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Fahrner M, Hwang J, Cho SM, Thakor NV, Habela CW, Kaplan PW, Geocadin RG. EEG Reactivity in Neurologic Prognostication in Post-Cardiac Arrest Patients: A Narrative Review. Resuscitation 2024:110398. [PMID: 39277070 DOI: 10.1016/j.resuscitation.2024.110398] [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: 07/08/2024] [Revised: 08/31/2024] [Accepted: 09/10/2024] [Indexed: 09/17/2024]
Abstract
Electroencephalographic reactivity (EEG-R) is a promising early predictor of arousal in comatose patients after cardiac arrest. Despite recent guidelines advocating for the integration of EEG-R into the multimodal prognostication model, EEG-R testing methods remain heterogeneous across studies. While efforts towards standardization have been made to reduce interrater variability by the development of quantitative approaches and machine learning models, future validation studies are needed to increase clinical applicability and generalization. Furthermore, the specific neurophysiological mechanisms and neuroanatomical correlates underlying EEG-R are not well understood. In this narrative review, we explore the value and possible mechanisms of EEG-R, focusing on post-cardiac arrest comatose patients. We aim to discuss the current standard of knowledge and future directions, as well as elucidate possible implications for patient care and research.
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Affiliation(s)
- Marlen Fahrner
- Department of Neurology, Division of Neurocritical Care, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
| | - Jaeho Hwang
- Department of Neurology, Division of Epilepsy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sung-Min Cho
- Departments of Neurology, Surgery, and Anesthesiology - Critical Care Medicine, Division of Neurocritical Care, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland USA
| | - Christa W Habela
- Department of Neurology, Division of Epilepsy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peter W Kaplan
- Department of Neurology, Division of Epilepsy, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Romergryko G Geocadin
- Departments of Neurology, Anesthesiology - Critical Care Medicine, and Neurosurgery, Division of Neurocritical Care, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Turella S, Dankiewicz J, Ben-Hamouda N, Nilsen KB, 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; 202:110319. [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] [MESH Headings] [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 B 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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3
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Stammet P. Neuroprognostication after cardiac arrest: Don't forget the good! Resuscitation 2024; 202:110350. [PMID: 39103032 DOI: 10.1016/j.resuscitation.2024.110350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Accepted: 07/29/2024] [Indexed: 08/07/2024]
Affiliation(s)
- Pascal Stammet
- Department of Anaesthesia and Intensive Care Medicine, Centre Hospitalier de Luxembourg, 4, rue Barblé, L-1210 Luxembourg, Luxembourg; Department of Life Sciences and Medicine, Faculty of Science, Technology and Medicine, University of Luxembourg, 2, place de l'Université, L-4365 Esch-sur-Alzette, Luxembourg.
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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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Liu G, Wang Y, Tian F, Chen W, Cui L, Jiang M, Zhang Y, Gao K, Su Y, Wang H. Quantitative EEG reactivity induced by electrical stimulation predicts good outcome in comatose patients after cardiac arrest. Ann Intensive Care 2024; 14:99. [PMID: 38935167 PMCID: PMC11211292 DOI: 10.1186/s13613-024-01339-6] [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: 04/06/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND EEG reactivity is a predictor for neurological outcome in comatose patients after cardiac arrest (CA); however, its application is limited by variability in stimulus types and visual assessment. We aimed to evaluate the prognostic value of the quantitative analysis of EEG reactivity induced by standardized electrical stimulation and for early prognostication in this population. METHODS This prospective observational study recruited post-CA comatose patients in Xuanwu Hospital, Capital Medical University (Beijing, China) between January 2016 and June 2023. EEG reactivity to electrical or traditional pain stimulation was randomly performed via visual and quantitative analysis. Neurological outcome within 6 months was dichotomized as good (Cerebral Performance Categories, CPC 1-2) or poor (CPC 3-5). RESULTS Fifty-eight post-CA comatose patients were admitted, and 52 patients were included in the final analysis, of which 19 (36.5%) had good outcomes. EEG reactivity induced with the electrical stimulation had superior performance to the traditional pain stimulation for good outcome prediction (quantitative analysis: AUC 0.932 vs. 0.849, p = 0.048). When using the electrical stimulation, the AUC of EEG reactivity to predict good outcome by visual analysis was 0.838, increasing to 0.932 by quantitative analysis (p = 0.039). Comparing to the traditional pain stimulation by visual analysis, the AUC of EEG reactivity for good prognostication by the electrical stimulation with quantitative analysis was significantly improved (0.932 vs. 0.770, p = 0.004). CONCLUSIONS EEG reactivity induced by the standardized electrical stimulation in combination with quantitative analysis is a promising formula for post-CA comatose patients, with increased predictive accuracy.
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Affiliation(s)
- Gang Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Yuan Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Fei Tian
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Weibi Chen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Lili Cui
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Mengdi Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Yan Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China
| | - Keming Gao
- Department of Psychiatry, Mood Disorders Program, University Hospitals Cleveland Medical Center/Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Yingying Su
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China.
| | - Hongxing Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, National Brain Injury Evaluation Quality Control Center, National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, 100053, China.
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Hwang J, Cho SM, Geocadin R, Ritzl EK. Methods of Evaluating EEG Reactivity in Adult Intensive Care Units: A Review. J Clin Neurophysiol 2024:00004691-990000000-00133. [PMID: 38857365 DOI: 10.1097/wnp.0000000000001078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
PURPOSE EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent. METHODS A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias. RESULTS One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds). CONCLUSIONS This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature.
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Affiliation(s)
- Jaeho Hwang
- Division of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
| | - Sung-Min Cho
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
| | - Romergryko Geocadin
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
| | - Eva K Ritzl
- Division of Epilepsy, Department of Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
- Division of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine and Neurology, Johns Hopkins Hospital, Baltimore, Maryland, U.S.A.; and
- Division of Intraoperative Monitoring, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A
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Bencsik CM, Kramer AH, Couillard P, MacKay M, Kromm JA. Postarrest Neuroprognostication: Practices and Opinions of Canadian Physicians. Can J Neurol Sci 2024; 51:404-415. [PMID: 37489539 DOI: 10.1017/cjn.2023.261] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
BACKGROUND Objective, evidence-based neuroprognostication of postarrest patients is crucial to avoid inappropriate withdrawal of life-sustaining therapies or prolonged, invasive, and costly therapies that could perpetuate suffering when there is no chance of an acceptable recovery. Postarrest prognostication guidelines exist; however, guideline adherence and practice variability are unknown. OBJECTIVE To investigate Canadian practices and opinions regarding assessment of neurological prognosis in postarrest patients. METHODS An anonymous electronic survey was distributed to physicians who care for adult postarrest patients. RESULTS Of the 134 physicians who responded to the survey, 63% had no institutional protocols for neuroprognostication. While the use of targeted temperature management did not affect the timing of neuroprognostication, an increasing number of clinical findings suggestive of a poor prognosis affected the timing of when physicians were comfortable concluding patients had a poor prognosis. Variability existed in what factors clinicians' thought were confounders. Physicians identified bilaterally absent pupillary light reflexes (85%), bilaterally absent corneal reflexes (80%), and status myoclonus (75%) as useful in determining poor prognosis. Computed tomography, magnetic resonance imaging, and spot electroencephalography were the most useful and accessible tests. Somatosensory evoked potentials were useful, but logistically challenging. Serum biomarkers were unavailable at most centers. Most (79%) physicians agreed ≥2 definitive findings on neurologic exam, electrophysiologic tests, neuroimaging, and/or biomarkers are required to determine a poor prognosis with a high degree of certainty. Distress during the process of neuroprognostication was reported by 70% of physicians and 51% request a second opinion from an external expert. CONCLUSION Significant variability exists in post-cardiac arrest neuroprognostication practices among Canadian physicians.
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Affiliation(s)
- Caralyn M Bencsik
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Health Services, Calgary, AB, Canada
| | - Andreas H 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
| | - Philippe Couillard
- 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 A 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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Yuan Y, Ma Y, Wu Q, Huo L, Liu CF, Liu X. Clinical and electroencephalogram characteristics of methylmalonic acidemia with MMACHC and MUT gene mutations. BMC Pediatr 2024; 24:119. [PMID: 38355526 PMCID: PMC10865547 DOI: 10.1186/s12887-024-04559-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
OBJECTIVE This study investigated the clinical, imaging, and electroencephalogram (EEG) characteristics of methylmalonic acidemia (MMA) with nervous system damage as the primary manifestation. METHODS From January 2017 to November 2022, patients with nervous system injury as the main clinical manifestation, diagnosed with methylmalonic acidemia by metabolic and genetic testing, were enrolled and analyzed. Their clinical, imaging, and electroencephalogram data were analyzed. RESULTS A total of 18 patients were enrolled, including 15 males and 3 females. The clinical symptoms were convulsions, poor feeding, growth retardation, disorder of consciousness, developmental delay, hypotonia, and blood system changes. There were 6 cases (33%) of hydrocephalus, 9 (50%) of extracerebral space widened, 5 (27%) of corpus callosum thinning, 3 (17%) of ventricular dilation, 3 (17%) of abnormal signals in the brain parenchyma (frontal lobe, basal ganglia region, and brain stem), and 3 (17%) of abnormal signals in the lateral paraventricular. In addition, there were 3 cases (17%) of cerebral white matter atrophy and 1 (5%) of cytotoxic edema in the basal ganglia and cerebral peduncle. EEG data displayed 2 cases (11%) of hypsarrhythmia, 3 (17%) of voltage reduction, 12(67%) of abnormal discharge, 13 (72%) of abnormal sleep physiological waves or abnormal sleep structure, 1 (5%) of immature (delayed) EEG development, and 8 (44%) of slow background. There were 2 cases (11%) of spasms, 1 (5%) of atonic seizures, and 1 (5%) of myoclonic seizures. There were 16 patients (89%) with hyperhomocysteinemia. During follow-up, 1 patient was lost to follow-up, and 1 died. In total, 87.5% (14/16) of the children had varying developmental delays. EEG was re-examined in 11 cases, of which 8 were normal, and 3 were abnormal. Treatments included intramuscular injections of vitamin B12, L-carnitine, betaine, folic acid, and oral antiepileptic therapy. Acute treatment included anti-infective, blood transfusion, fluid replacement, and correcting acidosis. The other treatments included low-protein diets and special formula milk powder. CONCLUSION Methylmalonic acidemia can affect the central nervous system, leading to structural changes or abnormal signals on brain MRI. Metabolic screening and genetic testing help clarify the diagnosis. EEG can reflect changes in brain waves during the acute phase.
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Affiliation(s)
- Yujun Yuan
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ying Ma
- Department of Neurology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qiong Wu
- Department of Neurology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Liang Huo
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China
| | - Chun-Feng Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Xueyan Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China.
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Berg KM, Bray JE, Ng KC, Liley HG, Greif R, Carlson JN, Morley PT, Drennan IR, Smyth M, Scholefield BR, Weiner GM, Cheng A, Djärv T, Abelairas-Gómez C, Acworth J, Andersen LW, Atkins DL, Berry DC, Bhanji F, Bierens J, Bittencourt Couto T, Borra V, Böttiger BW, Bradley RN, Breckwoldt J, Cassan P, Chang WT, Charlton NP, Chung SP, Considine J, Costa-Nobre DT, Couper K, Dainty KN, Dassanayake V, Davis PG, Dawson JA, Fernanda de Almeida M, De Caen AR, Deakin CD, Dicker B, Douma MJ, Eastwood K, El-Naggar W, Fabres JG, Fawke J, Fijacko N, Finn JC, Flores GE, Foglia EE, Folke F, Gilfoyle E, Goolsby CA, Granfeldt A, Guerguerian AM, Guinsburg R, Hatanaka T, Hirsch KG, Holmberg MJ, Hosono S, Hsieh MJ, Hsu CH, Ikeyama T, Isayama T, Johnson NJ, Kapadia VS, Daripa Kawakami M, Kim HS, Kleinman ME, Kloeck DA, Kudenchuk P, Kule A, Kurosawa H, Lagina AT, Lauridsen KG, Lavonas EJ, Lee HC, Lin Y, Lockey AS, Macneil F, Maconochie IK, John Madar R, Malta Hansen C, Masterson S, Matsuyama T, McKinlay CJD, Meyran D, Monnelly V, Nadkarni V, Nakwa FL, Nation KJ, Nehme Z, Nemeth M, Neumar RW, Nicholson T, Nikolaou N, Nishiyama C, Norii T, Nuthall GA, Ohshimo S, Olasveengen TM, Gene Ong YK, Orkin AM, Parr MJ, Patocka C, Perkins GD, Perlman JM, Rabi Y, Raitt J, Ramachandran S, Ramaswamy VV, Raymond TT, Reis AG, Reynolds JC, Ristagno G, Rodriguez-Nunez A, Roehr CC, Rüdiger M, Sakamoto T, Sandroni C, Sawyer TL, Schexnayder SM, Schmölzer GM, Schnaubelt S, Semeraro F, Singletary EM, Skrifvars MB, Smith CM, Soar J, Stassen W, Sugiura T, Tijssen JA, Topjian AA, Trevisanuto D, Vaillancourt C, Wyckoff MH, Wyllie JP, Yang CW, Yeung J, Zelop CM, Zideman DA, Nolan JP. 2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces. Resuscitation 2024; 195:109992. [PMID: 37937881 DOI: 10.1016/j.resuscitation.2023.109992] [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] [Indexed: 11/09/2023]
Abstract
The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates.
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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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11
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Berg KM, Bray JE, Ng KC, Liley HG, Greif R, Carlson JN, Morley PT, Drennan IR, Smyth M, Scholefield BR, Weiner GM, Cheng A, Djärv T, Abelairas-Gómez C, Acworth J, Andersen LW, Atkins DL, Berry DC, Bhanji F, Bierens J, Bittencourt Couto T, Borra V, Böttiger BW, Bradley RN, Breckwoldt J, Cassan P, Chang WT, Charlton NP, Chung SP, Considine J, Costa-Nobre DT, Couper K, Dainty KN, Dassanayake V, Davis PG, Dawson JA, de Almeida MF, De Caen AR, Deakin CD, Dicker B, Douma MJ, Eastwood K, El-Naggar W, Fabres JG, Fawke J, Fijacko N, Finn JC, Flores GE, Foglia EE, Folke F, Gilfoyle E, Goolsby CA, Granfeldt A, Guerguerian AM, Guinsburg R, Hatanaka T, Hirsch KG, Holmberg MJ, Hosono S, Hsieh MJ, Hsu CH, Ikeyama T, Isayama T, Johnson NJ, Kapadia VS, Kawakami MD, Kim HS, Kleinman ME, Kloeck DA, Kudenchuk P, Kule A, Kurosawa H, Lagina AT, Lauridsen KG, Lavonas EJ, Lee HC, Lin Y, Lockey AS, Macneil F, Maconochie IK, Madar RJ, Malta Hansen C, Masterson S, Matsuyama T, McKinlay CJD, Meyran D, Monnelly V, Nadkarni V, Nakwa FL, Nation KJ, Nehme Z, Nemeth M, Neumar RW, Nicholson T, Nikolaou N, Nishiyama C, Norii T, Nuthall GA, Ohshimo S, Olasveengen TM, Ong YKG, Orkin AM, Parr MJ, Patocka C, Perkins GD, Perlman JM, Rabi Y, Raitt J, Ramachandran S, Ramaswamy VV, Raymond TT, Reis AG, Reynolds JC, Ristagno G, Rodriguez-Nunez A, Roehr CC, Rüdiger M, Sakamoto T, Sandroni C, Sawyer TL, Schexnayder SM, Schmölzer GM, Schnaubelt S, Semeraro F, Singletary EM, Skrifvars MB, Smith CM, Soar J, Stassen W, Sugiura T, Tijssen JA, Topjian AA, Trevisanuto D, Vaillancourt C, Wyckoff MH, Wyllie JP, Yang CW, Yeung J, Zelop CM, Zideman DA, Nolan JP. 2023 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces. Circulation 2023; 148:e187-e280. [PMID: 37942682 PMCID: PMC10713008 DOI: 10.1161/cir.0000000000001179] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
The International Liaison Committee on Resuscitation engages in a continuous review of new, peer-reviewed, published cardiopulmonary resuscitation and first aid science. Draft Consensus on Science With Treatment Recommendations are posted online throughout the year, and this annual summary provides more concise versions of the final Consensus on Science With Treatment Recommendations from all task forces for the year. Topics addressed by systematic reviews this year include resuscitation of cardiac arrest from drowning, extracorporeal cardiopulmonary resuscitation for adults and children, calcium during cardiac arrest, double sequential defibrillation, neuroprognostication after cardiac arrest for adults and children, maintaining normal temperature after preterm birth, heart rate monitoring methods for diagnostics in neonates, detection of exhaled carbon dioxide in neonates, family presence during resuscitation of adults, and a stepwise approach to resuscitation skills training. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, using Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations. Insights into the deliberations of the task forces are provided in the Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces list priority knowledge gaps for further research. Additional topics are addressed with scoping reviews and evidence updates.
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12
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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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13
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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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14
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Sumner BD, Hahn CW. Prognosis of Cardiac Arrest-Peri-arrest and Post-arrest Considerations. Emerg Med Clin North Am 2023; 41:601-616. [PMID: 37391253 DOI: 10.1016/j.emc.2023.03.008] [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: 07/02/2023]
Abstract
There has been only a small improvement in survival and neurologic outcomes in patients with cardiac arrest in recent decades. Type of arrest, length of total arrest time, and location of arrest alter the trajectory of survival and neurologic outcome. In the post-arrest phase, clinical markers such as blood markers, pupillary light response, corneal reflex, myoclonic jerking, somatosensory evoked potential, and electroencephalography testing can be used to help guide neurological prognostication. Most of the testing should be performed 72 hours post-arrest with special considerations for longer observation periods in patients who underwent TTM or who had prolonged sedation and/or neuromuscular blockade.
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Affiliation(s)
- Brian D Sumner
- Institute for Critical Care Medicine, 1468 Madison Avenue, Guggenheim Pavilion 6 East Room 378, New York, NY 10029, USA.
| | - Christopher W Hahn
- Department of Emergency Medicine, Mount Sinai Morningside-West, 1000 10th Avenue, New York, NY 10019, USA
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15
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Lee S, Park JS, You Y, Min JH, Jeong W, Ahn HJ, In YN, Cho YC, Lee IH, Lee JK, Kang C. Preliminary Prognostication for Good Neurological Outcomes in the Early Stage of Post-Cardiac Arrest Care. Diagnostics (Basel) 2023; 13:2174. [PMID: 37443569 DOI: 10.3390/diagnostics13132174] [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: 05/29/2023] [Revised: 06/15/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
We investigated prognostic strategies for predicting good outcomes in the early stage of post-cardiac-arrest care using multiple prognostic tests that are available until 24 h after the return of spontaneous circulation (ROSC). A retrospective analysis was conducted on 138 out-of-hospital cardiac-arrest patients who underwent prognostic tests, including the gray-white-matter ratio (GWR-BG), the Glasgow Coma Scale motor (GCS-M) score before sedative administration, and the neuron-specific enolase (NSE) level measured at 24 h after the ROSC. We investigated the prognostic performances of the tests as single predictors and in various combination strategies. Classification and regression-tree analysis were used to provide a reliable model for the risk stratification. Out of all the patients, 55 (44.0%) had good outcomes. The NSE level showed the highest prognostic performance as a single prognostic test and provided improved specificities (>70%) and sensitivities (>98%) when used in combination strategies. Low NSE levels (≤32.1 ng/mL) and high GCS-M (≥4) scores identified good outcomes without misclassification. The overall accuracy for good outcomes was 81.8%. In comatose patients with low NSE levels or high GCS-M scores, the premature withdrawal of life-sustaining therapy should be avoided, thereby complying with the formal prognostication-strategy algorithm after at least 72 h from the ROSC.
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Affiliation(s)
- Sunghyuk Lee
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
| | - Jung Soo Park
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon 35015, Republic of Korea
| | - Yeonho You
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
| | - Jin Hong Min
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon 35015, Republic of Korea
- Department of Emergency Medicine, Chungnam National University Sejong Hospital, 20, Bodeum 7-ro, Sejong 30099, Republic of Korea
| | - Wonjoon Jeong
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon 35015, Republic of Korea
| | - Hong Joon Ahn
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon 35015, Republic of Korea
| | - Yong Nam In
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon 35015, Republic of Korea
- Department of Emergency Medicine, Chungnam National University Sejong Hospital, 20, Bodeum 7-ro, Sejong 30099, Republic of Korea
| | - Yong Chul Cho
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
| | - In Ho Lee
- Department of Radiology, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
- Department of Radiology, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon 35015, Republic of Korea
| | - Jae Kwang Lee
- Department of Emergency Medicine, Konyang University Hospital, College of Medicine, Daejeon 35365, Republic of Korea
| | - Changshin Kang
- Department of Emergency Medicine, Chungnam National University Hospital, 282 Munhwa-ro, Jung-gu, Daejeon 35015, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 282 Mokdong-ro, Jung-gu, Daejeon 35015, Republic of Korea
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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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17
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Bouchereau E, Marchi A, Hermann B, Pruvost-Robieux E, Guinard E, Legouy C, Schimpf C, Mazeraud A, Baron JC, Ramdani C, Gavaret M, Sharshar T, Turc G. Quantitative analysis of early-stage EEG reactivity predicts awakening and recovery of consciousness in patients with severe brain injury. Br J Anaesth 2023; 130:e225-e232. [PMID: 36243578 DOI: 10.1016/j.bja.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Decisions of withdrawal of life-sustaining therapy for patients with severe brain injury are often based on prognostic evaluations such as analysis of electroencephalography (EEG) reactivity (EEG-R). However, EEG-R usually relies on visual assessment, which requires neurophysiological expertise and is prone to inter-rater variability. We hypothesised that quantitative analysis of EEG-R obtained 3 days after patient admission can identify new markers of subsequent awakening and consciousness recovery. METHODS In this prospective observational study of patients with severe brain injury requiring mechanical ventilation, quantitative EEG-R was assessed using standard 11-lead EEG with frequency-based (power spectral density) and functional connectivity-based (phase-lag index) analyses. Associations between awakening in the intensive care unit (ICU) and reactivity to auditory and nociceptive stimulations were assessed with logistic regression. Secondary outcomes included in-ICU mortality and 3-month Coma Recovery Scale-Revised (CRS-R) score. RESULTS Of 116 patients, 86 (74%) awoke in the ICU. Among quantitative EEG-R markers, variation in phase-lag index connectivity in the delta frequency band after noise stimulation was associated with awakening (adjusted odds ratio=0.89, 95% confidence interval: 0.81-0.97, P=0.02 corrected for multiple tests), independently of age, baseline severity, and sedation. This new marker was independently associated with improved 3-month CRS-R (adjusted β=-0.16, standard error 0.075, P=0.048), but not with mortality (adjusted odds ratio=1.08, 95% CI: 0.99-1.18, P=0.10). CONCLUSIONS An early-stage quantitative EEG-R marker was independently associated with awakening and 3-month level of consciousness in patients with severe brain injury. This promising marker based on functional connectivity will need external validation before potential integration into a multimodal prognostic model.
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Affiliation(s)
- Eléonore Bouchereau
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France.
| | - Angela Marchi
- Epileptology and Cerebral Rhythmology Department, APHM, Timone Hospital, Marseille, France
| | - Bertrand Hermann
- ICU Department, Hôpital Européen Georges Pompidou, Paris, France; Institut du Cerveau et de la Moelle épinière - ICM, Paris, France; Université Paris Cité, Paris, France
| | - Estelle Pruvost-Robieux
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Eléonore Guinard
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France
| | - Camille Legouy
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Caroline Schimpf
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France
| | - Aurélien Mazeraud
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Université Paris Cité, Paris, France
| | - Jean-Claude Baron
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Céline Ramdani
- Institut de Recherche Biomédicale des Armées (IRBA), Brétigny-sur-Orge, France
| | - Martine Gavaret
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurophysiology Department, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
| | - Tarek Sharshar
- Anaesthesiology and ICU Department, Sainte Anne Hospital, Paris, France; Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; FHU NeuroVasc, Paris, France
| | - Guillaume Turc
- Institute of Psychiatry and Neurosciences of Paris (IPNP), INSERM U1266, Paris, France; Université Paris Cité, Paris, France; Neurology Department, GHU Paris Psychiatry and Neurosciences, Sainte Anne Hospital, Paris, France; FHU NeuroVasc, Paris, France
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18
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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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19
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Benghanem S, Pruvost-Robieux E, Bouchereau E, Gavaret M, Cariou A. Prognostication after cardiac arrest: how EEG and evoked potentials may improve the challenge. Ann Intensive Care 2022; 12:111. [PMID: 36480063 PMCID: PMC9732180 DOI: 10.1186/s13613-022-01083-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 11/07/2022] [Indexed: 12/13/2022] Open
Abstract
About 80% of patients resuscitated from CA are comatose at ICU admission and nearly 50% of survivors are still unawake at 72 h. Predicting neurological outcome of these patients is important to provide correct information to patient's relatives, avoid disproportionate care in patients with irreversible hypoxic-ischemic brain injury (HIBI) and inappropriate withdrawal of care in patients with a possible favorable neurological recovery. ERC/ESICM 2021 algorithm allows a classification as "poor outcome likely" in 32%, the outcome remaining "indeterminate" in 68%. The crucial question is to know how we could improve the assessment of both unfavorable but also favorable outcome prediction. Neurophysiological tests, i.e., electroencephalography (EEG) and evoked-potentials (EPs) are a non-invasive bedside investigations. The EEG is the record of brain electrical fields, characterized by a high temporal resolution but a low spatial resolution. EEG is largely available, and represented the most widely tool use in recent survey examining current neuro-prognostication practices. The severity of HIBI is correlated with the predominant frequency and background continuity of EEG leading to "highly malignant" patterns as suppression or burst suppression in the most severe HIBI. EPs differ from EEG signals as they are stimulus induced and represent the summated activities of large populations of neurons firing in synchrony, requiring the average of numerous stimulations. Different EPs (i.e., somato sensory EPs (SSEPs), brainstem auditory EPs (BAEPs), middle latency auditory EPs (MLAEPs) and long latency event-related potentials (ERPs) with mismatch negativity (MMN) and P300 responses) can be assessed in ICU, with different brain generators and prognostic values. In the present review, we summarize EEG and EPs signal generators, recording modalities, interpretation and prognostic values of these different neurophysiological tools. Finally, we assess the perspective for futures neurophysiological investigations, aiming to reduce prognostic uncertainty in comatose and disorders of consciousness (DoC) patients after CA.
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Affiliation(s)
- Sarah Benghanem
- grid.411784.f0000 0001 0274 3893Medical ICU, Cochin Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France ,grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,After ROSC Network, Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Estelle Pruvost-Robieux
- grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,Neurophysiology and Epileptology Department, GHU Psychiatry and Neurosciences, Sainte Anne, 75014 Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Eléonore Bouchereau
- Department of Neurocritical Care, G.H.U Paris Psychiatry and Neurosciences, 1, Rue Cabanis, 75014 Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Martine Gavaret
- grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,Neurophysiology and Epileptology Department, GHU Psychiatry and Neurosciences, Sainte Anne, 75014 Paris, France ,grid.7429.80000000121866389UMR 1266, Institut de Psychiatrie et, INSERM FHU NeuroVascNeurosciences de Paris-IPNP, 75014 Paris, France
| | - Alain Cariou
- grid.411784.f0000 0001 0274 3893Medical ICU, Cochin Hospital, Assistance Publique – Hôpitaux de Paris (AP-HP), 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France ,grid.508487.60000 0004 7885 7602Medical School, University Paris Cité, Paris, France ,After ROSC Network, Paris, France ,grid.462416.30000 0004 0495 1460Paris-Cardiovascular-Research-Center (Sudden-Death-Expertise-Center), INSERM U970, Paris, France
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20
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Noachtar S, Remi J, Kaufmann E. EEG-Update. KLIN NEUROPHYSIOL 2022. [DOI: 10.1055/a-1949-1691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Durch die rasante Entwicklung digitaler Computertechniken und neuer
Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme
(quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der
Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen
Möglichkeiten der Analyse des EEG durch Einsatz künstlicher
Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie
telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der
Methode vermutlich in den nächsten Jahren erweitern.
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21
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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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22
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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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23
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Zheng WL, Amorim E, Jing J, Wu O, Ghassemi M, Lee JW, Sivaraju A, Pang T, Herman ST, Gaspard N, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning. IEEE Trans Biomed Eng 2022; 69:1813-1825. [PMID: 34962860 PMCID: PMC9087641 DOI: 10.1109/tbme.2021.3139007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
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24
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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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25
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Fan J, Zhong Y, Wang H, Aierken N, He R. Repetitive transcranial magnetic stimulation improves consciousness in some patients with disorders of consciousness. Clin Rehabil 2022; 36:916-925. [PMID: 35322709 DOI: 10.1177/02692155221089455] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE To investigate the therapeutic efficacy of repetitive transcranial magnetic stimulation (rTMS) in patients with disorders of consciousness. DESIGN AND SETTING We performed a randomized, double-blinded, sham-controlled trial. PARTICIPANTS Patients (N = 40) with disorders of consciousness. INTERVENTIONS Forty patients with disorders of consciousness (time since onset of the disorder 49.0 ± 24.6 days) were enrolled and randomized to groups receiving either active-rTMS or sham-rTMS. The active-TMS protocol had a frequency of 20 Hz, was delivered over the left dorsolateral prefrontal cortex and had a 100% rest motor threshold. The sham-rTMS protocol was the same as the active protocol without magnetic stimulation over the cortex. MAIN OUTCOME MEASURES Consciousness was evaluated by the Coma Recovery Scale-Revised (CRS-R) before and after the four-week intervention. The ratio of patients that awakened from disorders of consciousness was followed up at discharge. RESULTS Before rTMS sessions, there were no significant differences in consciousness scores between groups. Compared to sham-rTMS (6.25 ± 1.29), patients with disorders of consciousness treated by active rTMS showed strikingly improved consciousness (8.45 ± 3.55). In-depth analysis revealed that only some patients showed obvious increases in consciousness scores induced by active rTMS. Furthermore, rTMS did not significantly enhance the awakening ratio. CONCLUSIONS rTMS showed therapeutic efficacy for improving consciousness in some, but not all, patients with disorders of consciousness. It is essential to discern the potential patients whose consciousness can be improved by rTMS.
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Affiliation(s)
- Jianzhong Fan
- Department of Rehabilitation Medicine, Nanfang Hospital, 198153Southern Medical University, Guangzhou 510515, China
| | - Yuhua Zhong
- Department of Rehabilitation Medicine, Nanfang Hospital, 198153Southern Medical University, Guangzhou 510515, China
| | - Huijuan Wang
- Department of Rehabilitation Medicine, Nanfang Hospital, 198153Southern Medical University, Guangzhou 510515, China
| | - Nilubaier Aierken
- Department of Rehabilitation Medicine, Nanfang Hospital, 198153Southern Medical University, Guangzhou 510515, China
| | - Renhong He
- Department of Rehabilitation Medicine, Nanfang Hospital, 198153Southern Medical University, Guangzhou 510515, China
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26
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Snider SB, Fischer D, McKeown ME, Cohen AL, Schaper FLWVJ, Amorim E, Fox MD, Scirica B, Bevers MB, Lee JW. Regional Distribution of Brain Injury After Cardiac Arrest: Clinical and Electrographic Correlates. Neurology 2022; 98:e1238-e1247. [PMID: 35017304 PMCID: PMC8967331 DOI: 10.1212/wnl.0000000000013301] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 12/27/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Disorders of consciousness, EEG background suppression, and epileptic seizures are associated with poor outcome after cardiac arrest. Our objective was to identify the distribution of diffusion MRI-measured anoxic brain injury after cardiac arrest and to define the regional correlates of disorders of consciousness, EEG background suppression, and seizures. METHODS We analyzed patients from a single-center database of unresponsive patients who underwent diffusion MRI after cardiac arrest (n = 204). We classified each patient according to recovery of consciousness (command following) before discharge, the most continuous EEG background (burst suppression vs continuous), and the presence or absence of seizures. Anoxic brain injury was measured with the apparent diffusion coefficient (ADC) signal. We identified ADC abnormalities relative to controls without cardiac arrest (n = 48) and used voxel lesion symptom mapping to identify regional associations with disorders of consciousness, EEG background suppression, and seizures. We then used a bootstrapped lasso regression procedure to identify robust, multivariate regional associations with each outcome variable. Last, using area under receiver operating characteristic curves, we then compared the classification ability of the strongest regional associations to that of brain-wide summary measures. RESULTS Compared to controls, patients with cardiac arrest demonstrated ADC signal reduction that was most significant in the occipital lobes. Disorders of consciousness were associated with reduced ADC most prominently in the occipital lobes but also in deep structures. Regional injury more accurately classified patients with disorders of consciousness than whole-brain injury. Background suppression mapped to a similar set of brain regions, but regional injury could no better classify patients than whole-brain measures. Seizures were less common in patients with more severe anoxic injury, particularly in those with injury to the lateral temporal white matter. DISCUSSION Anoxic brain injury was most prevalent in posterior cerebral regions, and this regional pattern of injury was a better predictor of disorders of consciousness than whole-brain injury measures. EEG background suppression lacked a specific regional association, but patients with injury to the temporal lobe were less likely to have seizures. Regional patterns of anoxic brain injury are relevant to the clinical and electrographic sequelae of cardiac arrest and may hold importance for prognosis. CLASSIFICATION OF EVIDENCE This study provides Class IV evidence that disorders of consciousness after cardiac arrest are associated with widely lower ADC values on diffusion MRI and are most strongly associated with reductions in occipital ADC.
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Affiliation(s)
- Samuel B Snider
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
| | - David Fischer
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Morgan E McKeown
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Alexander Li Cohen
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Frederic L W V J Schaper
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Edilberto Amorim
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Michael D Fox
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Benjamin Scirica
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Matthew B Bevers
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Jong Woo Lee
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
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27
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Sandroni C, D'Arrigo S, Cacciola S, Hoedemaekers CWE, Westhall E, Kamps MJA, Taccone FS, Poole D, Meijer FJA, Antonelli M, Hirsch KG, Soar J, Nolan JP, Cronberg T. Prediction of good neurological outcome in comatose survivors of cardiac arrest: a systematic review. Intensive Care Med 2022; 48:389-413. [PMID: 35244745 PMCID: PMC8940794 DOI: 10.1007/s00134-022-06618-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 01/03/2022] [Indexed: 12/11/2022]
Abstract
PURPOSE To assess the ability of clinical examination, blood biomarkers, electrophysiology or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict good neurological outcome, defined as no, mild, or moderate disability (CPC 1-2 or mRS 0-3) at discharge from intensive care unit or later, in comatose adult survivors from cardiac arrest (CA). METHODS PubMed, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews were searched. Sensitivity and specificity for good outcome were calculated for each predictor. The risk of bias was assessed using the QUIPS tool. RESULTS A total of 37 studies were included. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed. A withdrawal or localisation motor response to pain immediately or at 72-96 h after ROSC, normal blood values of neuron-specific enolase (NSE) at 24 h-72 h after ROSC, a short-latency somatosensory evoked potentials (SSEPs) N20 wave amplitude > 4 µV or a continuous background without discharges on electroencephalogram (EEG) within 72 h from ROSC, and absent diffusion restriction in the cortex or deep grey matter on MRI on days 2-7 after ROSC predicted good neurological outcome with more than 80% specificity and a sensitivity above 40% in most studies. Most studies had moderate or high risk of bias. CONCLUSIONS In comatose cardiac arrest survivors, clinical, biomarker, electrophysiology, and imaging studies identified patients destined to a good neurological outcome with high specificity within the first week after cardiac arrest (CA).
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Affiliation(s)
- Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"-IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.,Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Sonia D'Arrigo
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"-IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.
| | - Sofia Cacciola
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"-IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy
| | | | - Erik Westhall
- Department of Clinical Sciences Lund, Clinical Neurophysiology, Lund University, Skane University Hospital, Lund, Sweden
| | - Marlijn J A Kamps
- Intensive Care Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Fabio S Taccone
- Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Daniele Poole
- Department of Anaesthesiology and Intensive Care, San Martino Hospital, Belluno, Italy
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Massimo Antonelli
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"-IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.,Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Karen G Hirsch
- Department of Neurology, Stanford University, Stanford, USA
| | - Jasmeet Soar
- Critical Care Unit, Southmead Hospital, North Bristol NHS Trust, Bristol, UK
| | - Jerry P Nolan
- Department of Anaesthesia and Intensive Care Medicine, Royal United Hospital, Bath, UK
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Skane University Hospital, Lund, Sweden
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28
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Bronder J, Cho SM, Geocadin RG, Ritzl EK. Revisiting EEG as part of the multidisciplinary approach to post-cardiac arrest care and prognostication: A review. Resusc Plus 2022; 9:100189. [PMID: 34988537 PMCID: PMC8693464 DOI: 10.1016/j.resplu.2021.100189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 11/24/2021] [Accepted: 11/26/2021] [Indexed: 11/01/2022] Open
Abstract
Since the 1960s, EEG has been used to assess the neurologic function of patients in the hours and days after cardiac arrest. Accurate and reliable prognostication after cardiac arrest is vital for tailoring aggressive patient care for those with a high likelihood of recovery and setting appropriate goals of care for those who have a high likelihood of a poor outcome. Attempts to define EEG's role in this process has evolved over the years. In this review, we provide important historical context about EEG's use, it's perceived unreliability in the post-cardiac arrest patient in the past and provide a detailed analysis of how this role has changed recently. A review of the 71 most recent and highest quality studies demonstrates that the introduction of a uniform classification and a timed approach to EEG analysis have positioned EEG as a complementary tool in the multimodal approach for prognostication. The review was created and intended for medical staff in the intensive care units and emphasizes EEG patterns and timing which portend both favorable and poor prognoses. Also, the review addresses the overall quality of the existing studies and discusses future directions to address the knowledge gaps in this field.
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Affiliation(s)
- Jay Bronder
- Epilepsy Fellow, Department of Neurology, Johns Hopkins Hospital, 600 N. Wolfe St / Meyer 2-147, Baltimore, MD 21287-7247, USA
| | - Sung-Min Cho
- Neuroscience Critical Care Division, Departments of Neurology, Neurosurgery, and Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Baltimore, MD 21287, USA
| | - Romergryko G. Geocadin
- Professor of Neurology, Anesthesiology-Critical Care, Neurosurgery, and Joint Appointment in Medicine, The Johns Hopkins University School of Medicine, 600 N. Wolfe Street, Phipps 455, Baltimore, MD 21287, USA
| | - Eva Katharina Ritzl
- Department of Neurology and Anesthesia and Critical Care Medicine, Johns Hopkins Hospital, 1800 Orleans Street, Suite 3329, Baltimore, MD 21287, USA
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29
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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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30
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Pruvost-Robieux E, Marchi A, Martinelli I, Bouchereau E, Gavaret M. Evoked and Event-Related Potentials as Biomarkers of Consciousness State and Recovery. J Clin Neurophysiol 2022; 39:22-31. [PMID: 34474424 PMCID: PMC8715993 DOI: 10.1097/wnp.0000000000000762] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
SUMMARY The definition of consciousness has been the subject of great interest for many scientists and philosophers. To better understand how evoked potentials may be identified as biomarkers of consciousness and recovery, the different theoretical models sustaining neural correlates of consciousness are reviewed. A multimodal approach can help to better predict clinical outcome in patients presenting with disorders of consciousness. Evoked potentials are inexpensive and easy-to-implement bedside examination techniques. Evoked potentials are an integral part of prognostic evaluation, particularly in cases of cognitive motor dissociation. Prognostic criteria are well established in postanoxic disorders of consciousness, especially postcardiac arrest but are less well determined in other etiologies. In the early examination, bilateral absence of N20 in disorder of consciousness patients is strongly associated with unfavorable outcome (i.e., death or unresponsive wakefulness syndrome) especially in postanoxic etiologies. This predictive value is lower in other etiologies and probably also in children. Both N20 and mismatch negativity are proven outcome predictors for acute coma. Many studies have shown that mismatch negativity and P3a are characterized by a high prognostic value for awakening, but some patients presenting unresponsive wakefulness syndrome also process a P3a. The presence of long-latency event-related potential components in response to stimuli is indicative of a better recovery. All neurophysiological data must be integrated within a multimodal approach combining repeated clinical evaluation, neuroimaging, functional imaging, biology, and neurophysiology combining passive and active paradigms.
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Affiliation(s)
- Estelle Pruvost-Robieux
- Neurophysiology Department, GHU Paris Psychiatry & Neurosciences, Sainte Anne, Paris, France
- Paris University, Paris, France
| | - Angela Marchi
- Neurophysiology Department, GHU Paris Psychiatry & Neurosciences, Sainte Anne, Paris, France
| | - Ilaria Martinelli
- Department of Neurosciences, St. Agostino-Estense Hospital, Azienda Ospedaliero, Universitaria di Modena, Modena, Italy;
| | - Eléonore Bouchereau
- Department of Anesthesiology and intensive care, GHU Paris Psychiatry & Neurosciences, Sainte Anne, Paris, France; and
| | - Martine Gavaret
- Neurophysiology Department, GHU Paris Psychiatry & Neurosciences, Sainte Anne, Paris, France
- Paris University, Paris, France
- INSERM UMR 1266, Paris, France
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31
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Sandroni C, Cronberg T, Sekhon M. Brain injury after cardiac arrest: pathophysiology, treatment, and prognosis. Intensive Care Med 2021; 47:1393-1414. [PMID: 34705079 PMCID: PMC8548866 DOI: 10.1007/s00134-021-06548-2] [Citation(s) in RCA: 172] [Impact Index Per Article: 57.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 09/28/2021] [Indexed: 01/03/2023]
Abstract
Post-cardiac arrest brain injury (PCABI) is caused by initial ischaemia and subsequent reperfusion of the brain following resuscitation. In those who are admitted to intensive care unit after cardiac arrest, PCABI manifests as coma, and is the main cause of mortality and long-term disability. This review describes the mechanisms of PCABI, its treatment options, its outcomes, and the suggested strategies for outcome prediction.
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Affiliation(s)
- Claudio Sandroni
- Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy. .,Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli", IRCCS, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168, Rome, Italy.
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Mypinder Sekhon
- Division of Critical Care Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
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32
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Bauerschmidt A, Eliseyev A, Doyle KW, Velasquez A, Egbebike J, Chiu W, Kumar V, Alkhachroum A, Der Nigoghossian C, Al-Mufti F, Rabbani L, Brodie D, Rubinos C, Park S, Roh D, Agarwal S, Claassen J. Predicting early recovery of consciousness after cardiac arrest supported by quantitative electroencephalography. Resuscitation 2021; 165:130-137. [PMID: 34166746 PMCID: PMC10008439 DOI: 10.1016/j.resuscitation.2021.06.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 06/03/2021] [Accepted: 06/16/2021] [Indexed: 01/27/2023]
Abstract
OBJECTIVE To determine the ability of quantitative electroencephalography (QEEG) to improve the accuracy of predicting recovery of consciousness by post-cardiac arrest day 10. METHODS Unconscious survivors of cardiac arrest undergoing daily clinical and EEG assessments through post-cardiac arrest day 10 were studied in a prospective observational cohort study. Power spectral density, local coherence, and permutation entropy were calculated from daily EEG clips following a painful stimulus. Recovery of consciousness was defined as following at least simple commands by day 10. We determined the impact of EEG metrics to predict recovery when analyzed with established predictors of recovery using partial least squares regression models. Explained variance analysis identified which features contributed most to the predictive model. RESULTS 367 EEG epochs from 98 subjects were analyzed in conjunction with clinical measures. Highest prediction accuracy was achieved when adding QEEG features from post-arrest days 4-6 to established predictors (area under the receiver operating curve improved from 0.81 ± 0.04 to 0.86 ± 0.05). Prediction accuracy decreased from 0.84 ± 0.04 to 0.79 ± 0.04 when adding QEEG features from post-arrest days 1-3. Patients with recovery of command-following by day 10 showed higher coherence across the frequency spectrum and higher centro-occipital delta-frequency spectral power by days 4-6, and globally-higher theta range permutation entropy by days 7-10. CONCLUSIONS Adding quantitative EEG metrics to established predictors of recovery allows modest improvement of prediction accuracy for recovery of consciousness, when obtained within a week of cardiac arrest. Further research is needed to determine the best strategy for integration of QEEG data into prognostic models in this patient population.
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Affiliation(s)
- Andrew Bauerschmidt
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Andrey Eliseyev
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Kevin W Doyle
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Angela Velasquez
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Jennifer Egbebike
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Wendy Chiu
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Vedika Kumar
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Ayham Alkhachroum
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | - Fawaz Al-Mufti
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - LeRoy Rabbani
- Cardiac Care Unit, Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Daniel Brodie
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, Columbia University Medical Center, New York, NY, USA
| | - Clio Rubinos
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Soojin Park
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - David Roh
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Sachin Agarwal
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Jan Claassen
- Department of Neurology, Columbia University Medical Center, New York, NY, USA.
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33
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Admiraal MM, Ramos LA, Delgado Olabarriaga S, Marquering HA, Horn J, van Rootselaar AF. Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest. Clin Neurophysiol 2021; 132:2240-2247. [PMID: 34315065 DOI: 10.1016/j.clinph.2021.07.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 04/06/2021] [Accepted: 07/03/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA). METHODS Several types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3-5 within 6 months. RESULTS The Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80-86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40-51) and 89% specificity (95%-CI 86-92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83-88) at 24 h after CA, with 62% sensitivity (95%-CI 57-67) and 84% specificity (95%-CI 79-88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives. CONCLUSIONS Quantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data. SIGNIFICANCE Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.
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Affiliation(s)
- M M Admiraal
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands.
| | - L A Ramos
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - S Delgado Olabarriaga
- Amsterdam UMC, University of Amsterdam, Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam, the Netherlands
| | - H A Marquering
- Amsterdam UMC, University of Amsterdam, Department Biomedical Engineering & Physics, Amsterdam Neuroscience, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, the Netherlands
| | - J Horn
- Amsterdam UMC, University of Amsterdam, Laboratory for Experimental Intensive Care and Anesthesiology, Amsterdam, the Netherlands; Amsterdam UMC, University of Amsterdam, Department of Intensive Care, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - A F van Rootselaar
- Amsterdam UMC, University of Amsterdam, Department of Neurology/Clinical Neurophysiology, Amsterdam Neuroscience, Amsterdam, the Netherlands
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34
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Olasveengen TM, Skrifvars MB, Taccone F, Soar J. Postreanimationsbehandlung. Notf Rett Med 2021. [DOI: 10.1007/s10049-021-00892-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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35
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Chen S, Lachance BB, Gao L, Jia X. Targeted temperature management and early neuro-prognostication after cardiac arrest. J Cereb Blood Flow Metab 2021; 41:1193-1209. [PMID: 33444088 PMCID: PMC8142127 DOI: 10.1177/0271678x20970059] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Targeted temperature management (TTM) is a recommended neuroprotective intervention for coma after out-of-hospital cardiac arrest (OHCA). However, controversies exist concerning the proper implementation and overall efficacy of post-CA TTM, particularly related to optimal timing and depth of TTM and cooling methods. A review of the literature finds that optimizing and individualizing TTM remains an open question requiring further clinical investigation. This paper will summarize the preclinical and clinical trial data to-date, current recommendations, and future directions of this therapy, including new cooling methods under investigation. For now, early induction, maintenance for at least 24 hours, and slow rewarming utilizing endovascular methods may be preferred. Moreover, timely and accurate neuro-prognostication is valuable for guiding ethical and cost-effective management of post-CA coma. Current evidence for early neuro-prognostication after TTM suggests that a combination of initial prediction models, biomarkers, neuroimaging, and electrophysiological methods is the optimal strategy in predicting neurological functional outcomes.
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Affiliation(s)
- Songyu Chen
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA.,Department of Neurosurgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Brittany Bolduc Lachance
- Program in Trauma, Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Liang Gao
- Department of Neurosurgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiaofeng Jia
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA.,Department of Orthopedics, University of Maryland School of Medicine, Baltimore, MD, USA.,Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA.,Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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36
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Touchard C, Cartailler J, Vellieux G, de Montmollin E, Jaquet P, Wanono R, Reuter J, Para M, Bouadma L, Timsit JF, d'Ortho MP, Kubis N, Rouvel Tallec A, Sonneville R. Simplified frontal EEG in adults under veno-arterial extracorporeal membrane oxygenation. Ann Intensive Care 2021; 11:76. [PMID: 33987690 PMCID: PMC8119573 DOI: 10.1186/s13613-021-00854-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/12/2021] [Indexed: 12/04/2022] Open
Abstract
Background EEG-based prognostication studies in intensive care units often rely on a standard 21-electrode montage (stdEEG) requiring substantial human, technical, and financial resources. We here evaluate whether a simplified 4-frontal electrode montage (4-frontEEG) can detect EEG patterns associated with poor outcomes in adult patients under veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Methods We conducted a reanalysis of EEG data from a prospective cohort on 118 adult patients under VA-ECMO, in whom EEG was performed on admission to intensive care. EEG patterns of interest included background rhythm, discontinuity, reactivity, and the Synek’s score. They were all reassessed by an intensivist on a 4-frontEEG montage, whose analysis was then compared to an expert’s interpretation made on stdEEG recordings. The main outcome measure was the degree of correlation between 4-frontEEG and stdEEG montages to identify EEG patterns of interest. The performance of the Synek scores calculated on 4-frontEEG and stdEEG montage to predict outcomes (i.e., 28-day mortality and 90-day Rankin score \documentclass[12pt]{minimal}
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\begin{document}$${\ge {4}}$$\end{document}≥4) was investigated in a secondary exploratory analysis. Results The detection of EEG patterns using 4-frontEEG was statistically similar to that of stdEEG for background rhythm (Spearman rank test, ρ = 0.66, p < 0.001), discontinuity (Cohen’s kappa, \documentclass[12pt]{minimal}
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\begin{document}$$\kappa$$\end{document}κ = 0.955), reactivity (\documentclass[12pt]{minimal}
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\begin{document}$$\kappa$$\end{document}κ = 0.739) and the Synek’s score (ρ = 0.794, p < 0.001). Using the Synek classification, we found similar performances between 4-frontEEG and stdEEG montages in predicting 28-day mortality (AUC 4-frontEEG 0.71, AUC stdEEG 0.68) and for 90-day poor neurologic outcome (AUC 4-frontEEG 0.71, AUC stdEEG 0.66). An exploratory analysis confirmed that the Synek scores determined by 4 or 21 electrodes were independently associated with 28-day mortality and poor 90-day functional outcome. Conclusion In adult patients under VA-ECMO, a simplified 4-frontal electrode EEG montage interpreted by an intensivist, detected common EEG patterns associated with poor outcomes, with a performance similar to that of a standard EEG montage interpreted by expert neurophysiologists. This simplified montage could be implemented as part of a multimodal evaluation for bedside prognostication. Supplementary Information The online version contains supplementary material available at 10.1186/s13613-021-00854-0.
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Affiliation(s)
- Cyril Touchard
- Department of Anesthesiology and Intensive Care, APHP, Lariboisière-Saint Louis Hospitals, 75010, Paris, France
| | - Jérôme Cartailler
- Department of Anesthesiology and Intensive Care, APHP, Lariboisière-Saint Louis Hospitals, 75010, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France
| | - Geoffroy Vellieux
- Université de Paris, NeuroDiderot, Inserm, 75019, Paris, France.,Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Etienne de Montmollin
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Pierre Jaquet
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Ruben Wanono
- Université de Paris, NeuroDiderot, Inserm, 75019, Paris, France.,Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Jean Reuter
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Marylou Para
- Department of Cardiac Surgery, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Lila Bouadma
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Jean-François Timsit
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Marie-Pia d'Ortho
- Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Nathalie Kubis
- Laboratory for Vascular Translational Science, INSERM UMR1148, Team 6, Université de Paris, 75018, Paris, France.,Department of Clinical Physiology, APHP, Lariboisière - Saint Louis hospitals, DMU DREAM, 75010, Paris, France
| | - Anny Rouvel Tallec
- Université de Paris, NeuroDiderot, Inserm, 75019, Paris, France.,Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Romain Sonneville
- Laboratory for Vascular Translational Science, INSERM UMR1148, Team 6, Université de Paris, 75018, Paris, France. .,Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France.
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Caroyer S, Depondt C, Rikir E, Mavroudakis N, Peluso L, Silvio Taccone F, Legros B, Gaspard N. Assessment of a standardized EEG reactivity protocol after cardiac arrest. Clin Neurophysiol 2021; 132:1687-1693. [PMID: 34049028 DOI: 10.1016/j.clinph.2021.03.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 02/02/2021] [Accepted: 03/08/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Reactivity assessment during EEG might provide important prognostic information in post-anoxic coma. It is still unclear how best to perform reactivity testing and how it might be affected by hypothermia. Our primary aim was to determine and compare the effectiveness, inter-rater reliability and prognostic value of different types of stimulus for EEG reactivity testing, using a standardized stimulation protocol and standardized definitions. Our secondary aims were to assess the effect of hypothermia on these measures, and to determine the prognostic value of a simplified sequence with the three most efficient stimuli. METHODS Prospective single-center cohort of post-anoxic comatose patients admitted to the intensive care unit of an academic medical center between January 1, 2016 and December 31, 2018 and receiving continuous EEG monitoring (CEEG). Reactivity was assessed using standardized definitions and standardized sequence of stimuli: auditory (mild noise and loud noise), tactile (shaking), nociceptive (nostril tickling, trapezius muscle squeezing, endotracheal tube suctioning), and visual (passive eye opening). Gwet's AC1 and percent agreement (PA) were used to measure inter-rater agreement (IRA). Ability to predict favorable neurological outcome (defined as a Cerebral Performance Category of 1 to 2: no disability to moderate disability) was measured with sensitivity (Se), specificity (Sp), accuracy, and odds ratio [OR]. These were calculated for each stimulus type and at the level of the entire sequence comprising all the stimuli. RESULTS One-hundred and fifteen patients were included and 242 EEG epochs were analyzed. Loud noise, shaking and trapezius muscle squeezing most frequently elicited EEG reactivity (42%, 38% and 38%, respectively) but were all inferior to the entire sequence, which elicited reactivity in 58% cases. The IRA for reactivity to individual stimuli varied from moderate to good (AC1:58-69%; PA:56-68%) and was the highest for loud noise (AC1:69%; PA:68%), trapezius muscle squeezing (AC1:67%; PA:65%) and passive eye opening (AC1:68%; PA:64%). Mild (odds ratio [OR]:11.0; Se:70% and Sp:86%) and loud noises (OR:27.0; Se:73% and Sp:75%), and trapezius muscle squeezing (OR:15.3; Se:76% and Sp:83%) during hypothermia had the best predictive value for favorable neurological outcome, although each was inferior to the whole sequence (OR:60.2; Se:91% and Sp:73%). A simplified sequence of loud noise, shaking and trapezius muscle squeezing had the same performance for predicting neurological outcome as the entire sequence. Hypothermia did not significantly affect the effectiveness of stimulation, but IRA was slightly better during hypothermia, for all stimuli. Similarly, the predictive value was higher during hypothermia than during normothermia. CONCLUSIONS Despite a standardized stimulation protocol and standardized definitions, the IRA of EEG reactivity testing in post-anoxic comatose patients was only good at best (AC1 < 70%), and its predictive value for neurological outcome remained imperfect, in particular with Sp values < 90%. While no single stimulus appeared superior to others, a full sequence using all stimuli or a simplified sequence comprising loud noise, shaking and trapezius muscle squeezing had the best combination of IRA and predictive value. SIGNIFICANCE This study stresses the necessity to use multiple stimulus types to improve the predictive value of reactivity testing in post-anoxic coma and confirms that it is not affected by hypothermia.
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Affiliation(s)
- Sarah Caroyer
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Chantal Depondt
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Estelle Rikir
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Nicolas Mavroudakis
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Lorenzo Peluso
- Department of Intensive Care, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Fabio Silvio Taccone
- Department of Intensive Care, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium; Yale University Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory New Haven, CT, USA.
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Kim YM, Jeung KW, Kim WY, Park YS, Oh JS, You YH, Lee DH, Chae MK, Jeong YJ, Kim MC, Ha EJ, Hwang KJ, Kim WS, Lee JM, Cha KC, Chung SP, Park JD, Kim HS, Lee MJ, Na SH, Kim ARE, Hwang SO. 2020 Korean Guidelines for Cardiopulmonary Resuscitation. Part 5. Post-cardiac arrest care. Clin Exp Emerg Med 2021; 8:S41-S64. [PMID: 34034449 PMCID: PMC8171174 DOI: 10.15441/ceem.21.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 03/07/2021] [Accepted: 03/19/2021] [Indexed: 12/20/2022] Open
Affiliation(s)
- Young-Min Kim
- Department of Emergency Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea
| | - Kyung Woon Jeung
- Department of Emergency Medicine, Chonnam National University College of Medicine, Gwangju, Korea
| | - Won Young Kim
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
| | - Yoo Seok Park
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Joo Suk Oh
- Department of Emergency Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea
| | - Yeon Ho You
- Department of Emergency Medicine, Chungnam National University College of Medicine, Daejeon, Korea
| | - Dong Hoon Lee
- Department of Emergency Medicine, Chung-Ang University College of Medicine, Seoul, Korea
| | - Minjung Kathy Chae
- Department of Emergency Medicine, Ajou University College of Medicine, Suwon, Korea
| | - Yoo Jin Jeong
- Department of Emergency Medicine, Chonnam National University College of Medicine, Gwangju, Korea
| | - Min Chul Kim
- Department of Internal Medicine, Chonnam National University College of Medicine, Gwangju, Korea
| | - Eun Jin Ha
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea
| | - Kyoung Jin Hwang
- Department of Neurology, Kyung Hee University College of Medicine, Seoul, Korea
| | - Won-Seok Kim
- Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jae Myung Lee
- Department of General Surgery, Korea University College of Medicine, Seoul, Korea
| | - Kyoung-Chul Cha
- Department of Emergency Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sung Phil Chung
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - June Dong Park
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
| | - Han-Suk Kim
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
| | - Mi Jin Lee
- Department of Emergency Medicine, Kyoungbook University College of Medicine, Daegu, Korea
| | - Sang-Hoon Na
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
| | - Ai-Rhan Ellen Kim
- Department of Pediatrics, Ulsan University College of Medicine, Seoul, Korea
| | - Sung Oh Hwang
- Department of Emergency Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - on behalf of the Steering Committee of 2020 Korean Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care
- Department of Emergency Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Chonnam National University College of Medicine, Gwangju, Korea
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Yonsei University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Chungnam National University College of Medicine, Daejeon, Korea
- Department of Emergency Medicine, Chung-Ang University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Ajou University College of Medicine, Suwon, Korea
- Department of Internal Medicine, Chonnam National University College of Medicine, Gwangju, Korea
- Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Korea
- Department of Neurology, Kyung Hee University College of Medicine, Seoul, Korea
- Department of Rehabilitation Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of General Surgery, Korea University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Korea
- Department of Emergency Medicine, Kyoungbook University College of Medicine, Daegu, Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- Department of Pediatrics, Ulsan University College of Medicine, Seoul, Korea
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Wouters A, Scheldeman L, Plessers S, Peeters R, Cappelle S, Demaerel P, Van Paesschen W, Ferdinande B, Dupont M, Dens J, Janssens S, Ameloot K, Lemmens R. Added Value of Quantitative Apparent Diffusion Coefficient Values for Neuroprognostication After Cardiac Arrest. Neurology 2021; 96:e2611-e2618. [PMID: 33837117 DOI: 10.1212/wnl.0000000000011991] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 02/26/2021] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To test the prognostic value of brain MRI in addition to clinical and electrophysiologic variables in patients post-cardiac arrest (CA), we explored data from the randomized Neuroprotect Post-CA trial (NCT02541591). METHODS In this trial, brain MRIs were prospectively obtained. We calculated receiver operating characteristic (ROC) curves for the average apparent diffusion coefficient (ADC) value and percentage of brain voxels with an ADC value <650 × 10-6 mm2/s and <450 × 10-6 mm2/s. We constructed multivariable logistic regression models with clinical characteristics, EEG, somatosensory evoked potentials (SSEP), and ADC value as independent variables to predict good neurologic recovery. RESULTS In 79/102 patients, MRI data were available and in 58/79 patients all other data were available. At 180 days post-CA, 25/58 (43%) patients had good neurologic recovery. In univariable analysis of all tested MRI measures, average ADC value in the postcentral cortex had the highest accuracy to predict good neurologic recovery, with an area under the ROC curve (AUC) of 0.78. In the most optimal multivariable model, which also included corneal reflexes and EEG, this measure remained an independent predictor of good neurologic recovery (AUC 0.96, false-positive 27%). This model provided a more accurate prediction compared to the most optimal combination of EEG, corneal reflexes, and SSEP (p = 0.03). CONCLUSIONS Adding information on brain MRI in a multivariable model may improve the prediction of good neurologic recovery in patients post-CA. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that MRI ADC features predict neurologic recovery in patients post-CA.
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Affiliation(s)
- Anke Wouters
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium.
| | - Lauranne Scheldeman
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Sam Plessers
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Ronald Peeters
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Sarah Cappelle
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Philippe Demaerel
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Wim Van Paesschen
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Bert Ferdinande
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Matthias Dupont
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Jo Dens
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Stefan Janssens
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Koen Ameloot
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
| | - Robin Lemmens
- From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Olasveengen TM, Skrifvars MB, Taccone F, Soar J. European Resuscitation Council and European Society of Intensive Care Medicine guidelines 2021: post-resuscitation care. Intensive Care Med 2021; 47:369-421. [PMID: 33765189 PMCID: PMC7993077 DOI: 10.1007/s00134-021-06368-4] [Citation(s) in RCA: 462] [Impact Index Per Article: 154.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/08/2021] [Indexed: 12/13/2022]
Abstract
The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation and organ donation.
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Affiliation(s)
- Jerry P. Nolan
- University of Warwick, Warwick Medical School, Coventry, CV4 7AL UK
- Royal United Hospital, Bath, BA1 3NG UK
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy
- Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Bernd W. Böttiger
- Department of Anaesthesiology and Intensive Care Medicine, University Hospital of Cologne, Kerpener Straße 62, 50937 Cologne, Germany
| | - Alain Cariou
- Cochin University Hospital (APHP) and University of Paris (Medical School), Paris, France
| | - Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences, Anaesthesia and Intensive Care Medicine, Lund University, Skane University Hospital, Lund, Sweden
| | - Cornelia Genbrugge
- Acute Medicine Research Pole, Institute of Experimental and Clinical Research (IREC), Université Catholique de Louvain, Brussels, Belgium
- Emergency Department, University Hospitals Saint-Luc, Brussels, Belgium
| | - Kirstie Haywood
- Warwick Research in Nursing, Division of Health Sciences, Warwick Medical School, University of Warwick, Room A108, Coventry, CV4 7AL UK
| | - Gisela Lilja
- Department of Clinical Sciences Lund, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Véronique R. M. Moulaert
- Department of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Nikolaos Nikolaou
- Cardiology Department, Konstantopouleio General Hospital, Athens, Greece
| | - Theresa Mariero Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Markus B. Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Fabio Taccone
- Department of Intensive Care, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik, 808, 1070 Brussels, Belgium
| | - Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol, BS10 5NB UK
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41
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Nolan JP, Sandroni C, Böttiger BW, Cariou A, Cronberg T, Friberg H, Genbrugge C, Haywood K, Lilja G, Moulaert VRM, Nikolaou N, Mariero Olasveengen T, Skrifvars MB, Taccone F, Soar J. European Resuscitation Council and European Society of Intensive Care Medicine Guidelines 2021: Post-resuscitation care. Resuscitation 2021; 161:220-269. [PMID: 33773827 DOI: 10.1016/j.resuscitation.2021.02.012] [Citation(s) in RCA: 375] [Impact Index Per Article: 125.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) have collaborated to produce these post-resuscitation care guidelines for adults, which are based on the 2020 International Consensus on Cardiopulmonary Resuscitation Science with Treatment Recommendations. The topics covered include the post-cardiac arrest syndrome, diagnosis of cause of cardiac arrest, control of oxygenation and ventilation, coronary reperfusion, haemodynamic monitoring and management, control of seizures, temperature control, general intensive care management, prognostication, long-term outcome, rehabilitation, and organ donation.
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Affiliation(s)
- Jerry P Nolan
- University of Warwick, Warwick Medical School, Coventry CV4 7AL, UK; Royal United Hospital, Bath, BA1 3NG, UK.
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario A. Gemelli-IRCCS, Rome, Italy; Institute of Anaesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Bernd W Böttiger
- University Hospital of Cologne, Kerpener Straße 62, D-50937 Cologne, Germany
| | - Alain Cariou
- Cochin University Hospital (APHP) and University of Paris (Medical School), Paris, France
| | - Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Hans Friberg
- Department of Clinical Sciences, Anaesthesia and Intensive Care Medicine, Lund University, Skane University Hospital, Lund, Sweden
| | - Cornelia Genbrugge
- Acute Medicine Research Pole, Institute of Experimental and Clinical Research (IREC) Université Catholique de Louvain, Brussels, Belgium; Emergency Department, University Hospitals Saint-Luc, Brussels, Belgium
| | - Kirstie Haywood
- Warwick Research in Nursing, Room A108, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Gisela Lilja
- Lund University, Skane University Hospital, Department of Clinical Sciences Lund, Neurology, Lund, Sweden
| | - Véronique R M Moulaert
- University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Groningen, The Netherlands
| | - Nikolaos Nikolaou
- Cardiology Department, Konstantopouleio General Hospital, Athens, Greece
| | - Theresa Mariero Olasveengen
- Department of Anesthesiology, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Norway
| | - Markus B Skrifvars
- Department of Emergency Care and Services, University of Helsinki and Helsinki University Hospital, Finland
| | - Fabio Taccone
- Department of Intensive Care, Hôpital Erasme, Université Libre de Bruxelles, Route de Lennik, 808, 1070 Brussels, Belgium
| | - Jasmeet Soar
- Southmead Hospital, North Bristol NHS Trust, Bristol BS10 5NB, UK
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42
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Khazanova D, Douglas VC, Amorim E. A matter of timing: EEG monitoring for neurological prognostication after cardiac arrest in the era of targeted temperature management. Minerva Anestesiol 2021; 87:704-713. [PMID: 33591136 DOI: 10.23736/s0375-9393.21.14793-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Neuromonitoring with electroencephalography (EEG) is an essential tool in neurological prognostication post-cardiac arrest. EEG allows reliable and real-time assessment of early changes in background patterns, development of seizures and epileptiform activity, as well as testing for background reactivity to stimuli despite use of sedation or targeted temperature management. Delayed emergence of consciousness post-cardiac arrest is common, therefore longitudinal monitoring of EEG allows the detection of trends indicative of neurological improvement before coma recovery can be observed clinically. In this review, we summarize essential recent literature in EEG monitoring for neurological prognostication post-cardiac arrest in the context of targeted temperature management, with a particular focus on the importance of the evolution of EEG patterns in the first few days following resuscitation.
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Affiliation(s)
- Darya Khazanova
- Department of Neurology, University of California, San Francisco, CA, USA.,Division of Neurology, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA
| | - Vanja C Douglas
- Department of Neurology, University of California, San Francisco, CA, USA
| | - Edilberto Amorim
- Department of Neurology, University of California, San Francisco, CA, USA - .,Division of Neurology, Zuckerberg San Francisco General Hospital, San Francisco, CA, USA
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Carrai R, Spalletti M, Scarpino M, Lolli F, Lanzo G, Cossu C, Bonizzoli M, Socci F, Lazzeri C, Amantini A, Grippo A. Are neurophysiologic tests reliable, ultra-early prognostic indices after cardiac arrest? Neurophysiol Clin 2021; 51:133-144. [PMID: 33573889 DOI: 10.1016/j.neucli.2021.01.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 01/26/2021] [Accepted: 01/26/2021] [Indexed: 10/22/2022] Open
Abstract
OBJECTIVES Determining early and reliable prognosis in comatose subjects after cardiac arrest is a central component of post-cardiac arrest care both for developing realistic prognostic expectations for families, and for better determining which resources are mobilized or withheld for individual patients. The aim of the study was to evaluate the prognostic accuracy of EEG and SEP patterns during the very early period (within the first 6 h) after cardiac arrest. METHODS We retrospectively analysed comatose patients after CA, either inside or outside the hospital, in which prognostic evaluation was made during the first 6 h from CA. Prognostic evaluation comprised clinical evaluation (GCS and pupillary light reflex) and neurophysiological (electroencephalography (EEG) and somatosensory evoked potentials (SEP)) studies. Prognosis was evaluated with regards to likelihood of recovery of consciousness and also likelihood of failure to regain consciousness. RESULTS Forty-one comatose patients after cardiac arrest were included. All patients with continuous and nearly continuous EEG recovered consciousness. Isoelectric EEG was always associated with poor outcome. Burst-suppression, suppression and discontinuous patterns were usually associated with poor outcome although some consciousness recovery was observed. Bilaterally absent SEP responses were always associated with poor outcome. Continuous and nearly continuous EEG patterns were never associated with bilaterally absent SEP. CONCLUSIONS During the very early period following cardiac arrest (first 6 h), EEG and SEP maintain their high predictive value to predict respectively recovery and failure of recovery of consciousness. A very early EEG exam allows identification of patients with very high probability of a good outcome, allowing rapid use of the most appropriate therapeutic procedures.
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Affiliation(s)
- Riccardo Carrai
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy.
| | - Maddalena Spalletti
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Maenia Scarpino
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Francesco Lolli
- Dipartimento di Scienze Biomediche Mario Serio, Università di Firenze, Florence, Italy
| | - Giovanni Lanzo
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Cesarina Cossu
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Manuela Bonizzoli
- Unità di Terapia Intensiva, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Filippo Socci
- Unità di Terapia Intensiva, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Chiara Lazzeri
- Unità di Terapia Intensiva, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy
| | - Aldo Amantini
- IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Antonello Grippo
- SODc Neurofisiopatologia, Dipartimento Neuromuscolo-Scheletrico e degli Organi di Senso, AOU Careggi, Florence, Italy; IRCCS, Fondazione Don Carlo Gnocchi, Florence, Italy
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Abstract
PURPOSE OF REVIEW Randomized controlled trials investigating the initial pharmacological treatment of status epilepticus have been recently published. Furthermore, status epilepticus arising in comatose survivors after cardiac arrest has received increasing attention in the last years. This review offers an updated assessment of status epilepticus treatment in these different scenarios. RECENT FINDINGS Initial benzodiazepines underdosing is common and correlates with development of status epilepticus refractoriness. The recently published ESETT trial provides high-level evidence regarding the equivalence of fosphenytoin, valproate, and levetiracetam as a second-line option. Myoclonus or epileptiform transients on electroencephalography occur in up to 1/3 of patients surviving a cardiac arrest. Contrary to previous assumptions regarding an almost invariable association with death, at least 1/10 of them may awaken with reasonably good prognosis, if treated. Multimodal prognostication including clinical examination, EEG, somatosensory evoked potentials, biochemical markers, and neuroimaging help identifying patients with a chance to recover consciousness, in whom a trial with antimyoclonic compounds and at times general anesthetics is indicated. SUMMARY There is a continuous, albeit relatively slow progress in knowledge regarding different aspect of status epilepticus; recent findings refine some treatment strategies and help improving patients' outcomes. Further high-quality studies are clearly needed to further improve the management of these patients, especially those with severe, refractory status epilepticus forms.
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45
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Soar J, Berg KM, Andersen LW, Böttiger BW, Cacciola S, Callaway CW, Couper K, Cronberg T, D'Arrigo S, Deakin CD, Donnino MW, Drennan IR, Granfeldt A, Hoedemaekers CWE, Holmberg MJ, Hsu CH, Kamps M, Musiol S, Nation KJ, Neumar RW, Nicholson T, O'Neil BJ, Otto Q, de Paiva EF, Parr MJA, Reynolds JC, Sandroni C, Scholefield BR, Skrifvars MB, Wang TL, Wetsch WA, Yeung J, Morley PT, Morrison LJ, Welsford M, Hazinski MF, Nolan JP. Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations. Resuscitation 2020; 156:A80-A119. [PMID: 33099419 PMCID: PMC7576326 DOI: 10.1016/j.resuscitation.2020.09.012] [Citation(s) in RCA: 135] [Impact Index Per Article: 33.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
This 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.
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46
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Berg KM, Soar J, Andersen LW, Böttiger BW, Cacciola S, Callaway CW, Couper K, Cronberg T, D’Arrigo S, Deakin CD, Donnino MW, Drennan IR, Granfeldt A, Hoedemaekers CW, Holmberg MJ, Hsu CH, Kamps M, Musiol S, Nation KJ, Neumar RW, Nicholson T, O’Neil BJ, Otto Q, de Paiva EF, Parr MJ, Reynolds JC, Sandroni C, Scholefield BR, Skrifvars MB, Wang TL, Wetsch WA, Yeung J, Morley PT, Morrison LJ, Welsford M, Hazinski MF, Nolan JP, Issa M, Kleinman ME, Ristagno G, Arafeh J, Benoit JL, Chase M, Fischberg BL, Flores GE, Link MS, Ornato JP, Perman SM, Sasson C, Zelop CM. Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations. Circulation 2020; 142:S92-S139. [DOI: 10.1161/cir.0000000000000893] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
This
2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations
for advanced life support includes updates on multiple advanced life support topics addressed with 3 different types of reviews. Topics were prioritized on the basis of both recent interest within the resuscitation community and the amount of new evidence available since any previous review. Systematic reviews addressed higher-priority topics, and included double-sequential defibrillation, intravenous versus intraosseous route for drug administration during cardiac arrest, point-of-care echocardiography for intra-arrest prognostication, cardiac arrest caused by pulmonary embolism, postresuscitation oxygenation and ventilation, prophylactic antibiotics after resuscitation, postresuscitation seizure prophylaxis and treatment, and neuroprognostication. New or updated treatment recommendations on these topics are presented. Scoping reviews were conducted for anticipatory charging and monitoring of physiological parameters during cardiopulmonary resuscitation. Topics for which systematic reviews and new Consensuses on Science With Treatment Recommendations were completed since 2015 are also summarized here. All remaining topics reviewed were addressed with evidence updates to identify any new evidence and to help determine which topics should be the highest priority for systematic reviews in the next 1 to 2 years.
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47
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Panchal AR, Bartos JA, Cabañas JG, Donnino MW, Drennan IR, Hirsch KG, Kudenchuk PJ, Kurz MC, Lavonas EJ, Morley PT, O’Neil BJ, Peberdy MA, Rittenberger JC, Rodriguez AJ, Sawyer KN, Berg KM, Arafeh J, Benoit JL, Chase M, Fernandez A, de Paiva EF, Fischberg BL, Flores GE, Fromm P, Gazmuri R, Gibson BC, Hoadley T, Hsu CH, Issa M, Kessler A, Link MS, Magid DJ, Marrill K, Nicholson T, Ornato JP, Pacheco G, Parr M, Pawar R, Jaxton J, Perman SM, Pribble J, Robinett D, Rolston D, Sasson C, Satyapriya SV, Sharkey T, Soar J, Torman D, Von Schweinitz B, Uzendu A, Zelop CM, Magid DJ. Part 3: Adult Basic and Advanced Life Support: 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation 2020; 142:S366-S468. [DOI: 10.1161/cir.0000000000000916] [Citation(s) in RCA: 371] [Impact Index Per Article: 92.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Cronberg T, Greer DM, Lilja G, Moulaert V, Swindell P, Rossetti AO. Brain injury after cardiac arrest: from prognostication of comatose patients to rehabilitation. Lancet Neurol 2020; 19:611-622. [PMID: 32562686 DOI: 10.1016/s1474-4422(20)30117-4] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 03/30/2020] [Accepted: 04/01/2020] [Indexed: 02/08/2023]
Abstract
More patients are surviving cardiac arrest than ever before; however, the burden now lies with estimating neurological prognoses in a large number of patients who were initially comatose, in whom the ultimate outcome is unclear. Neurologists, neurointensivists, and clinical neurophysiologists must accurately balance the concern that overly conservative prognostication could leave patients in a severely disabled state, with the possibility that inaccurately pessimistic prognostication could lead to the withdrawal of life-sustaining treatment in patients who might otherwise have a good functional outcome. Prognostic tools have improved greatly, including electrophysiological tests, neuroimaging, and chemical biomarkers. Conclusions about the prognosis should be delayed at least 72 h after arrest to allow for the clearance of sedative drugs. Cognitive impairments, emotional problems, and fatigue are common among patients who have survived cardiac arrest, and often go unrecognised despite being related to caregiver burden and a decreased participation in society. Through simple screening, these problems can be identified, and patients can be provided with adequate information and rehabilitation.
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Affiliation(s)
- Tobias Cronberg
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden.
| | - David M Greer
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Gisela Lilja
- Department of Clinical Sciences, Neurology, Lund University, Skane University Hospital, Lund, Sweden
| | - Véronique Moulaert
- Department of Rehabilitation Medicine, University of Groningen, University Medical Centre Groningen, Netherlands
| | | | - Andrea O Rossetti
- Department of Clinical Neurosciences, University Hospital and University of Lausanne, Lausanne, Switzerland
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Sandroni C, D'Arrigo S, Cacciola S, Hoedemaekers CWE, Kamps MJA, Oddo M, Taccone FS, Di Rocco A, Meijer FJA, Westhall E, Antonelli M, Soar J, Nolan JP, Cronberg T. Prediction of poor neurological outcome in comatose survivors of cardiac arrest: a systematic review. Intensive Care Med 2020; 46:1803-1851. [PMID: 32915254 PMCID: PMC7527362 DOI: 10.1007/s00134-020-06198-w] [Citation(s) in RCA: 180] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 07/15/2020] [Indexed: 12/17/2022]
Abstract
Purpose To assess the ability of clinical examination, blood biomarkers, electrophysiology, or neuroimaging assessed within 7 days from return of spontaneous circulation (ROSC) to predict poor neurological outcome, defined as death, vegetative state, or severe disability (CPC 3–5) at hospital discharge/1 month or later, in comatose adult survivors from cardiac arrest (CA). Methods PubMed, EMBASE, Web of Science, and the Cochrane Database of Systematic Reviews (January 2013–April 2020) were searched. Sensitivity and false-positive rate (FPR) for each predictor were calculated. Due to heterogeneities in recording times, predictor thresholds, and definition of some predictors, meta-analysis was not performed. Results Ninety-four studies (30,200 patients) were included. Bilaterally absent pupillary or corneal reflexes after day 4 from ROSC, high blood values of neuron-specific enolase from 24 h after ROSC, absent N20 waves of short-latency somatosensory-evoked potentials (SSEPs) or unequivocal seizures on electroencephalogram (EEG) from the day of ROSC, EEG background suppression or burst-suppression from 24 h after ROSC, diffuse cerebral oedema on brain CT from 2 h after ROSC, or reduced diffusion on brain MRI at 2–5 days after ROSC had 0% FPR for poor outcome in most studies. Risk of bias assessed using the QUIPS tool was high for all predictors. Conclusion In comatose resuscitated patients, clinical, biochemical, neurophysiological, and radiological tests have a potential to predict poor neurological outcome with no false-positive predictions within the first week after CA. Guidelines should consider the methodological concerns and limited sensitivity for individual modalities. (PROSPERO CRD42019141169) Electronic supplementary material The online version of this article (10.1007/s00134-020-06198-w) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.,Institute of Anesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Sonia D'Arrigo
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.
| | - Sofia Cacciola
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy
| | | | - Marlijn J A Kamps
- Intensive Care Catharina Hospital Eindhoven, Eindhoven, The Netherlands
| | - Mauro Oddo
- Department of Intensive Care Medicine, University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Fabio S Taccone
- Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Arianna Di Rocco
- Department of Public Health and Infectious Disease, Sapienza University, Rome, Italy
| | - Frederick J A Meijer
- Department of Radiology and Nuclear Medicine, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands
| | - Erik Westhall
- Department of ClinicalSciences, Clinical Neurophysiology, Lund University, Skane University Hospital, Lund, Sweden
| | - Massimo Antonelli
- Department of Intensive Care, Emergency Medicine and Anesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"- IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy.,Institute of Anesthesiology and Intensive Care Medicine, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Jasmeet Soar
- Critical Care Unit, Southmead Hospital, North Bristol NHS Trust, Bristol, UK
| | - Jerry P Nolan
- Department of Anaesthesia and Intensive Care Medicine, Royal United Hospital, Bath, UK
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Skane University Hospital, Lund, Sweden
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Comanducci A, Boly M, Claassen J, De Lucia M, Gibson RM, Juan E, Laureys S, Naccache L, Owen AM, Rosanova M, Rossetti AO, Schnakers C, Sitt JD, Schiff ND, Massimini M. Clinical and advanced neurophysiology in the prognostic and diagnostic evaluation of disorders of consciousness: review of an IFCN-endorsed expert group. Clin Neurophysiol 2020; 131:2736-2765. [PMID: 32917521 DOI: 10.1016/j.clinph.2020.07.015] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 07/06/2020] [Accepted: 07/26/2020] [Indexed: 12/13/2022]
Abstract
The analysis of spontaneous EEG activity and evoked potentialsis a cornerstone of the instrumental evaluation of patients with disorders of consciousness (DoC). Thepast few years have witnessed an unprecedented surge in EEG-related research applied to the prediction and detection of recovery of consciousness after severe brain injury,opening up the prospect that new concepts and tools may be available at the bedside. This paper provides a comprehensive, critical overview of bothconsolidated and investigational electrophysiological techniquesfor the prognostic and diagnostic assessment of DoC.We describe conventional clinical EEG approaches, then focus on evoked and event-related potentials, and finally we analyze the potential of novel research findings. In doing so, we (i) draw a distinction between acute, prolonged and chronic phases of DoC, (ii) attempt to relate both clinical and research findings to the underlying neuronal processes and (iii) discuss technical and conceptual caveats.The primary aim of this narrative review is to bridge the gap between standard and emerging electrophysiological measures for the detection and prediction of recovery of consciousness. The ultimate scope is to provide a reference and common ground for academic researchers active in the field of neurophysiology and clinicians engaged in intensive care unit and rehabilitation.
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Affiliation(s)
- A Comanducci
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
| | - M Boly
- Department of Neurology and Department of Psychiatry, University of Wisconsin, Madison, USA; Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin-Madison, Madison, USA
| | - J Claassen
- Department of Neurology, Columbia University Medical Center, New York Presbyterian Hospital, New York, NY, USA
| | - M De Lucia
- Laboratoire de Recherche en Neuroimagerie, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - R M Gibson
- The Brain and Mind Institute and the Department of Physiology and Pharmacology, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, Ontario, Canada
| | - E Juan
- Wisconsin Institute for Sleep and Consciousness, Department of Psychiatry, University of Wisconsin-Madison, Madison, USA; Amsterdam Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
| | - S Laureys
- Coma Science Group, Centre du Cerveau, GIGA-Consciousness, University and University Hospital of Liège, 4000 Liège, Belgium; Fondazione Europea per la Ricerca Biomedica Onlus, Milan 20063, Italy
| | - L Naccache
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France; Sorbonne Université, UPMC Université Paris 06, Faculté de Médecine Pitié-Salpêtrière, Paris, France
| | - A M Owen
- The Brain and Mind Institute and the Department of Physiology and Pharmacology, Western Interdisciplinary Research Building, N6A 5B7 University of Western Ontario, London, Ontario, Canada
| | - M Rosanova
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy; Fondazione Europea per la Ricerca Biomedica Onlus, Milan 20063, Italy
| | - A O Rossetti
- Neurology Service, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - C Schnakers
- Research Institute, Casa Colina Hospital and Centers for Healthcare, Pomona, CA, USA
| | - J D Sitt
- Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France
| | - N D Schiff
- Feil Family Brain and Mind Research Institute, Weill Cornell Medical College, 1300 York Avenue, New York, NY 10065, USA
| | - M Massimini
- IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy; Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
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