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Mertens M. The self-fulfilling prophecy in medicine. THEORETICAL MEDICINE AND BIOETHICS 2024:10.1007/s11017-024-09677-z. [PMID: 39120693 DOI: 10.1007/s11017-024-09677-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/30/2024] [Indexed: 08/10/2024]
Abstract
This article first describes the mechanism of any self-fulfilling prophecy through discussion of its four conditions: credibility, employment, employment sensitivity, and realization. Each condition is illustrated with examples specific to the medical context. The descriptive account ends with the definition of self-fulfilling prophecy and an expansion on collective self-fulfilling prophecies. Second, the normative account then discusses the moral relevance of self-fulfilling prophecies in medicine. A self-fulfilling prophecy is typically considered problematic when the prediction itself changes the predicted outcome to match the prediction (transformative self-fulfillment). I argue that also self-fulfilling prophecies that do not change the outcome but change the ways in which the outcome was realized (operative self-fulfillment), have significant ethical and epistemic ramifications. Because it is difficult to distinguish, retrospectively, between a transformative and an operative self-fulfilling prophecy, and thus between a false or true positive, it becomes equally difficult to catch mistakes. Moreover, since the prediction necessarily turns out true, there is never an error signal warning that a mistake might have been made. On the contrary, accuracy is seen as the standard for quality assurance. As such, self-fulfilling prophecies inhibit our ability to learn, inviting repetition and exacerbation of mistakes. With the rise of automated diagnostic and prognostic procedures and the increased use of machine learning and artificial intelligence for the development of predictive algorithms, attention to self-fulfilling feedback loops is especially warranted. This account of self-fulfilling prophecies is practically relevant for medical research and clinical practice. With it, researchers and practitioners can detect and analyze potential self-fulfilling mechanisms in any medical case and take responsibility for their ethical and epistemic implications.
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Affiliation(s)
- Mayli Mertens
- Department of Philosophy, Center for Ethics, University of Antwerp, Antwerp, Belgium.
- Department of Public Health, Center for Medical Science and Technology Studies, University of Copenhagen, Copenhagen, Denmark.
- Atlas Bioethics Center, Andalusia, Spain.
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2
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Glimmerveen AB, Verhulst MMLH, de Kruijf NLM, van Gils P, Delnoij T, Bonnes J, van Heugten CM, Van Putten MJAM, Hofmeijer J. Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest. Resuscitation 2024; 201:110253. [PMID: 38797387 DOI: 10.1016/j.resuscitation.2024.110253] [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: 03/18/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Approximately half of cardiac arrest survivors have persistent cognitive impairment. Guidelines recommend early screening to identify patients at risk for cognitive impairment, but there is no consensus on the best screening method. We aimed to identify quantitative EEG measures relating with short- and long-term cognitive function after cardiac arrest for potential to cognitive outcome prediction. METHODS We analyzed data from a prospective longitudinal multicenter cohort study designed to develop a prediction model for cognitive outcome after cardiac arrest. For the current analysis, we used twenty-minute EEG registrations from 80 patients around one week after cardiac arrest. We calculated power spectral density, normalized alpha-to-theta ratio (nATR), peak frequency, and center of gravity (CoG) of this peak frequency. We related these with global cognitive functioning (scores on the Montreal Cognitive Assessment (MoCA)) at one week, three and twelve months follow-up with multivariate mixed effect models, and with performance on standard neuropsychological examination at twelve months using Pearson correlation coefficients. RESULTS Each individual EEG parameter related to MoCA at one week (βnATR = 7.36; P < 0.01; βpeak frequency = 1.73, P < 0.01; βCoG = -9.88, P < 0.01). The nATR also related with the MoCA at three months ((βnATR = 2.49; P 0.01). No EEG metrics significantly related to the MoCA score at twelve months. nATR and peak frequency related with memory performance at twelve months. Results were consistent in sensitivity analyses. CONCLUSION Early resting-state EEG parameters relate with short-term global cognitive functioning and with memory function at one year after cardiac arrest. Additional predictive values in multimodal prediction models need further study.
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Affiliation(s)
- A B Glimmerveen
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
| | - M M L H Verhulst
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - N L M de Kruijf
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - P van Gils
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Maastrich University, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands; Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands
| | - T Delnoij
- Department of Intensive Care Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - J Bonnes
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - C M van Heugten
- Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands; Maastricht University, Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht, The Netherlands
| | - M J A M Van Putten
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - J Hofmeijer
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
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Turella S, Dankiewicz J, Ben-Hamouda N, Bernhard Nilsen K, Düring J, Endisch C, Engstrøm M, Flügel D, Gaspard N, Grejs AM, Haenggi M, Haffey S, Imbach L, Johnsen B, Kemlink D, Leithner C, Legriel S, Lindehammar H, Mazzon G, Nielsen N, Peyre A, Ribalta Stanford B, Roman-Pognuz E, Rossetti AO, Schrag C, Valeriánová A, Wendel-Garcia P, Zubler F, Cronberg T, Westhall E. EEG for good outcome prediction after cardiac arrest: A multicentre cohort study. Resuscitation 2024:110319. [PMID: 39029579 DOI: 10.1016/j.resuscitation.2024.110319] [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: 06/12/2024] [Revised: 07/06/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024]
Abstract
AIM Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA). METHODS Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 h after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3. RESULTS 873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 h (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p < 0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account. CONCLUSION Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance.
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Affiliation(s)
- S Turella
- Department of Clinical Sciences Lund, Clinical Neurophysiology, Lund University, Lund, Sweden
| | - J Dankiewicz
- Department of Clinical Sciences Lund, Cardiology, Lund University, Lund, Sweden
| | - N Ben-Hamouda
- Department of Adult Intensive Care Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - K Bernhard Nilsen
- Section for Clinical Neurophysiology, Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - J Düring
- Department of Clinical Sciences, Anaesthesia and Intensive Care, Lund University, Malmö, Sweden
| | - C Endisch
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt - Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - M Engstrøm
- Department of Clinical Neurophysiology, St. Olavs University Hospital and Department of Neuromedicine and Movement Science (INB) NTNU, Trondheim, Norway
| | - D Flügel
- Department of Neurology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - N Gaspard
- Department of Neurology, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium; Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - A M Grejs
- Department of Intensive Care Medicine, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - M Haenggi
- Department of Intensive Care Medicine, Bern University Hospital, University of Bern, Bern, Switzerland
| | - S Haffey
- Department of Clinical Neurophysiology, Royal Victoria Hospital, Belfast, Ireland
| | - L Imbach
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - B Johnsen
- Department of Clinical Medicine, Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - D Kemlink
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - C Leithner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt - Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - S Legriel
- Intensive Care Unit, Versailles Hospital, France
| | - H Lindehammar
- Clinical Neurophysiology, Department of Clinical and Experimental Medicine, Linköping University, Sweden
| | - G Mazzon
- Department of Neurology, University Hospital of Trieste, Trieste, Italy
| | - N Nielsen
- Department of Clinical Sciences Lund, Anesthesiology and Intensive Care Medicine, Helsingborg Hospital, Helsingborg, Sweden
| | - A Peyre
- Department of Neurology, Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - B Ribalta Stanford
- Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
| | - E Roman-Pognuz
- Intensive Care Unit, University Hospital of Trieste, Trieste, Italy
| | - A O Rossetti
- Department of Neurology, University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - C Schrag
- Intensive Care Department, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - A Valeriánová
- General University Hospital in Prague, Prague, Czech Republic
| | - P Wendel-Garcia
- Institute of Intensive Care Medicine, University Hospital Zürich, Zürich, Switzerland
| | - F Zubler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - T Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - E Westhall
- Department of Clinical Sciences Lund, Clinical Neurophysiology, Lund University, Lund, Sweden.
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Bitar R, Khan UM, Rosenthal ES. Utility and rationale for continuous EEG monitoring: a primer for the general intensivist. Crit Care 2024; 28:244. [PMID: 39014421 PMCID: PMC11251356 DOI: 10.1186/s13054-024-04986-0] [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: 02/06/2024] [Accepted: 06/09/2024] [Indexed: 07/18/2024] Open
Abstract
This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.
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Affiliation(s)
- Ribal Bitar
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA
| | - Usaamah M Khan
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA
| | - Eric S Rosenthal
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA.
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Hsiao CL, Chen PY, Chen IA, Lin SK. The Role of Routine Electroencephalography in the Diagnosis of Seizures in Medical Intensive Care Units. Diagnostics (Basel) 2024; 14:1111. [PMID: 38893637 PMCID: PMC11171977 DOI: 10.3390/diagnostics14111111] [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: 04/18/2024] [Revised: 05/15/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
Seizures should be diagnosed and treated to ensure optimal health outcomes in critically ill patients admitted in the medical intensive care unit (MICU). Continuous electroencephalography is still infrequently used in the MICU. We investigated the effectiveness of routine EEG (rEEG) in detecting seizures in the MICU. A total of 560 patients admitted to the MICU between October 2018 and March 2023 and who underwent rEEG were reviewed. Seizure-related rEEG constituted 47% of all rEEG studies. Totally, 39% of the patients experienced clinical seizures during hospitalization; among them, 48% experienced the seizure, and 13% experienced their first seizure after undergoing an rEEG study. Seventy-seven percent of the patients had unfavorable short-term outcomes. Patients with cardiovascular diseases were the most likely to have the suppression/burst suppression (SBS) EEG pattern and the highest mortality rate. The rhythmic and periodic patterns (RPPs) and electrographic seizure (ESz) EEG pattern were associated with seizures within 24 h after rEEG, which was also related to unfavorable outcomes. Significant predictors of death were age > 59 years, the male gender, the presence of cardiovascular disease, a Glasgow Coma Scale score ≤ 5, and the SBS EEG pattern, with a predictive performance of 0.737 for death. rEEG can help identify patients at higher risk of seizures. We recommend repeated rEEG in patients with ESz or RPP EEG patterns to enable a more effective monitoring of seizure activities.
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Affiliation(s)
- Cheng-Lun Hsiao
- Stroke Center and Department of Neurology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan; (C.-L.H.); (P.-Y.C.)
- School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - Pei-Ya Chen
- Stroke Center and Department of Neurology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan; (C.-L.H.); (P.-Y.C.)
- School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - I-An Chen
- Taiwan Center for Drug Evaluation, Taipei 11557, Taiwan;
| | - Shinn-Kuang Lin
- Stroke Center and Department of Neurology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan; (C.-L.H.); (P.-Y.C.)
- School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
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Bencsik C, Josephson C, Soo A, Ainsworth C, Savard M, van Diepen S, Kramer A, Kromm J. The Evolving Role of Electroencephalography in Postarrest Care. Can J Neurol Sci 2024:1-13. [PMID: 38572611 DOI: 10.1017/cjn.2024.55] [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: 04/05/2024]
Abstract
Electroencephalography is an accessible, portable, noninvasive and safe means of evaluating a patient's brain activity. It can aid in diagnosis and management decisions for post-cardiac arrest patients with seizures, myoclonus and other non-epileptic movements. It also plays an important role in a multimodal approach to neuroprognostication predicting both poor and favorable outcomes. Individuals ordering, performing and interpreting these tests, regardless of the indication, should understand the supporting evidence, logistical considerations, limitations and impact the results may have on postarrest patients and their families as outlined herein.
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Affiliation(s)
- Caralyn Bencsik
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
| | - Colin Josephson
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Andrea Soo
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
| | - Craig Ainsworth
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Martin Savard
- Département de Médecine, Université Laval, Quebec City, QC, Canada
| | - Sean van Diepen
- Department of Critical Care Medicine, University of Alberta, Edmonton, AB, Canada
- Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Andreas Kramer
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Julie Kromm
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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Cerina M, Levers M, Keller JM, Frega M. Neuroprotective role of lactate in a human in vitro model of the ischemic penumbra. Sci Rep 2024; 14:7973. [PMID: 38575687 PMCID: PMC10994928 DOI: 10.1038/s41598-024-58669-5] [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: 11/21/2023] [Accepted: 04/02/2024] [Indexed: 04/06/2024] Open
Abstract
In patients suffering from cerebral ischemic stroke, there is an urgent need for treatments to protect stressed yet viable brain cells. Recently, treatment strategies that induce neuronal activity have been shown to be neuroprotective. Here, we hypothesized that neuronal activation might maintain or trigger the astrocyte-to-neuron lactate shuttle (ANLS), whereby lactate is released from astrocytes to support the energy requirements of ATP-starved hypoxic neurons, and this leads to the observed neuroprotection. We tested this by using a human cell based in vitro model of the ischemic penumbra and investigating whether lactate might be neuroprotective in this setting. We found that lactate transporters are involved in the neuroprotective effect mediated by neuronal activation. Furthermore, we showed that lactate exogenously administered before hypoxia correlated with neuroprotection in our cellular model. In addition, stimulation of astrocyte with consequent endogenous production of lactate resulted in neuroprotection. To conclude, here we presented evidence that lactate transport into neurons contributes to neuroprotection during hypoxia providing a potential basis for therapeutic approaches in ischemic stroke.
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Affiliation(s)
- Marta Cerina
- Department of Clinical Neurophysiology, University of Twente, 7522 NB, Enschede, The Netherlands
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, piazza della Scienza 2, 20126, Milan, Italy
| | - Marloes Levers
- Department of Clinical Neurophysiology, University of Twente, 7522 NB, Enschede, The Netherlands
| | - Jason M Keller
- Department of Human Genetics, Radboudumc, Donders Institute for Brain, Cognition, and Behaviour, 6500 HB, Nijmegen, The Netherlands
| | - Monica Frega
- Department of Clinical Neurophysiology, University of Twente, 7522 NB, Enschede, The Netherlands.
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Arctaedius I, Levin H, Larsson M, Friberg H, Cronberg T, Nielsen N, Moseby-Knappe M, Lybeck A. 2021 European Resuscitation Council/European Society of Intensive Care Medicine Algorithm for Prognostication of Poor Neurological Outcome After Cardiac Arrest-Can Entry Criteria Be Broadened? Crit Care Med 2024; 52:531-541. [PMID: 38059722 DOI: 10.1097/ccm.0000000000006113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
OBJECTIVES To explore broadened entry criteria of the 2021 European Resuscitation Council/European Society of Intensive Care Medicine (ERC/ESICM) algorithm for neuroprognostication including patients with ongoing sedation and Glasgow Coma Scale-Motor score (GCS-M) scores 4-5. DESIGN Retrospective multicenter observational study. SETTING Four ICUs, Skane, Sweden. PATIENTS Postcardiac arrest patients managed at targeted temperature 36°C, 2014-2018. Neurologic outcome was assessed after 2-6 months according to the Cerebral Performance Category scale. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS In 794 included patients, median age was 69.5 years (interquartile range, 60.6-77.0 yr), 241 (30.4%) were female, 550 (69.3%) had an out-of-hospital cardiac arrest, and 314 (41.3%) had a shockable rhythm. Four hundred ninety-five patients were dead at follow-up, 330 of 495 died after a decision on withdrawal of life-sustaining therapies. At 72 hours after cardiac arrest 218 patients remained unconscious. The entry criteria of the original algorithm (GCS-M 1-3) was fulfilled by 163 patients and 115 patients with poor outcome were identified, with false positive rate (FPR) of 0% (95% CI, 0-79.4%) and sensitivity of 71.0% (95% CI, 63.6-77.4%). Inclusion of patients with ongoing sedation identified another 13 patients with poor outcome, generating FPR of 0% (95% CI, 0-65.8%) and sensitivity of 69.6% (95% CI, 62.6-75.8%). Inclusion of all unconscious patients (GCS-M 1-5), regardless of sedation, identified one additional patient, generating FPR of 0% (95% CI, 0-22.8) and sensitivity of 62.9% (95% CI, 56.1-69.2). The few patients with true negative prediction (patients with good outcome not fulfilling guideline criteria of a poor outcome) generated wide 95% CI for FPR. CONCLUSION The 2021 ERC/ESICM algorithm for neuroprognostication predicted poor neurologic outcome with a FPR of 0%. Broadening inclusion criteria to include all unconscious patients regardless of ongoing sedation identified an additional small number of patients with poor outcome but did not affect the FPR. Results are limited by high rate of withdrawal of life-sustaining therapies and few patients with true negative prediction.
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Affiliation(s)
- Isabelle Arctaedius
- Anesthesia & Intensive Care, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden
| | - Helena Levin
- Anaesthesia & Intensive Care, Department of Clinical Sciences Lund, Lund University and Department of Research & Education, Skane University Hospital, Lund, Sweden
| | - Melker Larsson
- Anesthesia & Intensive Care, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden
| | - Hans Friberg
- Anesthesia & Intensive Care, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Malmö, Sweden
| | - Tobias Cronberg
- Neurology, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden
| | - Niklas Nielsen
- Anesthesia & Intensive Care, Department of Clinical Sciences Lund, Lund University, Helsingborg Hospital, Helsingborg, Sweden
| | - Marion Moseby-Knappe
- Neurology and Rehabilitation Medicine, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden
| | - Anna Lybeck
- Anesthesia & Intensive Care, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden
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9
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Peluso L, Stropeni S, Macchini E, Peratoner C, Ferlini L, Legros B, Minini A, Bogossian EG, Garone A, Creteur J, Taccone FS, Gaspard N. Delayed Deterioration of Electroencephalogram in Patients with Cardiac Arrest: A Cohort Study. Neurocrit Care 2024; 40:633-644. [PMID: 37498454 DOI: 10.1007/s12028-023-01791-5] [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: 12/27/2022] [Accepted: 06/23/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND The aim of this study was to assess the prevalence of delayed deterioration of electroencephalogram (EEG) in patients with cardiac arrest (CA) without early highly malignant patterns and to determine their associations with clinical findings. METHODS This was a retrospective study of adult patients with CA admitted to the intensive care unit (ICU) of a university hospital. We included all patients with CA who had a normal voltage EEG, no more than 10% discontinuity, and absence of sporadic epileptic discharges, periodic discharges, or electrographic seizures. Delayed deterioration was classified as the following: (1) epileptic deterioration, defined as the appearance, at least 24 h after CA, of sporadic epileptic discharges, periodic discharges, and status epilepticus; or (2) background deterioration, defined as increasing discontinuity or progressive attenuation of the background at least 24 h after CA. The end points were the incidence of EEG deteriorations and their association with clinical features and ICU mortality. RESULTS We enrolled 188 patients in the analysis. The ICU mortality was 46%. Overall, 30 (16%) patients presented with epileptic deterioration and 9 (5%) patients presented with background deterioration; of those, two patients presented both deteriorations. Patients with epileptic deterioration more frequently had an out-of-hospital CA, and higher time to return of spontaneous circulation and less frequently had bystander resuscitation than others. Patients with background deterioration showed a predominantly noncardiac cause, more frequently developed shock, and had multiple organ failure compared with others. Patients with epileptic deterioration presented with a higher ICU mortality (77% vs. 41%; p < 0.01) than others, whereas all patients with background deterioration died in the ICU. CONCLUSIONS Delayed EEG deterioration was associated with high mortality rate. Epileptic deterioration was associated with worse characteristics of CA, whereas background deterioration was associated with shock and multiple organ failure.
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Affiliation(s)
- Lorenzo Peluso
- Departement of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20072, Pieve Emanuele, Italy.
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium.
- Departement of Anesthesiology and Intensive Care, Humanitas Gavazzeni, Via Mauro Gavazzeni, 21, 24125, Bergamo, Italy.
| | - Serena Stropeni
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Elisabetta Macchini
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Caterina Peratoner
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Lorenzo Ferlini
- Department of Neurology, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Andrea Minini
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Elisa Gouvea Bogossian
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Andrea Garone
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Jacques Creteur
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Fabio Silvio Taccone
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
- Department of Neurology, Yale University Medical School, 15, York Street, New Haven, CT, 06510, USA
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10
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Nikolovski SS, Lazic AD, Fiser ZZ, Obradovic IA, Tijanic JZ, Raffay V. Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors. Cureus 2024; 16:e54827. [PMID: 38529434 PMCID: PMC10962929 DOI: 10.7759/cureus.54827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 03/27/2024] Open
Abstract
As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.
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Affiliation(s)
- Srdjan S Nikolovski
- Pathology and Laboratory Medicine, Cardiovascular Research Institute, Loyola University Chicago Health Science Campus, Maywood, USA
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
| | - Aleksandra D Lazic
- Emergency Center, Clinical Center of Vojvodina, Novi Sad, SRB
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
| | - Zoran Z Fiser
- Emergency Medicine, Department of Emergency Medicine, Novi Sad, SRB
| | - Ivana A Obradovic
- Anesthesiology, Resuscitation, and Intensive Care, Sveti Vračevi Hospital, Bijeljina, BIH
| | - Jelena Z Tijanic
- Emergency Medicine, Municipal Institute of Emergency Medicine, Kragujevac, SRB
| | - Violetta Raffay
- School of Medicine, European University Cyprus, Nicosia, CYP
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
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11
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Gonzalez D, Dahiya G, Mutirangura P, Ergando T, Mello G, Singh R, Bentho O, Elliott AM. Post Cardiac Arrest Care in the Cardiac Intensive Care Unit. Curr Cardiol Rep 2024; 26:35-49. [PMID: 38214836 DOI: 10.1007/s11886-023-02015-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 01/13/2024]
Abstract
PURPOSE OF REVIEW Cardiac arrests constitute a leading cause of mortality in the adult population and cardiologists are often tasked with the management of patients following cardiac arrest either as a consultant or primary provider in the cardiac intensive care unit. Familiarity with evidence-based practice for post-cardiac arrest care is a requisite for optimizing outcomes in this highly morbid group. This review will highlight important concepts necessary to managing these patients. RECENT FINDINGS Emerging evidence has further elucidated optimal care of post-arrest patients including timing for routine coronary angiography, utility of therapeutic hypothermia, permissive hypercapnia, and empiric aspiration pneumonia treatment. The complicated state of multi-organ failure following cardiac arrest needs to be carefully optimized by the clinician to prevent further neurologic injury and promote systemic recovery. Future studies should be aimed at understanding if these findings extend to specific patient populations, especially those at the highest risk for poor outcomes.
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Affiliation(s)
- Daniel Gonzalez
- Department of Medicine, Division of Cardiology, University of Minnesota, 420 Delaware St SE, MMC 508, Minneapolis, MN, 55455, USA
| | - Garima Dahiya
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Duke University, Durham, USA
| | | | | | - Gregory Mello
- University of Minnesota Medical School, Minneapolis, USA
| | - Rahul Singh
- Department of Medicine, Division of Cardiology, University of Minnesota, 420 Delaware St SE, MMC 508, Minneapolis, MN, 55455, USA
| | - Oladi Bentho
- Department of Neurology, University of Minnesota, Minneapolis, USA
| | - Andrea M Elliott
- Department of Medicine, Division of Cardiology, University of Minnesota, 420 Delaware St SE, MMC 508, Minneapolis, MN, 55455, USA.
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12
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Slovis JC, Bach A, Beaulieu F, Zuckerberg G, Topjian A, Kirschen MP. Neuromonitoring after Pediatric Cardiac Arrest: Cerebral Physiology and Injury Stratification. Neurocrit Care 2024; 40:99-115. [PMID: 37002474 PMCID: PMC10544744 DOI: 10.1007/s12028-023-01685-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 01/30/2023] [Indexed: 04/03/2023]
Abstract
BACKGROUND Significant long-term neurologic disability occurs in survivors of pediatric cardiac arrest, primarily due to hypoxic-ischemic brain injury. Postresuscitation care focuses on preventing secondary injury and the pathophysiologic cascade that leads to neuronal cell death. These injury processes include reperfusion injury, perturbations in cerebral blood flow, disturbed oxygen metabolism, impaired autoregulation, cerebral edema, and hyperthermia. Postresuscitation care also focuses on early injury stratification to allow clinicians to identify patients who could benefit from neuroprotective interventions in clinical trials and enable targeted therapeutics. METHODS In this review, we provide an overview of postcardiac arrest pathophysiology, explore the role of neuromonitoring in understanding postcardiac arrest cerebral physiology, and summarize the evidence supporting the use of neuromonitoring devices to guide pediatric postcardiac arrest care. We provide an in-depth review of the neuromonitoring modalities that measure cerebral perfusion, oxygenation, and function, as well as neuroimaging, serum biomarkers, and the implications of targeted temperature management. RESULTS For each modality, we provide an in-depth review of its impact on treatment, its ability to stratify hypoxic-ischemic brain injury severity, and its role in neuroprognostication. CONCLUSION Potential therapeutic targets and future directions are discussed, with the hope that multimodality monitoring can shift postarrest care from a one-size-fits-all model to an individualized model that uses cerebrovascular physiology to reduce secondary brain injury, increase accuracy of neuroprognostication, and improve outcomes.
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Affiliation(s)
- Julia C Slovis
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, 6 Wood - 6105, Philadelphia, PA, 19104, USA.
| | - Ashley Bach
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, 6 Wood - 6105, Philadelphia, PA, 19104, USA
| | - Forrest Beaulieu
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, 6 Wood - 6105, Philadelphia, PA, 19104, USA
| | - Gabe Zuckerberg
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, 6 Wood - 6105, Philadelphia, PA, 19104, USA
| | - Alexis Topjian
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, 6 Wood - 6105, Philadelphia, PA, 19104, USA
| | - Matthew P Kirschen
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, 6 Wood - 6105, Philadelphia, PA, 19104, USA
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13
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Rossetti AO, Claassen J, Gaspard N. Status epilepticus in the ICU. Intensive Care Med 2024; 50:1-16. [PMID: 38117319 DOI: 10.1007/s00134-023-07263-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/26/2023] [Indexed: 12/21/2023]
Abstract
Status epilepticus (SE) is a common medical emergency associated with significant morbidity and mortality. Management that follows published guidelines is best suited to improve outcomes, with the most severe cases frequently being managed in the intensive care unit (ICU). Diagnosis of convulsive SE can be made without electroencephalography (EEG), but EEG is required to reliably diagnose nonconvulsive SE. Rapidly narrowing down underlying causes for SE is crucial, as this may guide additional management steps. Causes may range from underlying epilepsy to acute brain injuries such as trauma, cardiac arrest, stroke, and infections. Initial management consists of rapid administration of benzodiazepines and one of the following non-sedating intravenous antiseizure medications (ASM): (fos-)phenytoin, levetiracetam, or valproate; other ASM are increasingly used, such as lacosamide or brivaracetam. SE that continues despite these medications is called refractory, and most commonly treated with continuous infusions of midazolam or propofol. Alternatives include further non-sedating ASM and non-pharmacologic approaches. SE that reemerges after weaning or continues despite management with propofol or midazolam is labeled super-refractory SE. At this step, management may include non-sedating or sedating compounds including ketamine and barbiturates. Continuous video EEG is necessary for the management of refractory and super-refractory SE, as these are almost always nonconvulsive. If possible, management of the underlying cause of seizures is crucial particularly for patients with autoimmune encephalitis. Short-term mortality ranges from 10 to 15% after SE and is primarily related to increasing age, underlying etiology, and medical comorbidities. Refractoriness of treatment is clearly related to outcome with mortality rising from 10% in responsive cases, to 25% in refractory, and nearly 40% in super-refractory SE.
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Affiliation(s)
- Andrea O Rossetti
- Department of Neurology, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Jan Claassen
- Department of Neurology, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY, USA
| | - Nicolas Gaspard
- Service de Neurologie, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik 808, 1070, Brussels, Belgium.
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA.
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14
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Turella S, Dankiewicz J, Friberg H, Jakobsen JC, Leithner C, Levin H, Lilja G, Moseby-Knappe M, Nielsen N, Rossetti AO, Sandroni C, Zubler F, Cronberg T, Westhall E. The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations. Intensive Care Med 2024; 50:90-102. [PMID: 38172300 PMCID: PMC10811097 DOI: 10.1007/s00134-023-07280-9] [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: 06/22/2023] [Accepted: 11/14/2023] [Indexed: 01/05/2024]
Abstract
PURPOSE The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity. METHODS This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA. RESULTS 845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008). CONCLUSION The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.
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Affiliation(s)
- Sara Turella
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli", IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Josef Dankiewicz
- Department of Clinical Sciences Lund, Cardiology, Lund University, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Lund, Sweden
| | - Janus Christian Jakobsen
- Copenhagen Trial Unit, Capital Region, Copenhagen, Denmark
- Department of Regional Health Research, The Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Christoph Leithner
- Department of Neurology and Experimental Neurology, Charité, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt- Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Helena Levin
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Gisela Lilja
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
- Skane University Hospital, Lund, Sweden
| | - Marion Moseby-Knappe
- Department of Clinical Sciences Lund, Neurology and Rehabilitation, Lund University, Lund, Sweden
| | - Niklas Nielsen
- Department of Clinical Sciences Lund, Anesthesiology and Intensive Care Medicine, Helsingborg Hospital, Helsingborg, Sweden
| | - Andrea O Rossetti
- Department of Neurology, University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli", IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Frédéric Zubler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Erik Westhall
- Department of Clinical Sciences, Clinical Neurophysiology, Lund University, S-221 85, Lund, Sweden.
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15
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research Consortium Electroencephalography Database. Crit Care Med 2023; 51:1802-1811. [PMID: 37855659 PMCID: PMC10841086 DOI: 10.1097/ccm.0000000000006074] [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] [Indexed: 10/20/2023]
Abstract
OBJECTIVES To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. DESIGN Multicenter cohort, partly prospective and partly retrospective. SETTING Seven academic or teaching hospitals from the United States and Europe. PATIENTS Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included. INTERVENTIONS Not applicable. MEASUREMENTS AND MAIN RESULTS Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. CONCLUSIONS The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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16
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Sohn G, Kim SE. Measurement of thalamus and cortical damages in hypoxic ischemic encephalopathy. IBRO Neurosci Rep 2023; 15:179-185. [PMID: 37731916 PMCID: PMC10507579 DOI: 10.1016/j.ibneur.2023.09.002] [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: 03/12/2023] [Revised: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023] Open
Abstract
Background The thalamic gray-white matter ratios (GWRs) on CT and quantitative suppression ratios (SRs) of background activities on EEG may reflect damages in the thalamus and cerebral hemispheres in patients with hypoxic-ischemic encephalopathy (HIE). Methods The inclusion criteria were (1) cardiac arrest patients over the age of 20 years from March 2010 to March 2020, and (2) patients who had both EEG and brain CT within 7 days after cardiac arrest. The thalamic GWRs were semi-quantitatively measured by using the region of interest (ROI). SRs of background were analyzed with the installed software (Persyst® v13) in EEG machine. Results 175 patients were included among 686 patients with HIE and the thalamic GWRs of 168 patients were successfully measured. 155 patients (89 %) showed poor outcomes. The poor outcome group revealed not only higher SRs, but also lower thalamic GWRs. The thalamic GWRs showed a negative correlation to the SRs (ρ (rho) = -0.36, p < 0.0001 for right side, ρ (rho) = -0.31, p < 0.0001 for left side). The good outcome group showed neither beyond the cut-off values of thalamic GWRs nor SRs [40 % (59/148) VS 0 % (0/20) in right side, p = 0.0005 %, and 28 % (42/148) VS 0 % (0/20) in left side, p = 0.0061]. Conclusion The thalamic GWRs and SRs may reflect the damage in the thalamus and cerebral hemispheres in patients with HIE. Insults in the thalamocortical circuit (TCC) or the thalamus might be responsible for the poor outcome.
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Affiliation(s)
| | - Sung Eun Kim
- Correspondence to: Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan 48108, Republic of Korea.
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17
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Orav K, Bosque Varela P, Prüwasser T, Machegger L, Leitinger M, Trinka E, Kuchukhidze G. Post-hypoxic status epilepticus - A distinct subtype of status epilepticus with poor prognosis. Epileptic Disord 2023; 25:823-832. [PMID: 37776308 PMCID: PMC10947449 DOI: 10.1002/epd2.20164] [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: 06/20/2023] [Revised: 08/31/2023] [Accepted: 09/23/2023] [Indexed: 10/02/2023]
Abstract
OBJECTIVE To evaluate the clinical outcome of patients with possible and definitive post-hypoxic status epilepticus (SE) and to describe the SE types in patients with definitive post-hypoxic SE. METHODS Patients with definitive or possible SE resulting from hypoxic brain injury after cardiac arrest (CA) were prospectively recruited. Intermittent EEG was used for the diagnosis of SE according to clinical practice. Two raters blinded to outcome analyzed EEGs retrospectively for possible and definitive SE patterns and background features (frequency, continuity, reactivity, and voltage). Definitive SE was classified according to semiology (ILAE). Mortality and Cerebral Performance Categories (CPC) score were evaluated 1 month after CA. RESULTS We included 64 patients of whom 92% died. Among the survivors, only one patient had a good neurological outcome (CPC 1). No patient survived with a burst suppression pattern, low voltage, or electro-cerebral silence in any EEG. Possible or definitive SE was diagnosed in a median of 47 h (IQR 39-72 h) after CA. EEG criteria for definitive electrographic SE were fulfilled in 39% of patients; in 38% - for electroclinical SE and in 23% - for ictal-interictal continuum (IIC). The outcome did not differ significantly between the three groups. The only patient with good functional outcome belonged to the IIC group. Comatose non-convulsive SE (NCSE) without subtle motor phenomenon occurred in 20% of patients with definitive electrographic SE and outcome was similar to other types of SE. SIGNIFICANCE Possible or definitive SE due to hypoxic brain injury is associated with poor prognosis. The outcome of patients with electrographic SE, electroclinical SE, and IIC did not differ significantly. Outcome was similar in patients with definitive electrographic SE with and without prominent motor features.
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Affiliation(s)
- Kateriine Orav
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Department of NeurologyNorth Estonia Medical CentreTallinnEstonia
| | - Pilar Bosque Varela
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
| | - Tanja Prüwasser
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Department of MathematicsParis‐Lodron UniversitySalzburgAustria
| | - Lukas Machegger
- Department of Neuroradiology, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
| | - Markus Leitinger
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
| | - Eugen Trinka
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Neuroscience InstituteChristian Doppler University HospitalSalzburgAustria
- Karl Landsteiner Institute for Neurorehabilitation and Space NeurologySalzburgAustria
| | - Giorgi Kuchukhidze
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Neuroscience InstituteChristian Doppler University HospitalSalzburgAustria
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18
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Ding X, Shen Z. Electroencephalography Prediction of Neurological Outcomes After Hypoxic-Ischemic Brain Injury: A Systematic Review and Meta-Analysis. Clin EEG Neurosci 2023:15500594231211105. [PMID: 37941351 DOI: 10.1177/15500594231211105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Background. Predicting neurological outcomes after hypoxic-ischemic brain injury (HIBI) is difficult. Objective. Electroencephalography (EEG) can identify acute and subacute brain abnormalities after hypoxic brain injury and predict HIBI recovery. We examined EEG's ability to predict neurologic outcomes following HIBI. Method. A PRISMA-compliant search was conducted in the Medline, Embase, Cochrane, and Central databases until January 2023. EEG-predicted neurological outcomes in HIBI patients were selected from relevant perspective and retrospective cohort studies. RevMan did meta-analysis, while QDAS2 assessed research quality. Results. Eleven studies with 3761 HIBI patients met the inclusion and exclusion criteria. We aggregated study-level estimates of sensitivity and specificity for EEG patterns determined a priori using random effect bivariate and univariate meta-analysis when appropriate. Positive indicators and anatomical area heterogeneity impacted prognosis accuracy. Funnel plots analyzed publication bias. Significant heterogeneity of greater than 80% was among the included studies with P < 0.001. The area under the curve was 0.94, the threshold effect was P < 0.001, and the sensitivity and specificity, with 95% confidence intervals, were 0.91 (0.84-0.99) and 0.86 (0.75-0.97). EEG detects status epilepticus and burst suppression with good sensitivity, specificity, and little probability of false-negative impairment result attribution. Study quality varied by domain, but patient flow and timing were well conducted in all. Conclusion. EEG can predict the outcome of HIBI with good prognostic accuracy, but more standardized cross-study protocols and descriptions of EEG patterns are needed to better evaluate its prognostic use for patients with HIBI.
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Affiliation(s)
- Xina Ding
- Department of Brain Function, Hospital of Nantong University, No. 20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
| | - Zhixiao Shen
- Department of Brain Function, Hospital of Nantong University, No. 20 Xisi Road, Chongchuan District, Nantong City, Jiangsu Province, 226001, China
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Candia-Rivera D, Machado C. Reduced Heartbeat-Evoked Responses in a Near-Death Case Report. J Clin Neurol 2023; 19:581-588. [PMID: 37455508 PMCID: PMC10622722 DOI: 10.3988/jcn.2022.0415] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/30/2022] [Accepted: 01/25/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND AND PURPOSE Whether brain-heart communication continues under ventricular fibrillation (VF) remains to be determined. There is weak evidence of physiological changes in cortical activity under VF. Moreover, brain-heart communication has not previously been studied in this condition. We aimed to measure parallel changes in heart-rate variability (HRV), cortical activity, and brain-heart interactions in a patient who experienced VF. METHODS The EEG and EKG signals for the case report were acquired for approximately 20 h. We selected different 1-min-long segments based on the changes in the EKG waveform. We present the changes in heartbeat-evoked responses (HERs), HRV, and EEG power for each selected segment. RESULTS The overall physiological activity appeared to deteriorate as VF proceeded. Brain-heart interactions measured using HERs disappeared, with a few aberrant amplitudes appearing occasionally. The parallel changes in EEG and HRV were not pronounced, suggesting the absence of bidirectional neural control. CONCLUSIONS Our measurements of brain-heart interactions suggested that the evolving VF impairs communication between the central and autonomic nervous systems. These results may support that reduced brain-heart interactions reflect loss of consciousness and deterioration in the overall health state.
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Affiliation(s)
| | - Calixto Machado
- Department of Clinical Neurophysiology, Institute of Neurology and Neurosurgery, Havana, Cuba
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Amorim E, Zheng WL, Jing J, Ghassemi MM, Lee JW, Wu O, Herman ST, Pang T, Sivaraju A, Gaspard N, Hirsch L, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest. Neurology 2023; 101:e940-e952. [PMID: 37414565 PMCID: PMC10501085 DOI: 10.1212/wnl.0000000000207537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 05/04/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest. METHODS Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months. RESULTS One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery. DISCUSSION Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.
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Affiliation(s)
- Edilberto Amorim
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands.
| | - Wei-Long Zheng
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jin Jing
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Mohammad M Ghassemi
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jong Woo Lee
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Ona Wu
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Susan T Herman
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Trudy Pang
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Adithya Sivaraju
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Nicolas Gaspard
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Lawrence Hirsch
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Barry J Ruijter
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Marleen C Tjepkema-Cloostermans
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jeannette Hofmeijer
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Michel J A M van Putten
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - M Brandon Westover
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.28.23294672. [PMID: 37693458 PMCID: PMC10491275 DOI: 10.1101/2023.08.28.23294672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Objective To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. Design Multicenter cohort, partly prospective and partly retrospective. Setting Seven academic or teaching hospitals from the U.S. and Europe. Patients Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included. Interventions not applicable. Measurements and Main Results Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. Conclusions The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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Horn J, Admiraal M, Hofmeijer J. Diagnosis and management of seizures and myoclonus after cardiac arrest. EUROPEAN HEART JOURNAL. ACUTE CARDIOVASCULAR CARE 2023; 12:525-531. [PMID: 37486703 DOI: 10.1093/ehjacc/zuad086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 07/25/2023]
Affiliation(s)
- Janneke Horn
- Department of Intensive care Medicine, AmsterdamUMC, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Neurosciences Institute, AmsterdamUMC, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Marjolein Admiraal
- Neurosciences Institute, AmsterdamUMC, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Department of Neurology and Clinical Neurophysiology, AmsterdamUMC, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, Technical Medical Center, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Wagnerlaan 55, 6815 AD Arnhem, The Netherlands
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Hoedemaekers C, Hofmeijer J, Horn J. Value of EEG in outcome prediction of hypoxic-ischemic brain injury in the ICU: A narrative review. Resuscitation 2023; 189:109900. [PMID: 37419237 DOI: 10.1016/j.resuscitation.2023.109900] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/26/2023] [Accepted: 06/29/2023] [Indexed: 07/09/2023]
Abstract
Prognostication of comatose patients after cardiac arrest aims to identify patients with a large probability of favourable or unfavouble outcome, usually within the first week after the event. Electroencephalography (EEG) is a technique that is increasingly used for this purpose and has many advantages, such as its non-invasive nature and the possibility to monitor the evolution of brain function over time. At the same time, use of EEG in a critical care environment faces a number of challenges. This narrative review describes the current role and future applications of EEG for outcome prediction of comatose patients with postanoxic encephalopathy.
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Affiliation(s)
- Cornelia Hoedemaekers
- Department of Critical Care, Radboud University Nijmegen Medical Center, Nijmegen, the Netherlands.
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Janneke Horn
- Department of Critical Care, Amsterdam University Medical Center, Location AMC, Amsterdam, the Netherlands
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24
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Tziakouri A, Novy J, Ben-Hamouda N, Rossetti AO. Relationship between serum neuron-specific enolase and EEG after cardiac arrest: A reappraisal. Clin Neurophysiol 2023; 151:100-106. [PMID: 37236128 DOI: 10.1016/j.clinph.2023.05.001] [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: 01/23/2023] [Revised: 04/05/2023] [Accepted: 05/01/2023] [Indexed: 05/28/2023]
Abstract
OBJECTIVE Electroencephalogram (EEG) and serum neuron specific enolase (NSE) are frequently used prognosticators after cardiac arrest (CA). This study explored the association between NSE and EEG, considering the role of EEG timing, its background continuity, reactivity, occurrence of epileptiform discharges, and pre-defined malignancy degree. METHODS Retrospective analysis including 445 consecutive adults from a prospective registry, surviving the first 24 hours after CA and undergoing multimodal evaluation. EEG were interpreted blinded to NSE results. RESULTS Higher NSE was associated with poor EEG prognosticators, such as increasing malignancy, repetitive epileptiform discharges and lack of background reactivity, independently of EEG timing (including sedation and temperature). When stratified for background continuity, NSE was higher with repetitive epileptiform discharges, except in the case of suppressed EEGs. This relationship showed some variation according to the recording time. CONCLUSIONS Neuronal injury after CA, reflected by NSE, correlates with several EEG features: increasing EEG malignancy, lack of background reactivity, and presence of repetitive epileptiform discharges. The correlation between epileptiform discharges and NSE is influenced by underlying EEG background and timing. SIGNIFICANCE This study, describing the complex interplay between serum NSE and epileptiform features, suggests that epileptiform discharges reflect neuronal injury particularly in non-suppressed EEG.
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Affiliation(s)
- Andria Tziakouri
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jan Novy
- 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
| | - Andrea O Rossetti
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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25
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van Gils PCW, Ruijter BJ, Bloo RJK, van Putten MJAM, Foudraine NA, van Hout MSE, Tromp SC, van Mook WNKA, Rouhl RPW, van Heugten CM, Hofmeijer J. Cognition, emotional state, and quality of life of survivors after cardiac arrest with rhythmic and periodic EEG patterns. Resuscitation 2023:109830. [PMID: 37182824 DOI: 10.1016/j.resuscitation.2023.109830] [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: 02/06/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 05/16/2023]
Abstract
AIM Rhythmic and periodic patterns (RPPs) on the electroencephalogram (EEG) in comatose patients after cardiac arrest have been associated with high case fatality rates. A good neurological outcome according to the Cerebral Performance Categories (CPC) has been reported in up to 10% of cases. Data on cognitive, emotional, and quality of life outcomes are lacking. We aimed to provide insight into these outcomes at one-year follow-up. METHODS We assessed outcome of surviving comatose patients after cardiac arrest with RPPs included in the 'treatment of electroencephalographic status epilepticus after cardiopulmonary resuscitation' (TELSTAR) trial at one-year follow-up, including the CPC for functional neurological outcome, a cognitive assessment, the hospital anxiety and depression scale (HADS) for emotional outcomes, and the 36-item short-form health survey (SF-36) for quality of life. Cognitive impairment was defined as a score of more than 1.5 SD below the mean on ≥ 2 (sub)tests within a cognitive domain. RESULTS Fourteen patients were included (median age 58 years, 21% female), of whom 13 had a cognitive impairment. Eleven of 14 were impaired in memory, 9/14 in executive functioning, and 7/14 in attention. The median scores on the HADS and SF-36 were all worse than expected. Based on the CPC alone, 8/14 had a good outcome (CPC 1-2). CONCLUSION Nearly all cardiac arrest survivors with RPPs during the comatose state have cognitive impairments at one-year follow-up. The incidence of anxiety and depression symptoms seem relatively high and quality of life relatively poor, despite 'good' outcomes according to the CPC.
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Affiliation(s)
- Pauline C W van Gils
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, Enschede, the Netherlands; Limburg Brain Injury Center, Maastricht University, Maastricht, the Netherlands.
| | - Barry J Ruijter
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, Enschede, the Netherlands; Department of Neurology, OLVG, Amsterdam, the Netherlands
| | - Rubia J K Bloo
- Department of medical psychology, Rijnstate Hospital, Arnhem, the Netherlands
| | - Michel J A M van Putten
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, Enschede, the Netherlands; Departments of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Norbert A Foudraine
- Department of Intensive Care, VieCuri Medical Center, Venlo, the Netherlands
| | | | - Selma C Tromp
- Departments of Neurology and Clinical Neurophysiology, St. Antonius Hospital, Nieuwegein, the Netherlands; Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
| | - Walther N K A van Mook
- Department of Intensive Care Medicine, and Academy for Postgraduate Training, Maastricht University Medical Centre+; School of Health Professions Education, Maastricht University, the Netherlands
| | - Rob P W Rouhl
- Department of Neurology, Maastricht University Medical Centre+, the Netherlands; Academic Centre for Epileptology Kempenhaeghe/MUMC+, the Netherlands
| | - Caroline M van Heugten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Limburg Brain Injury Center, Maastricht University, Maastricht, the Netherlands; Department of Neuropsychology and psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Jeannette Hofmeijer
- Clinical Neurophysiology (CNPH), TechMed Centre, University of Twente, Enschede, the Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
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26
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Admiraal MM, van Merkerk M, Horn J, Koelman JHTM, Hofmeijer J, Hoedemaekers CW, van Rootselaar AF. EEG in a four-electrode frontotemporal montage reliably predicts outcome after cardiac arrest. Resuscitation 2023; 188:109817. [PMID: 37164176 DOI: 10.1016/j.resuscitation.2023.109817] [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: 03/10/2023] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/12/2023]
Abstract
AIM To increase efficiency of continuous EEG monitoring for prognostication of neurological outcome in patients after cardiac arrest, we investigated the reliability of EEG in a four-electrode frontotemporal (4-FT) montage, compared to our standard nine-electrode (9-EL) montage. METHODS EEG recorded with Ag/AgCl cup-electrodes at 12 and/or 24h after cardiac arrest of 153 patients was available from a previous study. 220 EEG epochs of 5 minutes were reexamined in a 4-FT montage according to the ACNS criteria. Background classification was compared to the available 9-EL classification using Cohens kappa. Reliability for prognostication was assessed in 151 EEG epochs at 24h after CA using sensitivity and specificity for prediction of poor (cerebral performance categories (CPC) 3-5) and good (CPC 1-2) neurological outcome. RESULTS Agreement for EEG background classification between the two montages was substantial with a kappa of 0.85 (95%-CI 0.81-0.90). Specificity for prediction of poor outcome was 100% (95%-CI 95-100) for both montages, sensitivity was 31% (95%-CI 21-43) for the 4-FT montage and 35% (95%-CI 24-47) for the 9-EL montage. Good outcome was predicted with 65% specificity (95%-CI 53-76) and 81% sensitivity (95%-CI 71-89) for the 4-FT montage, similar to the 9-EL montage. CONCLUSION In this cohort, EEG background patterns determined in a four-electrode frontotemporal montage predict both poor and good outcome after CA with similar reliability. Our results may contribute to decreasing the workload of EEG monitoring in patients after CA without compromising reliability of outcome prediction. However, validation in a larger cohort is necessary, as is a multimodal approach.
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Affiliation(s)
- Marjolein M Admiraal
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands.
| | - Myrthe van Merkerk
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - Janneke Horn
- Amsterdam UMC, University of Amsterdam, Department of Intensive Care, Amsterdam Neuroscience Amsterdam Neuroscience, Meibergdreef 9, Amsterdam, The Netherlands
| | - J H T M Koelman
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
| | - J Hofmeijer
- Rijnstate Hospital, Department of Neurology, Arnhem, The Netherlands; University of Twente, Faculty of Science and Technology, Clinical Neurophysiology, Enschede, The Netherlands
| | - C W Hoedemaekers
- Radboud University Medical Center, Department of Intensive Care, Nijmegen, The Netherlands
| | - Anne-Fleur van Rootselaar
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, Meibergdreef 9, Amsterdam, The Netherlands
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Tewarie PKB, Tjepkema-Cloostermans MC, Abeysuriya RG, Hofmeijer J, van Putten MJAM. Preservation of thalamocortical circuitry is essential for good recovery after cardiac arrest. PNAS NEXUS 2023; 2:pgad119. [PMID: 37143862 PMCID: PMC10153639 DOI: 10.1093/pnasnexus/pgad119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/10/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023]
Abstract
Continuous electroencephalographam (EEG) monitoring contributes to prediction of neurological outcome in comatose cardiac arrest survivors. While the phenomenology of EEG abnormalities in postanoxic encephalopathy is well known, the pathophysiology, especially the presumed role of selective synaptic failure, is less understood. To further this understanding, we estimate biophysical model parameters from the EEG power spectra from individual patients with a good or poor recovery from a postanoxic encephalopathy. This biophysical model includes intracortical, intrathalamic, and corticothalamic synaptic strengths, as well as synaptic time constants and axonal conduction delays. We used continuous EEG measurements from hundred comatose patients recorded during the first 48 h postcardiac arrest, 50 with a poor neurological outcome [cerebral performance category ( CPC = 5 ) ] and 50 with a good neurological outcome ( CPC = 1 ). We only included patients that developed (dis-)continuous EEG activity within 48 h postcardiac arrest. For patients with a good outcome, we observed an initial relative excitation in the corticothalamic loop and corticothalamic propagation that subsequently evolved towards values observed in healthy controls. For patients with a poor outcome, we observed an initial increase in the cortical excitation-inhibition ratio, increased relative inhibition in the corticothalamic loop, delayed corticothalamic propagation of neuronal activity, and severely prolonged synaptic time constants that did not return to physiological values. We conclude that the abnormal EEG evolution in patients with a poor neurological recovery after cardiac arrest may result from persistent and selective synaptic failure that includes corticothalamic circuitry and also delayed corticothalamic propagation.
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Affiliation(s)
| | - Marleen C Tjepkema-Cloostermans
- Clinical Neurophysiology Group, University of Twente, 7522 NH Enschede, Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, 7512 KZ Enschede, Netherlands
| | - Romesh G Abeysuriya
- Computational Epidemic Modelling, Burnet Institute, 3004 Melbourne, Australia
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, 7522 NH Enschede, Netherlands
- Department of Neurology, Rijnstate Hospital, 6815 AD Arnhem, Netherlands
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28
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Inoue F, Inoue A, Nishimura T, Takahashi R, Nakatani Y, Suga M, Kikuta S, Tada S, Maemura S, Matsuyama S, Ishihara S. PCO 2 on arrival as a predictive biomarker in patients with out-of-hospital cardiac arrest. Am J Emerg Med 2023; 69:92-99. [PMID: 37084483 DOI: 10.1016/j.ajem.2023.04.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 03/31/2023] [Accepted: 04/07/2023] [Indexed: 04/23/2023] Open
Abstract
BACKGROUND Treating patients with out-of-hospital cardiac arrest (OHCA) requires early prediction of outcome, ideally on hospital arrival, as it can inform the clinical decisions involved. This study evaluated whether partial pressure of carbon dioxide (PCO2) on arrival is associated with outcome at one month OHCA patients. METHODS This was a single-center retrospective study of adult OHCA patients treated between January 2016 and December 2020. Outcomes were defined along the Cerebral Performance Category (CPC) scale. Primary outcome was mortality (CPC 5) at one month. Secondary outcomes were death or unfavorable neurological outcome (CPC 3-5) and unfavorable neurological outcome (CPC 3-4) at one month. Multivariable analysis was adjusted for age, sex, witnessed cardiac arrest, bystander cardiopulmonary resuscitation, initial shockable rhythm, and time from call to emergency medical services to hospital arrival. RESULTS Out of 977 OHCA patients in the study period, 19 were excluded because they were aged under 18 years, 79 because they underwent extracorporeal cardiopulmonary resuscitation, and 101 due to lack of PCO2 data. This study included 778 patients total; mortality (CPC 5) at one month was observed in 706 (90.7%), death or unfavorable neurological outcome (CPC 3-5) in 743 (95.5%), and unfavorable neurological outcome (CPC 3-4) in 37 (4.8%). In multivariable analysis, high PCO2 levels showed significant association with mortality (CPC 5) at one month (odds ratio [OR] [per 5 mmHg], 1.14; 95% confidence interval [CI], 1.08-1.21), death or unfavorable neurological outcome (CPC 3-5) (OR [per 5 mmHg], 1.29; 95% CI, 1.17-1.42), and unfavorable neurological outcome (CPC 3-4) (OR [per 5 mmHg], 1.21; 95% CI, 1.04-1.41). CONCLUSIONS High PCO2 on arrival was significantly associated with mortality and unfavorable neurological outcome in OHCA patients.
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Affiliation(s)
- Fumiya Inoue
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan; Department of Emergency Medicine, Hiroshima Citizens Hospital, Japan
| | - Akihiko Inoue
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan.
| | - Takeshi Nishimura
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Ryo Takahashi
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Yukihide Nakatani
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Masafumi Suga
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Shota Kikuta
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Shuhei Tada
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Saki Maemura
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Shigenari Matsuyama
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
| | - Satoshi Ishihara
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Japan
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Baker AK, Griffith JL. To Treat or Not to Treat: Ethics of Management of Refractory Status Myoclonus Following Pediatric Anoxic Brain Injury. Semin Pediatr Neurol 2023; 45:101033. [PMID: 37003631 DOI: 10.1016/j.spen.2023.101033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 02/01/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
The development of status myoclonus (SM) in a postcardiac arrest patient has historically been thought of as indicative of not only a poor neurologic outcome but of neurologic devastation. In many instances, this may lead clinicians to initiate conversations about withdrawal of life sustaining therapies (WLST) regardless of the time from return of spontaneous circulation (ROSC). Recent studies showing a percentage of patients may make a good recovery has called into question whether a self-fulfilling prophecy has developed where the concern for a poor neurologic outcome leads clinicians to prematurely discuss WLST. The issue is only further complicated by changing terminology, lack of neuro-axis localization, and limited data regarding association with electroencephalogram (EEG) characteristics, all of which could aid in the understanding of the severity of neurologic injury associated with SM. Here we review the initial literature reporting SM as indicative of poor neurologic outcome, the studies that call this into question, the various definitions of SM and related terms as well as data regarding association with EEG backgrounds. We propose that improved prognostication on outcomes results from combining the presence of SM with other clinical variables (eg EEG patterns, MRI findings, and clinical exam). We discuss the ethical implications of using SM as a prognostic tool and its impact on decisions about life-sustaining care in children following cardiac arrest. We advocate for prognostication efforts to be delayed for at least 72 hours following ROSC and thus to treat SM in those early hours and days.
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Affiliation(s)
- Alyson K Baker
- Department of Pediatrics, University of Nebraska Medical Center, Omaha, NE; Children's Hospital and Medical Center, Omaha, NE.
| | - Jennifer L Griffith
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO; Department of Neurology, Washington University School of Medicine, St. Louis, MO
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30
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GLIMMERVEEN A, VERHULST M, VERBUNT J, van HEUGTEN C, HOFMEIJER J. Predicting Long-Term Cognitive Impairments in Survivors after Cardiac Arrest: A Systematic Review. J Rehabil Med 2023; 55:jrm00368. [PMID: 36714933 PMCID: PMC9900490 DOI: 10.2340/jrm.v55.3497] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/29/2022] [Indexed: 01/31/2023] Open
Abstract
OBJECTIVE International guidelines recommend early screening for identification of patients who are at risk of long-term cognitive impairments after cardiac arrest. However, information about predictors is not provided. A systematic review of the literature was performed to identify early predictors of long-term cognitive outcome after cardiac arrest. METHODS Scopus and PubMed were systematically searched to identify studies on early predictors of long-term cognitive outcome in patients after cardiac arrest. The population included adult cardiac arrest survivors and potential early predictors were demographics, early cognitive screening scores, imaging measures, electroencephalographic measures, and levels of blood biomarkers. Two investigators reviewed studies for relevance, extracted data and assessed risk of bias. RESULTS Five articles were included. Risk of bias was assessed as low or moderate. Most detected longterm cognitive impairments were in the domain of memory. Coma duration (2 studies), early cognitive impairments by the self-developed clinical Bedside Neuropsychological Test Battery (BNTB) screener (2 studies), and high S-100B levels on day 3 (2 studies) were the most prominent identified determinants of cognitive impairment on the group level. On the individual patient level, a score on the BNTB of ≤ 94.5 predicted cognitive impairments at 6 months after cardiac arrest (1 study without external validation). Studies on brain imaging and electroencephalography are lacking. CONCLUSION Early bedside cognitive screening can contribute to prediction of long-term cognitive impairment after cardiac arrest. Evidence is scarce for S-100B levels and coma duration and absent for measures derived from brain imaging and electroencephalography.
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Affiliation(s)
- Astrid GLIMMERVEEN
- Department of Neurology, Rijnstate Hospital, Arnhem,Department of Clinical Neurophysiology, University of Twente, Faculty of Science and Technology, Enschede
| | - Marlous VERHULST
- Department of Neurology, Rijnstate Hospital, Arnhem,Department of Clinical Neurophysiology, University of Twente, Faculty of Science and Technology, Enschede
| | - Jeanine VERBUNT
- Department of Rehabilitation Medicine, Maastricht University, Care and Public Health Research Institute, Functioning and Rehabilitation,Adelante Centre of Expertise in Rehabilitation and Audiology
| | - Caroline van HEUGTEN
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastrich University,Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Jeannette HOFMEIJER
- Department of Neurology, Rijnstate Hospital, Arnhem,Department of Clinical Neurophysiology, University of Twente, Faculty of Science and Technology, Enschede
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31
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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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Free water corrected diffusion tensor imaging discriminates between good and poor outcomes of comatose patients after cardiac arrest. Eur Radiol 2023; 33:2139-2148. [PMID: 36418623 PMCID: PMC9935650 DOI: 10.1007/s00330-022-09245-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/26/2022] [Accepted: 10/16/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Approximately 50% of comatose patients after cardiac arrest never regain consciousness. Cerebral ischaemia may lead to cytotoxic and/or vasogenic oedema, which can be detected by diffusion tensor imaging (DTI). Here, we evaluate the potential value of free water corrected mean diffusivity (MD) and fractional anisotropy (FA) based on DTI, for the prediction of neurological recovery of comatose patients after cardiac arrest. METHODS A total of 50 patients after cardiac arrest were included in this prospective cohort study in two Dutch hospitals. DTI was obtained 2-4 days after cardiac arrest. Outcome was assessed at 6 months, dichotomised as poor (cerebral performance category 3-5; n = 20) or good (n = 30) neurological outcome. We calculated the whole brain mean MD and FA and compared between patients with good and poor outcomes. In addition, we compared a preliminary prediction model based on clinical parameters with or without the addition of MD and FA. RESULTS We found significant differences between patients with good and poor outcome of mean MD (good: 726 [702-740] × 10-6 mm2/s vs. poor: 663 [575-736] × 10-6 mm2/s; p = 0.01) and mean FA (0.30 ± 0.03 vs. 0.28 ± 0.03; p = 0.03). An exploratory prediction model combining clinical parameters, MD and FA increased the sensitivity for reliable prediction of poor outcome from 60 to 85%, compared to the model containing clinical parameters only, but confidence intervals are overlapping. CONCLUSIONS Free water-corrected MD and FA discriminate between patients with good and poor outcomes after cardiac arrest and hold the potential to add to multimodal outcome prediction. KEY POINTS • Whole brain mean MD and FA differ between patients with good and poor outcome after cardiac arrest. • Free water-corrected MD can better discriminate between patients with good and poor outcome than uncorrected MD. • A combination of free water-corrected MD (sensitive to grey matter abnormalities) and FA (sensitive to white matter abnormalities) holds potential to add to the prediction of outcome.
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Elmashala A, Busl KM, Maciel CB. Will shifting the lens let us see more clearly when prognosticating after cardiac arrest, or do we need new glasses? Resuscitation 2023; 182:109667. [PMID: 36565947 DOI: 10.1016/j.resuscitation.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Amjad Elmashala
- Department of Neurology, Division of Neurocritical Care, University of Florida College of Medicine, Gainesville, FL 32611, USA
| | - Katharina M Busl
- Department of Neurology, Division of Neurocritical Care, University of Florida College of Medicine, Gainesville, FL 32611, USA; Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL 32611, USA
| | - Carolina B Maciel
- Department of Neurology, Division of Neurocritical Care, University of Florida College of Medicine, Gainesville, FL 32611, USA; Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL 32611, USA; Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA.
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Coppler PJ, Flickinger KL, Darby JM, Doshi A, Guyette FX, Faro J, Callaway CW, Elmer J. Early risk stratification for progression to death by neurological criteria following out-of-hospital cardiac arrest. Resuscitation 2022; 179:248-255. [PMID: 35914657 DOI: 10.1016/j.resuscitation.2022.07.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/27/2022] [Accepted: 07/22/2022] [Indexed: 01/05/2023]
Abstract
BACKGROUND Some patients resuscitated from out-of-hospital cardiac arrest (OHCA) progress to death by neurological criteria (DNC). We hypothesized that initial brain imaging, electroencephalography (EEG), and arrest characteristics predict progression to DNC. METHODS We identified comatose OHCA patients from January 2010 to February 2020 treated at a single quaternary care facility in Western Pennsylvania. We abstracted demographics and arrest characteristics; Pittsburgh Cardiac Arrest Category, initial motor exam and pupillary light reflex; initial brain computed tomography (CT) grey-to-white ratio (GWR), sulcal or basal cistern effacement; initial EEG background and suppression ratio. We used two modeling approaches: fast and frugal tree (FFT) analysis to create an interpretable clinical risk stratification tool and ridge regression for comparison. We used bootstrapping to randomly partition cases into 80% training and 20% test sets and evaluated test set sensitivity and specificity. RESULTS We included 1,569 patients, of whom 147 (9%) had diagnosed DNC. Across bootstrap samples, >99% of FFTs included three predictors: sulcal effacement, and in cases without sulcal effacement, the combination of EEG background suppression and GWR ≤ 1.23. This tree had mean sensitivity and specificity of 87% and 81%. Ridge regression with all available predictors had mean sensitivity 91 % and mean specificity 83%. Subjects falsely predicted as likely to progress to DNC generally died of rearrest or withdrawal of life sustaining therapies due to poor neurological prognosis. Two of these cases awakened from coma during the index hospitalization. CONCLUSIONS Sulcal effacement on presenting brain CT or EEG suppression with GWR ≤ 1.23 predict progression to DNC after OHCA.
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Affiliation(s)
- Patrick J Coppler
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| | | | - Joseph M Darby
- Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ankur Doshi
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Francis X Guyette
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - John Faro
- Department of Family Medicine, Soin Medical Center - Kettering Health Network, Beavercreek, OH, USA
| | - Clifton W Callaway
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jonathan Elmer
- Department of Emergency Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
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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: 30] [Impact Index Per Article: 15.0] [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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Jonas S, Müller M, Rossetti AO, Rüegg S, Alvarez V, Schindler K, Zubler F. Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study. Neuroimage Clin 2022; 36:103167. [PMID: 36049354 PMCID: PMC9441331 DOI: 10.1016/j.nicl.2022.103167] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/16/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Abstract
Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine.
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Affiliation(s)
- Stefan Jonas
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Michael Müller
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrea O. Rossetti
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Stephan Rüegg
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Vincent Alvarez
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,Department of Neurology, Hôpital du Valais, Sion, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Frédéric Zubler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Corresponding author at: Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, Freiburgstrasse 10, 3010 Bern, Switzerland.
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Keijzer HM, Lange PA, Meijer FJ, Tonino BA, Blans MJ, Klijn CJ, Hoedemaekers CW, Hofmeijer J, Helmich RC. MRI markers of brain network integrity relate to neurological outcome in postanoxic coma. Neuroimage Clin 2022; 36:103171. [PMID: 36058165 PMCID: PMC9446009 DOI: 10.1016/j.nicl.2022.103171] [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: 02/10/2022] [Revised: 08/18/2022] [Accepted: 08/23/2022] [Indexed: 12/14/2022]
Abstract
AIM Current multimodal approaches leave approximately half of the comatose patients after cardiac arrest with an indeterminate prognosis. Here we investigated whether early MRI markers of brain network integrity can distinguish between comatose patients with a good versus poor neurological outcome six months later. METHODS We performed a prospective cohort study in 48 patients after cardiac arrest submitted in a comatose state to the Intensive Care Unit of two Dutch hospitals. MRI was performed at three days after cardiac arrest, including resting state functional MRI and diffusion-tensor imaging (DTI). Resting state fMRI was used to quantify functional connectivity within ten resting-state networks, and DTI to assess mean diffusivity (MD) in these same networks. We contrasted two groups of patients, those with good (n = 29, cerebral performance category 1-2) versus poor (n = 19, cerebral performance category 3-5) outcome at six months. Mutual associations between functional connectivity, MD, and clinical outcome were studied. RESULTS Patients with good outcome show higher within-network functional connectivity (fMRI) and higher MD (DTI) than patients with poor outcome across 8/10 networks, most prominent in the default mode network, salience network, and visual network. While the anatomical distribution of outcome-related changes was similar for functional connectivity and MD, the pattern of inter-individual differences was very different: functional connectivity showed larger inter-individual variability in good versus poor outcome, while the opposite was observed for MD. Exploratory analyses suggested that it is possible to define network-specific cut-off values that could help in outcome prediction: (1) high functional connectivity and high MD, associated with good outcome; (2) low functional connectivity and low MD, associated with poor outcome; (3) low functional connectivity and high MD, associated with uncertain outcome. DISCUSSION Resting-state functional connectivity and mean diffusivity-three days after cardiac arrest are strongly associated with neurological recovery-six months later in a complementary fashion. The combination of fMRI and MD holds potential to improve prediction of outcome.
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Affiliation(s)
- Hanneke M. Keijzer
- Department of Neurology, Rijnstate Hospital, 6800 TA Arnhem, the Netherlands,Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands,Corresponding author at: Department of Neurology, Rijnstate Hospital, PO box 9555 TA Arnhem, the Netherlands.
| | - Puck A.M. Lange
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Frederick J.A. Meijer
- Department of Medical Imaging, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Bart A.R. Tonino
- Department of Radiology, Rijnstate Hospital, 6800 TA Arnhem, the Netherlands
| | - Michiel J. Blans
- Department of Intensive Care Medicine, Rijnstate Hospital, Arnhem, the Netherlands
| | - Catharina J.M. Klijn
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Cornelia W.E. Hoedemaekers
- Department of Intensive Care Medicine, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
| | - Jeannette Hofmeijer
- Department of Neurology, Rijnstate Hospital, 6800 TA Arnhem, the Netherlands,Department of Clinical Neurophysiology, University of Twente, Faculty of Science and Technology, 7522 NB Enschede, the Netherlands
| | - Rick C. Helmich
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, 6500 HC Nijmegen, the Netherlands
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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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Neurological Prognostication Using Raw EEG Patterns and Spectrograms of Frontal EEG in Cardiac Arrest Patients. J Clin Neurophysiol 2022; 39:427-433. [DOI: 10.1097/wnp.0000000000000787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Grindegård L, Cronberg T, Backman S, Blennow K, Dankiewicz J, Friberg H, Hassager C, Horn J, Kjaer TW, Kjaergaard J, Kuiper M, Mattsson-Carlgren N, Nielsen N, van Rootselaar AF, Rossetti AO, Stammet P, Ullén S, Zetterberg H, Westhall E, Moseby-Knappe M. Association Between EEG Patterns and Serum Neurofilament Light After Cardiac Arrest. Neurology 2022; 98:e2487-e2498. [PMID: 35470143 PMCID: PMC9231840 DOI: 10.1212/wnl.0000000000200335] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 02/21/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Objectives EEG is widely used for prediction of neurologic outcome after cardiac arrest. To better understand the relationship between EEG and neuronal injury, we explored the association between EEG and neurofilament light (NfL) as a marker of neuroaxonal injury, evaluated whether highly malignant EEG patterns are reflected by high NfL levels, and explored the association of EEG backgrounds and EEG discharges with NfL. Methods We performed a post hoc analysis of the Target Temperature Management After Out-of-Hospital Cardiac Arrest trial. Routine EEGs were prospectively performed after the temperature intervention ≥36 hours postarrest. Patients who awoke or died prior to 36 hours postarrest were excluded. EEG experts blinded to clinical information classified EEG background, amount of discharges, and highly malignant EEG patterns according to the standardized American Clinical Neurophysiology Society terminology. Prospectively collected serum samples were analyzed for NfL after trial completion. The highest available concentration at 48 or 72 hours postarrest was used. Results A total of 262/939 patients with EEG and NfL data were included. Patients with highly malignant EEG patterns had 2.9 times higher NfL levels than patients with malignant patterns and NfL levels were 13 times higher in patients with malignant patterns than those with benign patterns (95% CI 1.4–6.1 and 6.5–26.2, respectively; effect size 0.47; p < 0.001). Both background and the amount of discharges were independently strongly associated with NfL levels (p < 0.001). The EEG background had a stronger association with NfL levels than EEG discharges (R2 = 0.30 and R2 = 0.10, respectively). NfL levels in patients with a continuous background were lower than for any other background (95% CI for discontinuous, burst-suppression, and suppression, respectively: 2.26–18.06, 3.91–41.71, and 5.74–41.74; effect size 0.30; p < 0.001 for all). NfL levels did not differ between suppression and burst suppression. Superimposed discharges were only associated with higher NfL levels if the EEG background was continuous. Discussion Benign, malignant, and highly malignant EEG patterns reflect the extent of brain injury as measured by NfL in serum. The extent of brain injury is more strongly related to the EEG background than superimposed discharges. Combining EEG and NfL may be useful to better identify patients misclassified by single methods. Trial Registration Information ClinicalTrials.gov NCT01020916.
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Affiliation(s)
- Linnéa Grindegård
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China.
| | - Tobias Cronberg
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Sofia Backman
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Kaj Blennow
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Josef Dankiewicz
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Hans Friberg
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Christian Hassager
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Janneke Horn
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Troels W Kjaer
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Jesper Kjaergaard
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Michael Kuiper
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Niklas Mattsson-Carlgren
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Niklas Nielsen
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Anne-Fleur van Rootselaar
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Andrea O Rossetti
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Pascal Stammet
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Susann Ullén
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Henrik Zetterberg
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Erik Westhall
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Marion Moseby-Knappe
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
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41
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Curley WH, Comanducci A, Fecchio M. Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness. Semin Neurol 2022; 42:309-324. [PMID: 36100227 DOI: 10.1055/s-0042-1755220] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.
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Affiliation(s)
- William H Curley
- Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, Massachusetts
| | - Angela Comanducci
- IRCSS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy.,Università Campus Bio-Medico di Roma, Rome, Italy
| | - Matteo Fecchio
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, Massachusetts
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42
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Urbano V, Alvarez V, Schindler K, Rüegg S, Ben-Hamouda N, Novy J, Rossetti AO. Continuous versus routine EEG in patients after cardiac arrest-Analysis of a randomized controlled trial (CERTA) - RESUS-D-22-00369. Resuscitation 2022; 176:68-73. [PMID: 35654226 DOI: 10.1016/j.resuscitation.2022.05.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 05/18/2022] [Accepted: 05/24/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Electroencephalography (EEG) is essential to assess prognosis in patients after cardiac arrest (CA). Use of continuous EEG (cEEG) is increasing in critically-ill patients, but it is more resource-consuming than routine EEG (rEEG). Observational studies did not show a major impact of cEEG versus rEEG on outcome, but randomized studies are lacking. METHODS We analyzed data of the CERTA trial (NCT03129438), including comatose adults after CA undergoing cEEG (30-48 hours) or two rEEG (20-30 minutes each). We explored correlations between recording EEG type and mortality (primary outcome), or Cerebral Performance Categories (CPC, secondary outcome), assessed blindly at 6 months, using uni- and multivariable analyses (adjusting for other prognostic variables showing some imbalance across groups). RESULTS We analyzed 112 adults (52 underwent rEEG, 60 cEEG,); 31 (27.7%) were women; 68 (60.7%) patients died. In univariate analysis, mortality (rEEG 59%, cEEG 65%, p=0.318) and good outcome (CPC 1-2; rEEG 33%, cEEG 27%, p=0.247) were comparable across EEG groups. This did not change after multiple logistic regressions, adjusting for shockable rhythm, time to return of spontaneous circulation, serum neuron-specific enolase, EEG background reactivity, regarding mortality (rEEG vs cEEG: OR 1.60, 95% CI 0.43 - 5.83, p=0.477), and good outcome (OR 0.51, 95% CI 0.14 - 1.90, p=0.318). CONCLUSION This analysis suggests that cEEG or repeated rEEG are related to comparable outcomes of comatose patients after CA. Pending a prospective, large randomized trial, this finding does not support the routine use of cEEG for prognostication in this setting. Trial registration Continuous EEG Randomized Trial in Adults (CERTA); NCT03129438; July 25, 2019.
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Affiliation(s)
- Valentina Urbano
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Vincent Alvarez
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Neurology, Hôpital du Valais, Sion, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stephan Rüegg
- Department of Neurology, University Hospital Basel, and University of Basel, Basel, 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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43
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Ben-Hamouda N, Ltaief Z, Kirsch M, Novy J, Liaudet L, Oddo M, Rossetti AO. Neuroprognostication Under ECMO After Cardiac Arrest: Are Classical Tools Still Performant? Neurocrit Care 2022; 37:293-301. [PMID: 35534658 DOI: 10.1007/s12028-022-01516-0] [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: 11/02/2021] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND According to international guidelines, neuroprognostication in comatose patients after cardiac arrest (CA) is performed using a multimodal approach. However, patients undergoing extracorporeal membrane oxygenation (ECMO) may have longer pharmacological sedation and show alteration in biological markers, potentially challenging prognostication. Here, we aimed to assess whether routinely used predictors of poor neurological outcome also exert an acceptable performance in patients undergoing ECMO after CA. METHODS This observational retrospective study of our registry includes consecutive comatose adults after CA. Patients deceased within 36 h and not undergoing prognostic tests were excluded. Veno-arterial ECMO was initiated in patients < 80 years old presenting a refractory CA, with a no flow < 5 min and a low flow ≤ 60 min on admission. Neuroprognostication test performance (including pupillary reflex, electroencephalogram, somatosensory-evoked potentials, neuron-specific enolase) toward mortality and poor functional outcome (Cerebral Performance Categories [CPC] score 3-5) was compared between patients undergoing ECMO and those without ECMO. RESULTS We analyzed 397 patients without ECMO and 50 undergoing ECMO. The median age was 65 (interquartile range 54-74), and 69.8% of patients were men. Most had a cardiac etiology (67.6%); 52% of the patients had a shockable rhythm, and the median time to return of an effective circulation was 20 (interquartile range 10-28) minutes. Compared with those without ECMO, patients receiving ECMO had worse functional outcome (74% with CPC scores 3-5 vs. 59%, p = 0.040) and a nonsignificant higher mortality (60% vs. 47%, p = 0.080). Apart from the neuron-specific enolase level (higher in patients with ECMO, p < 0.001), the presence of prognostic items (pupillary reflex, electroencephalogram background and reactivity, somatosensory-evoked potentials, and myoclonus) related to unfavorable outcome (CPC score 3-5) in both groups was similar, as was the prevalence of at least any two such items concomitantly. The specificity of each these variables toward poor outcome was between 92 and 100% in both groups, and of the combination of at least two items, it was 99.3% in patients without ECMO and 100% in those with ECMO. The predictive performance (receiver operating characteristic curve) of their combination toward poor outcome was 0.822 (patients without ECMO) and 0.681 (patients with ECMO) (p = 0.134). CONCLUSIONS Pending a prospective assessment on a larger cohort, in comatose patients after CA, the performance of prognostic factors seems comparable in patients with ECMO and those without ECMO. In particular, the combination of at least two poor outcome criteria appears valid across these two groups.
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Affiliation(s)
- Nawfel Ben-Hamouda
- Department of Adult Intensive Care Medicine, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland. .,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Zied Ltaief
- Department of Adult Intensive Care Medicine, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Matthias Kirsch
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Department of Cardiovascular Surgery, Lausanne University Hospital, Lausanne, Switzerland
| | - Jan Novy
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - Lucas Liaudet
- Department of Adult Intensive Care Medicine, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Mauro Oddo
- Department of Adult Intensive Care Medicine, Lausanne University Hospital, Rue du Bugnon 46, 1011, Lausanne, Switzerland.,Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Andrea O Rossetti
- Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
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44
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Prognosis After Cardiac Arrest: The Additional Value of DWI and FLAIR to EEG. Neurocrit Care 2022; 37:302-313. [DOI: 10.1007/s12028-022-01498-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/28/2022] [Indexed: 10/18/2022]
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Verdijk JPAJ, Pottkämper JCM, Verwijk E, van Wingen GA, van Putten MJAM, Hofmeijer J, van Waarde JA. Study of effect of nimodipine and acetaminophen on postictal symptoms in depressed patients after electroconvulsive therapy (SYNAPSE). Trials 2022; 23:324. [PMID: 35436940 PMCID: PMC9014277 DOI: 10.1186/s13063-022-06206-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 03/26/2022] [Indexed: 11/20/2022] Open
Abstract
Background Postictal phenomena as delirium, headache, nausea, myalgia, and anterograde and retrograde amnesia are common manifestations after seizures induced by electroconvulsive therapy (ECT). Comparable postictal phenomena also contribute to the burden of patients with epilepsy. The pathophysiology of postictal phenomena is poorly understood and effective treatments are not available. Recently, seizure-induced cyclooxygenase (COX)-mediated postictal vasoconstriction, accompanied by cerebral hypoperfusion and hypoxia, has been identified as a candidate mechanism in experimentally induced seizures in rats. Vasodilatory treatment with acetaminophen or calcium antagonists reduced postictal hypoxia and postictal symptoms. The aim of this clinical trial is to study the effects of acetaminophen and nimodipine on postictal phenomena after ECT-induced seizures in patients suffering major depressive disorder. We hypothesize that (1) acetaminophen and nimodipine will reduce postictal electroencephalographic (EEG) phenomena, (2) acetaminophen and nimodipine will reduce magnetic resonance imaging (MRI) measures of postictal cerebral hypoperfusion, (3) acetaminophen and nimodipine will reduce clinical postictal phenomena, and (4) postictal phenomena will correlate with measures of postictal hypoperfusion. Methods We propose a prospective, three-condition cross-over design trial with randomized condition allocation, open-label treatment, and blinded end-point evaluation (PROBE design). Thirty-three patients (age > 17 years) suffering from a depressive episode treated with ECT will be included. Randomly and alternately, single doses of nimodipine (60 mg), acetaminophen (1000 mg), or water will be given two hours prior to each ECT session with a maximum of twelve sessions per patient. The primary outcome measure is ‘postictal EEG recovery time’, expressed and quantified as an adapted version of the temporal brain symmetry index, yielding a time constant for the duration of the postictal state on EEG. Secondary outcome measures include postictal cerebral perfusion, measured by arterial spin labelling MRI, and the postictal clinical ‘time to orientation’. Discussion With this clinical trial, we will systematically study postictal EEG, MRI and clinical phenomena after ECT-induced seizures and will test the effects of vasodilatory treatment intending to reduce postictal symptoms. If an effect is established, this will provide a novel treatment of postictal symptoms in ECT patients. Ultimately, these findings may be generalized to patients with epilepsy. Trial registration Inclusion in SYNAPSE started in December 2019. Prospective trial registration number is NCT04028596 on the international clinical trial register on July 22, 2019.
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46
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Bouyaknouden D, Peddada TN, Ravishankar N, Fatima S, Fong-Isariyawongse J, Gilmore EJ, Lee JW, Struck AF, Gaspard N. Neurological Prognostication After Hypoglycemic Coma: Role of Clinical and EEG Findings. Neurocrit Care 2022; 37:273-280. [PMID: 35437670 DOI: 10.1007/s12028-022-01495-2] [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] [Received: 10/29/2021] [Accepted: 03/17/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND Hypoglycemic coma (HC) is an uncommon but severe clinical condition associated with poor neurological outcome. There is a dearth of robust neurological prognostic factors after HC. On the other hand, there is an increasing body of literature on reliable prognostic markers in the postanoxic coma, a similar-albeit not identical-situation. The objective of this study was thus to investigate the use and predictive value of these markers in HC. METHODS We conducted a retrospective, multicenter, cohort study within five centers of the Critical Care EEG Monitoring Research Consortium. We queried our electroencephalography (EEG) databases to identify all patients undergoing continuous EEG monitoring after admission to an intensive care unit with HC (defined as Glasgow Coma Scale < 8 on admission and a first blood glucose level < 50 mg/dL or not documented but in an obvious clinical context) between 01/01/2010 and 12/31/2020. We studied the association of findings at neurological examination (Glasgow Coma Scale motor subscale, pupillary light and corneal reflexes) and at continuous EEG monitoring(highly malignant patterns, reactivity, periodic discharges, seizures) with best neurological outcome within 3 months after hospital discharge, defined by the Cerebral Performance Category as favorable (1-3: recovery of consciousness) versus unfavorable (4-5: lack of recovery of consciousness). RESULTS We identified 60 patients (30 [50%] women; age 62 [51-72] years). Thirty-one and 29 patients had a favorable and unfavorable outcome, respectively. The presence of pupillary reflexes (24 [100%] vs. 17 [81%]; p value 0.04) and a motor subscore > 2 (22 [92%] vs. 12 [63%]; p value 0.03) at 48-72 h were associated with a favorable outcome. A highly malignant EEG pattern was observed in 7 of 29 (24%) patients with unfavorable outcome versus 0 of 31 (0%) with favorable outcome, whereas the presence of EEG reactivity was observed in 28 of 31 (90%) patients with favorable outcome versus 13 of 29 (45%) with unfavorable outcome (p < 0.001 for comparison of all background categories). CONCLUSIONS This preliminary study suggests that highly malignant EEG patterns might be reliable prognostic markers of unfavorable outcome after HC. Other EEG findings, including lack of EEG reactivity and seizures and clinical findings appear less accurate. These findings should be replicated in a larger multicenter prospective study.
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Affiliation(s)
- Douaae Bouyaknouden
- Department of Neurology, Hôpital Erasme - Cliniques Universitaires de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Teja N Peddada
- Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Safoora Fatima
- Department of Neurology, University of Wisconsin, Madison, WI, USA
| | | | - Emily J Gilmore
- Department of Neurology, Yale University, New Haven, CT, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin, Madison, WI, USA.,William S. Middleton Veterans Hospital, Madison, WI, USA
| | - Nicolas Gaspard
- Department of Neurology, Hôpital Erasme - Cliniques Universitaires de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium. .,Department of Neurology, Yale University, New Haven, CT, USA.
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Routine Reporting of Grey-White Matter differentiation in Early Brain Computed Tomography in comatose patients after cardiac arrest: a substudy of the COACT trial. Resuscitation 2022; 175:13-18. [DOI: 10.1016/j.resuscitation.2022.03.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 03/13/2022] [Accepted: 03/14/2022] [Indexed: 01/27/2023]
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48
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Lee JW, Sreepada LP, Bevers MB, Li K, Scirica BM, Santana da Silva D, Henderson GV, Bay C, Lin AP. Magnetic Resonance Spectroscopy of Hypoxic-Ischemic Encephalopathy After Cardiac Arrest. Neurology 2022; 98:e1226-e1237. [PMID: 35017308 PMCID: PMC8967333 DOI: 10.1212/wnl.0000000000013297] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 12/27/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To correlate brain metabolites with clinical outcome using magnetic resonance spectroscopy (MRS) in patients undergoing targeted temperature management (TTM) after cardiac arrest and assess their relationships to MRI and EEG variables. METHODS A prospective cohort of 50 patients was studied. The primary outcome was coma recovery to follow commands. Comparison of MRS measures in the posterior cingulate gyrus, parietal white matter, basal ganglia, and brainstem were also made to 25 normative controls. RESULTS Fourteen of 50 patients achieved coma recovery before hospital discharge. There was a significant decrease in total N-acetylaspartate (NAA/Cr) and an increase in lactate/creatine (Lac/Cr) in patients who did not recover, with changes most prominent in the posterior cingulate gyrus. Patients who recovered had decrease in NAA/Cr as compared to controls. NAA/Cr had a strong monotonic relationship with MRI cortical apparent diffusion coefficient (ADC); Lac level exponentially increased with decreasing ADC. EEG suppression/burst suppression was strongly associated with Lac elevation. DISCUSSION NAA and Lac changes are associated with clinical/MRI/EEG changes consistent with hypoxic-ischemic encephalopathy (HIE) and are most prominent in the posterior cingulate gyrus. NAA/Cr decrease observed in patients with good outcomes suggests mild HIE in patients asymptomatic at hospital discharge. The appearance of cortical Lac represents a deterioration of aerobic energy metabolism and is associated with EEG background suppression, synaptic transmission failure, and severe, potentially irreversible HIE. CLASSIFICATION OF EVIDENCE This study provides Class IV evidence that in patients undergoing TTM after cardiac arrest, brain MRS-determined decrease in total NAA/Cr and an increase in Lac/Cr are associated with an increased risk of not recovering.
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Affiliation(s)
- Jong Woo Lee
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA
| | - Lasya P Sreepada
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA
| | - Matthew B Bevers
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA
| | - Karen Li
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA.
| | - Benjamin M Scirica
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA
| | - Danuzia Santana da Silva
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA
| | - Galen V Henderson
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA
| | - Camden Bay
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA
| | - Alexander P Lin
- From the Department of Neurology (J.W.L., M.B., K.L., G.V.H.), Department of Radiology (L.S., C.B., A.P.L.), and Department of Medicine, Division of Cardiology (B.S., D.S.d.S.), Brigham and Women's Hospital, Boston, MA.
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Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods. Neurocrit Care 2022; 37:248-258. [PMID: 35233717 PMCID: PMC9343315 DOI: 10.1007/s12028-022-01449-8] [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: 10/25/2021] [Accepted: 01/10/2022] [Indexed: 12/03/2022]
Abstract
Background To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. Methods A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as “good” (Cerebral Performance Category 1–2) or “poor” (Cerebral Performance Category 3–5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. Results The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44–64%) at a false positive rate (FPR) of 0% (95% CI 0–2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52–100%) at a FPR of 12% (95% CI 0–24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83–83%) at a FPR of 3% (95% CI 3–3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. Conclusions A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.
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Critical care EEG standardized nomenclature in clinical practice: Strengths, limitations, and outlook on the example of prognostication after cardiac arrest. Clin Neurophysiol Pract 2022; 6:149-154. [PMID: 35112033 PMCID: PMC8790140 DOI: 10.1016/j.cnp.2021.03.002] [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: 11/01/2020] [Revised: 01/08/2021] [Accepted: 03/03/2021] [Indexed: 11/21/2022] Open
Abstract
Optimal use of the ACNS nomenclature implies integration of clinical information. Knowledge of pathophysiological mechanisms of EEG patterns may help interpretation. Standardized therapeutic procedures for critical care patients are needed.
We discuss the achievements of the ACNS critical care EEG nomenclature proposed in 2013 and, from a clinical angle, outline some limitations regarding translation into treatment implications. While the recently proposed updated 2021 version of the nomenclature will probable improve some uncertainty areas, a refined understanding of the mechanisms at the origin of the EEG patterns, and a multimodal integration of the nomenclature to the clinical context may help improving the rationale supporting therapeutic procedures. We illustrate these aspects on prognostication after cardiac arrest.
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Key Words
- ACNS, American Clinical Neurophysiology Society
- American Clinical Neurophysiology Society (ACNS) Standardized Terminology
- BIRD, Brief potentially ictal rhythmic discharge
- BS, Burst suppression
- Burst suppression
- CA, Cardiac arrest
- Cardiac arrest (CA)
- DWI, diffusion-weighted MRI
- ESI, electric source imaging
- GPD
- GPD, generalized periodic discharge
- GRDA, generalized rhythmic delta activity
- ICU, Intensive care unit
- ICU-EEG, intensive care unit-electroencephalography
- IIC, Ictal-Interictal Continuum
- Ictal-Interictal Continuum
- LPD, Lateralized periodic discharge
- MEG, Magneto-electroencephalography
- NCSE, Non-Convulsive Status Epilepticus
- NSE, Serum neuron-specific enolase
- PET, Positron emission tomography
- Prognostication assessment
- SE, Status epilepticus
- SPECT, Single Photon Emission Computed Tomography
- SSEP, Somatosensory evoked potentials
- WLST, Withdraw of life sustaining treatment
- fMRI, functional MRI
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