1
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Hwang J, Cho SM, Geocadin R, Ritzl EK. Methods of Evaluating EEG Reactivity in Adult Intensive Care Units: A Review. J Clin Neurophysiol 2024:00004691-990000000-00133. [PMID: 38857365 DOI: 10.1097/wnp.0000000000001078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
PURPOSE EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent. METHODS A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias. RESULTS One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds). CONCLUSIONS This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature.
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Affiliation(s)
- Jaeho Hwang
- Division of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
| | - Sung-Min Cho
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
| | - Romergryko Geocadin
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
| | - Eva K Ritzl
- Division of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
- Division of Intraoperative Monitoring, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A
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Ling Y, Xu C, Wen X, Li J, Gao J, Luo B. Cortical responses to auditory stimulation predict the prognosis of patients with disorders of consciousness. Clin Neurophysiol 2023; 153:11-20. [PMID: 37385110 DOI: 10.1016/j.clinph.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 05/15/2023] [Accepted: 06/03/2023] [Indexed: 07/01/2023]
Abstract
OBJECTIVE This study aimed to assess the prognosis of patients with disorders of consciousness (DoC) using auditory stimulation with electroencephalogram (EEG) recordings. METHODS We enrolled 72 patients with DoC in the study, which involved subjecting patients to auditory stimulation while EEG responses were recorded. Coma Recovery Scale-Revised (CRS-R) scores and Glasgow Outcome Scale (GOS) were determined for each patient and followed up for three months. A frequency spectrum analysis was performed on the EEG recordings. Finally, the power spectral density (PSD) index was used to predict the prognosis of patients with DoC based on a support vector machine (SVM) model. RESULTS Power spectral analyses revealed that the cortical response to auditory stimulation showed a decreasing trend with decreasing consciousness levels. Auditory stimulation-induced changes in absolute PSD at the delta and theta bands were positively correlated with the CRS-R and GOS scores. Furthermore, these cortical responses to auditory stimulation had a good ability to discriminate between good and poor prognoses of patients with DoC. CONCLUSIONS Auditory stimulation-induced changes in the PSD were highly predictive of DoC outcomes. SIGNIFICANCE Our findings showed that cortical responses to auditory stimulation may be an important electrophysiological indicator of prognosis in patients with DoC.
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Affiliation(s)
- Yi Ling
- Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310000, China
| | - Chuan Xu
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou 310016, China
| | - Xinrui Wen
- Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310000, China
| | - Jingqi Li
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou 311215, China
| | - Jian Gao
- Department of Rehabilitation, Hangzhou Mingzhou Brain Rehabilitation Hospital, Hangzhou 311215, China
| | - Benyan Luo
- Department of Neurology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310000, China.
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3
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Rajajee V, Muehlschlegel S, Wartenberg KE, Alexander SA, Busl KM, Chou SHY, Creutzfeldt CJ, Fontaine GV, Fried H, Hocker SE, Hwang DY, Kim KS, Madzar D, Mahanes D, Mainali S, Meixensberger J, Montellano F, Sakowitz OW, Weimar C, Westermaier T, Varelas PN. Guidelines for Neuroprognostication in Comatose Adult Survivors of Cardiac Arrest. Neurocrit Care 2023; 38:533-563. [PMID: 36949360 PMCID: PMC10241762 DOI: 10.1007/s12028-023-01688-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/30/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Among cardiac arrest survivors, about half remain comatose 72 h following return of spontaneous circulation (ROSC). Prognostication of poor neurological outcome in this population may result in withdrawal of life-sustaining therapy and death. The objective of this article is to provide recommendations on the reliability of select clinical predictors that serve as the basis of neuroprognostication and provide guidance to clinicians counseling surrogates of comatose cardiac arrest survivors. METHODS A narrative systematic review was completed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. Candidate predictors, which included clinical variables and prediction models, were selected based on clinical relevance and the presence of an appropriate body of evidence. The Population, Intervention, Comparator, Outcome, Timing, Setting (PICOTS) question was framed as follows: "When counseling surrogates of comatose adult survivors of cardiac arrest, should [predictor, with time of assessment if appropriate] be considered a reliable predictor of poor functional outcome assessed at 3 months or later?" Additional full-text screening criteria were used to exclude small and lower-quality studies. Following construction of the evidence profile and summary of findings, recommendations were based on four GRADE criteria: quality of evidence, balance of desirable and undesirable consequences, values and preferences, and resource use. In addition, good practice recommendations addressed essential principles of neuroprognostication that could not be framed in PICOTS format. RESULTS Eleven candidate clinical variables and three prediction models were selected based on clinical relevance and the presence of an appropriate body of literature. A total of 72 articles met our eligibility criteria to guide recommendations. Good practice recommendations include waiting 72 h following ROSC/rewarming prior to neuroprognostication, avoiding sedation or other confounders, the use of multimodal assessment, and an extended period of observation for awakening in patients with an indeterminate prognosis, if consistent with goals of care. The bilateral absence of pupillary light response > 72 h from ROSC and the bilateral absence of N20 response on somatosensory evoked potential testing were identified as reliable predictors. Computed tomography or magnetic resonance imaging of the brain > 48 h from ROSC and electroencephalography > 72 h from ROSC were identified as moderately reliable predictors. CONCLUSIONS These guidelines provide recommendations on the reliability of predictors of poor outcome in the context of counseling surrogates of comatose survivors of cardiac arrest and suggest broad principles of neuroprognostication. Few predictors were considered reliable or moderately reliable based on the available body of evidence.
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Affiliation(s)
- Venkatakrishna Rajajee
- Departments of Neurology and Neurosurgery, 3552 Taubman Health Care Center, SPC 5338, University of Michigan, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5338, USA.
| | - Susanne Muehlschlegel
- Departments of Neurology, Anesthesiology, and Surgery, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | | | - Katharina M Busl
- Departments of Neurology and Neurosurgery, College of Medicine, University of Florida, Gainesville, FL, USA
| | - Sherry H Y Chou
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Gabriel V Fontaine
- Departments of Pharmacy and Neurosciences, Intermountain Healthcare, Salt Lake City, UT, USA
| | - Herbert Fried
- Department of Neurosurgery, Denver Health Medical Center, Denver, CO, USA
| | - Sara E Hocker
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - David Y Hwang
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Keri S Kim
- Pharmacy Practice, University of Illinois, Chicago, IL, USA
| | - Dominik Madzar
- Department of Neurology, University of Erlangen, Erlangen, Germany
| | - Dea Mahanes
- Departments of Neurology and Neurosurgery, University of Virginia Health, Charlottesville, VA, USA
| | - Shraddha Mainali
- Department of Neurology, Virginia Commonwealth University, Richmond, VA, USA
| | | | | | - Oliver W Sakowitz
- Department of Neurosurgery, Neurosurgery Center Ludwigsburg-Heilbronn, Ludwigsburg, Germany
| | - Christian Weimar
- Institute of Medical Informatics, Biometry, and Epidemiology, University Hospital Essen, Essen, Germany
- BDH-Clinic Elzach, Elzach, Germany
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4
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A Simplified Electroencephalography Montage and Interpretation for Evaluation of Comatose Patients in the ICU. Crit Care Explor 2022; 4:e0781. [DOI: 10.1097/cce.0000000000000781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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5
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Hwang J, Cho SM, Ritzl EK. Recent applications of quantitative electroencephalography in adult intensive care units: a comprehensive review. J Neurol 2022; 269:6290-6309. [DOI: 10.1007/s00415-022-11337-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/10/2022] [Accepted: 08/11/2022] [Indexed: 10/15/2022]
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Johnsen B, Jeppesen J, Duez CHV. Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses. Clin Neurophysiol 2022; 142:143-153. [PMID: 36041343 DOI: 10.1016/j.clinph.2022.07.507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/03/2022]
Abstract
OBJECTIVE Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method. METHODS Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores. RESULTS EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932). CONCLUSIONS Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication. SIGNIFICANCE This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.
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Affiliation(s)
- Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark.
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark
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7
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Liuzzi P, Grippo A, Campagnini S, Scarpino M, Draghi F, Romoli A, Bahia H, Sterpu R, Maiorelli A, Macchi C, Cecchi F, Carrozza MC, Mannini A. Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1504-1513. [PMID: 35635833 DOI: 10.1109/tnsre.2022.3178801] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical data-based and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Data-driven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decision-making process the practitioners must face.
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8
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Huang H, Su Y, Niu Z, Liu G, Li X, Jiang M. Comatose Patients After Cardiopulmonary Resuscitation: An Analysis Based on Quantitative Methods of EEG Reactivity. Front Neurol 2022; 13:877406. [PMID: 35720067 PMCID: PMC9205205 DOI: 10.3389/fneur.2022.877406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Objective Every year, approximately 50–110/1,00,000 people worldwide suffer from cardiac arrest, followed by hypoxic-ischemic encephalopathy after cardiopulmonary resuscitation (CPR), and approximately 40–66% of patients do not recover. The purpose of this study was to identify the brain network parameters and key brain regions associated with awakening by comparing the reactivity characteristics of the brain networks between the awakening and unawakening groups of CPR patients after coma, thereby providing a basis for further awakening interventions. Method This study involved a prospective cohort study. Using a 64-electrode electroencephalography (EEG) wireless 64A system, EEG signals were recorded from 16 comatose patients after CPR in the acute phase (<1 month) from 2019 to 2020. MATLAB (2017b) was used to quantitatively analyze the reactivity (power spectrum and entropy) and brain network characteristics (coherence and phase lag index) after pain stimulation. The patients were divided into an awakening group and an unawakening group based on their ability to execute commands or engage in repeated and continuous purposeful behavior after 3 months. The above parameters were compared to determine whether there were differences between the two groups. Results (1) Power spectrum: the awakening group had higher gamma, beta and alpha spectral power after pain stimulation in the frontal and parietal lobes, and lower delta and theta spectral power in the bilateral temporal and occipital lobes than the unawakening group. (2) Entropy: after pain stimulation, the awakening group had higher entropy in the frontal and parietal lobes and lower entropy in the temporal occipital lobes than the unawakening group. (3) Connectivity: after pain stimulation, the awakening group had stronger gamma and beta connectivity in nearly the whole brain, but weaker theta and delta connectivity in some brain regions (e.g., the frontal-occipital lobe and parietal-occipital lobe) than the unawakening group. Conclusion After CPR, comatose patients were more likely to awaken if there was a higher stimulation of fast-frequency band spectral power, higher entropy, stronger whole-brain connectivity and better retention of frontal-parietal lobe function after pain stimulation.
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Affiliation(s)
- Huijin Huang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- *Correspondence: Yingying Su
| | - Zikang Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Beijing Normal University, Beijing, China
- Zikang Niu
| | - Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
- Gang Liu
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Beijing Normal University, Beijing, China
| | - Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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9
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Williams A, Zeng Y, Li Z, Thakor N, Geocadin RG, Bronder J, Martinez NC, Ritzl EK, Cho SM. Quantitative Assessment of Electroencephalogram Reactivity in Comatose Patients on Extracorporeal Membrane Oxygenation. Int J Neural Syst 2022; 32:2250025. [PMID: 35443895 DOI: 10.1142/s0129065722500253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Objective assessment of the brain's responsiveness in comatose patients on Extracorporeal Membrane Oxygenation (ECMO) support is essential to clinical care, but current approaches are limited by subjective methodology and inter-rater disagreement. Quantitative electroencephalogram (EEG) algorithms could potentially assist clinicians, improving diagnostic accuracy. We developed a quantitative, stimulus-based algorithm to assess EEG reactivity features in comatose patients on ECMO support. Patients underwent a stimulation protocol of increasing intensity (auditory, peripheral, and nostril stimulation). A total of 129 20-s EEG epochs were collected from 24 patients (age [Formula: see text], 10 females, 14 males) on ECMO support with a Glasgow Coma Scale[Formula: see text]8. EEG reactivity scores ([Formula: see text]-scores) were calculated using aggregated spectral power and permutation entropy for each of five frequency bands ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]. Parameter estimation techniques were applied to [Formula: see text]-scores to identify properties that replicate the decision process of experienced clinicians performing visual analysis. Spectral power changes from audio stimulation were concentrated in the [Formula: see text] band, whereas peripheral stimulation elicited an increase in spectral power across multiple bands, and nostril stimulation changed the entropy of the [Formula: see text] band. The findings of this pilot study on [Formula: see text]-score lay a foundation for a future prediction tool with clinical applications.
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Affiliation(s)
- Autumn Williams
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yinuo Zeng
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Ziwei Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nitish Thakor
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Romergryko G Geocadin
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jay Bronder
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Eva K Ritzl
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sung-Min Cho
- Department of Neurology, Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Phipps 455, Baltimore, MD, USA
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10
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Zheng WL, Amorim E, Jing J, Wu O, Ghassemi M, Lee JW, Sivaraju A, Pang T, Herman ST, Gaspard N, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning. IEEE Trans Biomed Eng 2021; 69:1813-1825. [PMID: 34962860 PMCID: PMC9087641 DOI: 10.1109/tbme.2021.3139007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
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11
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Bouchereau E, Sharshar T, Legouy C. Delayed awakening in neurocritical care. Rev Neurol (Paris) 2021; 178:21-33. [PMID: 34392974 DOI: 10.1016/j.neurol.2021.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 02/07/2023]
Abstract
Delayed awakening is defined as a persistent disorder of arousal or consciousness 48 to 72h after sedation interruption in critically ill patients. Delayed awakening is either a component of coma or delirium. It results in longer hospital stays and increased mortality. It is therefore a diagnostic, therapeutic and prognostic emergency. In severe brain injured patients, delayed awakening may be related to the primary neurological injury or to secondary systemic insults related to organ failure associated with intensive care. In the present review, we propose diagnostic, therapeutic and prognostic algorithms for managing delayed awaking in neuro-ICU brain injured patients.
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Affiliation(s)
- E Bouchereau
- G.H.U Paris Psychiatry & Neurosciences, department of Neurocritical care, Service d'Anesthésie-Réanimation Neurochirurgicale, 1, rue Cabanis, 75674 Paris Cedex 14, France; INSERM U1266, FHU NeuroVasc, Institut de Psychiatrie et Neuroscience de Paris, Paris, France
| | - T Sharshar
- G.H.U Paris Psychiatry & Neurosciences, department of Neurocritical care, Service d'Anesthésie-Réanimation Neurochirurgicale, 1, rue Cabanis, 75674 Paris Cedex 14, France; INSERM U1266, FHU NeuroVasc, Institut de Psychiatrie et Neuroscience de Paris, Paris, France.
| | - C Legouy
- G.H.U Paris Psychiatry & Neurosciences, department of Neurocritical care, Service d'Anesthésie-Réanimation Neurochirurgicale, 1, rue Cabanis, 75674 Paris Cedex 14, France
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12
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Admiraal MM, Ramos LA, Delgado Olabarriaga S, Marquering HA, Horn J, van Rootselaar AF. Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest. Clin Neurophysiol 2021; 132:2240-2247. [PMID: 34315065 DOI: 10.1016/j.clinph.2021.07.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 04/06/2021] [Accepted: 07/03/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA). METHODS Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months. RESULTS The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives. CONCLUSIONS Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data. SIGNIFICANCE Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.
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Affiliation(s)
- M M Admiraal
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - L A Ramos
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - S Delgado Olabarriaga
- Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - H A Marquering
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands
| | - J Horn
- Amsterdam UMC, University of Amsterdam, Laboratory for Experimental Intensive Care and Anesthesiology, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Intensive Care, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - A F van Rootselaar
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands
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13
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Urdanibia-Centelles O, Nielsen RM, Rostrup E, Vedel-Larsen E, Thomsen K, Nikolic M, Johnsen B, Møller K, Lauritzen M, Benedek K. Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis. Clin Neurophysiol 2021; 132:2075-2082. [PMID: 34284242 DOI: 10.1016/j.clinph.2021.05.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/12/2021] [Accepted: 05/06/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE In critical care, continuous EEG (cEEG) monitoring is useful for delirium diagnosis. Although visual cEEG analysis is most commonly used, automatic cEEG analysis has shown promising results in small samples. Here we aimed to compare visual versus automatic cEEG analysis for delirium diagnosis in septic patients. METHODS We obtained cEEG recordings from 102 septic patients who were scored for delirium six times daily. A total of 1252 cEEG blocks were visually analyzed, of which 805 blocks were also automatically analyzed. RESULTS Automatic cEEG analyses revealed that delirium was associated with 1) high mean global field power (p < 0.005), mainly driven by delta activity; 2) low average coherence across all electrode pairs and all frequencies (p < 0.01); 3) lack of intrahemispheric (fronto-temporal and temporo-occipital regions) and interhemispheric coherence (p < 0.05); and 4) lack of cEEG reactivity (p < 0.005). Classification accuracy was assessed by receiver operating characteristic (ROC) curve analysis, revealing a slightly higher area under the curve for visual analysis (0.88) than automatic analysis (0.74) (p < 0.05). CONCLUSIONS Automatic cEEG analysis is a useful supplement to visual analysis, and provides additional cEEG diagnostic classifiers. SIGNIFICANCE Automatic cEEG analysis provides useful information in septic patients.
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Affiliation(s)
- Olalla Urdanibia-Centelles
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark; Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Rikke M Nielsen
- Department of Neuroanesthesiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Egill Rostrup
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Esben Vedel-Larsen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Kirsten Thomsen
- Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Miki Nikolic
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Nørrebrogade 44, 8000 Aarhus C, Denmark.
| | - Kirsten Møller
- Department of Neuroanesthesiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Denmark.
| | - Martin Lauritzen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark; Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Krisztina Benedek
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
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Jing J, d'Angremont E, Ebrahim S, Tabaeizadeh M, Ng M, Herlopian A, Dauwels J, Brandon Westover M. Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns. J Neurosci Methods 2020; 347:108956. [PMID: 33099261 DOI: 10.1016/j.jneumeth.2020.108956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 09/16/2020] [Accepted: 09/18/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG. NEW METHOD We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters. RESULTS Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency. COMPARISON WITH EXISTING METHODS Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. CONCLUSIONS Our results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.
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Affiliation(s)
- Jin Jing
- Massachusetts General Hospital, Boston, MA, United States; Nanyang Technological University, Singapore, Singapore
| | | | - Senan Ebrahim
- Massachusetts General Hospital, Boston, MA, United States
| | | | - Marcus Ng
- University of Manitoba, Winnipeg, MB, Canada
| | - Aline Herlopian
- Yale University School of Medicine, New Haven, CT, United States
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Chen W, Liu G, Su Y, Zhang Y, Lin Y, Jiang M, Huang H, Ren G, Yan J. EEG signal varies with different outcomes in comatose patients: A quantitative method of electroencephalography reactivity. J Neurosci Methods 2020; 342:108812. [PMID: 32565224 DOI: 10.1016/j.jneumeth.2020.108812] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Revised: 06/05/2020] [Accepted: 06/15/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Electroencephalographic reactivity (EEG-R) is a major predictor of outcome in comatose patients; however, the inter-rater reliability is limited due to the lack of homogeneous stimuli and quantitative interpretation. NEW METHODS EEG-R testing was employed in comatose patients by quantifiable electrical stimulation. Reactivity at different frequency bands was computed as the difference between pre- and post-stimulations in power spectra and connectivity function (including magnitude squared coherence and transfer entropy). The clinical outcomes were dichotomized as good and poor according to the recovery of consciousness. Signal discrimination of EEG-R was compared between the two groups. RESULTS A total of 18 patients (43%) regained consciousness at a 3-month follow-up. In the patients who regained consciousness, the EEG power increased significantly (P < 0.05) at the Alpha and Beta frequency bands after stimulation as compared to those with no behavioral awakening. Also, connectivity enhancement (including linear and nonlinear analysis) in the Beta and Gamma bands and connectivity decrease (nonlinear transfer entropy analysis) in the Delta band after stimulus were observed in the good outcome group. COMPARISON WITH EXISTING METHOD(S) In this study, the combined use of quantifiable stimulation and quantitative analysis shed new light on differentiating brain responses in comatose patients with good and poor outcomes as well as exploring the nature of EEG changes concerning the recovery of consciousness. CONCLUSIONS The combination of quantifiable electrical stimulation and quantitative analysis with spectral power and connectivity for the EEG-R may be a promising method to predict the outcome of comatose patients.
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Affiliation(s)
- Weibi Chen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Yan Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yicong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Huijin Huang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jiaqing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, China.
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Abstract
PURPOSE We 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. METHODS This 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. RESULTS Eighty-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%. CONCLUSIONS The 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.
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Amorim E, van der Stoel M, Nagaraj SB, Ghassemi MM, Jing J, O'Reilly UM, Scirica BM, Lee JW, Cash SS, Westover MB. Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury. Clin Neurophysiol 2019; 130:1908-1916. [PMID: 31419742 PMCID: PMC6751020 DOI: 10.1016/j.clinph.2019.07.014] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 05/27/2019] [Accepted: 07/05/2019] [Indexed: 01/17/2023]
Abstract
OBJECTIVE Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury. METHODS We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1-2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review. RESULTS Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant). CONCLUSIONS Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest. SIGNIFICANCE A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | | | | | - Mohammad M Ghassemi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Una-May O'Reilly
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
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Lee JW. EEG Reactivity in Coma After Cardiac Arrest: Is it Enough to Wake Up the Dead? Epilepsy Curr 2019; 19:369-371. [PMID: 31526034 PMCID: PMC6891174 DOI: 10.1177/1535759719875134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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Geocadin RG, Callaway CW, Fink EL, Golan E, Greer DM, Ko NU, Lang E, Licht DJ, Marino BS, McNair ND, Peberdy MA, Perman SM, Sims DB, Soar J, Sandroni C. Standards for Studies of Neurological Prognostication in Comatose Survivors of Cardiac Arrest: A Scientific Statement From the American Heart Association. Circulation 2019; 140:e517-e542. [DOI: 10.1161/cir.0000000000000702] [Citation(s) in RCA: 153] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Significant improvements have been achieved in cardiac arrest resuscitation and postarrest resuscitation care, but mortality remains high. Most of the poor outcomes and deaths of cardiac arrest survivors have been attributed to widespread brain injury. This brain injury, commonly manifested as a comatose state, is a marker of poor outcome and a major basis for unfavorable neurological prognostication. Accurate prognostication is important to avoid pursuing futile treatments when poor outcome is inevitable but also to avoid an inappropriate withdrawal of life-sustaining treatment in patients who may otherwise have a chance of achieving meaningful neurological recovery. Inaccurate neurological prognostication leading to withdrawal of life-sustaining treatment and deaths may significantly bias clinical studies, leading to failure in detecting the true study outcomes. The American Heart Association Emergency Cardiovascular Care Science Subcommittee organized a writing group composed of adult and pediatric experts from neurology, cardiology, emergency medicine, intensive care medicine, and nursing to review existing neurological prognostication studies, the practice of neurological prognostication, and withdrawal of life-sustaining treatment. The writing group determined that the overall quality of existing neurological prognostication studies is low. As a consequence, the degree of confidence in the predictors and the subsequent outcomes is also low. Therefore, the writing group suggests that neurological prognostication parameters need to be approached as index tests based on relevant neurological functions that are directly related to the functional outcome and contribute to the quality of life of cardiac arrest survivors. Suggestions to improve the quality of adult and pediatric neurological prognostication studies are provided.
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Admiraal MM, van Rootselaar A, Hofmeijer J, Hoedemaekers CWE, van Kaam CR, Keijzer HM, van Putten MJAM, Schultz MJ, Horn J. Electroencephalographic reactivity as predictor of neurological outcome in postanoxic coma: A multicenter prospective cohort study. Ann Neurol 2019; 86:17-27. [PMID: 31124174 PMCID: PMC6618107 DOI: 10.1002/ana.25507] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 05/22/2019] [Accepted: 05/22/2019] [Indexed: 11/30/2022]
Abstract
Objective Outcome prediction in patients after cardiac arrest (CA) is challenging. Electroencephalographic reactivity (EEG‐R) might be a reliable predictor. We aimed to determine the prognostic value of EEG‐R using a standardized assessment. Methods In a prospective cohort study, a strictly defined EEG‐R assessment protocol was executed twice per day in adult patients after CA. EEG‐R was classified as present or absent by 3 EEG readers, blinded to patient characteristics. Uncertain reactivity was classified as present. Primary outcome was best Cerebral Performance Category score (CPC) in 6 months after CA, dichotomized as good (CPC = 1–2) or poor (CPC = 3–5). EEG‐R was considered reliable for predicting poor outcome if specificity was ≥95%. For good outcome prediction, a specificity of ≥80% was used. Added value of EEG‐R was the increase in specificity when combined with EEG background, neurological examination, and somatosensory evoked potentials (SSEPs). Results Of 160 patients enrolled, 149 were available for analyses. Absence of EEG‐R for poor outcome prediction had a specificity of 82% and a sensitivity of 73%. For good outcome prediction, specificity was 73% and sensitivity 82%. Specificity for poor outcome prediction increased from 98% to 99% when EEG‐R was added to a multimodal model. For good outcome prediction, specificity increased from 70% to 89%. Interpretation EEG‐R testing in itself is not sufficiently reliable for outcome prediction in patients after CA. For poor outcome prediction, it has no substantial added value to EEG background, neurological examination, and SSEPs. For prediction of good outcome, EEG‐R seems to have added value. ANN NEUROL 2019
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Affiliation(s)
- Marjolein M. Admiraal
- Amsterdam University Medical Centers, University of AmsterdamDepartment of Intensive Care, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Anne‐Fleur van Rootselaar
- Amsterdam University Medical Centers, University of AmsterdamDepartment of Neurology/Clinical Neurophysiology, Amsterdam NeuroscienceAmsterdamthe Netherlands
| | - Jeannette Hofmeijer
- Rijnstate HospitalDepartment of NeurologyArnhemthe Netherlands
- Clinical NeurophysiologyTechMed Centre, University of TwenteEnschedethe Netherlands
| | | | | | - Hanneke M. Keijzer
- Rijnstate HospitalDepartment of NeurologyArnhemthe Netherlands
- Department of Intensive Care Medicine and NeurologyDonders Institute for Brain, Cognition, and Behavior, Radboud University Medical CenterNijmegenthe Netherlands
| | - Michel J. A. M. van Putten
- Clinical NeurophysiologyTechMed Centre, University of TwenteEnschedethe Netherlands
- Department of Clinical NeurophysiologyMedisch Spectrum TwenteEnschedethe Netherlands
| | - Marcus J. Schultz
- Amsterdam University Medical Centers, University of AmsterdamDepartment of Intensive Care, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Amsterdam University Medical Centers, University of AmsterdamLaboratory for Experimental Intensive Care and AnesthesiologyAmsterdamthe Netherlands
- Mahidol UniversityMahidol Oxford Tropical Medicine Research UnitBangkokThailand
| | - Janneke Horn
- Amsterdam University Medical Centers, University of AmsterdamDepartment of Intensive Care, Amsterdam NeuroscienceAmsterdamthe Netherlands
- Amsterdam University Medical Centers, University of AmsterdamLaboratory for Experimental Intensive Care and AnesthesiologyAmsterdamthe Netherlands
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Caporro M, Rossetti AO, Seiler A, Kustermann T, Nguepnjo Nguissi NA, Pfeiffer C, Zimmermann R, Haenggi M, Oddo M, De Lucia M, Zubler F. Electromyographic reactivity measured with scalp-EEG contributes to prognostication after cardiac arrest. Resuscitation 2019; 138:146-152. [DOI: 10.1016/j.resuscitation.2019.03.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2018] [Revised: 03/03/2019] [Accepted: 03/06/2019] [Indexed: 01/02/2023]
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Neurological Prognostication After Cardiac Arrest in the Era of Target Temperature Management. Curr Neurol Neurosci Rep 2019; 19:10. [DOI: 10.1007/s11910-019-0922-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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EEG Reactivity Evaluation Practices for Adult and Pediatric Hypoxic-Ischemic Coma Prognostication in North America. J Clin Neurophysiol 2018; 35:510-514. [PMID: 30216207 DOI: 10.1097/wnp.0000000000000517] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
PURPOSE The aim of this study was to assess the variability in EEG reactivity evaluation practices during cardiac arrest prognostication. METHODS A survey of institutional representatives from North American academic hospitals participating in the Critical Care EEG Monitoring Research Consortium was conducted to assess practice patterns involving EEG reactivity evaluation. This 10-question multiple-choice survey evaluated metrics related to technical, interpretation, personnel, and procedural aspects of bedside EEG reactivity testing and interpretation specific to cardiac arrest prognostication. One response per hospital was obtained. RESULTS Responses were received from 25 hospitals, including 7 pediatric hospitals. A standardized EEG reactivity protocol was available in 44% of centers. Sixty percent of respondents believed that reactivity interpretation was subjective. Reactivity bedside testing always (100%) started during hypothermia and was performed daily during monitoring in the majority (71%) of hospitals. Stimulation was performed primarily by neurodiagnostic technologists (76%). The mean number of activation procedures modalities tested was 4.5 (SD 2.1). The most commonly used activation procedures were auditory (83.3%), nail bed pressure (63%), and light tactile stimuli (63%). Changes in EEG amplitude alone were not considered consistent with EEG reactivity in 21% of centers. CONCLUSIONS There is substantial variability in EEG reactivity evaluation practices during cardiac arrest prognostication among North American academic hospitals. Efforts are needed to standardize protocols and nomenclature according with national guidelines and promote best practices in EEG reactivity evaluation.
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Admiraal M, van Rootselaar A, Horn J. International consensus on EEG reactivity testing after cardiac arrest: Towards standardization. Resuscitation 2018; 131:36-41. [DOI: 10.1016/j.resuscitation.2018.07.025] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 07/20/2018] [Accepted: 07/25/2018] [Indexed: 10/28/2022]
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Backman S, Cronberg T, Friberg H, Ullén S, Horn J, Kjaergaard J, Hassager C, Wanscher M, Nielsen N, Westhall E. Highly malignant routine EEG predicts poor prognosis after cardiac arrest in the Target Temperature Management trial. Resuscitation 2018; 131:24-28. [DOI: 10.1016/j.resuscitation.2018.07.024] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/12/2018] [Accepted: 07/24/2018] [Indexed: 12/01/2022]
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Azabou E, Navarro V, Kubis N, Gavaret M, Heming N, Cariou A, Annane D, Lofaso F, Naccache L, Sharshar T. Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2018; 22:184. [PMID: 30071861 PMCID: PMC6091014 DOI: 10.1186/s13054-018-2104-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Accepted: 06/22/2018] [Indexed: 12/21/2022]
Abstract
Background Electroencephalography (EEG) is a well-established tool for assessing brain function that is available at the bedside in the intensive care unit (ICU). This review aims to discuss the relevance of electroencephalographic reactivity (EEG-R) in patients with impaired consciousness and to describe the neurophysiological mechanisms involved. Methods We conducted a systematic search of the term “EEG reactivity and coma” using the PubMed database. The search encompassed articles published from inception to March 2018 and produced 202 articles, of which 42 were deemed relevant, assessing the importance of EEG-R in relationship to outcomes in patients with impaired consciousness, and were therefore included in this review. Results Although definitions, characteristics and methods used to assess EEG-R are heterogeneous, several studies underline that a lack of EEG-R is associated with mortality and unfavorable outcome in patients with impaired consciousness. However, preserved EEG-R is linked to better odds of survival. Exploring EEG-R to nociceptive, auditory, and visual stimuli enables a noninvasive trimodal functional assessment of peripheral and central sensory ascending pathways that project to the brainstem, the thalamus and the cerebral cortex. A lack of EEG-R in patients with impaired consciousness may result from altered modulation of thalamocortical loop activity by afferent sensory input due to neural impairment. Assessing EEG-R is a valuable tool for the diagnosis and outcome prediction of severe brain dysfunction in critically ill patients. Conclusions This review emphasizes that whatever the etiology, patients with impaired consciousness featuring a reactive electroencephalogram are more likely to have a favorable outcome, whereas those with a nonreactive electroencephalogram are prone to having an unfavorable outcome. EEG-R is therefore a valuable prognostic parameter and warrants a rigorous assessment. However, current assessment methods are heterogeneous, and no consensus exists. Standardization of stimulation and interpretation methods is needed.
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Affiliation(s)
- Eric Azabou
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France. .,Clinical Neurophysiology Unit, Raymond Poincaré Hospital - Assistance - Publique Hôpitaux de Paris, INSERM U1173, University of Versailles-Saint Quentin (UVSQ), 104 Boulevard Raymond Poincaré, Garches, 92380, Paris, France.
| | - Vincent Navarro
- Department of Clinical Neurophysiology, Pitié-Salpêtrière Hospital, AP-HP, Inserm UMRS 1127, CNRS UMR 7225, Sorbonne Universities, Université Pierre et Marie Curie - UPMC Université Paris 06, Paris, France
| | - Nathalie Kubis
- Department of Clinical Physiology, Lariboisière Hospital, AP-HP, Inserm U965, University of Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Martine Gavaret
- Department of Clinical Neurophysiology, Sainte-Anne Hospital, Inserm U894, University Paris-Descartes, Paris, France
| | - Nicholas Heming
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France
| | - Alain Cariou
- Medical ICU, Cochin Hospital, AP-HP, Paris Cardiovascular Research Center, INSERM U970, Université Paris Descartes Sorbonne Paris Cité, Paris, France
| | - Djillali Annane
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France
| | - Fréderic Lofaso
- Department of Physiology and Department of Critical Care Medicine, Raymond Poincaré Hospital, Assistance Publique - Hôpitaux de Paris (AP-HP), Inserm UMR 1173 Infection and Inflammation, University of Versailles Saint Quentin (UVSQ), University Paris-Saclay, Garches, Paris, France
| | - Lionel Naccache
- Department of Clinical Neurophysiology, Pitié-Salpêtrière Hospital, AP-HP, Inserm UMRS 1127, CNRS UMR 7225, Sorbonne Universities, Université Pierre et Marie Curie - UPMC Université Paris 06, Paris, France
| | - Tarek Sharshar
- Department of Neuro-Intensive Care Medicine, Sainte-Anne Hospital, Paris-Descartes University, Paris, France
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Large inter-rater variability on EEG-reactivity is improved by a novel quantitative method. Clin Neurophysiol 2018; 129:724-730. [DOI: 10.1016/j.clinph.2018.01.054] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Revised: 12/28/2017] [Accepted: 01/24/2018] [Indexed: 11/21/2022]
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Kafashan M, Ryu S, Hargis MJ, Laurido-Soto O, Roberts DE, Thontakudi A, Eisenman L, Kummer TT, Ching S. EEG dynamical correlates of focal and diffuse causes of coma. BMC Neurol 2017; 17:197. [PMID: 29141595 PMCID: PMC5688694 DOI: 10.1186/s12883-017-0977-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 11/05/2017] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Rapidly determining the causes of a depressed level of consciousness (DLOC) including coma is a common clinical challenge. Quantitative analysis of the electroencephalogram (EEG) has the potential to improve DLOC assessment by providing readily deployable, temporally detailed characterization of brain activity in such patients. While used commonly for seizure detection, EEG-based assessment of DLOC etiology is less well-established. As a first step towards etiological diagnosis, we sought to distinguish focal and diffuse causes of DLOC through assessment of temporal dynamics within EEG signals. METHODS We retrospectively analyzed EEG recordings from 40 patients with DLOC with consensus focal or diffuse culprit pathology. For each recording, we performed a suite of time-series analyses, then used a statistical framework to identify which analyses (features) could be used to distinguish between focal and diffuse cases. RESULTS Using cross-validation approaches, we identified several spectral and non-spectral EEG features that were significantly different between DLOC patients with focal vs. diffuse etiologies, enabling EEG-based classification with an accuracy of 76%. CONCLUSIONS Our findings suggest that DLOC due to focal vs. diffuse injuries differ along several electrophysiological parameters. These results may form the basis of future classification strategies for DLOC and coma that are more etiologically-specific and therefore therapeutically-relevant.
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Affiliation(s)
- MohammadMehdi Kafashan
- Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Dr. Campus Box 1042, St. Louis, MO, 63130, USA.,Present Address: Harvard Medical School, Boston, USA
| | - Shoko Ryu
- Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Dr. Campus Box 1042, St. Louis, MO, 63130, USA
| | - Mitchell J Hargis
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave. Campus Box 8111, St. Louis, MO, 63110, USA.,Present Address: Department of Neurology, Novant Health Forsyth Medical Center, Winston-Salem, USA
| | - Osvaldo Laurido-Soto
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave. Campus Box 8111, St. Louis, MO, 63110, USA
| | - Debra E Roberts
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave. Campus Box 8111, St. Louis, MO, 63110, USA.,Present Address: Department of Neurology, University of Rochester, Rochester, USA
| | - Akshay Thontakudi
- Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Dr. Campus Box 1042, St. Louis, MO, 63130, USA
| | - Lawrence Eisenman
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave. Campus Box 8111, St. Louis, MO, 63110, USA
| | - Terrance T Kummer
- Department of Neurology, Washington University School of Medicine, 660 S Euclid Ave. Campus Box 8111, St. Louis, MO, 63110, USA.
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, 1 Brookings Dr. Campus Box 1042, St. Louis, MO, 63130, USA. .,Division of Biology and Biomedical Science, Washington University in St. Louis, St. Louis, MO, 63110, USA.
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31
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Johnsen B, Nøhr KB, Duez CHV, Ebbesen MQ. The Nature of EEG Reactivity to Light, Sound, and Pain Stimulation in Neurosurgical Comatose Patients Evaluated by a Quantitative Method. Clin EEG Neurosci 2017; 48:428-437. [PMID: 28844160 DOI: 10.1177/1550059417726475] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
EEG reactivity (EEG-R) is regarded as an important parameter in coma prognosis but knowledge is sparse on the nature of EEG changes due to different kinds of stimulation and their prognostic significance. EEG-R was quantified in a study of 39 comatose neurosurgical patients. Six 30-second standardized visual, auditory, and painful stimulations were applied. EEG-R in the delta, theta, alpha, and beta band was normalized in z-scores as the power of a stimulation epoch relative to average power of 6 resting epochs. Outcome measure was 3 months Glasgow Outcome Scale. Increase in EEG activity was related to poor outcome, was more common (13.4% of tests), and grew continuously during the 30-second stimulation epoch. Decrease in EEG activity was related to good outcome, was rarer (2.5%), and peaked around 15 seconds. Pain was the most provocative stimulation (20.4%) followed by sound (8.7%) and eye-opening (6.7%). Discrimination between good (n = 6) and poor (n = 33) outcome was best in the theta and alpha bands for pain stimulation in the first 10-20 seconds and for sound stimulation in the first 5 to 10 seconds, eye-opening did not discriminate. Increase in activity predicted poor outcome with a high specificity 100% (CI = 52%-100%) and a modest sensitivity of 39% (CI = 23%-58%). Decrease in activity predicted good outcome with a high specificity of 100% (CI = 87%-100%) and a modest sensitivity of 33% (CI = 6%-76%). This quantitative study reveals new knowledge about the nature of EEG-R, which contribute to the development of more reliable and objective clinical procedures for outcome prediction.
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Affiliation(s)
- Birger Johnsen
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Kristoffer B Nøhr
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Christophe H V Duez
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,2 Research Centre for Emergency Medicine, Aarhus University, Aarhus, Denmark
| | - Mads Q Ebbesen
- 1 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
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Efthymiou E, Renzel R, Baumann CR, Poryazova R, Imbach LL. Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach. Resuscitation 2017; 119:27-32. [PMID: 28750884 DOI: 10.1016/j.resuscitation.2017.07.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 07/14/2017] [Accepted: 07/21/2017] [Indexed: 11/25/2022]
Abstract
INTRODUCTION The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. METHODS We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. RESULTS Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (p<0.005) and correlated with independently identified visual EEG patterns such as generalized periodic discharges (p<0.02). Receiver operating characteristic (ROC) analysis confirmed the predictive value of lower state space velocity for poor clinical outcome after cardiac arrest (AUC 80.8, 70% sensitivity, 15% false positive rate). CONCLUSION Model-based quantitative EEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy.
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Affiliation(s)
- Evdokia Efthymiou
- Department of Neurology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Roland Renzel
- Department of Neurology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Christian R Baumann
- Department of Neurology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Rositsa Poryazova
- Department of Neurology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
| | - Lukas L Imbach
- Department of Neurology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland.
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Solari D, Rossetti AO, Carteron L, Miroz JP, Novy J, Eckert P, Oddo M. Early prediction of coma recovery after cardiac arrest with blinded pupillometry. Ann Neurol 2017; 81:804-810. [PMID: 28470675 DOI: 10.1002/ana.24943] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/27/2017] [Accepted: 04/27/2017] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Prognostication studies on comatose cardiac arrest (CA) patients are limited by lack of blinding, potentially causing overestimation of outcome predictors and self-fulfilling prophecy. Using a blinded approach, we analyzed the value of quantitative automated pupillometry to predict neurological recovery after CA. METHODS We examined a prospective cohort of 103 comatose adult patients who were unconscious 48 hours after CA and underwent repeated measurements of quantitative pupillary light reflex (PLR) using the Neurolight-Algiscan device. Clinical examination, electroencephalography (EEG), somatosensory evoked potentials (SSEP), and serum neuron-specific enolase were performed in parallel, as part of standard multimodal assessment. Automated pupillometry results were blinded to clinicians involved in patient care. Cerebral Performance Categories (CPC) at 1 year was the outcome endpoint. RESULTS Survivors (n = 50 patients; 32 CPC 1, 16 CPC 2, 2 CPC 3) had higher quantitative PLR (median = 20 [range = 13-41] vs 11 [0-55] %, p < 0.0001) and constriction velocity (1.46 [0.85-4.63] vs 0.94 [0.16-4.97] mm/s, p < 0.0001) than nonsurvivors. At 48 hours, a quantitative PLR < 13% had 100% specificity and positive predictive value to predict poor recovery (0% false-positive rate), and provided equal performance to that of EEG and SSEP. Reduced quantitative PLR correlated with higher serum neuron-specific enolase (Spearman r = -0.52, p < 0.0001). INTERPRETATION Reduced quantitative PLR correlates with postanoxic brain injury and, when compared to standard multimodal assessment, is highly accurate in predicting long-term prognosis after CA. This is the first prognostication study to show the value of automated pupillometry using a blinded approach to minimize self-fulfilling prophecy. Ann Neurol 2017;81:804-810.
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Affiliation(s)
| | - Andrea O Rossetti
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - Laurent Carteron
- Department of Intensive Care Medicine.,Neuroscience Critical Care Research Group.,Department of Anesthesiology and Intensive Care Medicine, University of Burgundy-Franche-Comté, Besançon, France
| | - John-Paul Miroz
- Department of Intensive Care Medicine.,Neuroscience Critical Care Research Group
| | - Jan Novy
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | | | - Mauro Oddo
- Department of Intensive Care Medicine.,Neuroscience Critical Care Research Group
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Neurologic Recovery After Cardiac Arrest: a Multifaceted Puzzle Requiring Comprehensive Coordinated Care. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2017; 19:52. [PMID: 28536893 DOI: 10.1007/s11936-017-0548-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OPINION STATEMENT Surviving cardiac arrest (CA) requires a longitudinal approach with multiple levels of responsibility, including fostering a culture of action by increasing public awareness and training, optimization of resuscitation measures including frequent updates of guidelines and their timely implementation into practice, and optimization of post-CA care. This clearly goes beyond resuscitation and targeted temperature management. Brain-directed physiologic goals should dictate the post-CA management, as accumulating evidence suggests that the degree of hypoxic brain injury is the main determinant of survival, regardless of the etiology of arrest. Early assessment of the need for further hemodynamic and electrophysiologic cardiac interventions, adjusting ventilator settings to avoid hyperoxia/hypoxia while targeting high-normal to mildly elevated PaCO2, maintaining mean arterial blood pressures >65 mmHg, evaluating for and treating seizures, maintaining euglycemia, and aggressively pursuing normothermia are key steps in reducing the bioenergetic failure that underlies secondary brain injury. Accurate neuroprognostication requires a multimodal approach with standardized assessments accounting for confounders while recognizing the importance of a delayed prognostication when there is any uncertainty regarding outcome. The concept of a highly specialized post-CA team with expertise in the management of post-CA syndrome (mindful of the brain-directed physiologic goals during the early post-resuscitation phase), TTM, and neuroprognostication, guiding the comprehensive care to the CA survivor, is likely cost-effective and should be explored by institutions that frequently care for these patients. Finally, providing tailored rehabilitation care with systematic reassessment of the needs and overall goals is key for increasing independence and improving quality-of-life in survivors, thereby also alleviating the burden on families. Emerging evidence from multicenter collaborations advances the field of resuscitation at an incredible pace, challenging previously well-established paradigms. There is no more room for "conventional wisdom" in saving the survivors of cardiac arrest.
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Neurophysiological assessment of brain dysfunction in critically ill patients: an update. Neurol Sci 2017; 38:715-726. [PMID: 28110410 DOI: 10.1007/s10072-017-2824-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 01/16/2017] [Indexed: 01/08/2023]
Abstract
The aim of this review was to provide up-to-date information about the usefulness of clinical neurophysiology testing in the management of critically ill patients. Evoked potentials (EPs) and electroencephalogram (EEG) are non-invasive clinical neurophysiology tools that allow an objective assessment of the central nervous system's function at the bedside in intensive care unit (ICU). These tests are quite useful in diagnosing cerebral complications, and establishing the vital and functional prognosis in ICU. EEG keeps a particularly privileged importance in detecting seizures phenomena such as subclinical seizures and non-convulsive status epilepticus. Quantitative EEG (QEEG) analysis techniques commonly called EEG Brain mapping can provide obvious topographic displays of digital EEG signals characteristics, showing the potential distribution over the entire scalp including filtering, frequency, and amplitude analysis and color mapping. Evidences of usefulness of QEEG for seizures detection in ICU are provided by several recent studies. Furthermore, beyond detection of epileptic phenomena, changes of some QEEG panels are early warning indicators of sedation level as well as brain damage or dysfunction in ICU. EPs offer the opportunity for assessing brainstem's functional integrity, as well as subcortical and cortical brain areas. A multimodal use, combining EEG and various modalities of EPs is recommended since this allows a more accurate functional exploration of the brain and helps caregivers to tailor therapeutic measures according to neurological worsening trends and to anticipate the prognosis in ICU.
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Identifying Brain Dysfunction Among Children With Acute Liver Failure-Can Spectral Electroencephalography Help? Pediatr Crit Care Med 2017; 18:88-90. [PMID: 28060158 DOI: 10.1097/pcc.0000000000001020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Şerban CA, Barborică A, Roceanu AM, Mîndruță IR, Ciurea J, Zăgrean AM, Zăgrean L, Moldovan M. EEG Assessment of Consciousness Rebooting from Coma. THE PHYSICS OF THE MIND AND BRAIN DISORDERS 2017. [DOI: 10.1007/978-3-319-29674-6_16] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Admiraal MM, van Rootselaar AF, Horn J. Electroencephalographic reactivity testing in unconscious patients: a systematic review of methods and definitions. Eur J Neurol 2016; 24:245-254. [PMID: 27981707 DOI: 10.1111/ene.13219] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 11/07/2016] [Indexed: 11/28/2022]
Abstract
Electroencephalographic (EEG) reactivity testing is often presented as a clear-cut element of electrophysiological testing. Absence of EEG reactivity is generally considered an indicator of poor outcome, especially in patients after cardiac arrest. However, guidelines do not clearly describe how to test for reactivity and how to evaluate the results. In a quest for clear guidelines, we performed a systematic review aimed at identifying testing methods and definitions of EEG reactivity. We systematically searched the literature between 1970 and May 2016. Methodological quality of the studies was assessed using the QUality In Prognostic Studies tool. Quality of the descriptions of stimulus protocol and reactivity definition was rated on a four-category grading scale based on reproducibility. We found that protocols for EEG reactivity testing vary greatly and descriptions of protocols are almost never replicable. Furthermore, replicable definitions of presence or absence of EEG reactivity are never provided. In order to draw firm conclusions on EEG reactivity as a prognostic factor, future studies should include a precise stimulation protocol and reactivity definition to facilitate guideline formation.
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Affiliation(s)
- M M Admiraal
- Department of Intensive Care, Academic Medical Center, Amsterdam, The Netherlands
| | - A-F van Rootselaar
- Department of Neurology/Clinical Neurophysiology, Academic Medical Center, Amsterdam, The Netherlands
| | - J Horn
- Department of Intensive Care, Academic Medical Center, Amsterdam, The Netherlands.,Laboratory for Experimental Intensive Care and Anesthesiology, University of Amsterdam, Amsterdam, The Netherlands
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Continuous EEG monitoring in post-cardiac arrest patients: Further prognostic insights. Resuscitation 2016; 109:A4. [PMID: 27750052 DOI: 10.1016/j.resuscitation.2016.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 10/06/2016] [Indexed: 11/20/2022]
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40
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Stimulus induced bursts in severe postanoxic encephalopathy. Clin Neurophysiol 2016; 127:3492-3497. [PMID: 27651213 DOI: 10.1016/j.clinph.2016.08.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 08/05/2016] [Accepted: 08/17/2016] [Indexed: 11/23/2022]
Abstract
OBJECTIVE To report on a distinct effect of auditory and sensory stimuli on the EEG in comatose patients with severe postanoxic encephalopathy. METHODS In two comatose patients admitted to the Intensive Care Unit (ICU) with severe postanoxic encephalopathy and burst-suppression EEG, we studied the effect of external stimuli (sound and touch) on the occurrence of bursts. RESULTS In patient A bursts could be induced by either auditory or sensory stimuli. In patient B bursts could only be induced by touching different facial regions (forehead, nose and chin). When stimuli were presented with relatively long intervals, bursts persistently followed the stimuli, while stimuli with short intervals (<1s) did not induce bursts. In both patients bursts were not accompanied by myoclonia. Both patients deceased. CONCLUSIONS Bursts in patients with a severe postanoxic encephalopathy can be induced by external stimuli, resulting in stimulus-dependent burst-suppression. SIGNIFICANCE Stimulus induced bursts should not be interpreted as prognostic favourable EEG reactivity.
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Predicting Outcome in Comatose Patients: The Role of EEG Reactivity to Quantifiable Electrical Stimuli. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2016; 2016:8273716. [PMID: 27127529 PMCID: PMC4834161 DOI: 10.1155/2016/8273716] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 02/14/2016] [Accepted: 03/17/2016] [Indexed: 11/24/2022]
Abstract
Objective. To test the value of quantifiable electrical stimuli as a reliable method to assess electroencephalogram reactivity (EEG-R) for the early prognostication of outcome in comatose patients. Methods. EEG was recorded in consecutive adults in coma after cardiopulmonary resuscitation (CPR) or stroke. EEG-R to standard electrical stimuli was tested. Each patient received a 3-month follow-up by the Glasgow-Pittsburgh cerebral performance categories (CPC) or modified Rankin scale (mRS) score. Results. Twenty-two patients met the inclusion criteria. In the CPR group, 6 of 7 patients with EEG-R had good outcomes (positive predictive value (PPV), 85.7%) and 4 of 5 patients without EEG-R had poor outcomes (negative predictive value (NPV), 80%). The sensitivity and specificity were 85.7% and 80%, respectively. In the stroke group, 6 of 7 patients with EEG-R had good outcomes (PPV, 85.7%); all of the 3 patients without EEG-R had poor outcomes (NPV, 100%). The sensitivity and specificity were 100% and 75%, respectively. Of all patients, the presence of EEG-R showed 92.3% sensitivity, 77.7% specificity, 85.7% PPV, and 87.5% NPV. Conclusion. EEG-R to quantifiable electrical stimuli might be a good positive predictive factor for the prognosis of outcome in comatose patients after CPR or stroke.
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De Lucia M, Tzovara A. Reply: Replicability and impact of statistics in the detection of neural responses of consciousness. Brain 2016; 139:e32. [PMID: 27017191 DOI: 10.1093/brain/aww063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Marzia De Lucia
- Laboratoire de Recherche en Neuroimagerie (LREN), Department of Clinical Neuroscience, Lausanne University and University Hospital, Lausanne, CH-1011, Switzerland
| | - Athina Tzovara
- Laboratoire de Recherche en Neuroimagerie (LREN), Department of Clinical Neuroscience, Lausanne University and University Hospital, Lausanne, CH-1011, Switzerland Department of Psychiatry, Psychotherapy, and Psychosomatics, University of Zurich, CH-8032, Switzerland Neuroscience Centre Zurich University of Zurich, CH-8032, Switzerland
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Hofmeijer J, van Putten MJAM. EEG in postanoxic coma: Prognostic and diagnostic value. Clin Neurophysiol 2016; 127:2047-55. [PMID: 26971488 DOI: 10.1016/j.clinph.2016.02.002] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Revised: 01/26/2016] [Accepted: 02/01/2016] [Indexed: 01/08/2023]
Abstract
Evolution of the EEG background pattern is a robust contributor to prediction of poor or good outcome of comatose patients after cardiac arrest. At 24h, persistent isoelectricity, low voltage activity, or burst-suppression with identical bursts predicts a poor outcome without false positives. Rapid recovery toward continuous patterns within 12h is strongly associated with a good neurological outcome. Predictive values are highest in the first 24h, despite the use of mild therapeutic hypothermia and sedative medication. Studies on reactivity or mismatch negativity have not included the EEG background pattern. Therefore, the additional predictive value of reactivity parameters remains unclear. Whether or not treatment of electrographic status epilepticus improves outcome is studied in the randomized multicenter Treatment of Electroencephalographic STatus epilepticus After cardiopulmonary Resuscitation (TELSTAR) trial (NCT02056236).
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Affiliation(s)
- J Hofmeijer
- Clinical Neurophysiology, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands.
| | - M J A M van Putten
- Clinical Neurophysiology, MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands; Department of Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands.
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