EEG monitoring after cardiac arrest.
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ABSTRACT:
SUBMITTER: Sandroni C
PROVIDER: S-EPMC9468095 | biostudies-literature | 2022 Oct
REPOSITORIES: biostudies-literature
Publications
EEG monitoring after cardiac arrest.
Sandroni Claudio C Cronberg Tobias T Hofmeijer Jeannette J
Intensive care medicine 20220426 10
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Project description:AimsAssessment of brain injury severity early after cardiac arrest (CA) may guide therapeutic interventions and help clinicians counsel families regarding neurologic prognosis. We aimed to determine whether adding EEG features to predictive models including clinical variables and examination signs increased the accuracy of short-term neurobehavioral outcome prediction.MethodsThis was a prospective, observational, single-center study of consecutive infants and children resuscitated from CA. Standardized EEG scoring was performed by an electroencephalographer for the initial EEG timepoint after return of spontaneous circulation (ROSC) and each 12-h segment from the time of ROSC up to 48 h. EEG Background Category was scored as: (1) normal; (2) slow-disorganized; (3) discontinuous or burst-suppression; or (4) attenuated-featureless. The primary outcome was neurobehavioral outcome at discharge from the Pediatric Intensive Care Unit. To develop the final predictive model, we compared areas under the receiver operating characteristic curves (AUROC) from models with varying combinations of Demographic/Arrest Variables, Examination Signs, and EEG Features.ResultsWe evaluated 89 infants and children. Initial EEG Background Category was normal in 9 subjects (10%), slow-disorganized in 44 (49%), discontinuous or burst suppression in 22 (25%), and attenuated-featureless in 14 (16%). The final model included Demographic/Arrest Variables (witnessed status, doses of epinephrine, initial lactate after ROSC) and EEG Background Category which achieved AUROC of 0.9 for unfavorable neurobehavioral outcome and 0.83 for mortality.ConclusionsThe addition of standardized EEG Background Categories to readily available CA variables significantly improved early stratification of brain injury severity after pediatric CA.
| S-EPMC8530861 | biostudies-literature
Project description:ObjectivesTherapeutic hypothermia (TH) after cardiac arrest (CA) improves outcomes in a fraction of patients. To enhance the administration of TH, we studied brain electrophysiological monitoring in determining the benefit of early initiation of TH compared to conventional administration in a rat model.MethodsUsing an asphyxial CA model, we compared the benefit of immediate hypothermia (IH, T=33 degrees C, immediately post-resuscitation, maintained 6h) to conventional hypothermia (CH, T=33 degrees C, starting 1h post-resuscitation, maintained 12h) via surface cooling. We tracked quantitative EEG using relative entropy (qEEG) with outcome verification by serial Neurological Deficit Score (NDS) and quantitative brain histopathological damage scoring (HDS). Thirty-two rats were divided into 4 groups based on CH/IH and 7/9-min duration of asphyxial CA. Four sham rats were included for evaluation of the effect of hypothermia on qEEG.ResultsThe 72-h NDS of the IH group was significantly better than the CH group for both 7-min (74/63; median, IH/CH, p<0.001) and 9-min (54/47, p=0.022) groups. qEEG showed greater recovery with IH (p<0.001) and significantly less neuronal cortical injury by HDS (IH: 18.9+/-2.5% versus CH: 33.2+/-4.4%, p=0.006). The 1-h post-resuscitation qEEG correlated well with 72-h NDS (p<0.05) and 72-h behavioral subgroup of NDS (p<0.01). No differences in qEEG were noted in the sham group.ConclusionsImmediate but shorter hypothermia compared to CH leads to better functional outcome in rats after 7- and 9-min CA. The beneficial effect of IH was readily detected by neuro-electrophysiological monitoring and histological changes supported the value of this observation.
| S-EPMC2323440 | biostudies-literature
Project description:Purpose of reviewTo describe the available neuromonitoring tools in patients who are comatose after resuscitation from cardiac arrest because of hypoxic-ischemic brain injury (HIBI).Recent findingsElectroencephalogram (EEG) is useful for detecting seizures and guiding antiepileptic treatment. Moreover, specific EEG patterns accurately identify patients with irreversible HIBI. Cerebral blood flow (CBF) decreases in HIBI, and a greater decrease with no CBF recovery indicates poor outcome. The CBF autoregulation curve is narrowed and right-shifted in some HIBI patients, most of whom have poor outcome. Parameters derived from near-infrared spectroscopy (NIRS), intracranial pressure (ICP) and transcranial Doppler (TCD), together with brain tissue oxygenation, are under investigation as tools to optimize CBF in patients with HIBI and altered autoregulation. Blood levels of brain biomarkers and their trend over time are used to assess the severity of HIBI in both the research and clinical setting, and to predict the outcome of postcardiac arrest coma. Neuron-specific enolase (NSE) is recommended as a prognostic tool for HIBI in the current postresuscitation guidelines, but other potentially more accurate biomarkers, such as neurofilament light chain (NfL) are under investigation.SummaryNeuromonitoring provides essential information to detect complications, individualize treatment and predict prognosis in patients with HIBI.
| S-EPMC9994800 | biostudies-literature
Project description:ObjectiveEEG is commonly used to predict prognosis in post anoxic coma. We investigated if stimulus-induced rhythmic, periodic or ictal discharges (SIRPIDs) add prognostic information after cardiac arrest.MethodsIn the multicenter Targeted Temperature Management trial, routine-EEGs were prospectively recorded after rewarming (≥36 h). Presence and subtype of SIRPIDs and main EEG-pattern (benign, malignant, highly malignant) were retrospectively reported according to a standardised classification. Patients were followed up after 180 days. Poor outcome was defined as severe neurological disability or death (Cerebral Performance Category 3-5).ResultsOf 142 patients, 71% had poor outcome and 14% had SIRPIDs. There was no significant difference in outcome between patients with and without SIRPIDs, even when subgrouped according to underlying main EEG-pattern. Comparing subtypes of SIRPIDs, 82% of patients with stimulus-induced periodic discharges had poor outcome compared to 44% of patients with stimulus-induced rhythmic delta activity, but the difference was not significant.ConclusionsIn EEGs performed ≥36 h after cardiac arrest, SIRPIDs cannot be used to reliably predict poor outcome. Whether certain subtypes of SIRPIDs indicate worse prognosis needs further investigation.SignificanceCategorising the main EEG-pattern has important prognostic implications, but assessment of late appearing SIRPIDs does not seem to add prognostic information.
| S-EPMC8350459 | biostudies-literature
Project description:ObjectiveTo identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society.MethodsIn this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3-5 until 180 days.ResultsEight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p < 0.001). Specificity and sensitivity were not significantly affected by targeted temperature or sedation. A benign EEG was found in 1% of the patients with a poor outcome.ConclusionsHighly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome.
| S-EPMC4836886 | biostudies-literature
Project description:PurposeWe aimed to determine which early EEG features and feature combinations most accurately predicted short-term neurobehavioral outcomes and survival in children resuscitated after cardiac arrest.MethodsThis was a prospective, single-center observational study of infants and children resuscitated from cardiac arrest who underwent conventional EEG monitoring with standardized EEG scoring. Logistic regression evaluated the marginal effect of each EEG variable or EEG variable combinations on the outcome. The primary outcome was neurobehavioral outcome (Pediatric Cerebral Performance Category score), and the secondary outcome was mortality. The authors identified the models with the highest areas under the receiver operating characteristic curve (AUC), evaluated the optimal models using a 5-fold cross-validation approach, and calculated test characteristics maximizing specificity.ResultsEighty-nine infants and children were evaluated. Unfavorable neurologic outcome (Pediatric Cerebral Performance Category score 4-6) occurred in 44 subjects (49%), including mortality in 30 subjects (34%). A model incorporating a four-level EEG Background Category (normal, slow-disorganized, discontinuous or burst-suppression, or attenuated-flat), stage 2 Sleep Transients (present or absent), and Reactivity-Variability (present or absent) had the highest AUC. Five-fold cross-validation for the optimal model predicting neurologic outcome indicated a mean AUC of 0.75 (range, 0.70-0.81) and for the optimal model predicting mortality indicated a mean AUC of 0.84 (range, 0.76-0.97). The specificity for unfavorable neurologic outcome and mortality were 95% and 97%, respectively. The positive predictive value for unfavorable neurologic outcome and mortality were both 86%.ConclusionsThe specificity of the optimal model using a combination of early EEG features was high for unfavorable neurologic outcome and mortality in critically ill children after cardiac arrest. However, the positive predictive value was only 86% for both outcomes. Therefore, EEG data must be considered in overall clinical context when used for neuroprognostication early after cardiac arrest.
| S-EPMC6731130 | biostudies-literature
Project description:ObjectivesHypothermia improves outcomes after cardiac arrest (CA), while hyperthermia worsens injury. EEG recovers through periodic bursting from isoelectricity after CA, the duration of which is associated with outcome in normothermia. We quantified burst frequency to study the effect of temperature on early EEG recovery after CA.MethodsTwenty-four rats were divided into three groups, based on 6h of hypothermia (T=33 degrees C), normothermia (T=37 degrees C), or hyperthermia (T=39 degrees C) immediately post-resuscitation from 7-min asphyxial CA. Temperature was maintained using surface cooling and re-warming. Neurological recovery was defined by 72-h neurological deficit score (NDS).ResultsBurst frequency was higher during the first 90min in rats treated with hypothermia (25.6+/-12.2min(-1)) and hyperthermia (22.6+/-8.3min(-1)) compared to normothermia (16.9+/-8.5min(-1)) (p<0.001). Burst frequency correlated strongly with 72-h NDS in normothermic rats (p<0.05) but not in hypothermic or hyperthermic rats. The 72-h NDS of the hypothermia group (74, 61-74; median, 25-75th percentile) was significantly higher than the normothermia (49, 47-61) and hyperthermia (43, 0-50) groups (p<0.001).ConclusionsIn normothermic rats resuscitated from CA, early EEG burst frequency is strongly associated with neurological recovery. Increased bursting followed by earlier restitution of continuous EEG activity with hypothermia may represent enhanced recovery, while heightened metabolic rate and worsening secondary injury is likely in the hyperthermia group. These factors may confound use of early burst frequency for outcome prediction.
| S-EPMC2570264 | biostudies-literature
Project description:ObjectiveAbnormal electroencephalography (EEG) patterns are common after resuscitation from cardiac arrest and have clinical and prognostic importance. Bedside continuous EEGs are not available in many institutions. We tested the feasibility of using a point-of-care system for EEG acquisition.MethodsWe prospectively enrolled a convenience sample of post-cardiac arrest patients between 9/2015-1/2017. Upon hospital arrival, a limited EEG montage was applied. We tested both continuous EEG (cEEG) and this point-of-care EEG (eEEG). A board-certified epileptologist and a board-certified neurointensivist jointly reviewed all EEGs. Cohen's kappa coefficient evaluated agreement between eEEG and cEEG and Fisher's exact test evaluated their associations with survival to hospital discharge and proximate cause of death.ResultsWe studied 95 comatose post-cardiac arrest patients. Mean age was 59 (SD17) years. Most (61%) were male, few (N = 22; 23%) demonstrated shockable rhythms, and PCAC IV illness severity was present in 58 (61%). eEEG was interpretable in 57 (60%) subjects. The most common eEEG interpretations were: continuous (21%), generalized suppression (14%), burst-suppression (12%) and burst-suppression with identical bursts (10%). Seizures were detected in 2 eEEG subjects (2%). No patient with seizure or burst-suppression with identical bursts survived. cEEG demonstrated generalized suppression (31%), burst-suppression with identical bursts (27%), continuous (18%) and seizure (4%). The eEEG and cEEG demonstrated fair agreement (kappa = 0.27). Neither eEEG nor cEEG was associated with survival (p = 0.19; p = 0.11) or proximate cause of death (p = 0.14; p = 0.8) CONCLUSIONS: eEEG is feasible, although artifact often precludes interpretation. eEEG is fairly associated with cEEG and may facilitate post-cardiac arrest care.
| S-EPMC7155922 | biostudies-literature
Project description:AimTo investigate how combined electrographic and radiologic data inform outcomes in children after cardiac arrest.MethodsRetrospective observational study of children admitted to the pediatric intensive care unit (PICU) of a tertiary children's hospital with diagnosis of cardiac arrest from 2009 to 2016. The first 20 min of electroencephalogram (EEG) background was blindly scored. Presence and location of magnetic resonance imaging (MRI) diffusion-weighted image (DWI) abnormalities were correlated with T2-weighted signal. Outcomes were categorized using Pediatric Cerebral Performance Category (PCPC) scores at hospital discharge, with "poor outcome" reflecting a PCPC score of 4-6. Logistic regression models examined the association of EEG and MRI variables with outcome.Results41 children met inclusion criteria and had both post-arrest EEG monitoring within 72 hours after ROSC and brain MRI performed within 8 days. Among the 19 children with poor outcome, 10 children did not survive to discharge. Severely abnormal EEG background (p < 0.0001) and any diffusion restriction (p < 0.0001) were associated with poor outcome. The area under the ROC curve (AUC) for identifying outcome based on EEG background alone was 0.86, which improved to 0.94 with combined EEG and MRI data (p = 0.02).ConclusionDiffusion abnormalities on MRI within 8 days after ROSC add to the prognostic value of EEG background in children surviving cardiac arrest.
| S-EPMC9001021 | biostudies-literature
Project description:PurposeTo investigate the prognostic value of a simple stratification system of electroencephalographical (EEG) patterns and spectral types for patients after cardiac arrest.MethodsIn this prospectively enrolled cohort, using manually selected EEG segments, patients after cardiac arrest were stratified into five independent EEG patterns (based on background continuity and burden of highly epileptiform discharges) and four independent power spectral types (based on the presence of frequency components). The primary outcome is cerebral performance category (CPC) at discharge. Results from multimodal prognostication testing were included for comparison.ResultsOf a total of 72 patients, 6 had CPC 1-2 by discharge, all of whom had mostly continuous EEG background without highly epileptiform activity at day 3. However, for the same EEG background pattern at day 3, 19 patients were discharged at CPC 3 and 15 patients at CPC 4-5. After adding spectral analysis, overall sensitivity for predicting good outcomes (CPC 1-2) was 83.3% (95% confidence interval 35.9% to 99.6%) and specificity was 97.0% (89.5% to 99.6%). In this cohort, standard prognostication testing all yielded 100% specificity but low sensitivity, with imaging being the most sensitive at 54.1% (36.9% to 70.5%).ConclusionsAdding spectral analysis to qualitative EEG analysis may further improve the diagnostic accuracy of EEG and may aid developing novel measures linked to good outcomes in postcardiac arrest coma.
| S-EPMC9905375 | biostudies-literature