketamine anesthesia, high-frequency EEG oscillations turned out to be the reason for the high entropy values seen despite deep anesthesia. In dexmedetomidine sedation, Entropy proved a rapid indicator of transition phases from conscious and unconscious states.Anestesian syvyyttä monitoroitaessa aivosähkökäyrä (EEG) auttaa välttämään potilaan kirurgian aikaisen tietoisuuden tunteen sekä pienentämään anestesian kustannuksia. Anestesian syvyyden monitorit laskevat otsalta mitatusta EEG-signaalista numeroarvon, joka pienenee monotonisesti anestesialääkityksen kasvaessa. Tässä työssä kehitettiin EEG-signaalinkäsittelymenetelmiä anestesian syvyyden monitorointiin. Työn ensimmäinen tavoite oli kehittää menetelmä purskevaimentuman ilmaisemiseksi ja yhdistää tämä osaksi anestesian syvyyden monitoria, parantaen monitoroinnin tarkkuutta syvässä anestesiassa. Kehitetty menetelmä perustuu adaptiiviseen segmentointiin, jossa hyödynnetään epälineaarista energiaoperaattoria. Kehitetty monitori on osoittautunut tarkaksi menetelmäksi lukuisissa tieteellisissä tutkimuksissa. Toinen tavoite oli kehittää menetelmä anestesian syvyyden monitorointiin, joka hyödyntää sekä aivokuorelta peräisin olevaa EEG-signaalia, että osittain aivorungosta peräisin olevaa kasvolihasten lihassähkökäyrää (EMG). Kehitetty menetelmä perustuu EEG- ja EMG-signaaleista laskettavaan spektraaliseen entropiaan. Menetelmä tuottaa kaksi muuttujaa, joiden avulla pyritään erottamaan potilaan aivokuoren tila sekä kirurgian aiheuttamat aivorunkovasteet. Tieteeliset tutkimukset ovat osoittaneet konseptin toimivuuden kirurgian aikaiseen monitorointiin. Kolmas tavoite oli kehittää menetelmä anestesian aikaisen epileptiformisen aivotoiminnan monitorointiin. Työssä kehitettiin täysin uusi EEG-signaalista johdettu suure; aallokemuunnoksen osakaistan entropia (WSE). WSE kykeni seuraaman epileptiformisen toiminnan kehitystä ennustetodennäköisyydellä 0,8 sekä tunnisti 97 %:sti tämän aiheuttamat harhaanjohtavat tapaukset anestesian syvyyden monitorin lukemissa. Lisäksi työssä arvioitiin kehitetyn Entropia-monitorin suorituskykyä S-ketamiinianestesiassa sekä deksmedetomidiinisedaatiossa. S-ketamiinianestesiassa korkeataajuiset EEG-oskillaatiot olivat syynä korkeille Entropia-arvoille huolimatta syvästä anestesiasta. Deksmedetomidiinisedaatiossa Entropia-monitori kykeni seuraamaan nopeita muutoksia tajuisuuden ja tajuttomuuden välillä.reviewe

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

Publisher Copyright: © 2021 Johanna E. Wennervirta et al.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.Peer reviewe

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

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 Prospective observational study. Teaching hospital general ICU. 30 intubated, mechanically ventilated patients without primary neurological diagnoses or drug overdose receiving continuous sedation. Monitoring of EEG and fEMG activity via forehead electrodes for up to 72 h and assessments of conscious level using a modified Ramsay Sedation Scale. 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) PK 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 [PK: 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. 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 range

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 &lt; .01) and variability of the relative fast theta power (AUC = 0.84, P &lt; .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. </jats:p