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Aanestad E, Beniczky S, Olberg H, Brogger J. Unveiling variability: A systematic review of reproducibility in visual EEG analysis, with focus on seizures. Epileptic Disord 2024. [PMID: 39340408 DOI: 10.1002/epd2.20291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 08/06/2024] [Accepted: 08/16/2024] [Indexed: 09/30/2024]
Abstract
OBJECTIVE Reproducibility is key for diagnostic tests involving subjective evaluation by experts. Our aim was to systematically review the reproducibility of visual analysis in clinical electroencephalogram (EEG). In this paper, we give data on the scope of EEG features found, and detailed reproducibility data for the most studied feature. METHODS We searched four databases for articles reporting reproducibility in clinical EEG, until June 2023. Two raters screened 24 553 citations, and then 2736 full texts. Quality was assessed according to the GRRAS guidelines. RESULTS We found 275 studies (268 interrater and 20 intrarater), addressing 606 different EEG features. Only 38 EEG features had been studied in >2 studies. Most studies had <50 patients and EEGs. The most often addressed feature was seizure detection (62 papers). Interrater reproducibility of seizure detection was substantial-to-almost-perfect with experienced raters and raw EEG (kappa .62-.88). With experienced raters and transformed EEG, reproducibility was substantial (kappa .63-.70). Inexperienced raters had lower reproducibility. Seizure lateralization reproducibility was moderate to substantial (kappa .58-.77) but lower than for seizure detection. SIGNIFICANCE Most EEG reproducibility studies are done only once. Intrarater studies are rare. The reproducibility of visual EEG analysis is variable. Interrater reproducibility for seizure detection is substantial-to-perfect with experienced raters and raw EEG, less with inexperienced raters or transformed EEG. The results of visual EEG analysis vary within the same rater, and between raters. There is a need for larger collaborative studies, using improved methodology, as well as more intrarater studies of EEG interpretation.
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Affiliation(s)
- Eivind Aanestad
- Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Sándor Beniczky
- Danish Epilepsy Centre, Dianalund, Denmark and Aarhus University, Aarhus, Denmark
| | - Henning Olberg
- Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway
| | - Jan Brogger
- Department of Clinical Neurophysiology, Haukeland University Hospital, Bergen, Norway
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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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Șerban CA, Barborică A, Roceanu AM, Mîndruță IR, Ciurea J, Stancu M, Pâslaru AC, Zăgrean AM, Zăgrean L, Moldovan M. Towards an electroencephalographic measure of awareness based on the reactivity of oscillatory macrostates to hearing a subject's own name. Eur J Neurosci 2024; 59:771-785. [PMID: 37675619 DOI: 10.1111/ejn.16138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
We proposed that the brain's electrical activity is composed of a sequence of alternating states with repeating topographic spectral distributions on scalp electroencephalogram (EEG), referred to as oscillatory macrostates. The macrostate showing the largest decrease in the probability of occurrence, measured as a percentage (reactivity), during sensory stimulation was labelled as the default EEG macrostate (DEM). This study aimed to assess the influence of awareness on DEM reactivity (DER). We included 11 middle cerebral artery ischaemic stroke patients with impaired awareness having a median Glasgow Coma Scale (GCS) of 6/15 and a group of 11 matched healthy controls. EEG recordings were carried out during auditory 1 min stimulation epochs repeating either the subject's own name (SON) or the SON in reverse (rSON). The DEM was identified across three SON epochs alternating with three rSON epochs. Compared with the patients, the DEM of controls contained more posterior theta activity reflecting source dipoles that could be mapped in the posterior cingulate cortex. The DER was measured from the 1 min quiet baseline preceding each stimulation epoch. The difference in mean DER between the SON and rSON epochs was measured by the salient EEG reactivity (SER) theoretically ranging from -100% to 100%. The SER was 12.4 ± 2.7% (Mean ± standard error of the mean) in controls and only 1.3 ± 1.9% in the patient group (P < 0.01). The patient SER decreased with the Glasgow Coma Scale. Our data suggest that awareness increases DER to SON as measured by SER.
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Affiliation(s)
- Cosmin-Andrei Șerban
- Physics Department, University of Bucharest, Bucharest, Romania
- Termobit Prod SRL, Bucharest, Romania
- FHC Inc, Bowdoin, Maine, USA
| | - Andrei Barborică
- Physics Department, University of Bucharest, Bucharest, Romania
- Termobit Prod SRL, Bucharest, Romania
- FHC Inc, Bowdoin, Maine, USA
| | | | | | - Jan Ciurea
- Department of Neurosurgery, Bagdasar-Arseni Emergency Hospital, Bucharest, Romania
| | - Mihai Stancu
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Division of Neurobiology, Faculty of Biology, Ludwig Maximilian University, Munich, Germany
| | - Alexandru C Pâslaru
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Ana-Maria Zăgrean
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Leon Zăgrean
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
| | - Mihai Moldovan
- Termobit Prod SRL, Bucharest, Romania
- Division of Physiology and Neuroscience, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
- Clinical Neurophysiology and Neurology, Rigshospitalet, Copenhagen, Denmark
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Gao Q, Hao J, Kang X, Yuan F, Liu Y, Chen R, Liu X, Li R, Jiang W. EEG dynamics induced by zolpidem forecast consciousness evolution in prolonged disorders of consciousness. Clin Neurophysiol 2023; 153:46-56. [PMID: 37454563 DOI: 10.1016/j.clinph.2023.06.012] [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: 10/31/2022] [Revised: 05/19/2023] [Accepted: 06/11/2023] [Indexed: 07/18/2023]
Abstract
OBJECTIVE To explore whether the EEG dynamics induced by zolpidem can predict consciousness evolution in patients with prolonged disorders of consciousness (PDOC). METHODS We conducted a prospective explorative analysis on thirty-six patients with PDOC and eleven healthy controls. The EEG power spectrum was analyzed and categorized into 'ABCD' patterns at baseline and one hour after zolpidem administration at 10 mg. The clinical outcome was defined as consciousness improvement and no improvement six months after enrollment using the Coma Recovery Scale-Revised (CRS-R) score. RESULTS Zolpidem administration significantly increased the EEG power in the delta & theta bands and decreased EEG power in the beta bands in healthy controls. Further follow-up studies indicated that the increased EEG beta-band power induced by zolpidem can predict an improved consciousness six months after enrollment with an area under the receiver operating characteristic curve (AUC) of 0.829, the sensitivity of 94.38% and an accuracy of 81.48%. CONCLUSIONS Our work revealed that the specific EEG responses to zolpidem can predict consciousness recovery in PDOC patients. SIGNIFICANCE The zolpidem-induced specific EEG responses could potentially predict the recovery of PDOC patients, which may help clinicians and patients' families in their decision-making process.
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Affiliation(s)
- Qiong Gao
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
| | - Jianmin Hao
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Xiaogang Kang
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Fang Yuan
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China; Department of Neurology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510000, China.
| | - Yu Liu
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
| | - Rong Chen
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China
| | - Xiuyun Liu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
| | - Rui Li
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
| | - Wen Jiang
- Department of Neurology, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
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Bouchereau E, Marchi A, Hermann B, Pruvost-Robieux E, Guinard E, Legouy C, Schimpf C, Mazeraud A, Baron JC, Ramdani C, Gavaret M, Sharshar T, Turc G. Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury. Br J Anaesth 2023; 130:e225-e232. [PMID: 36243578 DOI: 10.1016/j.bja.2022.09.005] [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: 04/08/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Decisions of withdrawal of life-sustaining therapy for patients with severe brain injury are often based on prognostic evaluations such as analysis of electroencephalography (EEG) reactivity (EEG-R). However, EEG-R usually relies on visual assessment, which requires neurophysiological expertise and is prone to inter-rater variability. We hypothesised that quantitative analysis of EEG-R obtained 3 days after patient admission can identify new markers of subsequent awakening and consciousness recovery. METHODS In this prospective observational study of patients with severe brain injury requiring mechanical ventilation, quantitative EEG-R was assessed using standard 11-lead EEG with frequency-based (power spectral density) and functional connectivity-based (phase-lag index) analyses. Associations between awakening in the intensive care unit (ICU) and reactivity to auditory and nociceptive stimulations were assessed with logistic regression. Secondary outcomes included in-ICU mortality and 3-month Coma Recovery Scale-Revised (CRS-R) score. RESULTS Of 116 patients, 86 (74%) awoke in the ICU. Among quantitative EEG-R markers, variation in phase-lag index connectivity in the delta frequency band after noise stimulation was associated with awakening (adjusted odds ratio=0.89, 95% confidence interval: 0.81-0.97, P=0.02 corrected for multiple tests), independently of age, baseline severity, and sedation. This new marker was independently associated with improved 3-month CRS-R (adjusted β=-0.16, standard error 0.075, P=0.048), but not with mortality (adjusted odds ratio=1.08, 95% CI: 0.99-1.18, P=0.10). CONCLUSIONS An early-stage quantitative EEG-R marker was independently associated with awakening and 3-month level of consciousness in patients with severe brain injury. This promising marker based on functional connectivity will need external validation before potential integration into a multimodal prognostic model.
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Affiliation(s)
- Eléonore Bouchereau
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France.
| | - Angela Marchi
- Epileptology and Cerebral Rhythmology Department, APHM, Timone Hospital, Marseille, France
| | - Bertrand Hermann
- ICU Department, Hôpital Européen Georges Pompidou, Paris, France; Institut du Cerveau et de la Moelle épinière - ICM, Paris, France; Université Paris Cité, Paris, France
| | - Estelle Pruvost-Robieux
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Eléonore Guinard
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Camille Legouy
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Caroline Schimpf
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Aurélien Mazeraud
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Université Paris Cité, Paris, France
| | - Jean-Claude Baron
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Céline Ramdani
- Institut de Recherche Biomédicale des Armées (IRBA), Brétigny-sur-Orge, France
| | - Martine Gavaret
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Tarek Sharshar
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; FHU NeuroVasc, Paris, France
| | - Guillaume Turc
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
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6
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Fenter H, Ben-Hamouda N, Novy J, Rossetti AO. Benign EEG for prognostication of favorable outcome after cardiac arrest: A reappraisal. Resuscitation 2023; 182:109637. [PMID: 36396011 DOI: 10.1016/j.resuscitation.2022.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/16/2022]
Abstract
AIM The current EEG role for prognostication after cardiac arrest (CA) essentially aims at reliably identifying patients with poor prognosis ("highly malignant" patterns, defined by Westhall et al. in 2014). Conversely, "benign EEGs", defined by the absence of elements of "highly malignant" and "malignant" categories, has limited sensitivity in detecting good prognosis. We postulate that a less stringent "benign EEG" definition would improve sensitivity to detect patients with favorable outcomes. METHODS Retrospectively assessing our registry of unconscious adults after CA (1.2018-8.2021), we scored EEGs within 72 h after CA using a modified "benign EEG" classification (allowing discontinuity, low-voltage, or reversed anterio-posterior amplitude development), versus Westhall's "benign EEG" classification (not allowing the former items). We compared predictive performances towards good outcome (Cerebral Performance Category 1-2 at 3 months), using 2x2 tables (and binomial 95% confidence intervals) and proportions comparisons. RESULTS Among 381 patients (mean age 61.9 ± 15.4 years, 104 (27.2%) females, 240 (62.9%) having cardiac origin), the modified "benign EEG" definition identified a higher number of patients with potential good outcome (252, 66%, vs 163, 43%). Sensitivity of the modified EEG definition was 0.97 (95% CI: 0.92-0.97) vs 0.71 (95% CI: 0.62-0.78) (p < 0.001). Positive predictive values (PPV) were 0.53 (95% CI: 0.46-0.59) versus 0.59 (95% CI: 0.51-0.67; p = 0.17). Similar statistics were observed at definite recording times, and for survivors. DISCUSSION The modified "benign EEG" classification demonstrated a markedly higher sensitivity towards favorable outcome, with minor impact on PPV. Adaptation of "benign EEG" criteria may improve efficient identification of patients who may reach a good outcome.
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Affiliation(s)
- Hélène Fenter
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nawfel Ben-Hamouda
- Department of Adult Intensive Care Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jan Novy
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Andrea O Rossetti
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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7
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Alkhachroum A, Appavu B, Egawa S, Foreman B, Gaspard N, Gilmore EJ, Hirsch LJ, Kurtz P, Lambrecq V, Kromm J, Vespa P, Zafar SF, Rohaut B, Claassen J. Electroencephalogram in the intensive care unit: a focused look at acute brain injury. Intensive Care Med 2022; 48:1443-1462. [PMID: 35997792 PMCID: PMC10008537 DOI: 10.1007/s00134-022-06854-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/31/2022] [Indexed: 02/04/2023]
Abstract
Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.
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Affiliation(s)
- Ayham Alkhachroum
- Department of Neurology, University of Miami, Miami, FL, USA
- Department of Neurology, Jackson Memorial Hospital, Miami, FL, USA
| | - Brian Appavu
- Department of Child Health and Neurology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
- Department of Neurosciences, Phoenix Children's Hospital, Phoenix, AZ, USA
| | - Satoshi Egawa
- Neurointensive Care Unit, Department of Neurosurgery, and Stroke and Epilepsy Center, TMG Asaka Medical Center, Saitama, Japan
| | - Brandon Foreman
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati, 231 Albert Sabin Way, Cincinnati, OH, USA
| | - Nicolas Gaspard
- Department of Neurology, Erasme Hospital, Free University of Brussels, Brussels, Belgium
| | - Emily J Gilmore
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Neurocritical Care and Emergency Neurology, Department of Neurology, Ale University School of Medicine, New Haven, CT, USA
| | - Lawrence J Hirsch
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Pedro Kurtz
- Department of Intensive Care Medicine, D'or Institute for Research and Education, Rio de Janeiro, Brazil
- Neurointensive Care, Paulo Niemeyer State Brain Institute, Rio de Janeiro, Brazil
| | - Virginie Lambrecq
- Department of Clinical Neurophysiology and Epilepsy Unit, AP-HP, Pitié Salpêtrière Hospital, Reference Center for Rare Epilepsies, 75013, Paris, France
| | - Julie Kromm
- Departments of Critical Care Medicine and Clinical Neurosciences, Cumming School of Medicine, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, Calgary, AB, Canada
| | - Paul Vespa
- Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles, USA
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Benjamin Rohaut
- Department of Neurology, Sorbonne Université, Pitié-Salpêtrière-AP-HP and Paris Brain Institute, ICM, Inserm, CNRS, Paris, France
| | - Jan Claassen
- Department of Neurology, Neurological Institute, Columbia University, New York Presbyterian Hospital, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA.
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8
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Amiri M, Fisher PM, Raimondo F, Sidaros A, Cacic Hribljan M, Othman MH, Zibrandtsen I, Albrechtsen SS, Bergdal O, Hansen AE, Hassager C, Højgaard JLS, Jakobsen EW, Jensen HR, Møller J, Nersesjan V, Nikolic M, Olsen MH, Sigurdsson ST, Sitt JD, Sølling C, Welling KL, Willumsen LM, Hauerberg J, Larsen VA, Fabricius M, Knudsen GM, Kjaergaard J, Møller K, Kondziella D. Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study. Brain 2022; 146:50-64. [PMID: 36097353 PMCID: PMC9825454 DOI: 10.1093/brain/awac335] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/25/2022] [Accepted: 08/14/2022] [Indexed: 01/15/2023] Open
Abstract
Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.
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Affiliation(s)
| | | | | | - Annette Sidaros
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melita Cacic Hribljan
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Marwan H Othman
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Ivan Zibrandtsen
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Simon S Albrechtsen
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Ove Bergdal
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Adam Espe Hansen
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Hassager
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark,Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Joan Lilja S Højgaard
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | | | - Helene Ravnholt Jensen
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jacob Møller
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Vardan Nersesjan
- Department of Neurology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Copenhagen University Hospital, Copenhagen, Denmark
| | - Miki Nikolic
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Markus Harboe Olsen
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Sigurdur Thor Sigurdsson
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jacobo D Sitt
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Christine Sølling
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Karen Lise Welling
- Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Lisette M Willumsen
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - John Hauerberg
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Vibeke Andrée Larsen
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Martin Fabricius
- Department of Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Jesper Kjaergaard
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark,Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Kirsten Møller
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark,Department of Neuroanaesthesiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Daniel Kondziella
- Correspondence to: Daniel Kondziella, MD, MSc, PhD FEBN Department of Neurology Copenhagen University Hospital, Rigshospitalet Blegdamsvej 9, DK-2100 Copenhagen E-mail:
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9
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Wang J, Chen X, Zhou L, Liu ZY, Xia YG, You J, Lan S, Liu JF. Assessment of electroencephalography and event-related potentials in unresponsive patients with brain injury. Neurophysiol Clin 2022; 52:384-393. [PMID: 36008205 DOI: 10.1016/j.neucli.2022.07.007] [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: 02/11/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 10/15/2022] Open
Abstract
OBJECTIVE To investigate the predictors of clinical outcomes in unresponsive patients with acquired brain injuries. METHODS Patients with coma or disorders of consciousness were enrolled from August 2019 to March 2021. A retrospective analysis of demographics, etiology, clinical score, diagnosis, electroencephalography (EEG), and event-related potential (ERP) data from 1 week to 2 months after coma onset was conducted. Findings were assessed for predicting favorable outcomes at 6 months post-coma, and functional outcomes were determined using the Glasgow Outcome Scale-Extended (GOS-E). RESULTS Of 68 patients, 22 patients had a good neurological outcome at 6 months, while 11 died. Univariate analysis showed that motor response (Motor-R; p < 0.001), EEG pattern (p = 0.015), sleep spindles (p = 0.018), EEG reactivity (EEG-R; p < 0.001), mismatch negativity (MMN) amplitude at electrode Fz (FzMMNA; p = 0.001), P3a latency (p = 0.044), and P3a amplitude at electrode Cz (CzP3aA; p < 0.001) were significantly correlated with patient prognosis. Multivariable logistic regression analysis showed that FzMMNA, CzP3aA, EEG-R, and Motor-R were significant independent predictors of a favorable outcome. The sensitivity and specificity of FzMMNA (dichotomized at 1.16 μV) were 86.4% and 58.5%, and of CzP3aA (cut-off value 2.76 μV) were 90.9% and 70.7%, respectively. ERP amplitude (ERP-A), a combination of FzMMNA and CzP3aA, improved prediction accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.884. A model incorporating Motor-R, EEG-R, and ERP-A yielded an outstanding predictive performance (AUC=0.921) for a favorable outcome. CONCLUSION ERP-A and the prognostic model resulted in the efficient prediction of a favorable outcome in unresponsive patients.
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Affiliation(s)
- Jian Wang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008
| | - Xin Chen
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008
| | - Liang Zhou
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008
| | - Zi-Yuan Liu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008
| | - Yu-Guo Xia
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008
| | - Jia You
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008
| | - Song Lan
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008
| | - Jin-Fang Liu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, Hunan, PR China, 410008.
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10
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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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11
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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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12
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Tatum WO. EEG Essentials. Continuum (Minneap Minn) 2022; 28:261-305. [PMID: 35393960 DOI: 10.1212/con.0000000000001129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
PURPOSE OF REVIEW EEG is the best study for evaluating the electrophysiologic function of the brain. The relevance of EEG is based on an accurate interpretation of the recording. Understanding the neuroscientific basis for EEG is essential. The basis for recording and interpreting EEG is both brain site-specific and technique-dependent to detect and represent a complex series of waveforms. Separating normal from abnormal EEG lies at the foundation of essential interpretative skills. RECENT FINDINGS Seizures and epilepsy are the primary targets for clinical use of EEG in diagnosis, seizure classification, and management. Interictal epileptiform discharges on EEG support a clinical diagnosis of seizures, but only when an electrographic seizure is recorded is the diagnosis confirmed. New variations of normal waveforms, benign variants, and artifacts can mimic epileptiform patterns and are potential pitfalls for misinterpretation for inexperienced interpreters. A plethora of medical conditions involve nonepileptiform and epileptiform abnormalities on EEG along the continuum of people who appear healthy to those who are critically ill. Emerging trends in long-term EEG monitoring to diagnose, classify, quantify, and characterize patients with seizures have unveiled epilepsy syndromes in patients and expanded medical and surgical options for treatment. Advances in terminology and application of continuous EEG help unify neurologists in the diagnosis of nonconvulsive seizures and status epilepticus in patients with encephalopathy and prognosticate recovery from serious neurologic injury involving the brain. SUMMARY After 100 years, EEG has retained a key role in the neurologist's toolkit as a safe, widely available, versatile, portable test of neurophysiology, and it is likely to remain at the forefront for patients with neurologic diseases. Interpreting EEG is based on qualitative review, and therefore, the accuracy of reporting is based on the interpreter's training, experience, and exposure to many new and older waveforms.
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13
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Serban CA, Barborica A, Roceanu AM, Mindruta I, Ciurea J, Pâslaru AC, Zăgrean AM, Zăgrean L, Moldovan M. A method to assess the default EEG macrostate and its reactivity to stimulation. Clin Neurophysiol 2021; 134:50-64. [PMID: 34973517 DOI: 10.1016/j.clinph.2021.12.002] [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: 02/07/2021] [Revised: 08/23/2021] [Accepted: 12/04/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The default mode network (DMN) is deactivated by stimulation. We aimed to assess the DMN reactivity impairment by routine EEG recordings in stroke patients with impaired consciousness. METHODS Binocular light flashes were delivered at 1 Hz in 1-minute epochs, following a 1-minute baseline (PRE). The EEG was decomposed in a series of binary oscillatory macrostates by topographic spectral clustering. The most deactivated macrostate was labeled the default EEG macrostate (DEM). Its reactivity (DER) was quantified as the decrease in DEM occurrence probability during stimulation. A normalized DER index (DERI) was calculated as DER/PRE. The measures were compared between 14 healthy controls and 32 comatose patients under EEG monitoring following an acute stroke. RESULTS The DEM was mapped to the posterior DMN hubs. In the patients, these DEM source dipoles were 3-4 times less frequent and were associated with an increased theta activity. Even in a reduced 6-channel montage, a DER below 6.26% corresponding to a DERI below 0.25 could discriminate the patients with sensitivity and specificity well above 80%. CONCLUSION The method detected the DMN impairment in post-stroke coma patients. SIGNIFICANCE The DEM and its reactivity to stimulation could be useful to monitor the DMN function at bedside.
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Affiliation(s)
- Cosmin-Andrei Serban
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | - Andrei Barborica
- Physics Department, University of Bucharest, Romania; Termobit Prod SRL, Bucharest, Romania; FHC Inc, Bowdoin, ME, USA.
| | | | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania.
| | - Jan Ciurea
- Department of Neurosurgery, Bagdasar-Arseni Emergency Hospital, Bucharest, Romania.
| | - Alexandru C Pâslaru
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Ana-Maria Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Leon Zăgrean
- Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
| | - Mihai Moldovan
- Termobit Prod SRL, Bucharest, Romania; Division of Physiology and Neuroscience, "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; Neuroscience, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark.
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14
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Kim YJ, Kim MJ, Kim YH, Youn CS, Cho IS, Kim SJ, Wee JH, Park YS, Oh JS, Lee DH, Kim WY. Background frequency can enhance the prognostication power of EEG patterns categories in comatose cardiac arrest survivors: a prospective, multicenter, observational cohort study. Crit Care 2021; 25:398. [PMID: 34789304 PMCID: PMC8596386 DOI: 10.1186/s13054-021-03823-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/10/2021] [Indexed: 11/10/2022] Open
Abstract
Background We assessed the prognostic accuracy of the standardized electroencephalography (EEG) patterns (“highly malignant,” “malignant,” and “benign”) according to the EEG timing (early vs. late) and investigated the EEG features to enhance the predictive power for poor neurologic outcome at 1 month after cardiac arrest. Methods This prospective, multicenter, observational, cohort study using data from Korean Hypothermia Network prospective registry included adult patients with out-of-hospital cardiac arrest (OHCA) treated with targeted temperature management (TTM) and underwent standard EEG within 7 days after cardiac arrest from 14 university-affiliated teaching hospitals in South Korea between October 2015 and December 2018. Early EEG was defined as EEG performed within 72 h after cardiac arrest. The primary outcome was poor neurological outcome (Cerebral Performance Category score 3–5) at 1 month. Results Among 489 comatose OHCA survivors with a median EEG time of 46.6 h, the “highly malignant” pattern (40.7%) was most prevalent, followed by the “benign” (33.9%) and “malignant” (25.4%) patterns. All patients with the highly malignant EEG pattern had poor neurologic outcomes, with 100% specificity in both groups but 59.3% and 56.1% sensitivity in the early and late EEG groups, respectively. However, for patients with “malignant” patterns, 84.8% sensitivity, 77.0% specificity, and 89.5% positive predictive value for poor neurologic outcome were observed. Only 3.5% (9/256) of patients with background EEG frequency of predominant delta waves or undetermined had good neurologic survival. The combination of “highly malignant” or “malignant” EEG pattern with background frequency of delta waves or undetermined increased specificity and positive predictive value, respectively, to up to 98.0% and 98.7%. Conclusions The “highly malignant” patterns predicted poor neurologic outcome with a high specificity regardless of EEG measurement time. The assessment of predominant background frequency in addition to EEG patterns can increase the prognostic value of OHCA survivors. Trial registration KORHN-PRO, NCT02827422. Registered 11 September 2016—Retrospectively registered. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-021-03823-y.
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Affiliation(s)
- Youn-Jung Kim
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Min-Jee Kim
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, Seoul, Korea
| | - Yong Hwan Kim
- Departments of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Chun Song Youn
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - In Soo Cho
- Department of Emergency Medicine, Hanil General Hospital, Seoul, Korea
| | - Su Jin Kim
- Department of Emergency Medicine, Korea University College of Medicine, Seoul, Korea
| | - Jung Hee Wee
- Department of Emergency Medicine, Yeouido St. Mary's Hospital, The Catholic University of Korea College of Medicine, Seoul, Korea
| | - Yoo Seok Park
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Joo Suk Oh
- Department of Emergency Medicine, Uijeongbu St. Mary's Hospital, The Catholic University of Korea College of Medicine, Uijeongbu-si, Korea
| | - Dong Hoon Lee
- Department of Emergency Medicine, Chung-Ang University, College of Medicine, Seoul, Korea
| | - Won Young Kim
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.
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15
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Association of Standard Electroencephalography Findings With Mortality and Command Following in Mechanically Ventilated Patients Remaining Unresponsive After Sedation Interruption. Crit Care Med 2021; 49:e423-e432. [PMID: 33591021 DOI: 10.1097/ccm.0000000000004874] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
CONTEXT Delayed awakening after sedation interruption is frequent in critically ill patients receiving mechanical ventilation. OBJECTIVES We aimed to investigate the association of standard electroencephalography with mortality and command following in this setting. DESIGN, SETTING, AND PATIENTS In a single-center study, we retrospectively analyzed standard electroencephalography performed in consecutive mechanically ventilated patients remaining unresponsive (comatose/stuporous or unable to follow commands) after sedation interruption. Standard electroencephalography parameters (background activity, continuity, and reactivity) were reassessed by neurophysiologists, blinded to patients' outcome. Patients were categorized during follow-up into three groups based on their best examination as: 1) command following, 2) unresponsive, or 3) deceased. Cause-specific models were used to identify independent standard electroencephalography parameters associated with main outcomes, that is, mortality and command following. Follow-up was right-censored 30 days after standard electroencephalography. MEASUREMENTS AND MAIN RESULTS Main standard electroencephalography parameters recorded in 121 unresponsive patients (median time between sedation interruption and standard electroencephalography: 2 d [interquartile range, 1-4 d]) consisted of a background frequency greater than 4 Hz in 71 (59%), a discontinuous background in 19 (16%), and a preserved reactivity in 98/120 (82%) patients. At 30 days, 66 patients (55%) were command following, nine (7%) were unresponsive, and 46 (38%) had died. In a multivariate analysis adjusted for nonneurologic organ failure, a reactive standard electroencephalography with a background frequency greater than 4 Hz was independently associated with a reduced risk of death (cause-specific hazard ratio, 0.38; CI 95%, 0.16-0.9). By contrast, none of the standard electroencephalography parameters were independently associated with command following. Sensitivity analyses conducted after exclusion of 29 patients with hypoxic brain injury revealed similar findings. CONCLUSIONS In patients remaining unresponsive after sedation interruption, a pattern consisting of a reactive standard electroencephalography with a background frequency greater than 4 Hz was associated with decreased odds of death. None of the standard electroencephalography parameters were independently associated with command following.
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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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Sinkin MV, Talypov AE, Yakovlev AA, Kordonskaya OO, Teplyshova AM, Trifonov IS, Guekht AB, Krylov VV. [Long-term EEG monitoring in patients with acute traumatic brain injury]. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:62-67. [PMID: 34184480 DOI: 10.17116/jnevro202112105162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To investigate the informativeness of long-term scalp EEG monitoring in patients with acute traumatic brain injury (TBI). MATERIAL AND METHODS The informativity of long-term EEG monitoring (LTM) was performed in 60 patients with acute severe TBI. Odd ratios (OR) of unfavorable outcome and non-convulsive status epilepticus (NCSE) among clinical, neurophysiological and radiological features were calculated. RESULTS EEG features of the unfavorable outcome are: slowing of the dominant background rhythm below q range (OR 3.5, CI 1.2-10.7), absence of frontal-occipital gradient (OR 10.2, CI 1.89-10.12), absence of reactivity (OR 8.75, CI 2.14-35.7), absence of variability (OR 6.25, CI 1.72-22.6) and absence of NREM sleep, stage 2 (OR 5.8, CI 1.79-18.91). Clinical features associated with the unfavorable outcome are: a decrease in GCS score (OR 1.25, CI 1.07-1.47), TBI severity (OR 2.46, CI 1.16-5.18), axial dislocation (OR 4.45, CI 1.08-18.29). ORs for NCSE are significant for the following EEG features: presence of rhythmic and periodic patterns (RPP) (OR 11.92, CI 1.37-103.39), stimulus induced RPP (OR 23.14, CI 2.56-209.34), "plus" modifier (OR 4.11, CI 1.13-14.91) and electrographic evolution (OR 13.05, CI 3.59-47.39). Background rhythm slowing below q range reduces NCSE probability (OR 3.33, CI 1.09-10). CONCLUSION Long-term EEG monitoring is an informative tool for prognosis of outcome and diagnosis of NCSE in patients with severe TBI. The risk of NCSE increases with Marshall score but NCSE is not associated with poor outcome that requires an individual selection of intensive care.
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Affiliation(s)
- M V Sinkin
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia.,Evdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia
| | - A E Talypov
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia
| | - A A Yakovlev
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia.,Soloviev Scientific and Practical Psychoneurological Center, Moscow, Russia
| | - O O Kordonskaya
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia.,Federal Center of Brain and Neurotechnology, Moscow, Russia
| | | | - I S Trifonov
- Evdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia
| | - A B Guekht
- Soloviev Scientific and Practical Psychoneurological Center, Moscow, Russia.,Pirogov Russian National Research Medical University, Moscow, Russia
| | - V V Krylov
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia.,Evdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Olasveengen TM, Skrifvars MB, Taccone F, Soar J. Postreanimationsbehandlung. Notf Rett Med 2021. [DOI: 10.1007/s10049-021-00892-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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19
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Caroyer S, Depondt C, Rikir E, Mavroudakis N, Peluso L, Silvio Taccone F, Legros B, Gaspard N. Assessment of a standardized EEG reactivity protocol after cardiac arrest. Clin Neurophysiol 2021; 132:1687-1693. [PMID: 34049028 DOI: 10.1016/j.clinph.2021.03.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 02/02/2021] [Accepted: 03/08/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Reactivity assessment during EEG might provide important prognostic information in post-anoxic coma. It is still unclear how best to perform reactivity testing and how it might be affected by hypothermia. Our primary aim was to determine and compare the effectiveness, inter-rater reliability and prognostic value of different types of stimulus for EEG reactivity testing, using a standardized stimulation protocol and standardized definitions. Our secondary aims were to assess the effect of hypothermia on these measures, and to determine the prognostic value of a simplified sequence with the three most efficient stimuli. METHODS Prospective single-center cohort of post-anoxic comatose patients admitted to the intensive care unit of an academic medical center between January 1, 2016 and December 31, 2018 and receiving continuous EEG monitoring (CEEG). Reactivity was assessed using standardized definitions and standardized sequence of stimuli: auditory (mild noise and loud noise), tactile (shaking), nociceptive (nostril tickling, trapezius muscle squeezing, endotracheal tube suctioning), and visual (passive eye opening). Gwet's AC1 and percent agreement (PA) were used to measure inter-rater agreement (IRA). Ability to predict favorable neurological outcome (defined as a Cerebral Performance Category of 1 to 2: no disability to moderate disability) was measured with sensitivity (Se), specificity (Sp), accuracy, and odds ratio [OR]. These were calculated for each stimulus type and at the level of the entire sequence comprising all the stimuli. RESULTS One-hundred and fifteen patients were included and 242 EEG epochs were analyzed. Loud noise, shaking and trapezius muscle squeezing most frequently elicited EEG reactivity (42%, 38% and 38%, respectively) but were all inferior to the entire sequence, which elicited reactivity in 58% cases. The IRA for reactivity to individual stimuli varied from moderate to good (AC1:58-69%; PA:56-68%) and was the highest for loud noise (AC1:69%; PA:68%), trapezius muscle squeezing (AC1:67%; PA:65%) and passive eye opening (AC1:68%; PA:64%). Mild (odds ratio [OR]:11.0; Se:70% and Sp:86%) and loud noises (OR:27.0; Se:73% and Sp:75%), and trapezius muscle squeezing (OR:15.3; Se:76% and Sp:83%) during hypothermia had the best predictive value for favorable neurological outcome, although each was inferior to the whole sequence (OR:60.2; Se:91% and Sp:73%). A simplified sequence of loud noise, shaking and trapezius muscle squeezing had the same performance for predicting neurological outcome as the entire sequence. Hypothermia did not significantly affect the effectiveness of stimulation, but IRA was slightly better during hypothermia, for all stimuli. Similarly, the predictive value was higher during hypothermia than during normothermia. CONCLUSIONS Despite a standardized stimulation protocol and standardized definitions, the IRA of EEG reactivity testing in post-anoxic comatose patients was only good at best (AC1 < 70%), and its predictive value for neurological outcome remained imperfect, in particular with Sp values < 90%. While no single stimulus appeared superior to others, a full sequence using all stimuli or a simplified sequence comprising loud noise, shaking and trapezius muscle squeezing had the best combination of IRA and predictive value. SIGNIFICANCE This study stresses the necessity to use multiple stimulus types to improve the predictive value of reactivity testing in post-anoxic coma and confirms that it is not affected by hypothermia.
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Affiliation(s)
- Sarah Caroyer
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Chantal Depondt
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Estelle Rikir
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Nicolas Mavroudakis
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Lorenzo Peluso
- Department of Intensive Care, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Fabio Silvio Taccone
- Department of Intensive Care, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium; Yale University Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory New Haven, CT, USA.
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Olasveengen TM, Skrifvars MB, Taccone F, Soar J. European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care. Intensive Care Med 2021; 47:369-421. [PMID: 33765189 PMCID: PMC7993077 DOI: 10.1007/s00134-021-06368-4] [Citation(s) in RCA: 468] [Impact Index Per Article: 156.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/08/2021] [Indexed: 12/13/2022]
Abstract
The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.
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Affiliation(s)
- Jerry P. Nolan
- University of Warwick, Warwick Medical School, Coventry, CV4 7AL UK
- Royal United Hospital, Bath, BA1 3NG UK
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Bernd W. Böttiger
- Department of Anaesthesiology and Intensive Care Medicine, University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Alain Cariou
- Cochin University Hospital (APHP) and University of Paris (Medical School), Paris, France
| | - Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences, Anaesthesia and Intensive Care Medicine, Lund University, Skane University Hospital, Lund, Sweden
| | - Cornelia Genbrugge
- Acute Medicine Research Pole, Institute of Experimental and Clinical Research (IREC), Université Catholique de Louvain, Brussels, Belgium
- Emergency Department, University Hospitals Saint-Luc, Brussels, Belgium
| | - Kirstie Haywood
- Warwick Research in Nursing, Division of Health Sciences, Warwick Medical School, University of Warwick, Room A108, Coventry, CV4 7AL UK
| | - Gisela Lilja
- Department of Clinical Sciences Lund, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Véronique R. M. Moulaert
- Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Nikolaos Nikolaou
- Cardiology Department, Konstantopouleio General Hospital, Athens, Greece
| | - Theresa Mariero Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Markus B. Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Fabio Taccone
- Department of Intensive Care, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik, 808, 1070 Brussels, Belgium
| | - Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol, BS10 5NB UK
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21
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Mariero Olasveengen T, Skrifvars MB, Taccone F, Soar J. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines 2021: Post-resuscitation care. Resuscitation 2021; 161:220-269. [PMID: 33773827 DOI: 10.1016/j.resuscitation.2021.02.012] [Citation(s) in RCA: 385] [Impact Index Per Article: 128.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation, and organ donation.
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Affiliation(s)
- Jerry P Nolan
- University of Warwick, Warwick Medical School, Coventry CV4 7AL, UK; Royal United Hospital, Bath, BA1 3NG, UK.
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy; Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Bernd W Böttiger
- University Hospital of Cologne, Kerpener Straße 62, D-50937 Cologne, Germany
| | - Alain Cariou
- Cochin University Hospital (APHP) and University of Paris (Medical School), Paris, France
| | - Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences, Anaesthesia and Intensive Care Medicine, Lund University, Skane University Hospital, Lund, Sweden
| | - Cornelia Genbrugge
- Acute Medicine Research Pole, Institute of Experimental and Clinical Research (IREC) Université Catholique de Louvain, Brussels, Belgium; Emergency Department, University Hospitals Saint-Luc, Brussels, Belgium
| | - Kirstie Haywood
- Warwick Research in Nursing, Room A108, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Gisela Lilja
- Lund University, Skane University Hospital, Department of Clinical Sciences Lund, Neurology, Lund, Sweden
| | - Véronique R M Moulaert
- University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Groningen, The Netherlands
| | - Nikolaos Nikolaou
- Cardiology Department, Konstantopouleio General Hospital, Athens, Greece
| | - Theresa Mariero Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Norway
| | - Markus B Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Finland
| | - Fabio Taccone
- Department of Intensive Care, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik, 808, 1070 Brussels, Belgium
| | - Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol BS10 5NB, UK
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22
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Carrai R, Spalletti M, Scarpino M, Lolli F, Lanzo G, Cossu C, Bonizzoli M, Socci F, Lazzeri C, Amantini A, Grippo A. Are neurophysiologic tests reliable, ultra-early prognostic indices after cardiac arrest? Neurophysiol Clin 2021; 51:133-144. [PMID: 33573889 DOI: 10.1016/j.neucli.2021.01.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/26/2021] [Accepted: 01/26/2021] [Indexed: 10/22/2022] Open
Abstract
OBJECTIVES Determining early and reliable prognosis in comatose subjects after cardiac arrest is a central component of post-cardiac arrest care both for developing realistic prognostic expectations for families, and for better determining which resources are mobilized or withheld for individual patients. The aim of the study was to evaluate the prognostic accuracy of EEG and SEP patterns during the very early period (within the first 6 h) after cardiac arrest. METHODS We retrospectively analysed comatose patients after CA, either inside or outside the hospital, in which prognostic evaluation was made during the first 6 h from CA. Prognostic evaluation comprised clinical evaluation (GCS and pupillary light reflex) and neurophysiological (electroencephalography (EEG) and somatosensory evoked potentials (SEP)) studies. Prognosis was evaluated with regards to likelihood of recovery of consciousness and also likelihood of failure to regain consciousness. RESULTS Forty-one comatose patients after cardiac arrest were included. All patients with continuous and nearly continuous EEG recovered consciousness. Isoelectric EEG was always associated with poor outcome. Burst-suppression, suppression and discontinuous patterns were usually associated with poor outcome although some consciousness recovery was observed. Bilaterally absent SEP responses were always associated with poor outcome. Continuous and nearly continuous EEG patterns were never associated with bilaterally absent SEP. CONCLUSIONS During the very early period following cardiac arrest (first 6 h), EEG and SEP maintain their high predictive value to predict respectively recovery and failure of recovery of consciousness. A very early EEG exam allows identification of patients with very high probability of a good outcome, allowing rapid use of the most appropriate therapeutic procedures.
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Affiliation(s)
- Riccardo Carrai
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy.
| | - Maddalena Spalletti
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Maenia Scarpino
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Francesco Lolli
- Dipartimento di Scienze Biomediche Mario Serio, Università di Firenze, Florence, Italy
| | - Giovanni Lanzo
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Cesarina Cossu
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Manuela Bonizzoli
- Unità di Terapia Intensiva, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Filippo Socci
- Unità di Terapia Intensiva, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Chiara Lazzeri
- Unità di Terapia Intensiva, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Aldo Amantini
- IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Antonello Grippo
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
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23
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Hirsch LJ, Fong MW, Leitinger M, LaRoche SM, Beniczky S, Abend NS, Lee JW, Wusthoff CJ, Hahn CD, Westover MB, Gerard EE, Herman ST, Haider HA, Osman G, Rodriguez-Ruiz A, Maciel CB, Gilmore EJ, Fernandez A, Rosenthal ES, Claassen J, Husain AM, Yoo JY, So EL, Kaplan PW, Nuwer MR, van Putten M, Sutter R, Drislane FW, Trinka E, Gaspard N. American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2021 Version. J Clin Neurophysiol 2021; 38:1-29. [PMID: 33475321 PMCID: PMC8135051 DOI: 10.1097/wnp.0000000000000806] [Citation(s) in RCA: 360] [Impact Index Per Article: 120.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Lawrence J. Hirsch
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - Michael W.K. Fong
- Westmead Comprehensive Epilepsy Unit, Westmead Hospital, University of Sydney, Sydney, Australia
| | - Markus Leitinger
- Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria
| | - Suzette M. LaRoche
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund and Aarhus University Hospital, Aarhus, Denmark
| | - Nicholas S. Abend
- Departments of Neurology and Pediatrics, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A
| | - Jong Woo Lee
- Brigham and Women’s Hospital, Boston, Massachusetts, U.S.A
| | | | - Cecil D. Hahn
- Division of Neurology, The Hospital for Sick Children, and Department of Pediatrics, University of Toronto, Toronto, Canada
| | | | - Elizabeth E. Gerard
- Comprehensive Epilepsy Center, Department of Neurology, Northwestern University, Chicago, Illinois, U.S.A
| | | | - Hiba Arif Haider
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Gamaleldin Osman
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, U.S.A
| | - Andres Rodriguez-Ruiz
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Carolina B. Maciel
- Division of Neurocritical Care, Department of Neurology, University of Florida, Gainesville, Florida, U.S.A
| | - Emily J. Gilmore
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - Andres Fernandez
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, U.S.A
| | - Eric S. Rosenthal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A
| | - Jan Claassen
- Neurocritical Care, Department of Neurology, Columbia University, New York, New York, U.S.A
| | - Aatif M. Husain
- Department of Medicine (Neurology), Duke University Medical Center, and Veterans Affairs Medical Center, Durham, North Carolina, U.S.A
| | - Ji Yeoun Yoo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Elson L. So
- Division of Epilepsy, Mayo Clinic, Rochester, Minnesota, U.S.A
| | - Peter W. Kaplan
- Department of Neurology, Johns Hopkins University School of Medicine, Johns Hopkins Bayview Medical Center, Baltimore, Maryland, U.S.A
| | - Marc R. Nuwer
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, U.S.A
| | - Michel van Putten
- Medisch Spectrum Twente and University of Twente, Enschede, The Netherlands
| | - Raoul Sutter
- Medical Intensive Care Units and Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Frank W. Drislane
- Department of Neurology, Harvard Medical School, and Comprehensive Epilepsy Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts, U.S.A
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria
| | - Nicolas Gaspard
- Department of Neurology, Université Libre de Bruxelles, Hôpital Erasme, Brussels, Belgium
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Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2020; 24:680. [PMID: 33287874 PMCID: PMC7720582 DOI: 10.1186/s13054-020-03407-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 11/24/2020] [Indexed: 01/08/2023]
Abstract
BACKGROUND Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI). METHOD Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2). RESULTS The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance. CONCLUSIONS While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.
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Backman S, Cronberg T, Rosén I, Westhall E. Reduced EEG montage has a high accuracy in the post cardiac arrest setting. Clin Neurophysiol 2020; 131:2216-2223. [DOI: 10.1016/j.clinph.2020.06.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 05/18/2020] [Accepted: 06/08/2020] [Indexed: 10/23/2022]
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Admiraal MM, Horn J, Hofmeijer J, Hoedemaekers CW, van Kaam C, Keijzer HM, van Putten MJ, Schultz MJ, van Rootselaar AF. EEG reactivity testing for prediction of good outcome in patients after cardiac arrest. Neurology 2020; 95:e653-e661. [DOI: 10.1212/wnl.0000000000009991] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 01/17/2020] [Indexed: 11/15/2022] Open
Abstract
ObjectiveTo determine the additional value of EEG reactivity (EEG-R) testing to EEG background pattern for prediction of good outcome in adult patients after cardiac arrest (CA).MethodsIn this post hoc analysis of a prospective cohort study, EEG-R was tested twice a day, using a strict protocol. Good outcome was defined as a Cerebral Performance Category score of 1–2 within 6 months. The additional value of EEG-R per EEG background pattern was evaluated using the diagnostic odds ratio (DOR). Prognostic value (sensitivity and specificity) of EEG-R was investigated in relation to time after CA, sedative medication, different stimuli, and repeated testing.ResultsBetween 12 and 24 hours after CA, data of 108 patients were available. Patients with a continuous (n = 64) or discontinuous (n = 19) normal voltage background pattern with reactivity were 3 and 8 times more likely to have a good outcome than without reactivity (continuous: DOR, 3.4; 95% confidence interval [CI], 0.97–12.0; p = 0.06; discontinuous: DOR, 8.0; 95% CI, 1.0–63.97; p = 0.0499). EEG-R was not observed in other background patterns within 24 hours after CA. In 119 patients with a normal voltage EEG background pattern, continuous or discontinuous, any time after CA, prognostic value was highest in sedated patients (sensitivity 81.3%, specificity 59.5%), irrespective of time after CA. EEG-R induced by handclapping and sternal rubbing, especially when combined, had highest prognostic value. Repeated EEG-R testing increased prognostic value.ConclusionEEG-R has additional value for prediction of good outcome in patients with discontinuous normal voltage EEG background pattern and possibly with continuous normal voltage. The best stimuli were clapping and sternal rubbing.
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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 whether clinical EEG reports obtained from children in the intensive care unit with refractory status epilepticus could provide data for comparative effectiveness research studies. METHODS We conducted a retrospective descriptive study to assess the documentation of key variables within clinical continuous EEG monitoring reports based on the American Clinical Neurophysiology Society's standardized EEG terminology for children with refractory status epilepticus from 10 academic centers. Two pediatric electroencephalographers reviewed the EEG reports. We compared reports generated using free text or templates. RESULTS We reviewed 191 EEG reports. Agreement between the electroencephalographers regarding whether a variable was described in the report ranged from fair to very good. The presence of electrographic seizures (ES) was documented in 46% (87/191) of reports, and these reports documented the time of first ES in 64% (56/87), ES duration in 72% (63/85), and ES frequency in 68% (59/87). Reactivity was documented in 16% (31/191) of reports, and it was more often documented in template than in free-text reports (40% vs. 14%, P = 0.006). Other variables were not differentially reported in template versus free-text reports. CONCLUSIONS Many key EEG features are not documented consistently in clinical continuous EEG monitoring reports, including ES characteristics and reactivity assessment. Standardization may be needed for clinical EEG reports to provide informative data for large multicenter observational studies.
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Ruijter BJ, Tjepkema-Cloostermans MC, Tromp SC, van den Bergh WM, Foudraine NA, Kornips FHM, Drost G, Scholten E, Bosch FH, Beishuizen A, van Putten MJAM, Hofmeijer J. Early electroencephalography for outcome prediction of postanoxic coma: A prospective cohort study. Ann Neurol 2019; 86:203-214. [PMID: 31155751 PMCID: PMC6771891 DOI: 10.1002/ana.25518] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 05/28/2019] [Accepted: 05/31/2019] [Indexed: 02/03/2023]
Abstract
Objective To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest. Methods In a 5‐center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five‐minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients’ actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (<10 μV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category [CPC] = 1–2) or poor (CPC = 3–5). Results We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval [CI] = 0.42–0.51) at 12 hours and 0.30 (95% CI = 0.26–0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99–1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46–0.55) and specificity of 0.91 (95% CI = 0.88–0.93); at 24 hours or later, specificity for the prediction of good outcome was <0.90. Interpretation EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019;86:203–214
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Affiliation(s)
- Barry J Ruijter
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede
| | | | - Selma C Tromp
- Departments of Neurology and Clinical Neurophysiology, St Antonius Hospital, Nieuwegein
| | - Walter M van den Bergh
- Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen
| | | | | | - Gea Drost
- Departments of Neurology and Neurosurgery, University Medical Center Groningen, University of Groningen, Groningen
| | - Erik Scholten
- Department of Intensive Care, St Antonius Hospital, Nieuwegein
| | - Frank H Bosch
- Department of Intensive Care, Rijnstate Hospital, Arnhem
| | | | - Michel J A M van Putten
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede.,Departments of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede.,Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
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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: 54] [Impact Index Per Article: 10.8] [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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Honorato-Cia C, Martinez-Simon A. The anesthesiologist and the EEG: Current uses and future trends in the operating room. TRENDS IN ANAESTHESIA AND CRITICAL CARE 2019. [DOI: 10.1016/j.tacc.2018.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Rittenberger JC, Weissman A, Baldwin M, Flickinger K, Repine MJ, Guyette FX, Doshi AA, Dezfulian C, Callaway CW, Elmer J. Preliminary experience with point-of-care EEG in post-cardiac arrest patients. Resuscitation 2018; 135:98-102. [PMID: 30605711 DOI: 10.1016/j.resuscitation.2018.12.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 08/09/2018] [Accepted: 12/13/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Abnormal electroencephalography (EEG) patterns are common after resuscitation from cardiac arrest and have clinical and prognostic importance. Bedside continuous EEGs are not available in many institutions. We tested the feasibility of using a point-of-care system for EEG acquisition. METHODS We prospectively enrolled a convenience sample of post-cardiac arrest patients between 9/2015-1/2017. Upon hospital arrival, a limited EEG montage was applied. We tested both continuous EEG (cEEG) and this point-of-care EEG (eEEG). A board-certified epileptologist and a board-certified neurointensivist jointly reviewed all EEGs. Cohen's kappa coefficient evaluated agreement between eEEG and cEEG and Fisher's exact test evaluated their associations with survival to hospital discharge and proximate cause of death. RESULTS We studied 95 comatose post-cardiac arrest patients. Mean age was 59 (SD17) years. Most (61%) were male, few (N = 22; 23%) demonstrated shockable rhythms, and PCAC IV illness severity was present in 58 (61%). eEEG was interpretable in 57 (60%) subjects. The most common eEEG interpretations were: continuous (21%), generalized suppression (14%), burst-suppression (12%) and burst-suppression with identical bursts (10%). Seizures were detected in 2 eEEG subjects (2%). No patient with seizure or burst-suppression with identical bursts survived. cEEG demonstrated generalized suppression (31%), burst-suppression with identical bursts (27%), continuous (18%) and seizure (4%). The eEEG and cEEG demonstrated fair agreement (kappa = 0.27). Neither eEEG nor cEEG was associated with survival (p = 0.19; p = 0.11) or proximate cause of death (p = 0.14; p = 0.8) CONCLUSIONS: eEEG is feasible, although artifact often precludes interpretation. eEEG is fairly associated with cEEG and may facilitate post-cardiac arrest care.
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Affiliation(s)
- Jon C Rittenberger
- Department of Emergency Medicine, University of Pittsburgh, United States.
| | - Alexandra Weissman
- Department of Emergency Medicine, University of Pittsburgh, United States
| | - Maria Baldwin
- Department of Neurology, University of Pittsburgh, United States
| | - Kathryn Flickinger
- Department of Emergency Medicine, University of Pittsburgh, United States
| | - Melissa J Repine
- Department of Emergency Medicine, University of Pittsburgh, United States
| | - Francis X Guyette
- Department of Emergency Medicine, University of Pittsburgh, United States
| | - Ankur A Doshi
- Department of Emergency Medicine, University of Pittsburgh, United States
| | - Cameron Dezfulian
- Department of Critical Care Medicine, University of Pittsburgh, United States
| | - Clifton W Callaway
- Department of Emergency Medicine, University of Pittsburgh, United States
| | - Jonathan Elmer
- Department of Emergency Medicine, University of Pittsburgh, United States; Department of Critical Care Medicine, University of Pittsburgh, United States
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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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Schapira AHV. Progress in neurology 2017-2018. Eur J Neurol 2018; 25:1389-1397. [DOI: 10.1111/ene.13846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- A. H. V. Schapira
- Department of Clinical and Movement Neurosciences; UCL Queen Square Institute of Neurology; London UK
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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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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: 68] [Impact Index Per Article: 11.3] [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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Early Electroencephalography for Neurologic Prognostication After Cardiac Arrest. Crit Care Med 2017. [DOI: 10.1097/ccm.0000000000002419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [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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