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Mertens M. The self-fulfilling prophecy in medicine. THEORETICAL MEDICINE AND BIOETHICS 2024; 45:363-385. [PMID: 39120693 PMCID: PMC11358258 DOI: 10.1007/s11017-024-09677-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/30/2024] [Indexed: 08/10/2024]
Abstract
This article first describes the mechanism of any self-fulfilling prophecy through discussion of its four conditions: credibility, employment, employment sensitivity, and realization. Each condition is illustrated with examples specific to the medical context. The descriptive account ends with the definition of self-fulfilling prophecy and an expansion on collective self-fulfilling prophecies. Second, the normative account then discusses the moral relevance of self-fulfilling prophecies in medicine. A self-fulfilling prophecy is typically considered problematic when the prediction itself changes the predicted outcome to match the prediction (transformative self-fulfillment). I argue that also self-fulfilling prophecies that do not change the outcome but change the ways in which the outcome was realized (operative self-fulfillment), have significant ethical and epistemic ramifications. Because it is difficult to distinguish, retrospectively, between a transformative and an operative self-fulfilling prophecy, and thus between a false or true positive, it becomes equally difficult to catch mistakes. Moreover, since the prediction necessarily turns out true, there is never an error signal warning that a mistake might have been made. On the contrary, accuracy is seen as the standard for quality assurance. As such, self-fulfilling prophecies inhibit our ability to learn, inviting repetition and exacerbation of mistakes. With the rise of automated diagnostic and prognostic procedures and the increased use of machine learning and artificial intelligence for the development of predictive algorithms, attention to self-fulfilling feedback loops is especially warranted. This account of self-fulfilling prophecies is practically relevant for medical research and clinical practice. With it, researchers and practitioners can detect and analyze potential self-fulfilling mechanisms in any medical case and take responsibility for their ethical and epistemic implications.
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Affiliation(s)
- Mayli Mertens
- Department of Philosophy, Center for Ethics, University of Antwerp, Antwerp, Belgium.
- Department of Public Health, Center for Medical Science and Technology Studies, University of Copenhagen, Copenhagen, Denmark.
- Atlas Bioethics Center, Andalusia, Spain.
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Van Roy S, Hsu L, Ho J, Scirica BM, Fischer D, Snider SB, Lee JW. Quantitative and Radiological Assessment of Post-cardiac-Arrest Comatose Patients with Diffusion-Weighted Magnetic Resonance Imaging. Neurocrit Care 2024:10.1007/s12028-024-02087-y. [PMID: 39164537 DOI: 10.1007/s12028-024-02087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 07/29/2024] [Indexed: 08/22/2024]
Abstract
BACKGROUND Although magnetic resonance imaging, particularly diffusion-weighted imaging, has increasingly been used as part of a multimodal approach to prognostication in patients who are comatose after cardiac arrest, the performance of quantitative analysis of apparent diffusion coefficient (ADC) maps, as compared to standard radiologist impression, has not been well characterized. This retrospective study evaluated quantitative ADC analysis to the identification of anoxic brain injury by diffusion abnormalities on standard clinical magnetic resonance imaging reports. METHODS The cohort included 204 previously described comatose patients after cardiac arrest. Clinical outcome was assessed by (1) 3-6 month post-cardiac-arrest cerebral performance category and (2) coma recovery to following commands. Radiological evaluation was obtained from clinical reports and characterized as diffuse, cortex only, deep gray matter structures only, or no anoxic injury. Quantitative analyses of ADC maps were obtained in specific regions of interest (ROIs), whole cortex, and whole brain. A subgroup analysis of 172 was performed after eliminating images with artifacts and preexisting lesions. RESULTS Radiological assessment outperformed quantitative assessment over all evaluated regions (area under the curve [AUC] 0.80 for radiological interpretation and 0.70 for the occipital region, the best performing ROI, p = 0.011); agreement was substantial for all regions. Radiological assessment still outperformed quantitative analysis in the subgroup analysis, though by smaller margins and with substantial to near-perfect agreement. When assessing for coma recovery only, the difference was no longer significant (AUC 0.83 vs. 0.81 for the occipital region, p = 0.70). CONCLUSIONS Although quantitative analysis eliminates interrater differences in the interpretation of abnormal diffusion imaging and avoids bias from other prediction modalities, clinical radiologist interpretation has a higher predictive value for outcome. Agreement between radiological and quantitative analysis improved when using high-quality scans and when assessing for coma recovery using following commands. Quantitative assessment may thus be more subject to variability in both clinical management and scan quality than radiological assessment.
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Affiliation(s)
- Sam Van Roy
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA
| | - Liangge Hsu
- Division of Neuroradiology, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Joseph Ho
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA
| | - Benjamin M Scirica
- Division of Cardiology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - David Fischer
- Department of Neurology, Hospital of Pennsylvania, Philadelphia, PA, USA
| | - Samuel B Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jong Woo Lee
- Division of EEG and Epilepsy, Department of Neurology, Brigham and Women's Hospital, 60 Fenwood Rd, Boston, MA, 02115, USA.
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3
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Glimmerveen AB, Verhulst MMLH, de Kruijf NLM, van Gils P, Delnoij T, Bonnes J, van Heugten CM, Van Putten MJAM, Hofmeijer J. Resting state EEG relates to short- and long-term cognitive functioning after cardiac arrest. Resuscitation 2024; 201:110253. [PMID: 38797387 DOI: 10.1016/j.resuscitation.2024.110253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/17/2024] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Approximately half of cardiac arrest survivors have persistent cognitive impairment. Guidelines recommend early screening to identify patients at risk for cognitive impairment, but there is no consensus on the best screening method. We aimed to identify quantitative EEG measures relating with short- and long-term cognitive function after cardiac arrest for potential to cognitive outcome prediction. METHODS We analyzed data from a prospective longitudinal multicenter cohort study designed to develop a prediction model for cognitive outcome after cardiac arrest. For the current analysis, we used twenty-minute EEG registrations from 80 patients around one week after cardiac arrest. We calculated power spectral density, normalized alpha-to-theta ratio (nATR), peak frequency, and center of gravity (CoG) of this peak frequency. We related these with global cognitive functioning (scores on the Montreal Cognitive Assessment (MoCA)) at one week, three and twelve months follow-up with multivariate mixed effect models, and with performance on standard neuropsychological examination at twelve months using Pearson correlation coefficients. RESULTS Each individual EEG parameter related to MoCA at one week (βnATR = 7.36; P < 0.01; βpeak frequency = 1.73, P < 0.01; βCoG = -9.88, P < 0.01). The nATR also related with the MoCA at three months ((βnATR = 2.49; P 0.01). No EEG metrics significantly related to the MoCA score at twelve months. nATR and peak frequency related with memory performance at twelve months. Results were consistent in sensitivity analyses. CONCLUSION Early resting-state EEG parameters relate with short-term global cognitive functioning and with memory function at one year after cardiac arrest. Additional predictive values in multimodal prediction models need further study.
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Affiliation(s)
- A B Glimmerveen
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
| | - M M L H Verhulst
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - N L M de Kruijf
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
| | - P van Gils
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Maastrich University, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht, The Netherlands; Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands
| | - T Delnoij
- Department of Intensive Care Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - J Bonnes
- Department of Cardiology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - C M van Heugten
- Maastricht University, Limburg Brain Injury Center, Maastricht, The Netherlands; Maastricht University, Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht, The Netherlands
| | - M J A M Van Putten
- Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands; Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, The Netherlands
| | - J Hofmeijer
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands; Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands
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Shivdat S, Zhan T, De Palma A, Zheng WL, Krishnamurthy P, Paneerselvam E, Snider S, Bevers M, O'Reilly UM, Lee JW, Westover MB, Amorim E. Early Burst Suppression Similarity Association with Structural Brain Injury Severity on MRI After Cardiac Arrest. Neurocrit Care 2024:10.1007/s12028-024-02047-6. [PMID: 39043984 DOI: 10.1007/s12028-024-02047-6] [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: 01/30/2024] [Accepted: 06/13/2024] [Indexed: 07/25/2024]
Abstract
BACKGROUND Identical bursts on electroencephalography (EEG) are considered a specific predictor of poor outcomes in cardiac arrest, but its relationship with structural brain injury severity on magnetic resonance imaging (MRI) is not known. METHODS This was a retrospective analysis of clinical, EEG, and MRI data from adult comatose patients after cardiac arrest. Burst similarity in first 72 h from the time of return of spontaneous circulation were calculated using dynamic time-warping (DTW) for bursts of equal (i.e., 500 ms) and varying (i.e., 100-500 ms) lengths and cross-correlation for bursts of equal lengths. Structural brain injury severity was measured using whole brain mean apparent diffusion coefficient (ADC) on MRI. Pearson's correlation coefficients were calculated between mean burst similarity across consecutive 12-24-h time blocks and mean whole brain ADC values. Good outcome was defined as Cerebral Performance Category of 1-2 (i.e., independence for activities of daily living) at the time of hospital discharge. RESULTS Of 113 patients with cardiac arrest, 45 patients had burst suppression (mean cardiac arrest to MRI time 4.3 days). Three study participants with burst suppression had a good outcome. Burst similarity calculated using DTW with bursts of varying lengths was correlated with mean ADC value in the first 36 h after cardiac arrest: Pearson's r: 0-12 h: - 0.69 (p = 0.039), 12-24 h: - 0.54 (p = 0.002), 24-36 h: - 0.41 (p = 0.049). Burst similarity measured with bursts of equal lengths was not associated with mean ADC value with cross-correlation or DTW, except for DTW at 60-72 h (- 0.96, p = 0.04). CONCLUSIONS Burst similarity on EEG after cardiac arrest may be associated with acute brain injury severity on MRI. This association was time dependent when measured using DTW.
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Affiliation(s)
- Shawn Shivdat
- Harvard College, Cambridge, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Tiange Zhan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alessandro De Palma
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Computing, Imperial College London, London, UK
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | | | - Ezhil Paneerselvam
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew Bevers
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Una-May O'Reilly
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jong Woo Lee
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Division of Epilepsy, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Edilberto Amorim
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, Zuckerberg San Francisco General Hospital, 1001 Potrero Ave, Building 1, Suite 312, San Francisco, CA, 94110, USA.
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Turella S, Dankiewicz J, Ben-Hamouda N, Bernhard Nilsen K, Düring J, Endisch C, Engstrøm M, Flügel D, Gaspard N, Grejs AM, Haenggi M, Haffey S, Imbach L, Johnsen B, Kemlink D, Leithner C, Legriel S, Lindehammar H, Mazzon G, Nielsen N, Peyre A, Ribalta Stanford B, Roman-Pognuz E, Rossetti AO, Schrag C, Valeriánová A, Wendel-Garcia P, Zubler F, Cronberg T, Westhall E. EEG for good outcome prediction after cardiac arrest: A multicentre cohort study. Resuscitation 2024:110319. [PMID: 39029579 DOI: 10.1016/j.resuscitation.2024.110319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 07/06/2024] [Accepted: 07/08/2024] [Indexed: 07/21/2024]
Abstract
AIM Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA). METHODS Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 h after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3. RESULTS 873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 h (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p < 0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account. CONCLUSION Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance.
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Affiliation(s)
- S Turella
- Department of Clinical Sciences Lund, Clinical Neurophysiology, Lund University, Lund, Sweden
| | - J Dankiewicz
- Department of Clinical Sciences Lund, Cardiology, Lund University, Lund, Sweden
| | - N Ben-Hamouda
- Department of Adult Intensive Care Medicine, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - K Bernhard Nilsen
- Section for Clinical Neurophysiology, Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - J Düring
- Department of Clinical Sciences, Anaesthesia and Intensive Care, Lund University, Malmö, Sweden
| | - C Endisch
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt - Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - M Engstrøm
- Department of Clinical Neurophysiology, St. Olavs University Hospital and Department of Neuromedicine and Movement Science (INB) NTNU, Trondheim, Norway
| | - D Flügel
- Department of Neurology, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - N Gaspard
- Department of Neurology, Erasme University Hospital, Université Libre de Bruxelles, Brussels, Belgium; Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - A M Grejs
- Department of Intensive Care Medicine, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - M Haenggi
- Department of Intensive Care Medicine, Bern University Hospital, University of Bern, Bern, Switzerland
| | - S Haffey
- Department of Clinical Neurophysiology, Royal Victoria Hospital, Belfast, Ireland
| | - L Imbach
- Department of Neurology, University Hospital Zurich, Zurich, Switzerland
| | - B Johnsen
- Department of Clinical Medicine, Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - D Kemlink
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - C Leithner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt - Universität zu Berlin, Department of Neurology and Experimental Neurology, Augustenburger Platz 1, 13353 Berlin, Germany
| | - S Legriel
- Intensive Care Unit, Versailles Hospital, France
| | - H Lindehammar
- Clinical Neurophysiology, Department of Clinical and Experimental Medicine, Linköping University, Sweden
| | - G Mazzon
- Department of Neurology, University Hospital of Trieste, Trieste, Italy
| | - N Nielsen
- Department of Clinical Sciences Lund, Anesthesiology and Intensive Care Medicine, Helsingborg Hospital, Helsingborg, Sweden
| | - A Peyre
- Department of Neurology, Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - B Ribalta Stanford
- Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
| | - E Roman-Pognuz
- Intensive Care Unit, University Hospital of Trieste, Trieste, Italy
| | - A O Rossetti
- Department of Neurology, University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - C Schrag
- Intensive Care Department, Kantonsspital St. Gallen, St. Gallen, Switzerland
| | - A Valeriánová
- General University Hospital in Prague, Prague, Czech Republic
| | - P Wendel-Garcia
- Institute of Intensive Care Medicine, University Hospital Zürich, Zürich, Switzerland
| | - F Zubler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - T Cronberg
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - E Westhall
- Department of Clinical Sciences Lund, Clinical Neurophysiology, Lund University, Lund, Sweden.
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Bitar R, Khan UM, Rosenthal ES. Utility and rationale for continuous EEG monitoring: a primer for the general intensivist. Crit Care 2024; 28:244. [PMID: 39014421 PMCID: PMC11251356 DOI: 10.1186/s13054-024-04986-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 06/09/2024] [Indexed: 07/18/2024] Open
Abstract
This review offers a comprehensive guide for general intensivists on the utility of continuous EEG (cEEG) monitoring for critically ill patients. Beyond the primary role of EEG in detecting seizures, this review explores its utility in neuroprognostication, monitoring neurological deterioration, assessing treatment responses, and aiding rehabilitation in patients with encephalopathy, coma, or other consciousness disorders. Most seizures and status epilepticus (SE) events in the intensive care unit (ICU) setting are nonconvulsive or subtle, making cEEG essential for identifying these otherwise silent events. Imaging and invasive approaches can add to the diagnosis of seizures for specific populations, given that scalp electrodes may fail to identify seizures that may be detected by depth electrodes or electroradiologic findings. When cEEG identifies SE, the risk of secondary neuronal injury related to the time-intensity "burden" often prompts treatment with anti-seizure medications. Similarly, treatment may be administered for seizure-spectrum activity, such as periodic discharges or lateralized rhythmic delta slowing on the ictal-interictal continuum (IIC), even when frank seizures are not evident on the scalp. In this setting, cEEG is utilized empirically to monitor treatment response. Separately, cEEG has other versatile uses for neurotelemetry, including identifying the level of sedation or consciousness. Specific conditions such as sepsis, traumatic brain injury, subarachnoid hemorrhage, and cardiac arrest may each be associated with a unique application of cEEG; for example, predicting impending events of delayed cerebral ischemia, a feared complication in the first two weeks after subarachnoid hemorrhage. After brief training, non-neurophysiologists can learn to interpret quantitative EEG trends that summarize elements of EEG activity, enhancing clinical responsiveness in collaboration with clinical neurophysiologists. Intensivists and other healthcare professionals also play crucial roles in facilitating timely cEEG setup, preventing electrode-related skin injuries, and maintaining patient mobility during monitoring.
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Affiliation(s)
- Ribal Bitar
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA
| | - Usaamah M Khan
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA
| | - Eric S Rosenthal
- Department of Neurology, Massachusetts General Hospital, 55 Fruit St., Lunder 644, Boston, MA, 02114, USA.
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Bang HJ, Youn CS, Sandroni C, Park KN, Lee BK, Oh SH, Cho IS, Choi SP. Good outcome prediction after out-of-hospital cardiac arrest: A prospective multicenter observational study in Korea (the KORHN-PRO registry). Resuscitation 2024; 199:110207. [PMID: 38582440 DOI: 10.1016/j.resuscitation.2024.110207] [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: 02/07/2024] [Revised: 03/29/2024] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
AIM To assess the ability of clinical examination, biomarkers, electrophysiology and brain imaging, individually or in combination to predict good neurological outcomes at 6 months after CA. METHODS This was a retrospective analysis of the Korean Hypothermia Network Prospective Registry 1.0, which included adult out-of-hospital cardiac arrest (OHCA) patients (≥18 years). Good outcome predictors were defined as both pupillary light reflex (PLR) and corneal reflex (CR) at admission, Glasgow Coma Scale Motor score (GCS-M) >3 at admission, neuron-specific enolase (NSE) <17 µg/L at 24-72 h, a median nerve somatosensory evoked potential (SSEP) N20/P25 amplitude >4 µV, continuous background without discharges on electroencephalogram (EEG), and absence of anoxic injury on brain CT and diffusion-weighted imaging (DWI). RESULTS A total of 1327 subjects were included in the final analysis, and their median age was 59 years; among them, 412 subjects had a good neurological outcome at 6 months. GCS-M >3 at admission had the highest specificity of 96.7% (95% CI 95.3-97.8), and normal brain DWI had the highest sensitivity of 96.3% (95% CI 92.9-98.4). When the two predictors were combined, the sensitivities tended to decrease (ranging from 2.7-81.1%), and the specificities tended to increase, ranging from81.3-100%. Through the explorative variation of the 2021 European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) prognostication strategy algorithms, good outcomes were predicted, with a specificity of 83.2% and a sensitivity of 83.5% in patients by the algorithm. CONCLUSIONS Clinical examination, biomarker, electrophysiology, and brain imaging predicted good outcomes at 6 months after CA. When the two predictors were combined, the specificity further improved. With the 2021 ERC/ESICM guidelines, the number of indeterminate patients and the uncertainty of prognostication can be reduced by using a good outcome prediction algorithm.
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Affiliation(s)
- Hyo Jin Bang
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Chun Song Youn
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea.
| | - Claudio Sandroni
- Department of Intensive Care, Emergency Medicine and Anaesthesiology, Fondazione Policlinico Universitario "Agostino Gemelli"-IRCCS, Largo Francesco Vito, 1, 00168, Rome, Italy
| | - Kyu Nam Park
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - Byung Kook Lee
- Department of Emergency Medicine, Chonnam National University Hospital, 42, Jebong-ro, Donggu, Gwangju, Republic of Korea
| | - Sang Hoon Oh
- Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222, Banpo-daero, Seocho-gu, Seoul 06591, Republic of Korea
| | - In Soo Cho
- Department of Emergency Medicine, KEPCO Medical Center, 308, Uicheon-ro, Dobong-gu, Seoul, Republic of Korea
| | - Seung Pill Choi
- Department of Emergency Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
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8
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Nutma S, Beishuizen A, van den Bergh WM, Foudraine NA, le Feber J, Filius PMG, Cornet AD, van der Palen J, van Putten MJAM, Hofmeijer J. Ghrelin for Neuroprotection in Post-Cardiac Arrest Coma: A Randomized Clinical Trial. JAMA Neurol 2024; 81:603-610. [PMID: 38709502 PMCID: PMC11074931 DOI: 10.1001/jamaneurol.2024.1088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/08/2024] [Indexed: 05/07/2024]
Abstract
Importance Out-of-hospital cardiac arrest survival rates have markedly risen in the last decades, but neurological outcome only improved marginally. Despite research on more than 20 neuroprotective strategies involving patients in comas after cardiac arrest, none have demonstrated unequivocal evidence of efficacy; however, treatment with acyl-ghrelin has shown improved functional and histological brain recovery in experimental models of cardiac arrest and was safe in a wide variety of human study populations. Objective To determine safety and potential efficacy of intravenous acyl-ghrelin to improve neurological outcome in patients in a coma after cardiac arrest. Design, Setting, and Participants A phase 2, double-blind, placebo-controlled, multicenter, randomized clinical trial, Ghrelin Treatment of Comatose Patients After Cardiac Arrest: A Clinical Trial to Promote Cerebral Recovery (GRECO), was conducted between January 18, 2019, and October 17, 2022. Adult patients 18 years or older who were in a comatose state after cardiac arrest were assessed for eligibility; patients were from 3 intensive care units in the Netherlands. Expected death within 48 hours or unfeasibility of treatment initiation within 12 hours were exclusion criteria. Interventions Patients were randomized to receive intravenous acyl-ghrelin, 600 μg (intervention group), or placebo (control group) within 12 hours after cardiac arrest, continued for 7 days, twice daily, in addition to standard care. Main Outcomes and Measures Primary outcome was the score on the Cerebral Performance Categories (CPC) scale at 6 months. Safety outcomes included any serious adverse events. Secondary outcomes were mortality and neuron-specific enolase (NSE) levels on days 1 and 3. Results A total of 783 adult patients in a coma after cardiac arrest were assessed for eligibility, and 160 patients (median [IQR] age, 68 [57-75] years; 120 male [75%]) were enrolled. A total of 81 patients (51%) were assigned to the intervention group, and 79 (49%) were assigned to the control group. The common odds ratio (OR) for any CPC improvement in the intervention group was 1.78 (95% CI, 0.98-3.22; P = .06). This was consistent over all CPC categories. Mean (SD) NSE levels on day 1 after cardiac arrest were significantly lower in the intervention group (34 [6] μg/L vs 56 [13] μg/L; P = .04) and on day 3 (28 [6] μg/L vs 52 [14] μg/L; P = .08). Serious adverse events were comparable in incidence and type between the groups. Mortality was 37% (30 of 81) in the intervention group vs 51% (40 of 79) in the control group (absolute risk reduction, 14%; 95% CI, -2% to 29%; P = .08). Conclusions and Relevance In patients in a coma after cardiac arrest, intravenous treatment with acyl-ghrelin was safe and potentially effective to improve neurological outcome. Phase 3 trials are needed for conclusive evidence. Trial Registration Clinicaltrialsregister.eu: EUCTR2018-000005-23-NL.
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Affiliation(s)
- Sjoukje Nutma
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede, the Netherlands
- Department of Neurology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Albertus Beishuizen
- Department of Critical Care, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Walter M. van den Bergh
- Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Joost le Feber
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede, the Netherlands
| | | | - Alexander D. Cornet
- Department of Critical Care, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Job van der Palen
- Department of Epidemiology, Medisch Spectrum Twente, Enschede, the Netherlands
- Section of Cognition, Data and Education, Faculty of Behavioral, Management and Social Sciences, University of Twente, Enschede, the Netherlands
| | - Michel J. A. M. van Putten
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede, the Netherlands
- Department of Neurology, Medisch Spectrum Twente, Enschede, 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
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9
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Tsai H, Chi CY, Wang LW, Su YJ, Chen YF, Tsai MS, Wang CH, Hsu C, Huang CH, Wang W. Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images. Crit Care 2024; 28:118. [PMID: 38594772 PMCID: PMC11005205 DOI: 10.1186/s13054-024-04895-2] [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: 12/22/2023] [Accepted: 03/29/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes. METHODS Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations. RESULTS Overall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR. CONCLUSIONS Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.
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Affiliation(s)
- Hsinhan Tsai
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 106216, Taiwan R.O.C
| | - Chien-Yu Chi
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Liang-Wei Wang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Yu-Jen Su
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Ya-Fang Chen
- Department of Medical Imaging, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Min-Shan Tsai
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Chih-Hung Wang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Cheyu Hsu
- Department of Oncology, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C
| | - Chien-Hua Huang
- Department of Emergency Medicine, National Taiwan University Hospital, Taipei, 100225, Taiwan R.O.C..
| | - Weichung Wang
- Institute of Applied Mathematical Sciences, National Taiwan University, Taipei, 106216, Taiwan R.O.C..
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10
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Rutiku R, Fiscone C, Massimini M, Sarasso S. Assessing mismatch negativity (MMN) and P3b within-individual sensitivity - A comparison between the local-global paradigm and two specialized oddball sequences. Eur J Neurosci 2024; 59:842-859. [PMID: 38439197 DOI: 10.1111/ejn.16302] [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: 04/19/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/06/2024]
Abstract
Mismatch negativity (MMN) and P3b are well known for their clinical utility. There exists no gold standard, however, for acquiring them as EEG markers of consciousness in clinical settings. This may explain why the within-individual sensitivity of MMN/P3b paradigms is often quite poor and why seemingly identical EEG markers can behave differently across Disorders of consciousness (DoC) studies. Here, we compare two traditional paradigms for MMN or P3b assessment with the recently more popular local-global paradigm that promises to assess MMN and P3b orthogonally within one oddball sequence. All three paradigms were administered to healthy participants (N = 15) with concurrent EEG. A clear MMN and local effect were found for 15/15 participants. The P3b and global effect were found for 14/15 and 13/15 participants, respectively. There were no systematic differences between the global effect and P3b. Indeed, P3b amplitude was highly correlated across paradigms. The local effect differed clearly from the MMN, however. It occurred earlier than MMN and was followed by a much more prominent P3a. The peak latencies and amplitudes were also not correlated across paradigms. Caution should therefore be exercised when comparing the local effect and MMN across studies. We conclude that the within-individual MMN sensitivity is adequate for both the local-global and a dedicated MMN paradigm. The within-individual sensitivity of P3b was lower than expected for both the local-global and a dedicated P3b paradigm, which may explain the often-low sensitivity of P3b paradigms in patients with DoC.
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Affiliation(s)
- Renate Rutiku
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- C-lab, Institute of Psychology, Jagiellonian University, Krakow, Poland
| | - Chiara Fiscone
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
- IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | - Simone Sarasso
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
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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, 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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12
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Nikolovski SS, Lazic AD, Fiser ZZ, Obradovic IA, Tijanic JZ, Raffay V. Recovery and Survival of Patients After Out-of-Hospital Cardiac Arrest: A Literature Review Showcasing the Big Picture of Intensive Care Unit-Related Factors. Cureus 2024; 16:e54827. [PMID: 38529434 PMCID: PMC10962929 DOI: 10.7759/cureus.54827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 03/27/2024] Open
Abstract
As an important public health issue, out-of-hospital cardiac arrest (OHCA) requires several stages of high quality medical care, both on-field and after hospital admission. Post-cardiac arrest shock can lead to severe neurological injury, resulting in poor recovery outcome and increased risk of death. These characteristics make this condition one of the most important issues to deal with in post-OHCA patients hospitalized in intensive care units (ICUs). Also, the majority of initial post-resuscitation survivors have underlying coronary diseases making revascularization procedure another crucial step in early management of these patients. Besides keeping myocardial blood flow at a satisfactory level, other tissues must not be neglected as well, and maintaining mean arterial pressure within optimal range is also preferable. All these procedures can be simplified to a certain level along with using targeted temperature management methods in order to decrease metabolic demands in ICU-hospitalized post-OHCA patients. Additionally, withdrawal of life-sustaining therapy as a controversial ethical topic is under constant re-evaluation due to its possible influence on overall mortality rates in patients initially surviving OHCA. Focusing on all of these important points in process of managing ICU patients is an imperative towards better survival and complete recovery rates.
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Affiliation(s)
- Srdjan S Nikolovski
- Pathology and Laboratory Medicine, Cardiovascular Research Institute, Loyola University Chicago Health Science Campus, Maywood, USA
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
| | - Aleksandra D Lazic
- Emergency Center, Clinical Center of Vojvodina, Novi Sad, SRB
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
| | - Zoran Z Fiser
- Emergency Medicine, Department of Emergency Medicine, Novi Sad, SRB
| | - Ivana A Obradovic
- Anesthesiology, Resuscitation, and Intensive Care, Sveti Vračevi Hospital, Bijeljina, BIH
| | - Jelena Z Tijanic
- Emergency Medicine, Municipal Institute of Emergency Medicine, Kragujevac, SRB
| | - Violetta Raffay
- School of Medicine, European University Cyprus, Nicosia, CYP
- Emergency Medicine, Serbian Resuscitation Council, Novi Sad, SRB
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13
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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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14
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Tao T, Lu S, Hu N, Xu D, Xu C, Li F, Wang Q, Peng Y. Prognosis of comatose patients with reduced EEG montage by combining quantitative EEG features in various domains. Front Neurosci 2023; 17:1302318. [PMID: 38144206 PMCID: PMC10748426 DOI: 10.3389/fnins.2023.1302318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Objective As the frontoparietal network underlies recovery from coma, a limited frontoparietal montage was used, and the prognostic values of EEG features for comatose patients were assessed. Methods Collected with a limited frontoparietal EEG montage, continuous EEG recordings of 81 comatose patients in ICU were used retrospectively. By the 60-day Glasgow outcome scale (GOS), the patients were dichotomized into favorable and unfavorable outcome groups. Temporal-, frequency-, and spatial-domain features were automatically extracted for comparison. Partial correlation analysis was applied to eliminate redundant factors, and multiple correspondence analysis was used to explore discrimination between groups. Prognostic characteristics were calculated to assess the performance of EEG feature-based predictors established by logistic regression. Analyses were performed on all-patients group, strokes subgroup, and traumatic brain injury (TBI) subgroup. Results By analysis of all patients, raised burst suppression ratio (BSR), suppressed root mean square (RMS), raised power ratio of β to α rhythm (β/α), and suppressed phase-lag index between F3 and P4 (PLI [F3, P4]) were associated with unfavorable outcome, and yielded AUC of 0.790, 0.811, 0.722, and 0.844, respectively. For the strokes subgroup, the significant variables were BSR, RMS, θ/total, θ/δ, and PLI (F3, P4), while for the TBI subgroup, only PLI (F3, P4) was significant. BSR combined with PLI (F3, P4) gave the best predictor by cross-validation analysis in the all-patients group (AUC = 0.889, 95% CI: 0.819-0.960). Conclusion Features extracted from limited frontoparietal montage EEG served as valuable coma prognostic tools, where PLI (F3, P4) was always significant. Combining PLI (F3, P4) with features in other domains may achieve better performance. Significance A limited-montage EEG coupled with an automated algorithm is valuable for coma prognosis.
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Affiliation(s)
- Tao Tao
- Intensive Care Unit, The First People’s Hospital of Kunshan, Kunshan Affiliated Hospital of Jiangsu University, Kunshan, Jiangsu, China
| | - Shiqi Lu
- Emergency Department, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Nan Hu
- School of Electronics and Information Engineering, Soochow University, Suzhou, Jiangsu, China
| | - Dongyang Xu
- Center for Intelligent Acoustics and Signal Processing, Huzhou Institute of Zhejiang University, Huzhou, China
| | - Chenyang Xu
- Intensive Care Unit, The First People’s Hospital of Kunshan, Kunshan Affiliated Hospital of Jiangsu University, Kunshan, Jiangsu, China
| | - Fajun Li
- Intensive Care Unit, The First People’s Hospital of Kunshan, Kunshan Affiliated Hospital of Jiangsu University, Kunshan, Jiangsu, China
| | - Qin Wang
- Intensive Care Unit, The First People’s Hospital of Kunshan, Kunshan Affiliated Hospital of Jiangsu University, Kunshan, Jiangsu, China
| | - Yuan Peng
- Intensive Care Unit, The First People’s Hospital of Kunshan, Kunshan Affiliated Hospital of Jiangsu University, Kunshan, Jiangsu, China
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15
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research Consortium Electroencephalography Database. Crit Care Med 2023; 51:1802-1811. [PMID: 37855659 PMCID: PMC10841086 DOI: 10.1097/ccm.0000000000006074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2023]
Abstract
OBJECTIVES To develop the International Cardiac Arrest Research (I-CARE), a harmonized multicenter clinical and electroencephalography database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. DESIGN Multicenter cohort, partly prospective and partly retrospective. SETTING Seven academic or teaching hospitals from the United States and Europe. PATIENTS Individuals 16 years old or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous electroencephalography monitoring were included. INTERVENTIONS Not applicable. MEASUREMENTS AND MAIN RESULTS Clinical and electroencephalography data were harmonized and stored in a common Waveform Database-compatible format. Automated spike frequency, background continuity, and artifact detection on electroencephalography were calculated with 10-second resolution and summarized hourly. Neurologic outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical data and 56,676 hours (3.9 terabytes) of continuous electroencephalography data for 1,020 patients. Most patients died ( n = 603, 59%), 48 (5%) had severe neurologic disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean electroencephalography recording duration depending on the neurologic outcome (range, 53-102 hr for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least 1 hour was seen in 258 patients (25%) (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least 1 hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. CONCLUSIONS The I-CARE consortium electroencephalography database provides a comprehensive real-world clinical and electroencephalography dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal electroencephalography patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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16
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Sohn G, Kim SE. Measurement of thalamus and cortical damages in hypoxic ischemic encephalopathy. IBRO Neurosci Rep 2023; 15:179-185. [PMID: 37731916 PMCID: PMC10507579 DOI: 10.1016/j.ibneur.2023.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 09/04/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023] Open
Abstract
Background The thalamic gray-white matter ratios (GWRs) on CT and quantitative suppression ratios (SRs) of background activities on EEG may reflect damages in the thalamus and cerebral hemispheres in patients with hypoxic-ischemic encephalopathy (HIE). Methods The inclusion criteria were (1) cardiac arrest patients over the age of 20 years from March 2010 to March 2020, and (2) patients who had both EEG and brain CT within 7 days after cardiac arrest. The thalamic GWRs were semi-quantitatively measured by using the region of interest (ROI). SRs of background were analyzed with the installed software (Persyst® v13) in EEG machine. Results 175 patients were included among 686 patients with HIE and the thalamic GWRs of 168 patients were successfully measured. 155 patients (89 %) showed poor outcomes. The poor outcome group revealed not only higher SRs, but also lower thalamic GWRs. The thalamic GWRs showed a negative correlation to the SRs (ρ (rho) = -0.36, p < 0.0001 for right side, ρ (rho) = -0.31, p < 0.0001 for left side). The good outcome group showed neither beyond the cut-off values of thalamic GWRs nor SRs [40 % (59/148) VS 0 % (0/20) in right side, p = 0.0005 %, and 28 % (42/148) VS 0 % (0/20) in left side, p = 0.0061]. Conclusion The thalamic GWRs and SRs may reflect the damage in the thalamus and cerebral hemispheres in patients with HIE. Insults in the thalamocortical circuit (TCC) or the thalamus might be responsible for the poor outcome.
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Affiliation(s)
| | - Sung Eun Kim
- Correspondence to: Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan 48108, Republic of Korea.
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17
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Orav K, Bosque Varela P, Prüwasser T, Machegger L, Leitinger M, Trinka E, Kuchukhidze G. Post-hypoxic status epilepticus - A distinct subtype of status epilepticus with poor prognosis. Epileptic Disord 2023; 25:823-832. [PMID: 37776308 PMCID: PMC10947449 DOI: 10.1002/epd2.20164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/31/2023] [Accepted: 09/23/2023] [Indexed: 10/02/2023]
Abstract
OBJECTIVE To evaluate the clinical outcome of patients with possible and definitive post-hypoxic status epilepticus (SE) and to describe the SE types in patients with definitive post-hypoxic SE. METHODS Patients with definitive or possible SE resulting from hypoxic brain injury after cardiac arrest (CA) were prospectively recruited. Intermittent EEG was used for the diagnosis of SE according to clinical practice. Two raters blinded to outcome analyzed EEGs retrospectively for possible and definitive SE patterns and background features (frequency, continuity, reactivity, and voltage). Definitive SE was classified according to semiology (ILAE). Mortality and Cerebral Performance Categories (CPC) score were evaluated 1 month after CA. RESULTS We included 64 patients of whom 92% died. Among the survivors, only one patient had a good neurological outcome (CPC 1). No patient survived with a burst suppression pattern, low voltage, or electro-cerebral silence in any EEG. Possible or definitive SE was diagnosed in a median of 47 h (IQR 39-72 h) after CA. EEG criteria for definitive electrographic SE were fulfilled in 39% of patients; in 38% - for electroclinical SE and in 23% - for ictal-interictal continuum (IIC). The outcome did not differ significantly between the three groups. The only patient with good functional outcome belonged to the IIC group. Comatose non-convulsive SE (NCSE) without subtle motor phenomenon occurred in 20% of patients with definitive electrographic SE and outcome was similar to other types of SE. SIGNIFICANCE Possible or definitive SE due to hypoxic brain injury is associated with poor prognosis. The outcome of patients with electrographic SE, electroclinical SE, and IIC did not differ significantly. Outcome was similar in patients with definitive electrographic SE with and without prominent motor features.
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Affiliation(s)
- Kateriine Orav
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Department of NeurologyNorth Estonia Medical CentreTallinnEstonia
| | - Pilar Bosque Varela
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
| | - Tanja Prüwasser
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Department of MathematicsParis‐Lodron UniversitySalzburgAustria
| | - Lukas Machegger
- Department of Neuroradiology, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
| | - Markus Leitinger
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
| | - Eugen Trinka
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Neuroscience InstituteChristian Doppler University HospitalSalzburgAustria
- Karl Landsteiner Institute for Neurorehabilitation and Space NeurologySalzburgAustria
| | - Giorgi Kuchukhidze
- Department of Neurology, Member of the European Reference Network EpiCARE, Centre for Cognitive Neuroscience, Christian Doppler University HospitalParacelsus Medical University of SalzburgSalzburgAustria
- Neuroscience InstituteChristian Doppler University HospitalSalzburgAustria
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Rubinos C, Bruzzone MJ, Viswanathan V, Figueredo L, Maciel CB, LaRoche S. Electroencephalography as a Biomarker of Prognosis in Acute Brain Injury. Semin Neurol 2023; 43:675-688. [PMID: 37832589 DOI: 10.1055/s-0043-1775816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
Electroencephalography (EEG) is a noninvasive tool that allows the monitoring of cerebral brain function in critically ill patients, aiding with diagnosis, management, and prognostication. Specific EEG features have shown utility in the prediction of outcomes in critically ill patients with status epilepticus, acute brain injury (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and traumatic brain injury), anoxic brain injury, and toxic-metabolic encephalopathy. Studies have also found an association between particular EEG patterns and long-term functional and cognitive outcomes as well as prediction of recovery of consciousness following acute brain injury. This review summarizes these findings and demonstrates the value of utilizing EEG findings in the determination of prognosis.
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Affiliation(s)
- Clio Rubinos
- Department of Neurology, University of North Carolina, Chapel Hill, North Carolina
| | | | - Vyas Viswanathan
- Department of Neurology, University of North Carolina, Chapel Hill, North Carolina
| | - Lorena Figueredo
- Department of Neurology, University of Florida, Gainesville, Florida
| | - Carolina B Maciel
- Department of Neurology, University of Florida, Gainesville, Florida
| | - Suzette LaRoche
- Department of Neurology, University of North Carolina, Chapel Hill, North Carolina
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Amorim E, Zheng WL, Jing J, Ghassemi MM, Lee JW, Wu O, Herman ST, Pang T, Sivaraju A, Gaspard N, Hirsch L, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest. Neurology 2023; 101:e940-e952. [PMID: 37414565 PMCID: PMC10501085 DOI: 10.1212/wnl.0000000000207537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 05/04/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest. METHODS Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months. RESULTS One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery. DISCUSSION Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.
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Affiliation(s)
- Edilberto Amorim
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands.
| | - Wei-Long Zheng
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jin Jing
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Mohammad M Ghassemi
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jong Woo Lee
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Ona Wu
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Susan T Herman
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Trudy Pang
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Adithya Sivaraju
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Nicolas Gaspard
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Lawrence Hirsch
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Barry J Ruijter
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Marleen C Tjepkema-Cloostermans
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jeannette Hofmeijer
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - Michel J A M van Putten
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
| | - M Brandon Westover
- From the Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California, San Francisco; Department of Neurology (E.A., W.-L.Z., J.J., M.B.W.), Massachusetts General Hospital, Boston; Department of Computer Science and Engineering (W.-L.Z.), Shanghai Jiao Tong University, China; Department of Neurology (J.J., T.P., M.B.W.), Beth Israel Deaconess Medical Center, Boston, MA; Department of Computer Science and Engineering (M.M.G.), Michigan State University, East Lansing; Department of Neurology (J.W.L.), Brigham and Women's Hospital; Athinoula A. Martinos Center for Biomedical Imaging (O.W.), Department of Radiology, Massachusetts General Hospital, Boston; Department of Neurology (S.T.H.), Barrow Neurological Institute Comprehensive Epilepsy Center, Phoenix, AZ; Department of Neurology (A.S., N.G., L.H.), Yale School of Medicine, New Haven, CT; Department of Neurology (N.G.), Universite Libre de Bruxelles, Belgium; Clinical Neurophysiology Group (B.J.R., M.C.T.-C., J.H., M.J.A.M.v.P.), University of Twente, Enschede; Department of Neurology (J.H.), Rijnstate Hospital, Arnhem; and Department of Neurology and Clinical Neurophysiology (M.J.A.M.v.P.), Medisch Spectrum Twente, Enschede, the Netherlands
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Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval M, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.28.23294672. [PMID: 37693458 PMCID: PMC10491275 DOI: 10.1101/2023.08.28.23294672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Objective To develop a harmonized multicenter clinical and electroencephalography (EEG) database for acute hypoxic-ischemic brain injury research involving patients with cardiac arrest. Design Multicenter cohort, partly prospective and partly retrospective. Setting Seven academic or teaching hospitals from the U.S. and Europe. Patients Individuals aged 16 or older who were comatose after return of spontaneous circulation following a cardiac arrest who had continuous EEG monitoring were included. Interventions not applicable. Measurements and Main Results Clinical and EEG data were harmonized and stored in a common Waveform Database (WFDB)-compatible format. Automated spike frequency, background continuity, and artifact detection on EEG were calculated with 10 second resolution and summarized hourly. Neurological outcome was determined at 3-6 months using the best Cerebral Performance Category (CPC) scale. This database includes clinical and 56,676 hours (3.9 TB) of continuous EEG data for 1,020 patients. Most patients died (N=603, 59%), 48 (5%) had severe neurological disability (CPC 3 or 4), and 369 (36%) had good functional recovery (CPC 1-2). There is significant variability in mean EEG recording duration depending on the neurological outcome (range 53-102h for CPC 1 and CPC 4, respectively). Epileptiform activity averaging 1 Hz or more in frequency for at least one hour was seen in 258 (25%) patients (19% for CPC 1-2 and 29% for CPC 3-5). Burst suppression was observed for at least one hour in 207 (56%) and 635 (97%) patients with CPC 1-2 and CPC 3-5, respectively. Conclusions The International Cardiac Arrest Research (I-CARE) consortium database provides a comprehensive real-world clinical and EEG dataset for neurophysiology research of comatose patients after cardiac arrest. This dataset covers the spectrum of abnormal EEG patterns after cardiac arrest, including epileptiform patterns and those in the ictal-interictal continuum.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, CN
| | - Mohammad M. Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Mahsa Aghaeeaval
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Pravinkumar Kandhare
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Vishnu Karukonda
- Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Jong Woo Lee
- Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Susan T. Herman
- Department of Neurology, Barrow Neurological Institute, Comprehensive Epilepsy Center, Phoenix, Arizona, USA
| | - Adithya Sivaraju
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Nicolas Gaspard
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Neurology, Universite Libre de Bruxelles, Brussels, Belgium
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Michel J. A. M. van Putten
- Clinical Neurophysiology Group, University of Twente, Enschede, The Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Matthew A. Reyna
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
- Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, Georgia, USA
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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21
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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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22
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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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23
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Zubler F, Tzovara A. Deep learning for EEG-based prognostication after cardiac arrest: from current research to future clinical applications. Front Neurol 2023; 14:1183810. [PMID: 37560450 PMCID: PMC10408678 DOI: 10.3389/fneur.2023.1183810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023] Open
Abstract
Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic encephalopathy. Electroencephalography (EEG) is routinely used to assess neural functions in comatose patients. Currently, EEG-based outcome prognosis relies on visual evaluation by medical experts, which is time consuming, prone to subjectivity, and oblivious to complex patterns. The field of deep learning has given rise to powerful algorithms for detecting patterns in large amounts of data. Analyzing EEG signals of coma patients with deep neural networks with the goal of assisting in outcome prognosis is therefore a natural application of these algorithms. Here, we provide the first narrative literature review on the use of deep learning for prognostication after CA. Existing studies show overall high performance in predicting outcome, relying either on spontaneous or on auditory evoked EEG signals. Moreover, the literature is concerned with algorithmic interpretability, and has shown that largely, deep neural networks base their decisions on clinically or neurophysiologically meaningful features. We conclude this review by discussing considerations that the fields of artificial intelligence and neurology will need to jointly address in the future, in order for deep learning algorithms to break the publication barrier, and to be integrated in clinical practice.
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Affiliation(s)
- Frederic Zubler
- Department of Neurology, Spitalzentrum Biel, University of Bern, Biel/Bienne, Switzerland
| | - Athina Tzovara
- Institute of Computer Science, University of Bern, Bern, Switzerland
- Department of Neurology, Zentrum für Experimentelle Neurologie and Sleep Wake Epilepsy Center—Neurotec, Inselspital University Hospital Bern, Bern, Switzerland
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24
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Tziakouri A, Novy J, Ben-Hamouda N, Rossetti AO. Relationship between serum neuron-specific enolase and EEG after cardiac arrest: A reappraisal. Clin Neurophysiol 2023; 151:100-106. [PMID: 37236128 DOI: 10.1016/j.clinph.2023.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/05/2023] [Accepted: 05/01/2023] [Indexed: 05/28/2023]
Abstract
OBJECTIVE Electroencephalogram (EEG) and serum neuron specific enolase (NSE) are frequently used prognosticators after cardiac arrest (CA). This study explored the association between NSE and EEG, considering the role of EEG timing, its background continuity, reactivity, occurrence of epileptiform discharges, and pre-defined malignancy degree. METHODS Retrospective analysis including 445 consecutive adults from a prospective registry, surviving the first 24 hours after CA and undergoing multimodal evaluation. EEG were interpreted blinded to NSE results. RESULTS Higher NSE was associated with poor EEG prognosticators, such as increasing malignancy, repetitive epileptiform discharges and lack of background reactivity, independently of EEG timing (including sedation and temperature). When stratified for background continuity, NSE was higher with repetitive epileptiform discharges, except in the case of suppressed EEGs. This relationship showed some variation according to the recording time. CONCLUSIONS Neuronal injury after CA, reflected by NSE, correlates with several EEG features: increasing EEG malignancy, lack of background reactivity, and presence of repetitive epileptiform discharges. The correlation between epileptiform discharges and NSE is influenced by underlying EEG background and timing. SIGNIFICANCE This study, describing the complex interplay between serum NSE and epileptiform features, suggests that epileptiform discharges reflect neuronal injury particularly in non-suppressed EEG.
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Affiliation(s)
- Andria Tziakouri
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jan Novy
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nawfel Ben-Hamouda
- Department of Adult Intensive Care Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Andrea O Rossetti
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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25
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Pelentritou A, Nguissi NAN, Iten M, Haenggi M, Zubler F, Rossetti AO, De Lucia M. The effect of sedation and time after cardiac arrest on coma outcome prognostication based on EEG power spectra. Brain Commun 2023; 5:fcad190. [PMID: 37469860 PMCID: PMC10353761 DOI: 10.1093/braincomms/fcad190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 05/11/2023] [Accepted: 06/27/2023] [Indexed: 07/21/2023] Open
Abstract
Early prognostication of long-term outcome of comatose patients after cardiac arrest remains challenging. Electroencephalography-based power spectra after cardiac arrest have been shown to help with the identification of patients with favourable outcome during the first day of coma. Here, we aim at comparing the power spectra prognostic value during the first and second day after coma onset following cardiac arrest and to investigate the impact of sedation on prognostication. In this cohort observational study, we included comatose patients (N = 91) after cardiac arrest for whom resting-state electroencephalography was collected on the first and second day after cardiac arrest in four Swiss hospitals. We evaluated whether the average power spectra values at 4.6-15.2 Hz were predictive of patients' outcome based on the best cerebral performance category score at 3 months, with scores ranging from 1 to 5 and dichotomized as favourable (1-2) and unfavourable (3-5). We assessed the effect of sedation and its interaction with the electroencephalography-based power spectra on patient outcome prediction through a generalized linear mixed model. Power spectra values provided 100% positive predictive value (95% confidence intervals: 0.81-1.00) on the first day of coma, with correctly predicted 18 out of 45 favourable outcome patients. On the second day, power spectra values were not predictive of patients' outcome (positive predictive value: 0.46, 95% confidence intervals: 0.19-0.75). On the first day, we did not find evidence of any significant contribution of sedative infusion rates to the patient outcome prediction (P > 0.05). Comatose patients' outcome prediction based on electroencephalographic power spectra is higher on the first compared with the second day after cardiac arrest. Sedation does not appear to impact patient outcome prediction.
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Affiliation(s)
| | | | - Manuela Iten
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Matthias Haenggi
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Frederic Zubler
- Department of Neurology, Spitalzentrum Biel, University of Bern, 2501 Biel, Switzerland
| | - Andrea O Rossetti
- Department of Clinical Neurosciences, University Hospital (CHUV) & University of Lausanne, 1011 Lausanne, Switzerland
| | - Marzia De Lucia
- Correspondence to: Marzia De Lucia, Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), MP16 05 559, Chemin de Mont-Paisible 16, Lausanne 1010, Switzerland. E-mail:
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26
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Wahlster S, Danielson K, Craft L, Matin N, Town JA, Srinivasan V, Schubert G, Carlbom D, Kim F, Johnson NJ, Tirschwell D. Factors Associated with Early Withdrawal of Life-Sustaining Treatments After Out-of-Hospital Cardiac Arrest: A Subanalysis of a Randomized Trial of Prehospital Therapeutic Hypothermia. Neurocrit Care 2023; 38:676-687. [PMID: 36380126 DOI: 10.1007/s12028-022-01636-7] [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: 05/28/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND The objective of this study is to describe incidence and factors associated with early withdrawal of life-sustaining therapies based on presumed poor neurologic prognosis (WLST-N) and practices around multimodal prognostication after out-of-hospital cardiac arrest (OHCA). METHODS We performed a subanalysis of a randomized controlled trial assessing prehospital therapeutic hypothermia in adult patients admitted to nine hospitals in King County with nontraumatic OHCA between 2007 and 2012. Patients who underwent tracheal intubation and were unconscious following return of spontaneous circulation were included. Our outcomes were (1) incidence of early WLST-N (WLST-N within < 72 h from return of spontaneous circulation), (2) factors associated with early WLST-N compared with patients who remained comatose at 72 h without WLST-N, (3) institutional variation in early WLST-N, (4) use of multimodal prognostication, and (5) use of sedative medications in patients with early WLST-N. Analysis included descriptive statistics and multivariable logistic regression. RESULTS We included 1,040 patients (mean age was 65 years, 37% were female, 41% were White, and 44% presented with arrest due to ventricular fibrillation) admitted to nine hospitals. Early WLST-N accounted for 24% (n = 154) of patient deaths and occurred in half (51%) of patients with WLST-N. Factors associated with early WLST-N in multivariate regressions were older age (odds ratio [OR] 1.02, 95% confidence interval [CI]: 1.01-1.03), preexisting do-not-attempt-resuscitation orders (OR 4.67, 95% CI: 1.55-14.01), bilateral absent pupillary reflexes (OR 2.4, 95% CI: 1.42-4.10), and lack of neurological consultation (OR 2.60, 95% CI: 1.52-4.46). The proportion of patients with early WLST-N among all OHCA admissions ranged from 19-60% between institutions. A head computed tomography scan was obtained in 54% (n = 84) of patients with early WLST-N; 22% (n = 34) and 5% (n = 8) underwent ≥ 1 and ≥ 2 additional prognostic tests, respectively. Prognostic tests were more frequently performed when neurological consultation occurred. Most patients received sedating medications (90%) within 24 h before early WLST-N; the median time from last sedation to early WLST-N was 4.2 h (interquartile range 0.4-15). CONCLUSIONS Nearly one quarter of deaths after OHCA were due to early WLST-N. The presence of concerning neurological examination findings appeared to impact early WLST-N decisions, even though these are not fully reliable in this time frame. Lack of neurological consultation was associated with early WLST-N and resulted in underuse of guideline-concordant multimodal prognostication. Sedating medications were often coadministered prior to early WLST-N and may have further confounded the neurological examination. Standardizing prognostication, restricting early WLST-N, and a multidisciplinary approach including neurological consultation might improve outcomes after OHCA.
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Affiliation(s)
- Sarah Wahlster
- Department of Neurology, Harborview Medical Center, University of Washington, 325 9th Avenue, Box 359702, Seattle, WA, USA.
- Department of Anesthesiology, University of Washington, Seattle, WA, USA.
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA.
| | - Kyle Danielson
- Airlift Northwest, University of Washington Medicine, Seattle, WA, USA
| | - Lindy Craft
- Department of Anesthesiology, University of Washington, Seattle, WA, USA
| | - Nassim Matin
- Department of Neurology, Harborview Medical Center, University of Washington, 325 9th Avenue, Box 359702, Seattle, WA, USA
| | - James A Town
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Vasisht Srinivasan
- Department of Emergency Medicine, University of Washington, Seattle, WA, USA
| | - Glenn Schubert
- Department of Neurology, Harborview Medical Center, University of Washington, 325 9th Avenue, Box 359702, Seattle, WA, USA
| | - David Carlbom
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Francis Kim
- Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Nicholas J Johnson
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Emergency Medicine, University of Washington, Seattle, WA, USA
| | - David Tirschwell
- Department of Neurology, Harborview Medical Center, University of Washington, 325 9th Avenue, Box 359702, Seattle, WA, USA
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27
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Tewarie PKB, Tjepkema-Cloostermans MC, Abeysuriya RG, Hofmeijer J, van Putten MJAM. Preservation of thalamocortical circuitry is essential for good recovery after cardiac arrest. PNAS NEXUS 2023; 2:pgad119. [PMID: 37143862 PMCID: PMC10153639 DOI: 10.1093/pnasnexus/pgad119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/10/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023]
Abstract
Continuous electroencephalographam (EEG) monitoring contributes to prediction of neurological outcome in comatose cardiac arrest survivors. While the phenomenology of EEG abnormalities in postanoxic encephalopathy is well known, the pathophysiology, especially the presumed role of selective synaptic failure, is less understood. To further this understanding, we estimate biophysical model parameters from the EEG power spectra from individual patients with a good or poor recovery from a postanoxic encephalopathy. This biophysical model includes intracortical, intrathalamic, and corticothalamic synaptic strengths, as well as synaptic time constants and axonal conduction delays. We used continuous EEG measurements from hundred comatose patients recorded during the first 48 h postcardiac arrest, 50 with a poor neurological outcome [cerebral performance category ( CPC = 5 ) ] and 50 with a good neurological outcome ( CPC = 1 ). We only included patients that developed (dis-)continuous EEG activity within 48 h postcardiac arrest. For patients with a good outcome, we observed an initial relative excitation in the corticothalamic loop and corticothalamic propagation that subsequently evolved towards values observed in healthy controls. For patients with a poor outcome, we observed an initial increase in the cortical excitation-inhibition ratio, increased relative inhibition in the corticothalamic loop, delayed corticothalamic propagation of neuronal activity, and severely prolonged synaptic time constants that did not return to physiological values. We conclude that the abnormal EEG evolution in patients with a poor neurological recovery after cardiac arrest may result from persistent and selective synaptic failure that includes corticothalamic circuitry and also delayed corticothalamic propagation.
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Affiliation(s)
| | - Marleen C Tjepkema-Cloostermans
- Clinical Neurophysiology Group, University of Twente, 7522 NH Enschede, Netherlands
- Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, 7512 KZ Enschede, Netherlands
| | - Romesh G Abeysuriya
- Computational Epidemic Modelling, Burnet Institute, 3004 Melbourne, Australia
| | - Jeannette Hofmeijer
- Clinical Neurophysiology Group, University of Twente, 7522 NH Enschede, Netherlands
- Department of Neurology, Rijnstate Hospital, 6815 AD Arnhem, Netherlands
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28
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Fordyce CB, Kramer AH, Ainsworth C, Christenson J, Hunter G, Kromm J, Lopez Soto C, Scales DC, Sekhon M, van Diepen S, Dragoi L, Josephson C, Kutsogiannis J, Le May MR, Overgaard CB, Savard M, Schnell G, Wong GC, Belley-Côté E, Fantaneanu TA, Granger CB, Luk A, Mathew R, McCredie V, Murphy L, Teitelbaum J. Neuroprognostication in the Post Cardiac Arrest Patient: A Canadian Cardiovascular Society Position Statement. Can J Cardiol 2023; 39:366-380. [PMID: 37028905 DOI: 10.1016/j.cjca.2022.12.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 04/08/2023] Open
Abstract
Cardiac arrest (CA) is associated with a low rate of survival with favourable neurologic recovery. The most common mechanism of death after successful resuscitation from CA is withdrawal of life-sustaining measures on the basis of perceived poor neurologic prognosis due to underlying hypoxic-ischemic brain injury. Neuroprognostication is an important component of the care pathway for CA patients admitted to hospital but is complex, challenging, and often guided by limited evidence. Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to evaluate the evidence underlying factors or diagnostic modalities available to determine prognosis, recommendations were generated in the following domains: (1) circumstances immediately after CA; (2) focused neurologic exam; (3) myoclonus and seizures; (4) serum biomarkers; (5) neuroimaging; (6) neurophysiologic testing; and (7) multimodal neuroprognostication. This position statement aims to serve as a practical guide to enhance in-hospital care of CA patients and emphasizes the adoption of a systematic, multimodal approach to neuroprognostication. It also highlights evidence gaps.
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Affiliation(s)
- Christopher B Fordyce
- Division of Cardiology, Department of Medicine, Vancouver General Hospital, and the Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, British Columbia.
| | - Andreas H Kramer
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Craig Ainsworth
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Jim Christenson
- Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia
| | - Gary Hunter
- Division of Neurology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Julie Kromm
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Carmen Lopez Soto
- Department of Critical Care, King's College Hospital NHS Foundation Trust, London, United Kingdom
| | - Damon C Scales
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Mypinder Sekhon
- Division of Critical Care, Department of Medicine, Vancouver General Hospital, Djavad Mowafaghian Centre for Brain Health, International Centre for Repair Discoveries, University of British Columbia, Vancouver, British Columbia
| | - Sean van Diepen
- Department of Critical Care Medicine, University of Alberta, Edmonton, Alberta; Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, Alberta
| | - Laura Dragoi
- Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Colin Josephson
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta; Department of Critical Care, University of Calgary, Alberta
| | - Jim Kutsogiannis
- Department of Critical Care Medicine, University of Alberta, Edmonton, Alberta
| | - Michel R Le May
- Division of Cardiology, Department of Medicine, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Christopher B Overgaard
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Martin Savard
- Department of Neurological Sciences CHU de Québec - Hôpital de l'Enfant-Jésus Quebec City, Quebec, Canada
| | - Gregory Schnell
- Division of Cardiology, Department of Medicine, University of Calgary, Calgary, Alberta
| | - Graham C Wong
- Division of Cardiology, Department of Medicine, Vancouver General Hospital, and the Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, British Columbia
| | - Emilie Belley-Côté
- Division of Cardiology, Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Tadeu A Fantaneanu
- Division of Neurology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Adriana Luk
- Division of Cardiology, Department of Medicine, University of Toronto and the Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Rebecca Mathew
- CAPITAL Research Group, Division of Cardiology, University of Ottawa Heart Institute, and the Faculty of Medicine, Division of Critical Care, University of Ottawa, Ottawa, Ontario, Canada
| | - Victoria McCredie
- Interdepartmental Division of Critical Care Medicine, University of Toronto, the Krembil Research Institute, Toronto Western Hospital, University Health Network, and Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Laurel Murphy
- Departments of Emergency Medicine and Critical Care, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jeanne Teitelbaum
- Neurological Intensive Care Unit, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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29
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Levin H, Lybeck A, Frigyesi A, Arctaedius I, Thorgeirsdóttir B, Annborn M, Moseby-Knappe M, Nielsen N, Cronberg T, Ashton NJ, Zetterberg H, Blennow K, Friberg H, Mattsson-Carlgren N. Plasma neurofilament light is a predictor of neurological outcome 12 h after cardiac arrest. Crit Care 2023; 27:74. [PMID: 36829239 PMCID: PMC9960417 DOI: 10.1186/s13054-023-04355-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/12/2023] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Previous studies have reported high prognostic accuracy of circulating neurofilament light (NfL) at 24-72 h after out-of-hospital cardiac arrest (OHCA), but performance at earlier time points and after in-hospital cardiac arrest (IHCA) is less investigated. We aimed to assess plasma NfL during the first 48 h after OHCA and IHCA to predict long-term outcomes. METHODS Observational multicentre cohort study in adults admitted to intensive care after cardiac arrest. NfL was retrospectively analysed in plasma collected on admission to intensive care, 12 and 48 h after cardiac arrest. The outcome was assessed at two to six months using the Cerebral Performance Category (CPC) scale, where CPC 1-2 was considered a good outcome and CPC 3-5 a poor outcome. Predictive performance was measured with the area under the receiver operating characteristic curve (AUROC). RESULTS Of 428 patients, 328 (77%) suffered OHCA and 100 (23%) IHCA. Poor outcome was found in 68% of OHCA and 55% of IHCA patients. The overall prognostic performance of NfL was excellent at 12 and 48 h after OHCA, with AUROCs of 0.93 and 0.97, respectively. The predictive ability was lower after IHCA than OHCA at 12 and 48 h, with AUROCs of 0.81 and 0.86 (p ≤ 0.03). AUROCs on admission were 0.77 and 0.67 after OHCA and IHCA, respectively. At 12 and 48 h after OHCA, high NfL levels predicted poor outcome at 95% specificity with 70 and 89% sensitivity, while low NfL levels predicted good outcome at 95% sensitivity with 71 and 74% specificity and negative predictive values of 86 and 88%. CONCLUSIONS The prognostic accuracy of NfL for predicting good and poor outcomes is excellent as early as 12 h after OHCA. NfL is less reliable for the prediction of outcome after IHCA.
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Affiliation(s)
- Helena Levin
- Anesthesia & Intensive Care, Department of Clinical Sciences, Lund University, Lund, Sweden. .,Department of Research & Education, Skane University Hospital, Lund, Sweden.
| | - Anna Lybeck
- grid.4514.40000 0001 0930 2361Anesthesia & Intensive Care, Department of Clinical Sciences, Skane University Hospital, Lund University, Lund, Sweden
| | - Attila Frigyesi
- grid.4514.40000 0001 0930 2361Anesthesia & Intensive Care, Department of Clinical Sciences, Skane University Hospital, Lund University, Lund, Sweden
| | - Isabelle Arctaedius
- grid.4514.40000 0001 0930 2361Anesthesia & Intensive Care, Department of Clinical Sciences, Skane University Hospital, Lund University, Lund, Sweden
| | - Bergthóra Thorgeirsdóttir
- grid.4514.40000 0001 0930 2361Anesthesia & Intensive Care, Department of Clinical Sciences, Skane University Hospital, Lund University, Malmö, Sweden
| | - Martin Annborn
- grid.4514.40000 0001 0930 2361Anesthesia & Intensive Care, Department of Clinical Sciences, Helsingborg Hospital, Lund University, Helsingborg, Sweden
| | - Marion Moseby-Knappe
- grid.4514.40000 0001 0930 2361Neurology, Department of Clinical Sciences Lund, Skane University Hospital, Lund University, Lund, Sweden
| | - Niklas Nielsen
- grid.4514.40000 0001 0930 2361Anesthesia & Intensive Care, Department of Clinical Sciences, Helsingborg Hospital, Lund University, Helsingborg, Sweden
| | - Tobias Cronberg
- grid.4514.40000 0001 0930 2361Neurology, Department of Clinical Sciences Lund, Skane University Hospital, Lund University, Lund, Sweden
| | - Nicholas J. Ashton
- grid.13097.3c0000 0001 2322 6764Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK ,grid.454378.9NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK ,grid.412835.90000 0004 0627 2891Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway ,grid.8761.80000 0000 9919 9582Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Henrik Zetterberg
- grid.8761.80000 0000 9919 9582Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden ,grid.1649.a000000009445082XClinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden ,grid.83440.3b0000000121901201Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK ,grid.83440.3b0000000121901201UK Dementia Research Institute at UCL, London, UK ,grid.24515.370000 0004 1937 1450Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Kaj Blennow
- grid.8761.80000 0000 9919 9582Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden ,grid.1649.a000000009445082XClinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Hans Friberg
- grid.4514.40000 0001 0930 2361Anesthesia & Intensive Care, Department of Clinical Sciences, Skane University Hospital, Lund University, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- grid.4514.40000 0001 0930 2361Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden ,grid.411843.b0000 0004 0623 9987Department of Neurology, Skane University Hospital, Lund, Sweden ,grid.4514.40000 0001 0930 2361Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
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Misirocchi F, Bernabè G, Zinno L, Spallazzi M, Zilioli A, Mannini E, Lazzari S, Tontini V, Mutti C, Parrino L, Picetti E, Florindo I. Epileptiform patterns predicting unfavorable outcome in postanoxic patients: A matter of time? Neurophysiol Clin 2023; 53:102860. [PMID: 37011480 DOI: 10.1016/j.neucli.2023.102860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 03/17/2023] [Accepted: 03/18/2023] [Indexed: 04/03/2023] Open
Abstract
OBJECTIVE Historically, epileptiform malignant EEG patterns (EMPs) have been considered to anticipate an unfavorable outcome, but an increasing amount of evidence suggests that they are not always or invariably associated with poor prognosis. We evaluated the prognostic significance of an EMP onset in two different timeframes in comatose patients after cardiac arrest (CA): early-EMPs and late-EMPs, respectively. METHODS We included all comatose post-CA survivors admitted to our intensive care unit (ICU) between 2016 and 2018 who underwent at least two 30-minute EEGs, collected at T0 (12-36 h after CA) and T1 (36-72 h after CA). All EEGs recordings were re-analyzed following the 2021 ACNS terminology by two senior EEG specialists, blinded to outcome. Malignant EEGs with abundant sporadic spikes/sharp waves, rhythmic and periodic patterns, or electrographic seizure/status epilepticus, were included in the EMP definition. The primary outcome was the cerebral performance category (CPC) score at 6 months, dichotomized as good (CPC 1-2) or poor (CPC 3-5) outcome. RESULTS A total of 58 patients and 116 EEG recording were included in the study. Poor outcome was seen in 28 (48%) patients. In contrast to late-EMPs, early-EMPs were associated with a poor outcome (p = 0.037), persisting after multiple regression analysis. Moreover, a multivariate binomial model coupling the timing of EMP onset with other EEG predictors such as T1 reactivity and T1 normal voltage background can predict outcome in the presence of an otherwise non-specific malignant EEG pattern with quite high specificity (82%) and moderate sensitivity (77%). CONCLUSIONS The prognostic significance of EMPs seems strongly time-dependent and only their early-onset may be associated with an unfavorable outcome. The time of onset of EMP combined with other EEG features could aid in defining prognosis in patients with intermediate EEG patterns.
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Elmashala A, Busl KM, Maciel CB. Will shifting the lens let us see more clearly when prognosticating after cardiac arrest, or do we need new glasses? Resuscitation 2023; 182:109667. [PMID: 36565947 DOI: 10.1016/j.resuscitation.2022.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022]
Affiliation(s)
- Amjad Elmashala
- Department of Neurology, Division of Neurocritical Care, University of Florida College of Medicine, Gainesville, FL 32611, USA
| | - Katharina M Busl
- Department of Neurology, Division of Neurocritical Care, University of Florida College of Medicine, Gainesville, FL 32611, USA; Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL 32611, USA
| | - Carolina B Maciel
- Department of Neurology, Division of Neurocritical Care, University of Florida College of Medicine, Gainesville, FL 32611, USA; Department of Neurosurgery, University of Florida College of Medicine, Gainesville, FL 32611, USA; Department of Neurology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Neurology, University of Utah, Salt Lake City, UT 84132, USA.
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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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Arbas-Redondo E, Rosillo-Rodríguez SO, Merino-Argos C, Marco-Clement I, Rodríguez-Sotelo L, Martínez-Marín LA, Martín-Polo L, Vélez-Salas A, Caro-Codón J, García-Arribas D, Armada-Romero E, López-De-Sa E. Bispectral index and suppression ratio after cardiac arrest: are they useful as bedside tools for rational treatment escalation plans? REVISTA ESPANOLA DE CARDIOLOGIA (ENGLISH ED.) 2022; 75:992-1000. [PMID: 35570124 DOI: 10.1016/j.rec.2022.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/15/2022] [Indexed: 06/15/2023]
Abstract
INTRODUCTION AND OBJECTIVES Myocardial dysfunction contributes to early mortality (24-72 hours) among survivors of a cardiac arrest (CA). The benefits of mechanical support in refractory shock should be balanced against the patient's potential for neurological recovery. To date, these early treatment decisions have been taken based on limited information leading mainly to undertreatment. Therefore, there is a need for early, reliable, accessible, and simple tools that offer information on the possibilities of neurological improvement. METHODS We collected data from bispectral index (BIS) and suppression ratio (SR) monitoring of adult comatose survivors of CA managed with targeted temperature management (TTM). Neurological status was assessed according to the Cerebral Performance Category (CPC) scale. RESULTS We included 340 patients. At the first full neurological evaluation, 211 patients (62.1%) achieved good outcome or CPC 1-2. Mean BIS values were significantly higher and median SR lower in patients with CPC 1-2. An average BIS> 26 during first 12 hours of TTM predicted good outcome with 89.5% sensitivity and 75.8% specificity (AUC of 0.869), while average SR values> 24 during the first 12 hours of TTM predicted poor outcome (CPC 3-5) with 91.5% sensitivity and 81.8% specificity (AUC, 0.906). Hourly BIS and SR values exhibited good predictive performance (AUC> 0.85), as soon as hour 2 for SR and hour 4 for BIS. CONCLUSIONS BIS/SR are associated with patients' potential for neurological recovery after CA. This finding could help to create awareness of the possibility of a better outcome in patients who might otherwise be wrongly considered as nonviable and to establish personalized treatment escalation plans.
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Affiliation(s)
| | - Sandra O Rosillo-Rodríguez
- Unidad de Cuidados Agudos Cardiovasculares, Servicio de Cardiología, Hospital Universitario La Paz, Madrid, Spain; Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Madrid, Spain
| | | | | | | | | | | | | | - Juan Caro-Codón
- Unidad de Cuidados Agudos Cardiovasculares, Servicio de Cardiología, Hospital Universitario La Paz, Madrid, Spain; Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Madrid, Spain
| | - Daniel García-Arribas
- Unidad de Cuidados Agudos Cardiovasculares, Servicio de Cardiología, Hospital Universitario La Paz, Madrid, Spain
| | - Eduardo Armada-Romero
- Unidad de Cuidados Agudos Cardiovasculares, Servicio de Cardiología, Hospital Universitario La Paz, Madrid, Spain; Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Madrid, Spain
| | - Esteban López-De-Sa
- Unidad de Cuidados Agudos Cardiovasculares, Servicio de Cardiología, Hospital Universitario La Paz, Madrid, Spain; Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Madrid, Spain
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Mertens M, King OC, van Putten MJAM, Boenink M. Can we learn from hidden mistakes? Self-fulfilling prophecy and responsible neuroprognostic innovation. JOURNAL OF MEDICAL ETHICS 2022; 48:922-928. [PMID: 34253620 PMCID: PMC9626909 DOI: 10.1136/medethics-2020-106636] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 05/22/2021] [Indexed: 05/24/2023]
Abstract
A self-fulfilling prophecy (SFP) in neuroprognostication occurs when a patient in coma is predicted to have a poor outcome, and life-sustaining treatment is withdrawn on the basis of that prediction, thus directly bringing about a poor outcome (viz. death) for that patient. In contrast to the predominant emphasis in the bioethics literature, we look beyond the moral issues raised by the possibility that an erroneous prediction might lead to the death of a patient who otherwise would have lived. Instead, we focus on the problematic epistemic consequences of neuroprognostic SFPs in settings where research and practice intersect. When this sort of SFP occurs, the problem is that physicians and researchers are never in a position to notice whether their original prognosis was correct or incorrect, since the patient dies anyway. Thus, SFPs keep us from discerning false positives from true positives, inhibiting proper assessment of novel prognostic tests. This epistemic problem of SFPs thus impedes learning, but ethical obligations of patient care make it difficult to avoid SFPs. We then show how the impediment to catching false positive indicators of poor outcome distorts research on novel techniques for neuroprognostication, allowing biases to persist in prognostic tests. We finally highlight a particular risk that a precautionary bias towards early withdrawal of life-sustaining treatment may be amplified. We conclude with guidelines about how researchers can mitigate the epistemic problems of SFPs, to achieve more responsible innovation of neuroprognostication for patients in coma.
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Affiliation(s)
- Mayli Mertens
- Center for Medical Science and Technology Studies, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
- Department of Philosophy, University of Twente, Enschede, Overijssel, The Netherlands
| | - Owen C King
- Department of Philosophy, University of Twente, Enschede, Overijssel, The Netherlands
| | - Michel J A M van Putten
- MIRA-Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, Overijssel, The Netherlands
- Department of Clinical Neurophysiology, Medisch Spectrum Twente, Enschede, Overijssel, The Netherlands
| | - Marianne Boenink
- Department of Philosophy, University of Twente, Enschede, Overijssel, The Netherlands
- Department IQ Healthcare, RadboudUMC - Radboud University, Nijmegen, Gelderland, the Netherlands
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Repetitive Electroencephalography as Biomarker for the Prediction of Survival in Patients with Post-Hypoxic Encephalopathy. J Clin Med 2022; 11:jcm11216253. [DOI: 10.3390/jcm11216253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 11/07/2022] Open
Abstract
Predicting survival in patients with post-hypoxic encephalopathy (HE) after cardiopulmonary resuscitation is a challenging aspect of modern neurocritical care. Here, continuous electroencephalography (cEEG) has been established as the gold standard for neurophysiological outcome prediction. Unfortunately, cEEG is not comprehensively available, especially in rural regions and developing countries. The objective of this monocentric study was to investigate the predictive properties of repetitive EEGs (rEEGs) with respect to 12-month survival based on data for 199 adult patients with HE, using log-rank and multivariate Cox regression analysis (MCRA). A total number of 59 patients (29.6%) received more than one EEG during the first 14 days of acute neurocritical care. These patients were analyzed for the presence of and changes in specific EEG patterns that have been shown to be associated with favorable or poor outcomes in HE. Based on MCRA, an initially normal amplitude with secondary low-voltage EEG remained as the only significant predictor for an unfavorable outcome, whereas all other relevant parameters identified by univariate analysis remained non-significant in the model. In conclusion, rEEG during early neurocritical care may help to assess the prognosis of HE patients if cEEG is not available.
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Alkhachroum A, Appavu B, Egawa S, Foreman B, Gaspard N, Gilmore EJ, Hirsch LJ, Kurtz P, Lambrecq V, Kromm J, Vespa P, Zafar SF, Rohaut B, Claassen J. Electroencephalogram in the intensive care unit: a focused look at acute brain injury. Intensive Care Med 2022; 48:1443-1462. [PMID: 35997792 PMCID: PMC10008537 DOI: 10.1007/s00134-022-06854-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 07/31/2022] [Indexed: 02/04/2023]
Abstract
Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.
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Affiliation(s)
- Ayham Alkhachroum
- Department of Neurology, University of Miami, Miami, FL, USA
- Department of Neurology, Jackson Memorial Hospital, Miami, FL, USA
| | - Brian Appavu
- Department of Child Health and Neurology, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, USA
- Department of Neurosciences, Phoenix Children's Hospital, Phoenix, AZ, USA
| | - Satoshi Egawa
- Neurointensive Care Unit, Department of Neurosurgery, and Stroke and Epilepsy Center, TMG Asaka Medical Center, Saitama, Japan
| | - Brandon Foreman
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati, 231 Albert Sabin Way, Cincinnati, OH, USA
| | - Nicolas Gaspard
- Department of Neurology, Erasme Hospital, Free University of Brussels, Brussels, Belgium
| | - Emily J Gilmore
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Neurocritical Care and Emergency Neurology, Department of Neurology, Ale University School of Medicine, New Haven, CT, USA
| | - Lawrence J Hirsch
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Pedro Kurtz
- Department of Intensive Care Medicine, D'or Institute for Research and Education, Rio de Janeiro, Brazil
- Neurointensive Care, Paulo Niemeyer State Brain Institute, Rio de Janeiro, Brazil
| | - Virginie Lambrecq
- Department of Clinical Neurophysiology and Epilepsy Unit, AP-HP, Pitié Salpêtrière Hospital, Reference Center for Rare Epilepsies, 75013, Paris, France
| | - Julie Kromm
- Departments of Critical Care Medicine and Clinical Neurosciences, Cumming School of Medicine, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, Calgary, AB, Canada
| | - Paul Vespa
- Brain Injury Research Center, Department of Neurosurgery, University of California, Los Angeles, USA
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Benjamin Rohaut
- Department of Neurology, Sorbonne Université, Pitié-Salpêtrière-AP-HP and Paris Brain Institute, ICM, Inserm, CNRS, Paris, France
| | - Jan Claassen
- Department of Neurology, Neurological Institute, Columbia University, New York Presbyterian Hospital, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA.
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Jonas S, Müller M, Rossetti AO, Rüegg S, Alvarez V, Schindler K, Zubler F. Diagnostic and prognostic EEG analysis of critically ill patients: A deep learning study. Neuroimage Clin 2022; 36:103167. [PMID: 36049354 PMCID: PMC9441331 DOI: 10.1016/j.nicl.2022.103167] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/16/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Abstract
Visual interpretation of electroencephalography (EEG) is time consuming, may lack objectivity, and is restricted to features detectable by a human. Computer-based approaches, especially deep learning, could potentially overcome these limitations. However, most deep learning studies focus on a specific question or a single pathology. Here we explore the potential of deep learning for EEG-based diagnostic and prognostic assessment of patients with acute consciousness impairment (ACI) of various etiologies. EEGs from 358 adults from a randomized controlled trial (CERTA, NCT03129438) were retrospectively analyzed. A convolutional neural network was used to predict the clinical outcome (based either on survival or on best cerebral performance category) and to determine the etiology (four diagnostic categories). The largest probability output served as marker for the confidence of the network in its prediction ("certainty factor"); we also systematically compared the predictions with raw EEG data, and used a visualization algorithm (Grad-CAM) to highlight discriminative patterns. When all patients were considered, the area under the receiver operating characteristic curve (AUC) was 0.721 for predicting survival and 0.703 for predicting the outcome based on best CPC; for patients with certainty factor ≥ 60 % the AUCs increased to 0.776 and 0.755 respectively; and for certainty factor ≥ 75 % to 0.852 and 0.879. The accuracy for predicting the etiology was 54.5 %; the accuracy increased to 67.7 %, 70.3 % and 84.1 % for patients with certainty factor of 50 %, 60 % and 75 % respectively. Visual analysis showed that the network learnt EEG patterns typically recognized by human experts, and suggested new criteria. This work demonstrates for the first time the potential of deep learning-based EEG analysis in critically ill patients with various etiologies of ACI. Certainty factor and post-hoc correlation of input data with prediction help to better characterize the method and pave the route for future implementations in clinical routine.
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Affiliation(s)
- Stefan Jonas
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Michael Müller
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrea O. Rossetti
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Stephan Rüegg
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Vincent Alvarez
- Department of Neurology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland,Department of Neurology, Hôpital du Valais, Sion, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Frédéric Zubler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland,Corresponding author at: Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital, Bern University Hospital, Freiburgstrasse 10, 3010 Bern, Switzerland.
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Amorim E, Firme MS, Zheng WL, Shelton KT, Akeju O, Cudemus G, Yuval R, Westover MB. High incidence of epileptiform activity in adults undergoing extracorporeal membrane oxygenation. Clin Neurophysiol 2022; 140:4-11. [PMID: 35691268 PMCID: PMC9340813 DOI: 10.1016/j.clinph.2022.04.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 02/20/2022] [Accepted: 04/27/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE The prevalence of seizures and other types of epileptiform brain activity in patients undergoing extracorporeal membrane oxygenation (ECMO) is unknown. We aimed to estimate the prevalence of seizures and ictal-interictal continuum patterns in patients undergoing electroencephalography (EEG) during ECMO. METHODS Retrospective review of a prospective ECMO registry from 2011-2018 in a university-affiliated academic hospital. Adult subjects who had decreased level of consciousness and underwent EEG monitoring for seizure screening were included. EEG classification followed the American Clinical Neurophysiology Society criteria. Poor neurological outcome was defined as a Cerebral Performance Category of 3-5 at hospital discharge. RESULTS Three hundred and ninety-five subjects had ECMO, and one hundred and thirteen (28.6%) had EEG monitoring. Ninety-two (23.3%) subjects had EEG performed during ECMO and were included in the study (average EEG duration 54 h). Veno-arterial ECMO was the most common cannulation strategy (83%) and 26 (28%) subjects had extracorporeal cardiopulmonary resuscitation. Fifty-eight subjects (63%) had epileptiform activity or ictal-interictal continuum patterns on EEG, including three (3%) subjects with nonconvulsive status epilepticus, 33 (36%) generalized periodic discharges, and 4 (5%) lateralized periodic discharges. Comparison between subjects with or without epileptiform activity showed comparable in-hospital mortality (57% vs. 47%, p = 0.38) and poor neurological outcome (and 56% and 36%, p = 0.23). Twenty-seven subjects (33%) had acute neuroimaging abnormalities (stroke N = 21). CONCLUSIONS Seizures and ictal-interictal continuum patterns are commonly observed in patients managed with ECMO. Further studies are needed to evaluate whether epileptiform activity is an actionable target for interventions. SIGNIFICANCE Epileptiform and ictal-interictal continuum abnormalities are frequently observed in patients supported with ECMO undergoing EEG monitoring.
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Affiliation(s)
- Edilberto Amorim
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA; Neurology Service, Zuckerberg San Francisco General Hospital, San Francisco, California, USA; Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
| | - Marcos S Firme
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Wei-Long Zheng
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Kenneth T Shelton
- Department of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Oluwaseun Akeju
- Department of Anesthesia, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Gaston Cudemus
- Department of Anesthesia, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Raz Yuval
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.
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39
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Grindegård L, Cronberg T, Backman S, Blennow K, Dankiewicz J, Friberg H, Hassager C, Horn J, Kjaer TW, Kjaergaard J, Kuiper M, Mattsson-Carlgren N, Nielsen N, van Rootselaar AF, Rossetti AO, Stammet P, Ullén S, Zetterberg H, Westhall E, Moseby-Knappe M. Association Between EEG Patterns and Serum Neurofilament Light After Cardiac Arrest. Neurology 2022; 98:e2487-e2498. [PMID: 35470143 PMCID: PMC9231840 DOI: 10.1212/wnl.0000000000200335] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 02/21/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Objectives EEG is widely used for prediction of neurologic outcome after cardiac arrest. To better understand the relationship between EEG and neuronal injury, we explored the association between EEG and neurofilament light (NfL) as a marker of neuroaxonal injury, evaluated whether highly malignant EEG patterns are reflected by high NfL levels, and explored the association of EEG backgrounds and EEG discharges with NfL. Methods We performed a post hoc analysis of the Target Temperature Management After Out-of-Hospital Cardiac Arrest trial. Routine EEGs were prospectively performed after the temperature intervention ≥36 hours postarrest. Patients who awoke or died prior to 36 hours postarrest were excluded. EEG experts blinded to clinical information classified EEG background, amount of discharges, and highly malignant EEG patterns according to the standardized American Clinical Neurophysiology Society terminology. Prospectively collected serum samples were analyzed for NfL after trial completion. The highest available concentration at 48 or 72 hours postarrest was used. Results A total of 262/939 patients with EEG and NfL data were included. Patients with highly malignant EEG patterns had 2.9 times higher NfL levels than patients with malignant patterns and NfL levels were 13 times higher in patients with malignant patterns than those with benign patterns (95% CI 1.4–6.1 and 6.5–26.2, respectively; effect size 0.47; p < 0.001). Both background and the amount of discharges were independently strongly associated with NfL levels (p < 0.001). The EEG background had a stronger association with NfL levels than EEG discharges (R2 = 0.30 and R2 = 0.10, respectively). NfL levels in patients with a continuous background were lower than for any other background (95% CI for discontinuous, burst-suppression, and suppression, respectively: 2.26–18.06, 3.91–41.71, and 5.74–41.74; effect size 0.30; p < 0.001 for all). NfL levels did not differ between suppression and burst suppression. Superimposed discharges were only associated with higher NfL levels if the EEG background was continuous. Discussion Benign, malignant, and highly malignant EEG patterns reflect the extent of brain injury as measured by NfL in serum. The extent of brain injury is more strongly related to the EEG background than superimposed discharges. Combining EEG and NfL may be useful to better identify patients misclassified by single methods. Trial Registration Information ClinicalTrials.gov NCT01020916.
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Affiliation(s)
- Linnéa Grindegård
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China.
| | - Tobias Cronberg
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Sofia Backman
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Kaj Blennow
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Josef Dankiewicz
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Hans Friberg
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Christian Hassager
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Janneke Horn
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Troels W Kjaer
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Jesper Kjaergaard
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Michael Kuiper
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Niklas Mattsson-Carlgren
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Niklas Nielsen
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Anne-Fleur van Rootselaar
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Andrea O Rossetti
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Pascal Stammet
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Susann Ullén
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Henrik Zetterberg
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Erik Westhall
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
| | - Marion Moseby-Knappe
- From Neurology (L.G., T.C., N.M.-C., M.M.-K.), Clinical Neurophysiology (S.B., E.W.), Cardiology (J.D.), and Anaesthesia and Intensive Care (H.F.), Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Malmö; Department of Psychiatry and Neurochemistry (K.B., H.Z.), Institute of Neuroscience and Physiology, the Sahlgrenska Academy, University of Gothenburg; Clinical Neurochemistry Laboratory (K.B., H.Z.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Cardiology (C.H.), Rigshospitalet and Department of Clinical Medicine, University of Copenhagen, Denmark; Departments of Intensive Care (J.H.) and Neurology/Clinical Neurophysiology (A.-F-V.R.), Amsterdam Neuroscience, Amsterdam UMC, Academic Medical Center, University of Amsterdam, the Netherlands; Departments of Clinical Neurophysiology (T.W.K.) and Cardiology (J.K.), Rigshospitalet University Hospital, Copenhagen, Denmark; Department of Intensive Care (M.K.), Medical Center Leeuwarden, the Netherlands; Clinical Memory Research Unit, Faculty of Medicine (N.M.-C.), and Wallenberg Centre for Molecular Medicine (N.M.-C.), Lund University; Anaesthesia and Intensive Care, Department of Clinical Sciences Lund (N.N.), Lund University, Helsingborg Hospital, Sweden; Department of Neurology (A.O.R.), CHUV and University of Lausanne, Switzerland; Department of Anesthesia and Intensive Care (P.S.), Centre Hospitalier de Luxembourg; Department of Life Sciences and Medicine (P.S.), Faculty of Science, Technology and Medicine, University of Luxembourg; Clinical Studies Sweden (S.U.), Skåne University Hospital, Lund; Department of Neurodegenerative Disease (H.Z.), UCL Institute of Neurology; UK Dementia Research Institute at UCL (H.Z.), London, UK; and Hong Kong Center for Neurodegenerative Diseases (H.Z.), China
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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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41
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Índice biespectral y tasa de supresión tras parada cardiaca: ¿son útiles para individualizar planes de escalada terapéutica? Rev Esp Cardiol 2022. [DOI: 10.1016/j.recesp.2022.03.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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42
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Prognosis After Cardiac Arrest: The Additional Value of DWI and FLAIR to EEG. Neurocrit Care 2022; 37:302-313. [DOI: 10.1007/s12028-022-01498-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/28/2022] [Indexed: 10/18/2022]
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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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Outcome Prediction of Postanoxic Coma: A Comparison of Automated Electroencephalography Analysis Methods. Neurocrit Care 2022; 37:248-258. [PMID: 35233717 PMCID: PMC9343315 DOI: 10.1007/s12028-022-01449-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/10/2022] [Indexed: 12/03/2022]
Abstract
Background To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. Methods A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as “good” (Cerebral Performance Category 1–2) or “poor” (Cerebral Performance Category 3–5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. Results The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44–64%) at a false positive rate (FPR) of 0% (95% CI 0–2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52–100%) at a FPR of 12% (95% CI 0–24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83–83%) at a FPR of 3% (95% CI 3–3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. Conclusions A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest.
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Nutma S, Tjepkema-Cloostermans MC, Ruijter BJ, Tromp SC, van den Bergh WM, Foudraine NA, H M Kornips F, Drost G, Scholten E, Strang A, Beishuizen A, J A M van Putten M, Hofmeijer J. Effects of targeted temperature management at 33°C vs. 36°C on comatose patients after cardiac arrest stratified by the severity of encephalopathy. Resuscitation 2022; 173:147-153. [PMID: 35122892 DOI: 10.1016/j.resuscitation.2022.01.026] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To assess neurological outcome after targeted temperature management (TTM) at 33°C vs. 36°C, stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24h. DESIGN Post hoc analysis of prospective cohort study. SETTING Five Dutch Intensive Care units. PATIENTS 479 adult comatose post-cardiac arrest patients. INTERVENTIONS TTM at 33°C (n=270) or 36°C (n=209) and continuous EEG monitoring. MEASUREMENTS AND MAIN RESULTS Outcome according to the cerebral performance category (CPC) score at 6 months post-cardiac arrest was similar after 33°C and 36°C. However, when stratified by the severity of encephalopathy based on EEG-patterns at 12 and 24h after cardiac arrest, the proportion of good outcome (CPC 1-2) in patients with moderate encephalopathy was significantly larger after TTM at 33°C (66% vs. 45%; Odds Ratios 2.38, 95% CI=1.32-4.30; p=0.004). In contrast, with mild encephalopathy, there was no statistically significant difference in the proportion of patients with good outcome between 33°C and 36°C (88% vs. 81%; OR 1.68, 95% CI=0.65-4.38; p=0.282). Ordinal regression analysis showed a shift towards higher CPC scores when treated with TTM 33°C as compared with 36°C in moderate encephalopathy (cOR 2.39; 95% CI=1.40-4.08; p=0.001), but not in mild encephalopathy (cOR 0.81 95% CI=0.41-1.59; p=0.537). Adjustment for initial cardiac rhythm and cause of arrest did not change this relationship. CONCLUSIONS Effects of TTM probably depend on the severity of encephalopathy in comatose patients after cardiac arrest. These results support inclusion of predefined subgroup analyses based on EEG measures of the severity of encephalopathy in future clinical trials.
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Affiliation(s)
- Sjoukje Nutma
- Departments of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede; Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede.
| | | | - Barry J Ruijter
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede
| | - Selma C Tromp
- Departments of Neurology and Clinical Neurophysiology, St Antonius Hospital, Nieuwegein
| | - Walter M van den Bergh
- Department of Critical Care, University Medical Center Groningen, University of Groningen, Groningen
| | | | | | - Gea Drost
- Departments of Neurology and Neurosurgery, University Medical Center Groningen, University of Groningen, Groningen
| | - Erik Scholten
- Department of Intensive Care, St Antonius Hospital, Nieuwegein
| | - Aart Strang
- Department of Intensive Care, Rijnstate Hospital, Arnhem
| | | | - Michel J A M van Putten
- Departments of Neurology and Clinical Neurophysiology, Medical Spectrum Twente, Enschede; Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede
| | - Jeannette Hofmeijer
- Department of Clinical Neurophysiology, Technical Medical Center, University of Twente, Enschede; Department of Neurology, Rijnstate Hospital, Arnhem, the Netherlands
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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] [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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Clinical Significance of Gray to White Matter Ratio after Cardiopulmonary Resuscitation in Children. CHILDREN 2022; 9:children9010036. [PMID: 35053661 PMCID: PMC8774629 DOI: 10.3390/children9010036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 11/27/2021] [Accepted: 12/04/2021] [Indexed: 11/17/2022]
Abstract
Cardiopulmonary resuscitation (CPR) successfully restores systemic circulation approximately 50% of the time; however, many successfully restored patients have severe neurologic damage. In adults, the gray matter to white matter attenuation ratio (GWR) in brain computed tomography (CT) correlates with the neurologic outcome. However, in children, the clinical significance of GWR still remains unclear. The aim of this study was to evaluate the clinical characteristics of children who underwent CPR for cardiac arrest according to the survival and to demonstrate the differentiation of grey/white matter by Hounsfield units of brain CT and to characterize the attenuations of grey and white matters. Methods: This is a retrospective single-center study. We enrolled those who underwent brain CT within 24 h after return of spontaneous circulation (ROSC) from January 2005 to June 2018. Brain CTs were taken within 24 h of ROSC. We measured the attenuation of grey and white matter in Hounsfield units and calculated GWR. They were compared with healthy controls. Patients were analyzed as follows: survivors vs. non-survivors and better neurologic outcome vs. worse neurologic outcome. Results: Among 100 pediatric patients who had CPR, 56 met inclusion criteria. There were 24 patients who survived and 32 non-survivors. Our study revealed that the incidence of seizure, duration of CPR, and instances of hypothermia were significantly different between survivors and non-survivors. In both survivors and non-survivors, the attenuation of the caudate nucleus, putamen, GWR-basal ganglia, and average GWR were significantly different from controls. In regression analyses, the medial cortex and average GWR were the significant variables to predict survival, and the receiver operating curves revealed areas under curve of 0.733 and 0.666, respectively. Also, the medial cortex 1 was the only variable that predicted the neurologic outcome. Conclusions: There was some predictive survival value of GWR and medial cortex at the centrum semiovale level in early brain CT within 24 h after cardiac arrest. Although we could not find the predictive value of GWR in the neurologic outcome of pediatric patients, we found that the absolute attenuation of the medial cortex was low in patients with worse neurologic outcomes. Further prospective, multicenter studies are needed to determine the predictive value of GWR and the medial cortex.
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Zheng WL, Amorim E, Jing J, Wu O, Ghassemi M, Lee JW, Sivaraju A, Pang T, Herman ST, Gaspard N, Ruijter BJ, Tjepkema-Cloostermans MC, Hofmeijer J, van Putten MJAM, Westover MB. Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning. IEEE Trans Biomed Eng 2021; 69:1813-1825. [PMID: 34962860 PMCID: PMC9087641 DOI: 10.1109/tbme.2021.3139007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information. METHODS We present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation. RESULTS The proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
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Heart rate complexity: An early prognostic marker of patient outcome after cardiac arrest. Clin Neurophysiol 2021; 134:27-33. [PMID: 34953334 DOI: 10.1016/j.clinph.2021.10.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/21/2021] [Accepted: 10/23/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Early prognostication in comatose patients after cardiac arrest (CA) is difficult but essential to inform relatives and optimize treatment. Here we investigate the predictive value of heart-rate variability captured by multiscale entropy (MSE) for long-term outcomes in comatose patients during the first 24 hours after CA. METHODS In this retrospective analysis of prospective multi-centric cohort, we analyzed MSE of the heart rate in 79 comatose patients after CA while undergoing targeted temperature management and sedation during the first day of coma. From the MSE, two complexity indices were derived by summing values over short and long time scales (CIs and CIl). We splitted the data in training and test datasets for analysing the predictive value for patient outcomes (defined as best cerebral performance category within 3 months) of CIs and CIl. RESULTS Across the whole dataset, CIl provided the best sensitivity, specificity, and accuracy (88%, 75%, and 82%, respectively). Positive and negative predictive power were 81% and 84%. CONCLUSIONS Characterizing the complexity of the ECG in patients after CA provides an accurate prediction of both favorable and unfavorable outcomes. SIGNIFICANCE The analysis of heartrate variability by means of MSE provides accurate outcome prediction on the first day of coma.
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Wu CI, Hsu PF, Lee IH, Lin YJ, Lin CF, Pan JP, Hsu TF, How CK, Kwan SY, Chung FP, Wu CH, Chen SA. Utilization of Amplitude-Integrated Electroencephalography to Predict Neurologic Function after Resuscitation in Adults with Cardiogenic Cardiac Arrest. ACTA CARDIOLOGICA SINICA 2021; 37:632-642. [PMID: 34812237 PMCID: PMC8593491 DOI: 10.6515/acs.202111_37(6).20210630b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/20/2021] [Indexed: 01/24/2023]
Abstract
BACKGROUND Amplitude-integrated electroencephalography (aEEG) has been used as a tool to recognize brain activity in children with hypoxic encephalopathy. OBJECTIVES To assess the prognostic value of aEEG during the post-resuscitation period of adult cardiogenic cardiac arrest, comatose survivors were monitored within 24 h of a return of spontaneous circulation using aEEG. METHODS Forty-two consecutive patients experiencing cardiac arrest were retrospectively enrolled, and a return of spontaneous circulation was achieved in all cases. These patients were admitted to the Coronary Intensive Care Unit due to cardiogenic cardiac arrest. The primary outcome was the best neurologic outcome within 6 months after resuscitation, and the registered patients were divided into two groups based on the Cerebral Performance Category (CPC) scale (CPC 1-2, good neurologic function group; CPC 3-5, poor neurologic function group). All patients received an aEEG examination within 24 h after a return of spontaneous circulation, and the parameters and patterns of aEEG recordings were compared. RESULTS Nineteen patients were in the good neurologic function group, and 23 were in the poor group. The four voltage parameters (minimum, maximum, span, average) of the aEEG recordings in the good neurologic function groups were significantly higher than in the poor group. Moreover, the continuous pattern, but not the status epilepticus or burst suppression patterns, could predict mid-term good neurologic function. CONCLUSIONS aEEG can be used to predict neurologic outcomes based on the recordings' parameters and patterns in unconscious adults who have experienced a cardiac collapse, resuscitation, and return of spontaneous circulation.
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Affiliation(s)
- Cheng-I Wu
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital;
,
Institute of Clinical Medicine, National Yang Ming Chiao Tung University
| | - Pai-Feng Hsu
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital;
,
Institute of Clinical Medicine, National Yang Ming Chiao Tung University;
,
Director of Coronary Care Unit
| | | | - Yenn-Jiang Lin
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital;
,
Institute of Clinical Medicine, National Yang Ming Chiao Tung University
| | - Chun-Fu Lin
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei
| | - Ju-Pin Pan
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital
| | | | | | - Shang-Yeong Kwan
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei
| | - Fa-Po Chung
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital;
,
Institute of Clinical Medicine, National Yang Ming Chiao Tung University
| | - Cheng-Hsueh Wu
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital;
,
Institute of Clinical Medicine, National Yang Ming Chiao Tung University
| | - Shih-Ann Chen
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University;
,
Cardiovascular Center, Taichung Veterans General Hospital, Taichung, Taiwan
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