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Fernández-Torre JL, Hernández-Hernández MA, Cherchi MS, Mato-Mañas D, de Lucas EM, Gómez-Ruiz E, Vázquez-Higuera JL, Fanjul-Vélez F, Arce-Diego JL, Martín-Láez R. Comparison of Continuous Intracortical and Scalp Electroencephalography in Comatose Patients with Acute Brain Injury. Neurocrit Care 2024:10.1007/s12028-024-02016-z. [PMID: 38918336 DOI: 10.1007/s12028-024-02016-z] [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: 11/29/2023] [Accepted: 05/16/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND Depth electroencephalography (dEEG) is a recent invasive monitoring technique used in patients with acute brain injury. This study aimed to describe in detail the clinical manifestations of nonconvulsive seizures (NCSzs) with and without a surface EEG correlate, analyze their long-standing effects, and provide data that contribute to understanding the significance of certain scalp EEG patterns observed in critically ill patients. METHODS We prospectively enrolled a cohort of 33 adults with severe acute brain injury admitted to the neurological intensive care unit. All of them underwent multimodal invasive monitoring, including dEEG. All patients were scanned on a 3T magnetic resonance imaging scanner at 6 months after hospital discharge, and mesial temporal atrophy (MTA) was calculated using a visual scale. RESULTS In 21 (65.6%) of 32 study participants, highly epileptiform intracortical patterns were observed. A total of 11 (34.3%) patients had electrographic or electroclinical seizures in the dEEG, of whom 8 had both spontaneous and stimulus-induced (SI) seizures, and 3 patients had only spontaneous intracortical seizures. An unequivocal ictal scalp correlate was observed in only 3 (27.2%) of the 11 study participants. SI-NCSzs occurred during nursing care, medical procedures, and family visits. Subtle clinical manifestations, such as restlessness, purposeless stereotyped movements of the upper limbs, ventilation disturbances, jerks, head movements, hyperextension posturing, chewing, and oroalimentary automatisms, occurred during intracortical electroclinical seizures. MTA was detected in 18 (81.8%) of the 22 patients. There were no statistically significant differences between patients with MTA with and without seizures or status epilepticus. CONCLUSIONS Most NCSzs in critically ill comatose patients remain undetectable on scalp EEG. SI-NCSzs frequently occur during nursing care, medical procedures, and family visits. Semiology of NCSzs included ictal minor signs and subtle symptoms, such as breathing pattern changes manifested as patient-ventilator dyssynchrony.
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Affiliation(s)
- José L Fernández-Torre
- Department of Clinical Neurophysiology, Marqués de Valdecilla University Hospital, 39008, Santander, Cantabria, Spain.
- Department of Physiology and Pharmacology, School of Medicine, University of Cantabria, 39008, Santander, Cantabria, Spain.
- Biomedical Research Institute (IDIVAL), 39011, Santander, Cantabria, Spain.
| | - Miguel A Hernández-Hernández
- Biomedical Research Institute (IDIVAL), 39011, Santander, Cantabria, Spain
- Department of Intensive Medicine, Marqués de Valdecilla University Hospital, 39008, Santander, Cantabria, Spain
| | - Marina S Cherchi
- Biomedical Research Institute (IDIVAL), 39011, Santander, Cantabria, Spain
- Department of Intensive Medicine, Marqués de Valdecilla University Hospital, 39008, Santander, Cantabria, Spain
| | - David Mato-Mañas
- Biomedical Research Institute (IDIVAL), 39011, Santander, Cantabria, Spain
- Department of Neurosurgery, Marqués de Valdecilla University Hospital, 39008, Santander, Cantabria, Spain
| | - Enrique Marco de Lucas
- Biomedical Research Institute (IDIVAL), 39011, Santander, Cantabria, Spain
- Department of Radiology, Marqués de Valdecilla University Hospital, 39008, Santander, Cantabria, Spain
- Department of Medical-Surgical Sciences, School of Medicine, University of Cantabria, 39008, Santander, Cantabria, Spain
| | - Elsa Gómez-Ruiz
- Department of Psychiatry, Marqués de Valdecilla University Hospital Santander, 39008, Cantabria, Spain
| | - José L Vázquez-Higuera
- Biomedical Research Institute (IDIVAL), 39011, Santander, Cantabria, Spain
- Department of Neurology, Marqués de Valdecilla University Hospital, 39008, Santander, Cantabria, Spain
| | - Félix Fanjul-Vélez
- Biomedical Engineering Group, Tecnología Electrónica, Ingeniería de Sistemas y Automática (TEISA) Department, University of Cantabria, 39005, Santander, Cantabria, Spain
| | - José L Arce-Diego
- Biomedical Engineering Group, Tecnología Electrónica, Ingeniería de Sistemas y Automática (TEISA) Department, University of Cantabria, 39005, Santander, Cantabria, Spain
| | - Rubén Martín-Láez
- Biomedical Research Institute (IDIVAL), 39011, Santander, Cantabria, Spain
- Department of Neurosurgery, Marqués de Valdecilla University Hospital, 39008, Santander, Cantabria, Spain
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Peluso L, Stropeni S, Macchini E, Peratoner C, Ferlini L, Legros B, Minini A, Bogossian EG, Garone A, Creteur J, Taccone FS, Gaspard N. Delayed Deterioration of Electroencephalogram in Patients with Cardiac Arrest: A Cohort Study. Neurocrit Care 2024; 40:633-644. [PMID: 37498454 DOI: 10.1007/s12028-023-01791-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/23/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND The aim of this study was to assess the prevalence of delayed deterioration of electroencephalogram (EEG) in patients with cardiac arrest (CA) without early highly malignant patterns and to determine their associations with clinical findings. METHODS This was a retrospective study of adult patients with CA admitted to the intensive care unit (ICU) of a university hospital. We included all patients with CA who had a normal voltage EEG, no more than 10% discontinuity, and absence of sporadic epileptic discharges, periodic discharges, or electrographic seizures. Delayed deterioration was classified as the following: (1) epileptic deterioration, defined as the appearance, at least 24 h after CA, of sporadic epileptic discharges, periodic discharges, and status epilepticus; or (2) background deterioration, defined as increasing discontinuity or progressive attenuation of the background at least 24 h after CA. The end points were the incidence of EEG deteriorations and their association with clinical features and ICU mortality. RESULTS We enrolled 188 patients in the analysis. The ICU mortality was 46%. Overall, 30 (16%) patients presented with epileptic deterioration and 9 (5%) patients presented with background deterioration; of those, two patients presented both deteriorations. Patients with epileptic deterioration more frequently had an out-of-hospital CA, and higher time to return of spontaneous circulation and less frequently had bystander resuscitation than others. Patients with background deterioration showed a predominantly noncardiac cause, more frequently developed shock, and had multiple organ failure compared with others. Patients with epileptic deterioration presented with a higher ICU mortality (77% vs. 41%; p < 0.01) than others, whereas all patients with background deterioration died in the ICU. CONCLUSIONS Delayed EEG deterioration was associated with high mortality rate. Epileptic deterioration was associated with worse characteristics of CA, whereas background deterioration was associated with shock and multiple organ failure.
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Affiliation(s)
- Lorenzo Peluso
- Departement of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini, 4, 20072, Pieve Emanuele, Italy.
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium.
- Departement of Anesthesiology and Intensive Care, Humanitas Gavazzeni, Via Mauro Gavazzeni, 21, 24125, Bergamo, Italy.
| | - Serena Stropeni
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Elisabetta Macchini
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Caterina Peratoner
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Lorenzo Ferlini
- Department of Neurology, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Andrea Minini
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Elisa Gouvea Bogossian
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Andrea Garone
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Jacques Creteur
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Fabio Silvio Taccone
- Department of Intensive Care, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Erasme Hospital, Route de Lennik, 808, 1070, Brussels, Belgium
- Department of Neurology, Yale University Medical School, 15, York Street, New Haven, CT, 06510, USA
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Fatima S, Krishnamurthy PV, Sun M, Aparicio MK, Gjini K, Struck AF. Estimate of Patients With Missed Seizures Because of Delay in Conventional EEG. J Clin Neurophysiol 2024; 41:230-235. [PMID: 38436390 PMCID: PMC10912745 DOI: 10.1097/wnp.0000000000000957] [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: 11/26/2022] Open
Abstract
PURPOSE There is frequent delay between ordering and placement of conventional EEG. Here we estimate how many patients had seizures during this delay. METHODS Two hundred fifty consecutive adult patients who underwent conventional EEG monitoring at the University of Wisconsin Hospital were retrospectively chart reviewed for demographics, time of EEG order, clinical and other EEG-related information. Patients were stratified by use of anti-seizure medications before EEG and into low-risk, medium-risk, and high-risk groups based on 2HELPS2B score (0, 1, or >1). Monte Carlo simulations (500 trials) were performed to estimate seizures during delay. RESULTS The median delay from EEG order to performing EEG was 2.00 hours (range of 0.5-8.00 hours) in the total cohort. For EEGs ordered after-hours, it was 2.00 hours (range 0.5-8.00 hours), and during business hours, it was 2.00 hours (range 0.5-6.00 hours). The place of EEG, intensive care unit, emergency department, and general floor, did not show significant difference (P = 0.84). Anti-seizure medication did not affect time to first seizure in the low-risk (P = 0.37), medium-risk (P = 0.44), or high-risk (P = 0.12) groups. The estimated % of patients who had a seizure in the delay period for low-risk group (2HELPS2B = 0) was 0.8%, for the medium-risk group (2HELPS2B = 1) was 10.3%, and for the high-risk group (2HELPS2B > 1) was 17.6%, and overall risk was 7.2%. CONCLUSIONS The University of Wisconsin Hospital with 24-hour in-house EEG technologists has a median delay of 2 hours from order to start of EEG, shorter than published reports from other centers. Nonetheless, seizures were likely missed in about 7.2% of patients.
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Affiliation(s)
- Safoora Fatima
- University of Wisconsin-Madison, Department of Neurology
| | | | - Mengzhen Sun
- University of Wisconsin-Madison, Department of Neurology
| | | | - Klevest Gjini
- University of Wisconsin-Madison, Department of Neurology
| | - Aaron F Struck
- University of Wisconsin-Madison, Department of Neurology
- William S Middleton Veterans Hospital, Madison, WI
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Xie D, Toutant D, Ng MC. Residual Seizure Rate of Intermittent Inpatient EEG Compared to a Continuous EEG Model. Can J Neurol Sci 2024; 51:246-254. [PMID: 37282558 DOI: 10.1017/cjn.2023.241] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
BACKGROUND Subclinical seizures are common in hospitalized patients and require electroencephalography (EEG) for detection and intervention. At our institution, continuous EEG (cEEG) is not available, but intermittent EEGs are subject to constant live interpretation. As part of quality improvement (QI), we sought to estimate the residual missed seizure rate at a typical quaternary Canadian health care center without cEEG. METHODS We calculated residual risk percentages using the clinically validated 2HELPS2B score to risk-stratify EEGs before deriving a risk percentage using a MATLAB calculator which modeled the risk decay curve for each recording. We generated a range of estimated residual seizure rates depending on whether a pre-cEEG screening EEG was simulated, EEGs showing seizures were included, or repeat EEGs on the same patient were excluded. RESULTS Over a 4-month QI period, 499 inpatient EEGs were scored as low (n = 125), medium (n = 123), and high (n = 251) seizure risk according to 2HELPS2B criteria. Median recording duration was 1:00:06 (interquartile range, IQR 30:40-2:21:10). The model with highest residual seizure rate included recordings with confirmed electrographic seizures (median 20.83%, IQR 20.6-26.6%), while the model with lowest residual seizure rate was in seizure-free recordings (median 10.59%, IQR 4%-20.6%). These rates were significantly higher than the benchmark 5% miss-rate threshold set by 2HELPS2B (p<0.0001). CONCLUSIONS We estimate that intermittent inpatient EEG misses 2-4 times more subclinical seizures than the 2HELPS2B-determined acceptable 5% seizure miss-rate threshold for cEEG. Future research is needed to determine the impact of potentially missed seizures on clinical care.
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Affiliation(s)
- Dave Xie
- Undergraduate Medical Education, University of Manitoba, Winnipeg, MB, Canada
| | - Darion Toutant
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB, Canada
| | - Marcus C Ng
- Biomedical Engineering Program, University of Manitoba, Winnipeg, MB, Canada
- Section of Neurology, University of Manitoba, Winnipeg, MB, Canada
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Hirsch KG, Abella BS, Amorim E, Bader MK, Barletta JF, Berg K, Callaway CW, Friberg H, Gilmore EJ, Greer DM, Kern KB, Livesay S, May TL, Neumar RW, Nolan JP, Oddo M, Peberdy MA, Poloyac SM, Seder D, Taccone FS, Uzendu A, Walsh B, Zimmerman JL, Geocadin RG. Critical Care Management of Patients After Cardiac Arrest: A Scientific Statement from the American Heart Association and Neurocritical Care Society. Neurocrit Care 2024; 40:1-37. [PMID: 38040992 PMCID: PMC10861627 DOI: 10.1007/s12028-023-01871-6] [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: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 12/03/2023]
Abstract
The critical care management of patients after cardiac arrest is burdened by a lack of high-quality clinical studies and the resultant lack of high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty and variability in management. Critical care management is crucial in patients after cardiac arrest and affects outcome. Although guidelines address some relevant topics (including temperature control and neurological prognostication of comatose survivors, 2 topics for which there are more robust clinical studies), many important subject areas have limited or nonexistent clinical studies, leading to the absence of guidelines or low-certainty evidence. The American Heart Association Emergency Cardiovascular Care Committee and the Neurocritical Care Society collaborated to address this gap by organizing an expert consensus panel and conference. Twenty-four experienced practitioners (including physicians, nurses, pharmacists, and a respiratory therapist) from multiple medical specialties, levels, institutions, and countries made up the panel. Topics were identified and prioritized by the panel and arranged by organ system to facilitate discussion, debate, and consensus building. Statements related to postarrest management were generated, and 80% agreement was required to approve a statement. Voting was anonymous and web based. Topics addressed include neurological, cardiac, pulmonary, hematological, infectious, gastrointestinal, endocrine, and general critical care management. Areas of uncertainty, areas for which no consensus was reached, and future research directions are also included. Until high-quality studies that inform practice guidelines in these areas are available, the expert panel consensus statements that are provided can advise clinicians on the critical care management of patients after cardiac arrest.
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Affiliation(s)
| | | | - Edilberto Amorim
- San Francisco-Weill Institute for Neurosciences, University of California, San Francisco, USA
| | - Mary Kay Bader
- Providence Mission Hospital Nursing Center of Excellence/Critical Care Services, Mission Viejo, USA
| | | | | | | | | | | | | | - Karl B Kern
- Sarver Heart Center, University of Arizona, Tucson, USA
| | | | | | | | - Jerry P Nolan
- Warwick Medical School, University of Warwick, Coventry, UK
- Royal United Hospital, Bath, UK
| | - Mauro Oddo
- CHUV-Lausanne University Hospital, Lausanne, Switzerland
| | | | | | | | | | - Anezi Uzendu
- St. Luke's Mid America Heart Institute, Kansas City, USA
| | - Brian Walsh
- University of Texas Medical Branch School of Health Sciences, Galveston, USA
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Hirsch KG, Abella BS, Amorim E, Bader MK, Barletta JF, Berg K, Callaway CW, Friberg H, Gilmore EJ, Greer DM, Kern KB, Livesay S, May TL, Neumar RW, Nolan JP, Oddo M, Peberdy MA, Poloyac SM, Seder D, Taccone FS, Uzendu A, Walsh B, Zimmerman JL, Geocadin RG. Critical Care Management of Patients After Cardiac Arrest: A Scientific Statement From the American Heart Association and Neurocritical Care Society. Circulation 2024; 149:e168-e200. [PMID: 38014539 PMCID: PMC10775969 DOI: 10.1161/cir.0000000000001163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The critical care management of patients after cardiac arrest is burdened by a lack of high-quality clinical studies and the resultant lack of high-certainty evidence. This results in limited practice guideline recommendations, which may lead to uncertainty and variability in management. Critical care management is crucial in patients after cardiac arrest and affects outcome. Although guidelines address some relevant topics (including temperature control and neurological prognostication of comatose survivors, 2 topics for which there are more robust clinical studies), many important subject areas have limited or nonexistent clinical studies, leading to the absence of guidelines or low-certainty evidence. The American Heart Association Emergency Cardiovascular Care Committee and the Neurocritical Care Society collaborated to address this gap by organizing an expert consensus panel and conference. Twenty-four experienced practitioners (including physicians, nurses, pharmacists, and a respiratory therapist) from multiple medical specialties, levels, institutions, and countries made up the panel. Topics were identified and prioritized by the panel and arranged by organ system to facilitate discussion, debate, and consensus building. Statements related to postarrest management were generated, and 80% agreement was required to approve a statement. Voting was anonymous and web based. Topics addressed include neurological, cardiac, pulmonary, hematological, infectious, gastrointestinal, endocrine, and general critical care management. Areas of uncertainty, areas for which no consensus was reached, and future research directions are also included. Until high-quality studies that inform practice guidelines in these areas are available, the expert panel consensus statements that are provided can advise clinicians on the critical care management of patients after cardiac arrest.
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Carroll EE, Der-Nigoghossian C, Alkhachroum A, Appavu B, Gilmore E, Kromm J, Rohaut B, Rosanova M, Sitt JD, Claassen J. Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group. Neurocrit Care 2023; 39:578-585. [PMID: 37606737 DOI: 10.1007/s12028-023-01795-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 06/22/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group. METHODS After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs. RESULTS After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group. CONCLUSIONS Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.
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Affiliation(s)
- Elizabeth E Carroll
- Department of Neurology, Columbia University Medical Center, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA
- NewYork-Presbyterian Hospital, New York, NY, USA
| | | | | | - Brian Appavu
- Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, USA
- University of Arizona College of Medicine - Phoenix, Phoenix, AZ, USA
| | - Emily Gilmore
- Divisions of Neurocritical Care and Emergency Neurology and Epilepsy, Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Yale New Haven Hospital, New Haven, CT, USA
| | - Julie Kromm
- Departments of Critical Care Medicine and Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Benjamin Rohaut
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, Centre national de la recherche scientifique, Assistance Publique-Hôpitaux de Paris, Neurosciences, Hôpital de La Pitié Salpêtrière, Paris, France
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Jacobo Diego Sitt
- Paris Brain Institute (ICM), Centre national de la recherche scientifique, Paris, France
| | - Jan Claassen
- Department of Neurology, Columbia University Medical Center, 177 Fort Washington Avenue, MHB 8 Center, Room 300, New York, NY, 10032, USA.
- NewYork-Presbyterian Hospital, New York, NY, USA.
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Greenblatt AS, Beniczky S, Nascimento FA. Pitfalls in scalp EEG: Current obstacles and future directions. Epilepsy Behav 2023; 149:109500. [PMID: 37931388 DOI: 10.1016/j.yebeh.2023.109500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.
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Affiliation(s)
- Adam S Greenblatt
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund and Aarhus University Hospital, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Fábio A Nascimento
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
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Fenter H, Rossetti AO, Beuchat I. Continuous versus Routine Electroencephalography in the Intensive Care Unit: A Review of Current Evidence. Eur Neurol 2023; 87:17-25. [PMID: 37952533 PMCID: PMC11003555 DOI: 10.1159/000535085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/05/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Electroencephalography (EEG) has long been used to detect seizures in patients with disorders of consciousness. In recent years, there has been a drastically increased adoption of continuous EEG (cEEG) in the intensive care units (ICUs). Given the resources necessary to record and interpret cEEG, this is still not available in every center and widespread recommendations to use continuous instead of routine EEG (typically lasting 20 min) are still a matter of some debate. Considering recent literature and personal experience, this review offers a rationale and practical advice to address this question. SUMMARY Despite the development of increasingly performant imaging techniques and several validated biomarkers, EEG remains central to clinicians in the intensive care unit and has been experiencing expanding popularity for at least 2 decades. Not only does EEG allow seizure or status epilepticus detection, which in the ICU often present without clinical movements, but it is also paramount for the prognostic evaluation of comatose patients, especially after cardiac arrest, and for detecting delayed ischemia after subarachnoid hemorrhage. At the end of the last Century, improvements of technical and digital aspects regarding recording and storage of EEG tracings have progressively led to the era of cEEG and automated quantitative analysis. KEY MESSAGES As compared to repeated rEEG, cEEG in comatose patients does not seem to improve clinical prognosis to a relevant extent, despite allowing a more performant of detection ictal events and consequent therapeutic modifications. The choice between cEEG and rEEG must therefore always be patient-tailored.
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Affiliation(s)
- Helene Fenter
- Department of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Andrea O Rossetti
- Department of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Isabelle Beuchat
- Department of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, Lausanne, Switzerland
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Dhakar MB, Sheikh ZB, Desai M, Desai RA, Sternberg EJ, Popescu C, Baron-Lee J, Rampal N, Hirsch LJ, Gilmore EJ, Maciel CB. Developing a Standardized Approach to Grading the Level of Brain Dysfunction on EEG. J Clin Neurophysiol 2023; 40:553-561. [PMID: 35239553 DOI: 10.1097/wnp.0000000000000919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE To assess variability in interpretation of electroencephalogram (EEG) background activity and qualitative grading of cerebral dysfunction based on EEG findings, including which EEG features are deemed most important in this determination. METHODS A web-based survey (Qualtrics) was disseminated to electroencephalographers practicing in institutions participating in the Critical Care EEG Monitoring Research Consortium between May 2017 and August 2018. Respondents answered 12 questions pertaining to their training and EEG interpretation practices and graded 40 EEG segments (15-second epochs depicting patients' most stimulated state) using a 6-grade scale. Fleiss' Kappa statistic evaluated interrater agreement. RESULTS Of 110 respondents, 78.2% were attending electroencephalographers with a mean of 8.3 years of experience beyond training. Despite 83% supporting the need for a standardized approach to interpreting the degree of dysfunction on EEG, only 13.6% used a previously published or an institutional grading scale. The overall interrater agreement was fair ( k = 0.35). Having Critical Care EEG Monitoring Research Consortium nomenclature certification (40.9%) or EEG board certification (70%) did not improve interrater agreement ( k = 0.26). Predominant awake frequencies and posterior dominant rhythm were ranked as the most important variables in grading background dysfunction, followed by continuity and reactivity. CONCLUSIONS Despite the preference for a standardized grading scale for background EEG interpretation, the lack of interrater agreement on levels of dysfunction even among experienced academic electroencephalographers unveils a barrier to the widespread use of EEG as a clinical and research neuromonitoring tool. There was reasonable agreement on the features that are most important in this determination. A standardized approach to grading cerebral dysfunction, currently used by the authors, and based on this work, is proposed.
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Affiliation(s)
- Monica B Dhakar
- Department of Neurology, Warren Alpert Medical School of Brown University, Providence, Rhode Island, U.S.A
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - Zubeda B Sheikh
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
- Department of Neurology, West Virginia University School of Medicine, Morgantown, West Virginia, U.S.A
| | - Masoom Desai
- Department of Neurology, University of Oklahoma Health Science Center, Oklahoma City, Oklahoma, U.S.A
| | - Raj A Desai
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida College of Pharmacy, Gainesville, Florida, U.S.A
| | - Eliezer J Sternberg
- Division of Neurology, Milford Regional Medical Center, Milford, Massachusetts, U.S.A
- Department of Neurology, University of Massachusetts Medical School, Worcester, Massachusetts, U.S.A
| | - Cristina Popescu
- Department of Social and Public Health, Ohio University, Athens, Ohio, U.S.A
| | - Jacqueline Baron-Lee
- Department of Neurology, UF-Health Shands Hospital, University of Florida College of Medicine, Gainesville, Florida, U.S.A.; and
| | | | - Lawrence J Hirsch
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - Emily J Gilmore
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - Carolina B Maciel
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
- Department of Neurology, UF-Health Shands Hospital, University of Florida College of Medicine, Gainesville, Florida, U.S.A.; and
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11
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Snider SB, Fong MWK, Nolan NM, Ruiz AR, Wang W, LaRoche S, Hirsch LJ, Lee JW. Clinical and Electroencephalographic Predictors of Seizures and Status Epilepticus in 12,450 Critically Ill Adults: A Retrospective Cohort Study. Crit Care Med 2023; 51:1001-1011. [PMID: 37010290 DOI: 10.1097/ccm.0000000000005872] [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: 04/04/2023]
Abstract
OBJECTIVES Status epilepticus (SE) is associated with significantly higher morbidity and mortality than isolated seizures. Our objective was to identify clinical diagnoses and rhythmic and periodic electroencephalogram patterns (RPPs) associated with SE and seizures. DESIGN Retrospective cohort study. SETTING Tertiary-care hospitals. SUBJECTS Twelve thousand four hundred fifty adult hospitalized patients undergoing continuous electroencephalogram (cEEG) monitoring in selected participating sites in the Critical Care EEG Monitoring Research Consortium database (February 2013 to June 2021). INTERVENTIONS Not applicable. MEASUREMENTS AND MAIN RESULTS We defined an ordinal outcome in the first 72 hours of cEEG: no seizures, isolated seizures without SE, or SE (with or without isolated seizures). Composite groups included isolated seizures or SE (AnySz) and no seizure or isolated seizures. In this cohort (mean age: 60 ± 17 yr), 1,226 patients (9.8%) had AnySz and 439 patients (3.5%) had SE. In a multivariate model, factors independently associated with SE were cardiac arrest (9.2% with SE; adjusted odds ratio, 8.8 [6.3-12.1]), clinical seizures before cEEG (5.7%; 3.3 [2.5-4.3]), brain neoplasms (3.2%; 1.6 [1.0-2.6]), lateralized periodic discharges (LPDs) (15.4%; 7.3 [5.7-9.4]), brief potentially ictal rhythmic discharges (BIRDs) (22.5%; 3.8 [2.6-5.5]), and generalized periodic discharges (GPDs) (7.2%; 2.4 [1.7-3.3]). All above variables and lateralized rhythmic delta activity (LRDA) were also associated with AnySz. Factors disproportionately increasing odds of SE over isolated seizures were cardiac arrest (7.3 [4.4-12.1]), clinical seizures (1.7 [1.3-2.4]), GPDs (2.3 [1.4-3.5]), and LPDs (1.4 [1.0-1.9]). LRDA had lower odds of SE compared with isolated seizures (0.5 [0.3-0.9]). RPP modifiers did not improve SE prediction beyond RPPs presence/absence ( p = 0.8). CONCLUSIONS Using the largest existing cEEG database, we identified specific predictors of SE (cardiac arrest, clinical seizures prior to cEEG, brain neoplasms, LPDs, GPDs, and BIRDs) and seizures (all previous and LRDA). These findings could be used to tailor cEEG monitoring for critically ill patients.
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Affiliation(s)
- Samuel B Snider
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Michael W K Fong
- Westmead Comprehensive Epilepsy Unit, Westmead Hospital, University of Sydney, Sydney, NSW, Australia
- Comprehensive Epilepsy Center, Dept. of Neurology, Yale University School of Medicine, New Haven, CT
| | - Neal M Nolan
- Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | | | - Wei Wang
- Division of Sleep Medicine, Departments of Medicine and Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Suzette LaRoche
- Department of Neurology, University of North Carolina, Chapel Hill, NC
| | - Lawrence J Hirsch
- Comprehensive Epilepsy Center, Dept. of Neurology, Yale University School of Medicine, New Haven, CT
| | - Jong W Lee
- Division of Epilepsy, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
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12
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Schaefer SM. Rethinking our Approach to Educating New Learners in Movement Disorders Phenomenological Diagnosis. Mov Disord 2023; 38:1379-1383. [PMID: 37403965 DOI: 10.1002/mds.29530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/08/2023] [Accepted: 06/20/2023] [Indexed: 07/06/2023] Open
Affiliation(s)
- Sara M Schaefer
- Department of Neurology, Yale School of Medicine, New Haven, Connecticut, USA
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13
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Kim SH, Kim H, Kim JB. Differences in functional network between focal onset nonconvulsive status epilepticus and toxic metabolic encephalopathy: application to machine learning models for differential diagnosis. Cogn Neurodyn 2023; 17:845-853. [PMID: 37522045 PMCID: PMC10374505 DOI: 10.1007/s11571-022-09877-0] [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: 01/07/2022] [Revised: 05/19/2022] [Accepted: 08/19/2022] [Indexed: 11/29/2022] Open
Abstract
We aimed to compare network properties between focal-onset nonconvulsive status epilepticus (NCSE) and toxic/metabolic encephalopathy (TME) during periods of periodic discharge using graph theoretical analysis, and to evaluate the applicability of graph measures as markers for the differential diagnosis between focal-onset NCSE and TME, using machine learning algorithms. Electroencephalography (EEG) data from 50 focal-onset NCSE and 44 TMEs were analyzed. Epochs with nonictal periodic discharges were selected, and the coherence in each frequency band was analyzed. Graph theoretical analysis was performed to compare brain network properties between the groups. Eight different traditional machine learning methods were implemented to evaluate the utility of graph theoretical measures as input features to discriminate between the two conditions. The average degree (in delta, alpha, beta, and gamma bands), strength (in delta band), global efficiency (in delta and alpha bands), local efficiency (in delta band), clustering coefficient (in delta band), and transitivity (in delta band) were higher in TME than in NCSE. TME showed lower modularity (in delta band) and assortativity (in alpha, beta, and gamma bands) than NCSE. Machine learning algorithms based on EEG global graph measures classified NCSE and TME with high accuracy, and gradient boosting was the most accurate classification model with an area under the receiver operating characteristics curve of 0.904. Our findings on differences in network properties may provide novel insights that graph measures reflecting the network properties could be quantitative markers for the differential diagnosis between focal-onset NCSE and TME.
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Affiliation(s)
- Seong Hwan Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Hayom Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jung Bin Kim
- Department of Neurology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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14
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Sheikh Z, Selioutski O, Taraschenko O, Gilmore EJ, Westover MB, Cohen AB. Systematic Evaluation of Research Priorities in Critical Care Electroencephalography. J Clin Neurophysiol 2023; 40:426-433. [PMID: 35066530 PMCID: PMC9296700 DOI: 10.1097/wnp.0000000000000916] [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: 11/25/2022] Open
Abstract
PURPOSE The Critical Care EEG Monitoring Research Consortium (CCEMRC) is an international research group focusing on critical care EEG and epilepsy. As CCEMRC grew to include 50+ institutions over the past decade, members met to establish research priorities. METHODS The authors used an analytical hierarchy process-based research prioritization method, adapted from an approach previously applied to a Department of Defense health-related research program. Forty-six CCEMRC members identified and scored a set of eight clinical problems (CPs) and 15 research topic areas (RTAs) at an annual CCEMRC meeting. Members scored CPs on three criteria using a five-point ordinal scale: Incidence, Impact, and Gap Size; and RTAs on four additional criteria: Niche, Feasibility, Scientific Importance, and Medical Importance, each of which was assigned a weight. The first three RTA criteria were scored using a five-point scale, and CPs were mapped to RTAs using a four-point scale. The Medical Importance score was a weighted average of its mapping scores and the CP score. Finally, a Priority score was calculated for each RTA as a product of the four RTA criteria scores. RESULTS The CPs with the highest scores were "Altered mental status" and "Long-term neurologic disability after hospital discharge." The RTAs with the highest priority scores were "Development of risk prediction tools," "Multicenter observational studies," and "Outcome prediction." CONCLUSIONS Research prioritization helped CCEMRC evaluate its current research trajectory, identify high-priority near-term research pursuits, and create a roadmap for future research plans aligned with its mission. This approach may be helpful to other academic consortia and research programs.
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Affiliation(s)
- Zubeda Sheikh
- Department of Neurology, West Virginia University School of Medicine, Morgantown, West Virginia, U.S.A
| | - Olga Selioutski
- Epilepsy Division, Department of Neurology, University of Rochester School of Medicine and Dentistry, Rochester, New York, U.S.A
| | - Olga Taraschenko
- Comprehensive Epilepsy Center, Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, U.S.A
| | - Emily J Gilmore
- Division of Neurocritical Care, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, U.S.A
- Harvard Medical School, Boston, Massachusetts, U.S.A
| | - Adam B Cohen
- The Johns Hopkins University Applied Physics Lab, National Health Mission Area, Laurel, Maryland, U.S.A.; and
- Department of Neurology, The Johns Hopkins Hospital, Baltimore, Maryland, U.S.A
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15
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Jing J, Ge W, Struck AF, Fernandes MB, Hong S, An S, Fatima S, Herlopian A, Karakis I, Halford JJ, Ng MC, Johnson EL, Appavu BL, Sarkis RA, Osman G, Kaplan PW, Dhakar MB, Jayagopal LA, Sheikh Z, Taraschenko O, Schmitt S, Haider HA, Kim JA, Swisher CB, Gaspard N, Cervenka MC, Rodriguez Ruiz AA, Lee JW, Tabaeizadeh M, Gilmore EJ, Nordstrom K, Yoo JY, Holmes MG, Herman ST, Williams JA, Pathmanathan J, Nascimento FA, Fan Z, Nasiri S, Shafi MM, Cash SS, Hoch DB, Cole AJ, Rosenthal ES, Zafar SF, Sun J, Westover MB. Interrater Reliability of Expert Electroencephalographers Identifying Seizures and Rhythmic and Periodic Patterns in EEGs. Neurology 2023; 100:e1737-e1749. [PMID: 36460472 PMCID: PMC10136018 DOI: 10.1212/wnl.0000000000201670] [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: 04/13/2022] [Accepted: 10/25/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The validity of brain monitoring using electroencephalography (EEG), particularly to guide care in patients with acute or critical illness, requires that experts can reliably identify seizures and other potentially harmful rhythmic and periodic brain activity, collectively referred to as "ictal-interictal-injury continuum" (IIIC). Previous interrater reliability (IRR) studies are limited by small samples and selection bias. This study was conducted to assess the reliability of experts in identifying IIIC. METHODS This prospective analysis included 30 experts with subspecialty clinical neurophysiology training from 18 institutions. Experts independently scored varying numbers of ten-second EEG segments as "seizure (SZ)," "lateralized periodic discharges (LPDs)," "generalized periodic discharges (GPDs)," "lateralized rhythmic delta activity (LRDA)," "generalized rhythmic delta activity (GRDA)," or "other." EEGs were performed for clinical indications at Massachusetts General Hospital between 2006 and 2020. Primary outcome measures were pairwise IRR (average percent agreement [PA] between pairs of experts) and majority IRR (average PA with group consensus) for each class and beyond chance agreement (κ). Secondary outcomes were calibration of expert scoring to group consensus, and latent trait analysis to investigate contributions of bias and noise to scoring variability. RESULTS Among 2,711 EEGs, 49% were from women, and the median (IQR) age was 55 (41) years. In total, experts scored 50,697 EEG segments; the median [range] number scored by each expert was 6,287.5 [1,002, 45,267]. Overall pairwise IRR was moderate (PA 52%, κ 42%), and majority IRR was substantial (PA 65%, κ 61%). Noise-bias analysis demonstrated that a single underlying receiver operating curve can account for most variation in experts' false-positive vs true-positive characteristics (median [range] of variance explained ([Formula: see text]): 95 [93, 98]%) and for most variation in experts' precision vs sensitivity characteristics ([Formula: see text]: 75 [59, 89]%). Thus, variation between experts is mostly attributable not to differences in expertise but rather to variation in decision thresholds. DISCUSSION Our results provide precise estimates of expert reliability from a large and diverse sample and a parsimonious theory to explain the origin of disagreements between experts. The results also establish a standard for how well an automated IIIC classifier must perform to match experts. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that an independent expert review reliably identifies ictal-interictal injury continuum patterns on EEG compared with expert consensus.
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Affiliation(s)
- Jin Jing
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Wendong Ge
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aaron F Struck
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marta Bento Fernandes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Shenda Hong
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sungtae An
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Safoora Fatima
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Aline Herlopian
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ioannis Karakis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jonathan J Halford
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Marcus C Ng
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily L Johnson
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Brian L Appavu
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Rani A Sarkis
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Gamaleldin Osman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Peter W Kaplan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Monica B Dhakar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Lakshman Arcot Jayagopal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Zubeda Sheikh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Olga Taraschenko
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sarah Schmitt
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Hiba A Haider
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Kim
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Christa B Swisher
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Nicolas Gaspard
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mackenzie C Cervenka
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andres A Rodriguez Ruiz
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jong Woo Lee
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mohammad Tabaeizadeh
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Emily J Gilmore
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Kristy Nordstrom
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ji Yeoun Yoo
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Manisha G Holmes
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Susan T Herman
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jennifer A Williams
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jay Pathmanathan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Fábio A Nascimento
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Ziwei Fan
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Samaneh Nasiri
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Mouhsin M Shafi
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sydney S Cash
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Daniel B Hoch
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Andrew J Cole
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Eric S Rosenthal
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Sahar F Zafar
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - Jimeng Sun
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL
| | - M Brandon Westover
- From the Massachusetts General Hospital/Harvard Medical School Department of Neurology (J.J., W.G., M.B.F., S.S.C., A.J.C., D.B.H., E.S.R., S.F.Z., M.B.W.), MA; Massachusetts General Hospital Clinical Data Animation Center (CDAC) (J.J., W.G., M.B.F., S.S.C., D.B.H., A.J.C., E.S.R., S.F.Z., M.B.W.), MA; University of Wisconsin-Madison Department of Neurology (A.F.S., S.F.); William S. Middleton Memorial Veterans Hospital Madison (A.F.S.), WI; National Institute of Health Data Science (S.H.), Peking University, Beijing, China; Georgia Institute of Technology (S.A.), College of Computing, Atlanta, GA; Yale University-Yale New Haven Hospital (A.H.), CT; Emory University School of Medicine (I.K.), GA; Medical University of South Carolina (J.J.H.), SC; University of Manitoba (M.C.N.), Canada; Johns Hopkins School of Medicine (E.L.J.), MD; University of Arizona College of Medicine (B.L.A.), AZ; Brigham and Women's Hospital (R.A.S.), MA; Mayo Clinic-Rochester (G.O.), MN; Warren Alpert School of Medicine of Brown University (M.B.D.), Providence, RI; University of Nebraska Medical Center (L.A.J.), NE; West Virginia University Hospitals (Z.S.), WV; University of Chicago (H.A.H.), Chicago, IL; Atrium Health (C.B.S.), NC; Université Libre de Bruxelles - Hôpital Erasme (N.G.), Belgium; Icahn School of Medicine, Mount Sinai (J.Y.Y.), NY; New York University (NYU) Grossman School of Medicine (M.G.H.), NY; Barrow Neurological Institute (S.T.H.), Phoenix, AZ; Mater Misericordiae University Hospital (J.A.W.), Dublin, Ireland; University of Pennsylvania (J.P.), PA; Beth Israel Deaconess Medical Center/Harvard Medical School (M.M.S.), MA; and University of Illinois at Urbana-Champaign (J.S.), College of Computing, Champaign, IL.
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16
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Liuzzi P, Campagnini S, Hakiki B, Burali R, Scarpino M, Macchi C, Cecchi F, Mannini A, Grippo A. Heart rate variability for the evaluation of patients with disorders of consciousness. Clin Neurophysiol 2023; 150:31-39. [PMID: 37002978 DOI: 10.1016/j.clinph.2023.03.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 12/12/2022] [Accepted: 03/03/2023] [Indexed: 04/03/2023]
Abstract
OBJECTIVE Clinical responsiveness of patients with a Disorder of Consciousness (DoC) correlates to sympathetic/parasympathetic homeostatic balance. Heart Rate Variability (HRV) metrics result in non-invasive proxies of modulation capabilities of visceral states. In this work, our aim was to evaluate whether HRV measures could improve the differential diagnosis between Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS) with respect to multivariate models based on standard clinical electroencephalography (EEG) labeling only in a rehabilitation setting. METHODS A prospective observational study was performed consecutively enrolling 82 DoC patients. Polygraphic recordings were performed. HRV-metrics and EEG descriptors derived from the American Clinical Neurophysiology Society's Standardized Critical Care terminology were included. Descriptors entered univariate and then multivariate logistic regressions with the target set to the UWS/MCS diagnosis. RESULTS HRV measures resulted significantly different between UWS and MCS patients, with higher values being associated with better consciousness levels. Specifically, adding HRV-related metrics to ACNS EEG descriptors increased the Nagelkerke R2 from 0.350 (only EEG descriptors) to 0.565 (HRV-EEG combination) with the outcome set to the consciousness diagnosis. CONCLUSIONS HRV changes across the lowest states of consciousness. Rapid changes in heart rate, occurring in better consciousness levels, confirm the mutual correlation between visceral state functioning patterns and consciousness alterations. SIGNIFICANCE Quantitative analysis of heart rate in patients with a DoC paves the way for the implementation of low-cost pipelines supporting medical decisions within multimodal consciousness assessments.
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Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy; Scuola Superiore Sant'Anna, Istituto di BioRobotica, Pontedera, Viale Rinaldo Piaggio 34, Italy
| | - Silvia Campagnini
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy; Scuola Superiore Sant'Anna, Istituto di BioRobotica, Pontedera, Viale Rinaldo Piaggio 34, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy.
| | - Rachele Burali
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy
| | - Maenia Scarpino
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy
| | - Claudio Macchi
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy; Università di Firenze, Dipartimento di Medicina Sperimentale e Clinica, Firenze, Largo Brambilla 3, Italy
| | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy; Università di Firenze, Dipartimento di Medicina Sperimentale e Clinica, Firenze, Largo Brambilla 3, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi, Firenze, Via di Scandicci 269, Italy
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17
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Gavaret M, Iftimovici A, Pruvost-Robieux E. EEG: Current relevance and promising quantitative analyses. Rev Neurol (Paris) 2023; 179:352-360. [PMID: 36907708 DOI: 10.1016/j.neurol.2022.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/02/2022] [Accepted: 12/06/2022] [Indexed: 03/12/2023]
Abstract
Electroencephalography (EEG) remains an essential tool, characterized by an excellent temporal resolution and offering a real window on cerebral functions. Surface EEG signals are mainly generated by the postsynaptic activities of synchronously activated neural assemblies. EEG is also a low-cost tool, easy to use at bed-side, allowing to record brain electrical activities with a low number or up to 256 surface electrodes. For clinical purpose, EEG remains a critical investigation for epilepsies, sleep disorders, disorders of consciousness. Its temporal resolution and practicability also make EEG a necessary tool for cognitive neurosciences and brain-computer interfaces. EEG visual analysis is essential in clinical practice and the subject of recent progresses. Several EEG-based quantitative analyses may complete the visual analysis, such as event-related potentials, source localizations, brain connectivity and microstates analyses. Some developments in surface EEG electrodes appear also, potentially promising for long term continuous EEGs. We overview in this article some recent progresses in visual EEG analysis and promising quantitative analyses.
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Affiliation(s)
- M Gavaret
- Université Paris Cité, INSERM UMR 1266, IPNP (Institute of Psychiatry and Neuroscience of Paris), France; Service de Neurophysiologie Clinique et Epileptologie, GHU Paris Psychiatrie et Neurosciences, Paris, France; FHU NeuroVasc, Paris, France.
| | - A Iftimovici
- Université Paris Cité, INSERM UMR 1266, IPNP (Institute of Psychiatry and Neuroscience of Paris), France; NeuroSpin, Atomic Energy Commission, Gif-sur-Yvette, France; Pôle PEPIT, GHU Paris Psychiatrie et Neurosciences, Paris, France
| | - E Pruvost-Robieux
- Université Paris Cité, INSERM UMR 1266, IPNP (Institute of Psychiatry and Neuroscience of Paris), France; Service de Neurophysiologie Clinique et Epileptologie, GHU Paris Psychiatrie et Neurosciences, Paris, France; FHU NeuroVasc, Paris, France
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18
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Waak M, Laing J, Nagarajan L, Lawn N, Harvey AS. Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective. CRIT CARE RESUSC 2023; 25:9-19. [PMID: 37876987 PMCID: PMC10581281 DOI: 10.1016/j.ccrj.2023.04.004] [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: 10/26/2023]
Abstract
Objectives This article aims to critically review the literature on continuous electroencephalography (cEEG) monitoring in the intensive care unit (ICU) from an Australian and New Zealand perspective and provide recommendations for clinicians. Design and review methods A taskforce of adult and paediatric neurologists, selected by the Epilepsy Society of Australia, reviewed the literature on cEEG for seizure detection in critically ill neonates, children, and adults in the ICU. The literature on routine EEG and cEEG for other indications was not reviewed. Following an evaluation of the evidence and discussion of controversial issues, consensus was reached, and a document that highlighted important clinical, practical, and economic considerations regarding cEEG in Australia and New Zealand was drafted. Results This review represents a summary of the literature and consensus opinion regarding the use of cEEG in the ICU for detection of seizures, highlighting gaps in evidence, practical problems with implementation, funding shortfalls, and areas for future research. Conclusion While cEEG detects electrographic seizures in a significant proportion of at-risk neonates, children, and adults in the ICU, conferring poorer neurological outcomes and guiding treatment in many settings, the health economic benefits of treating such seizures remain to be proven. Presently, cEEG in Australian and New Zealand ICUs is a largely unfunded clinical resource that is subsequently reserved for the highest-impact patient groups. Wider adoption of cEEG requires further research into impact on functional and health economic outcomes, education and training of the neurology and ICU teams involved, and securement of the necessary resources and funding to support the service.
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Affiliation(s)
- Michaela Waak
- Paediatric Critical Care Research Group, Child Health Research Centre, The University of Queensland, Brisbane, Australia
- Paediatric Intensive Care Unit, Queensland Children's Hospital, South Brisbane, Australia
| | - Joshua Laing
- Department of Neurosciences, Central Clinical School, Monash University, Melbourne, Australia
- Comprehensive Epilepsy Program, Alfred Health, Melbourne, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Australia
| | - Lakshmi Nagarajan
- Department of Neurology, Perth Children's Hospital, Perth, Australia
- Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
- Telethon Kids Institute, Perth Children's Hospital, Perth, Australia
| | - Nicholas Lawn
- Western Australian Adult Epilepsy Service, Sir Charles Gardiner Hospital, Perth, Australia
| | - A. Simon Harvey
- Department of Neurology, The Royal Children's Hospital, Melbourne, Australia
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia
- Neurosciences Research Group, Murdoch Children's Research Institute, Melbourne, Australia
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19
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Sporadic and Periodic Interictal Discharges in Critically Ill Children: Seizure Associations and Time to Seizure Identification. J Clin Neurophysiol 2023; 40:130-135. [PMID: 34144575 DOI: 10.1097/wnp.0000000000000860] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
PURPOSE We evaluated interictal discharges (IEDs) as a biomarker for the time to development of electrographic seizures (ES). METHODS Prospective observational study of 254 critically ill children who underwent continuous electroencephalography (cEEG) monitoring. We excluded neonates and patients with known epilepsy or the sole cEEG indication to characterize events. Interictal discharges included sporadic epileptiform discharges and periodic and rhythmic patterns. Sporadic epileptiform discharges were categorized as low frequency (rare [<1/hour] and occasional [≥1/hour but <1/minute]) and high frequency (frequent, [≥1/minute] and abundant [≥1/10 seconds]). Time variables included time from cEEG start to first IED and time between first IED and ES. RESULTS Interictal discharges were present in 33% (83/254) of patients. We identified ES in 20% (50/254), and 86% (43/50) had IEDs. High-frequency sporadic epileptiform discharges (odds ratio [OR], 35; 95% confidence interval [CI], 14.5-88; P < 0.0001) and lateralized periodic discharges (OR, 27; 95% CI, 7.3-100; P < 0.0001) were associated with ES. Mildly abnormal EEG background without IEDs or background asymmetry was associated with the absence of seizures (OR, 0.1; 95% CI, 0.04-0.3; P < 0.0001). Time from cEEG start to first IED was 36 minutes (interquartile range, 3-131 minutes), and time between first IED and ES was 9.6 minutes (interquartile range, 0.6-165 minutes). CONCLUSIONS Interictal discharges are associated with ES and are identified in the first 3 hours of cEEG. High-frequency sporadic epileptiform discharges and periodic patterns have the highest risk of ES. Our findings define a window of high seizure risk after the identification of IEDs in which to allocate resources to improve seizure identification and subsequent treatment.
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20
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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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21
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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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22
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Wesselingh R, Broadley J, Buzzard K, Tarlinton D, Seneviratne U, Kyndt C, Stankovich J, Sanfilippo P, Nesbitt C, D'Souza W, Macdonell R, Butzkueven H, O'Brien TJ, Monif M. Prevalence, risk factors, and prognosis of drug-resistant epilepsy in autoimmune encephalitis. Epilepsy Behav 2022; 132:108729. [PMID: 35623203 DOI: 10.1016/j.yebeh.2022.108729] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/10/2022] [Accepted: 05/05/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE To evaluate the prevalence and biomarkers of drug-resistant epilepsy (DRE) in patients with autoimmune encephalitis (AIE). METHODS Sixty-nine patients with AIE were recruited retrospectively and electroencephalographies (EEGs) were reviewed using a standard reporting proforma. Associations between EEG biomarkers and DRE development at 12 months were examined using logistic regression modeling and were utilized to create a DRE risk score. RESULTS Sixteen percent of patients with AIE developed DRE at 12-month follow-up. The presence of status epilepticus (SE) (OR 11.50, 95% CI [2.81, 51.86], p-value <0.001), temporal lobe focality (OR 9.90, 95% CI [2.60, 50.71], p-value 0.001) and periodic discharges (OR 19.12, 95% CI [3.79, 191.10], p-value 0.001) on the admission EEG were associated with the development of DRE at 12 months. These variables were utilized to create a clinically applicable risk score for the prediction of DRE development. CONCLUSIONS Drug-resistant epilepsy is an infrequent complication of AIE. Electroencephalography changes during the acute illness can predict the risk of DRE at 12 months post-acute AIE. SIGNIFICANCE The identified EEG biomarkers provide the basis to generate a clinically applicable prediction tool which could be used to inform treatment, prognosis, and select patients for acute treatment trials.
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Affiliation(s)
- Robb Wesselingh
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - James Broadley
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Katherine Buzzard
- Department of Neurology, Melbourne Health, 300 Grattan Street, Parkville, Victoria 3050, Australia; Department of Neuroscience, Eastern Health, Level 2, 5 Arnold Street, Box Hill, Victoria 3128, Australia
| | - David Tarlinton
- Department of Immunology and Pathology, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Burnett Building, 89 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Udaya Seneviratne
- Department of Neurosciences, Monash Health, Clayton Road, Clayton, Victoria 3168, Australia
| | - Chris Kyndt
- Department of Neurology, Melbourne Health, 300 Grattan Street, Parkville, Victoria 3050, Australia; Department of Neuroscience, Eastern Health, Level 2, 5 Arnold Street, Box Hill, Victoria 3128, Australia
| | - Jim Stankovich
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Paul Sanfilippo
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Cassie Nesbitt
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, University Hospital of Geelong, Level 2, Kardinia House, Bellerine Street, Geelong, Victoria 3220, Australia
| | - Wendyl D'Souza
- Department of Neurosciences, Building D - Daly Wing, Level 5, St Vincent's Hospital, Fitzroy, Victoria 3065, Australia
| | - Richard Macdonell
- Department of Neurology, Austin Health, Level 6 North Austin Tower, 145 Studley Road, Heidelberg, Victoria 3084, Australia
| | - Helmut Butzkueven
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Terence J O'Brien
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Mastura Monif
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Melbourne Health, 300 Grattan Street, Parkville, Victoria 3050, Australia.
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23
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Kim MJ, Kim YJ, Yum MS, Kim WY. Alpha-power in electroencephalography as good outcome predictor for out-of-hospital cardiac arrest survivors. Sci Rep 2022; 12:10907. [PMID: 35764807 PMCID: PMC9240023 DOI: 10.1038/s41598-022-15144-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/20/2022] [Indexed: 11/09/2022] Open
Abstract
This study aimed to investigate the utility of quantitative EEG biomarkers for predicting good neurologic outcomes in OHCA survivors treated with targeted temperature management (TTM) using power spectral density (PSD), event-related spectral perturbation (ERSP), and spectral entropy (SE). This observational registry-based study was conducted at a tertiary care hospital in Korea using data of adult nontraumatic comatose OHCA survivors who underwent standard EEG and treated with TTM between 2010 and 2018. Good neurological outcome at 1 month (Cerebral Performance Category scores 1 and 2) was the primary outcome. The linear mixed model analysis was performed for PSD, ESRP, and SE values of all and each frequency band. Thirteen of the 54 comatose OHCA survivors with TTM and EEG were excluded due to poor EEG quality or periodic/rhythmic pattern, and EEG data of 41 patients were used for analysis. The median time to EEG was 21 h, and the rate of the good neurologic outcome at 1 month was 52.5%. The good neurologic outcome group was significantly younger and showed higher PSD and ERSP and lower SE features for each frequency than the poor outcome group. After age adjustment, only the alpha-PSD was significantly higher in the good neurologic outcome group (1.13 ± 1.11 vs. 0.09 ± 0.09, p = 0.031) and had best performance with 0.903 of the area under the curve for predicting good neurologic outcome. Alpha-PSD best predicts good neurologic outcome in OHCA survivors and is an early biomarker for prognostication. Larger studies are needed to conclusively confirm these findings.
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Affiliation(s)
- Min-Jee Kim
- Division of Pediatric Neurology, Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Youn-Jung Kim
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea
| | - Mi-Sun Yum
- Division of Pediatric Neurology, Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
| | - Won Young Kim
- Department of Emergency Medicine, Asan Medical Center, Ulsan University College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
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Waak M, Gibbons K, Sparkes L, Harnischfeger J, Gurr S, Schibler A, Slater A, Malone S. Real-time seizure detection in paediatric intensive care patients: the RESET child brain protocol. BMJ Open 2022; 12:e059301. [PMID: 36691237 PMCID: PMC9171209 DOI: 10.1136/bmjopen-2021-059301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 04/19/2022] [Indexed: 01/27/2023] Open
Abstract
INTRODUCTION Approximately 20%-40% of comatose children with risk factors in intensive care have electrographic-only seizures; these go unrecognised due to the absence of continuous electroencephalography (EEG) monitoring (cEEG). Utility of cEEG with high-quality assessment is currently limited due to high-resource requirements. New software analysis tools are available to facilitate bedside cEEG assessment using quantitative EEG (QEEG) trends. The primary aim of this study is to describe accuracy of interpretation of QEEG trends by paediatric intensive care unit (PICU) nurses compared with cEEG assessment by neurologist (standard clinical care) in children at risk of seizures and status epilepticus utilising diagnostic test statistics. The secondary aims are to determine time to seizure detection for QEEG users compared with standard clinical care and describe impact of confounders on accuracy of seizure detection. METHODS AND ANALYSIS This will be a single-centre, prospective observational cohort study evaluating a paediatric QEEG programme utilising the full 19 electrode set. The setting will be a 36-bed quaternary PICU with medical, cardiac and general surgical cases. cEEG studies in PICU patients identified as 'at risk of seizures' will be analysed. Trained bedside clinical nurses will interpret the QEEG. Seizure events will be marked as seizures if >3 QEEG criteria occur. Post-hoc dedicated neurologists, who remain blinded to the QEEG analysis, will interpret the cEEG. Determination of standard test characteristics will assess the primary hypothesis. To calculate 95% (CIs) around the sensitivity and specificity estimates with a CI width of 10%, the sample size needed for sensitivity is 80 patients assuming each EEG will have approximately 9 to 18 1-hour epochs. ETHICS AND DISSEMINATION The study has received approval by the Children's Health Queensland Human Research Ethics Committee (HREC/19/QCHQ/58145). Results will be made available to the funders, critical care survivors and their caregivers, the relevant societies, and other researchers. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry (ANZCTR) 12621001471875.
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Affiliation(s)
- Michaela Waak
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
- Centre for Children's Health Research, Brisbane, Queensland, Australia
| | - Kristen Gibbons
- Centre for Children's Health Research, Brisbane, Queensland, Australia
- The University of Queensland, Saint Lucia, Queensland, Australia
| | - Louise Sparkes
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
- Centre for Children's Health Research, Brisbane, Queensland, Australia
| | - Jane Harnischfeger
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
| | - Sandra Gurr
- Neurosciences, Queensland Children's Hospital, South Brisbane, Queensland, Australia
| | - Andreas Schibler
- St Andrew's War Memorial Hospital, Spring Hill, Queensland, Australia
| | - Anthony Slater
- Queensland Children's Hospital Paediatric Intensive Care Unit, South Brisbane, Queensland, Australia
| | - Stephen Malone
- The University of Queensland, Saint Lucia, Queensland, Australia
- Neurosciences, Queensland Children's Hospital, South Brisbane, Queensland, Australia
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25
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Tantillo GB, Jetté N, Gururangan K, Agarwal P, Marcuse L, Singh A, Goldstein J, Kwon CS, Dhamoon MS, Navis A, Nadkarni GN, Charney AW, Young JJ, Blank LJ, Fields M, Yoo JY. Electroencephalography at the height of a pandemic: EEG findings in patients with COVID-19. Clin Neurophysiol 2022; 137:102-112. [PMID: 35305494 PMCID: PMC8901228 DOI: 10.1016/j.clinph.2022.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 02/25/2022] [Accepted: 03/01/2022] [Indexed: 01/08/2023]
Abstract
OBJECTIVE To characterize continuous video electroencephalogram (VEEG) findings of hospitalized COVID-19 patients. METHODS We performed a retrospective chart review of patients admitted at three New York City hospitals who underwent VEEG at the peak of the COVID-19 pandemic. Demographics, comorbidities, neuroimaging, VEEG indications and findings, treatment, and outcomes were collected. RESULTS Of 93 patients monitored, 77% had severe COVID-19 and 40% died. Acute ischemic or hemorrhagic stroke was present in 26% and 15%, respectively. Most common VEEG indications were encephalopathy/coma (60%) and seizure-like movements (38%). Most common VEEG findings were generalized slowing (97%), generalized attenuation (31%), generalized periodic discharges (17%) and generalized sharp waves (15%). Epileptiform abnormalities were present in 43% and seizures in 8% of patients, all of whom had seizure risk factors. Factors associated with an epileptiform VEEG included increasing age (OR 1.07, p = 0.001) and hepatic/renal failure (OR 2.99, p = 0.03). CONCLUSIONS Most COVID-19 patients who underwent VEEG monitoring had severe COVID-19 and over one-third had acute cerebral injury (e.g., stroke, anoxia). Seizures were uncommon. VEEG findings were nonspecific. SIGNIFICANCE VEEG findings in this cohort of hospitalized COVID-19 patients were those often seen in critical illness. Seizures were uncommon and occurred in the setting of common seizure risk factors.
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Affiliation(s)
- Gabriela B Tantillo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nathalie Jetté
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Healthcare Delivery Science, Department of Population Health and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Kapil Gururangan
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Parul Agarwal
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Healthcare Delivery Science, Department of Population Health and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Lara Marcuse
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Anuradha Singh
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | | | - Churl-Su Kwon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Healthcare Delivery Science, Department of Population Health and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mandip S Dhamoon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Allison Navis
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Girish N Nadkarni
- Icahn School of Medicine at Mount Sinai, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, USA; Mount Sinai COVID-19 Informatics Center, USA
| | - Alexander W Charney
- Icahn School of Medicine at Mount Sinai, USA; Mount Sinai COVID-19 Informatics Center, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, USA
| | - James J Young
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Leah J Blank
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Institute for Healthcare Delivery Science, Department of Population Health and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Madeline Fields
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA
| | - Ji Yeoun Yoo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Icahn School of Medicine at Mount Sinai, USA.
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26
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Wesselingh R, Broadley J, Buzzard K, Tarlinton D, Seneviratne U, Kyndt C, Stankovich J, Sanfilippo P, Nesbitt C, D'Souza W, Macdonell R, Butzkueven H, O'Brien TJ, Monif M. Electroclinical biomarkers of autoimmune encephalitis. Epilepsy Behav 2022; 128:108571. [PMID: 35101840 DOI: 10.1016/j.yebeh.2022.108571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/25/2021] [Accepted: 01/10/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To evaluate the utility of electroencephalography (EEG) changes as diagnostic and prognostic biomarkers in acute autoimmune encephalitis (AIE). METHODS One hundred and thirty-one patients with AIE were recruited retrospectively across 7 hospitals. Clinical data were collected during admission and at 12 months. EEGs were reviewed using a standard reporting proforma. Associations between EEG biomarkers, AIE subtypes, and clinical outcomes were assessed using logistic regression modeling. RESULTS Presence of superimposed fast activity (OR 34.33; 95% CI 3.90, 4527.27; p < 0.001), fluctuating EEG abnormality (OR 6.60; 95% CI 1.60, 37.59; p = 0.008), and hemispheric focality (OR 28.48; 95% CI 3.14, 3773.14; p < 0.001) were significantly more common in N-methyl-d-aspartate receptor (NMDAR) antibody-associated patients with AIE compared to other AIE subtypes. Abnormal background rhythm was associated with a poor mRS (modified Rankin score) at discharge (OR 0.29; 95% CI 0.10, 0.75; p = 0.01) and improvement in mRS at 12 months compared with admission mRS (3.72; 95% CI 1.14, 15.23; p = 0.04). SIGNIFICANCE We have identified EEG biomarkers that differentiate NMDAR AIE from other subtypes. We have also demonstrated EEG biomarkers that are associated with poor functional outcomes.
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Affiliation(s)
- Robb Wesselingh
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - James Broadley
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Katherine Buzzard
- Department of Neurology, Melbourne Health, 300 Grattan Street, Parkville, Victoria 3050, Australia; Department of Neuroscience, Eastern Health, Level 2, 5 Arnold Street, Box Hill, Victoria 3128, Australia
| | - David Tarlinton
- Department of Immunology, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Burnett Building, 89 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Udaya Seneviratne
- Department of Neurosciences, Monash Health, Clayton Road, Clayton, Victoria 3168, Australia
| | - Chris Kyndt
- Department of Neurology, Melbourne Health, 300 Grattan Street, Parkville, Victoria 3050, Australia; Department of Neuroscience, Eastern Health, Level 2, 5 Arnold Street, Box Hill, Victoria 3128, Australia
| | - Jim Stankovich
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Paul Sanfilippo
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Cassie Nesbitt
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Barwon Neurology, Level 2, Kardinia House, Bellerine Street, Geelong, Victoria 3220, Australia
| | - Wendyl D'Souza
- Department of Neurosciences, Building D - Daly Wing, Level 5, St Vincent's Hospital, Fitzroy, Victoria 3065, Australia
| | - Richard Macdonell
- Department of Neurology, Austin Health, Level 6 North Austin Tower, 145 Studley Road, Heidelberg, Victoria 3084, Australia
| | - Helmut Butzkueven
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Terence J O'Brien
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia
| | - Mastura Monif
- Department of Neurosciences, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Alfred Health, Level 6, Alfred Centre, 99 Commercial Road, Melbourne, Victoria 3004, Australia; Department of Neurology, Melbourne Health, 300 Grattan Street, Parkville, Victoria 3050, Australia.
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27
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Critical care EEG standardized nomenclature in clinical practice: Strengths, limitations, and outlook on the example of prognostication after cardiac arrest. Clin Neurophysiol Pract 2022; 6:149-154. [PMID: 35112033 PMCID: PMC8790140 DOI: 10.1016/j.cnp.2021.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 01/08/2021] [Accepted: 03/03/2021] [Indexed: 11/21/2022] Open
Abstract
Optimal use of the ACNS nomenclature implies integration of clinical information. Knowledge of pathophysiological mechanisms of EEG patterns may help interpretation. Standardized therapeutic procedures for critical care patients are needed.
We discuss the achievements of the ACNS critical care EEG nomenclature proposed in 2013 and, from a clinical angle, outline some limitations regarding translation into treatment implications. While the recently proposed updated 2021 version of the nomenclature will probable improve some uncertainty areas, a refined understanding of the mechanisms at the origin of the EEG patterns, and a multimodal integration of the nomenclature to the clinical context may help improving the rationale supporting therapeutic procedures. We illustrate these aspects on prognostication after cardiac arrest.
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Key Words
- ACNS, American Clinical Neurophysiology Society
- American Clinical Neurophysiology Society (ACNS) Standardized Terminology
- BIRD, Brief potentially ictal rhythmic discharge
- BS, Burst suppression
- Burst suppression
- CA, Cardiac arrest
- Cardiac arrest (CA)
- DWI, diffusion-weighted MRI
- ESI, electric source imaging
- GPD
- GPD, generalized periodic discharge
- GRDA, generalized rhythmic delta activity
- ICU, Intensive care unit
- ICU-EEG, intensive care unit-electroencephalography
- IIC, Ictal-Interictal Continuum
- Ictal-Interictal Continuum
- LPD, Lateralized periodic discharge
- MEG, Magneto-electroencephalography
- NCSE, Non-Convulsive Status Epilepticus
- NSE, Serum neuron-specific enolase
- PET, Positron emission tomography
- Prognostication assessment
- SE, Status epilepticus
- SPECT, Single Photon Emission Computed Tomography
- SSEP, Somatosensory evoked potentials
- WLST, Withdraw of life sustaining treatment
- fMRI, functional MRI
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28
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FIERAIN A, GASPARD N, LEJEUNE N, EL TAHRY R, SPEYBROECK N, DERMAUW V, FERRAO SANTOS S. Beware of nonconvulsive seizures in prolonged disorders of consciousness: long-term EEG monitoring is the key. Clin Neurophysiol 2022; 136:228-234. [DOI: 10.1016/j.clinph.2021.12.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 11/22/2021] [Accepted: 12/14/2021] [Indexed: 11/24/2022]
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29
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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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30
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Tatum WO, Mani J, Jin K, Halford JJ, Gloss D, Fahoum F, Maillard L, Mothersill I, Beniczky S. Minimum standards for inpatient long-term video-EEG monitoring: A clinical practice guideline of the international league against epilepsy and international federation of clinical neurophysiology. Clin Neurophysiol 2021; 134:111-128. [PMID: 34955428 DOI: 10.1016/j.clinph.2021.07.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events (see Table S1). For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and establish its clinical utility.
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Affiliation(s)
- William O Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA.
| | - Jayanti Mani
- Department of Neurology, Kokilaben Dhirubai Ambani Hospital, Mumbai, India
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University Graduate School of Medicine, Japan
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
| | - David Gloss
- Department of Neurology, Charleston Area Medical Center, Charleston, WV, USA
| | - Firas Fahoum
- Department of Neurology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Louis Maillard
- Department of Neurology, University of Nancy, UMR7039, University of Lorraine, France.
| | - Ian Mothersill
- Department of Clinical Neurophysiology, Swiss Epilepsy Center, Zurich Switzerland.
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Danish Epilepsy Center, Dianalund, Denmark.
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31
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Tatum WO, Mani J, Jin K, Halford JJ, Gloss D, Fahoum F, Maillard L, Mothersill I, Beniczky S. Minimum standards for inpatient long-term video-electroencephalographic monitoring: A clinical practice guideline of the International League Against Epilepsy and International Federation of Clinical Neurophysiology. Epilepsia 2021; 63:290-315. [PMID: 34897662 DOI: 10.1111/epi.16977] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 01/02/2023]
Abstract
The objective of this clinical practice guideline is to provide recommendations on the indications and minimum standards for inpatient long-term video-electroencephalographic monitoring (LTVEM). The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology develop guidelines aligned with the Epilepsy Guidelines Task Force. We reviewed published evidence using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement. We found limited high-level evidence aimed at specific aspects of diagnosis for LTVEM performed to evaluate patients with seizures and nonepileptic events. For classification of evidence, we used the Clinical Practice Guideline Process Manual of the American Academy of Neurology. We formulated recommendations for the indications, technical requirements, and essential practice elements of LTVEM to derive minimum standards used in the evaluation of patients with suspected epilepsy using GRADE (Grading of Recommendations Assessment, Development, and Evaluation). Further research is needed to obtain evidence about long-term outcome effects of LTVEM and to establish its clinical utility.
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Affiliation(s)
- William O Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Jayanti Mani
- Department of Neurology, Kokilaben Dhirubai Ambani Hospital, Mumbai, India
| | - Kazutaka Jin
- Department of Epileptology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - David Gloss
- Department of Neurology, Charleston Area Medical Center, Charleston, West Virginia, USA
| | - Firas Fahoum
- Department of Neurology, Tel Aviv Sourasky Medical Center and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Louis Maillard
- Department of Neurology, University of Nancy, UMR7039, University of Lorraine, Nancy, France
| | - Ian Mothersill
- Department of Clinical Neurophysiology, Swiss Epilepsy Center, Zurich,, Switzerland
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Danish Epilepsy Center, Dianalund, Denmark
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Fung FW, Parikh DS, Massey SL, Fitzgerald MP, Vala L, Donnelly M, Jacobwitz M, Kessler SK, Topjian AA, Abend NS. Periodic and rhythmic patterns in critically ill children: Incidence, interrater agreement, and seizures. Epilepsia 2021; 62:2955-2967. [PMID: 34642942 DOI: 10.1111/epi.17068] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/27/2021] [Accepted: 09/01/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVES We aimed to determine the incidence of periodic and rhythmic patterns (PRP), assess the interrater agreement between electroencephalographers scoring PRP using standardized terminology, and analyze associations between PRP and electrographic seizures (ES) in critically ill children. METHODS This was a prospective observational study of consecutive critically ill children undergoing continuous electroencephalographic monitoring (CEEG). PRP were identified by one electroencephalographer, and then two pediatric electroencephalographers independently scored the first 1-h epoch that contained PRP using standardized terminology. We determined the incidence of PRPs, evaluated interrater agreement between electroencephalographers scoring PRP, and evaluated associations between PRP and ES. RESULTS One thousand three hundred ninety-nine patients underwent CEEG. ES occurred in 345 (25%) subjects. PRP, ES + PRP, and ictal-interictal continuum (IIC) patterns occurred in 142 (10%), 81 (6%), and 93 (7%) subjects, respectively. The most common PRP were generalized periodic discharges (GPD; 43, 30%), lateralized periodic discharges (LPD; 34, 24%), generalized rhythmic delta activity (GRDA; 34, 24%), bilateral independent periodic discharges (BIPD; 14, 10%), and lateralized rhythmic delta activity (LRDA; 11, 8%). ES risk varied by PRP type (p < .01). ES occurrence was associated with GPD (odds ratio [OR] = 6.35, p < .01), LPD (OR = 10.45, p < .01), BIPD (OR = 6.77, p < .01), and LRDA (OR = 6.58, p < .01). Some modifying features increased the risk of ES for each of those PRP. GRDA was not significantly associated with ES (OR = 1.34, p = .44). Each of the IIC patterns was associated with ES (OR = 6.83-8.81, p < .01). ES and PRP occurred within 6 h (before or after) in 45 (56%) subjects. SIGNIFICANCE PRP occurred in 10% of critically ill children who underwent CEEG. The most common patterns were GPD, LPD, GRDA, BIPD, and LRDA. The GPD, LPD, BIPD, LRDA, and IIC patterns were associated with ES. GRDA was not associated with ES.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Shavonne L Massey
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Mark P Fitzgerald
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Sudha K Kessler
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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Abstract
SUMMARY Generalized periodic patterns with triphasic wave morphology, long referred to as triphasic waves [TWs], had been associated with metabolic encephalopathies, although other neurologic and systemic causes have since been identified. In a recent classification of periodic patterns, TWs were formally grouped with the generalized periodic discharges, which are often associated with ictal activity. The interpretation of generalized periodic patterns with TWs as nonictal can have significant implications in the management of comatose patients in nonconvulsive status epilepticus. Electrographic characteristics that help distinguish nonictal periodic patterns with TWs from generalized periodic discharge ictal patterns include (1) TWs in long runs of periodic bilaterally synchronous and symmetric discharges, maximal in frontocentral or posterior head regions with and without a frontal-to-occipital lag or posterior-to-anterior lag, respectively; (2) recurrent spontaneous and/or low-dose benzodiazepine-induced attenuation and/or suppression of the periodic pattern and replacement with a diffuse slow wave activity throughout a prolonged EEG recording; and (3) stimulation-induced activation and/or increase in frequency and/or organization of TWs. We coined the term of status triphasicus to describe the electrographic periodic pattern of TWs with these three distinct characteristics. In this article, we discuss the advantages and limitations of keeping the status triphasicus pattern as a distinct electrographic entity different from periodic ictal generalized periodic discharge patterns. We discuss the circumstances in which a status triphasicus pattern can be associated with ictal activity and propose a simple pragmatic classification of status triphasicus that encompasses the different clinical scenarios it can be associated with.
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Affiliation(s)
- Manuel M Bicchi
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida, U.S.A.; and
- Neurology Services, University of Miami Hospital and Clinics and Jackson Memorial Hospital, Miami, Florida, U.S.A
| | - Ayham Alkhachroum
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida, U.S.A.; and
- Neurology Services, University of Miami Hospital and Clinics and Jackson Memorial Hospital, Miami, Florida, U.S.A
| | - Andres M Kanner
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida, U.S.A.; and
- Neurology Services, University of Miami Hospital and Clinics and Jackson Memorial Hospital, Miami, Florida, U.S.A
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Kural MA, Fabricius M, Christensen J, Kaplan PW, Beniczky S. Triphasic Waves Are Generated by Widespread Bilateral Cortical Networks. J Clin Neurophysiol 2021; 38:415-419. [PMID: 32852286 DOI: 10.1097/wnp.0000000000000770] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Triphasic waves (TWs) have been observed in the EEG recorded in patients with various types of encephalopathy, yet their genesis and significance is still debated. The aim of this study was to elucidate the localization of the cortical generators of TWs using EEG source imaging. METHODS In 20 consecutive patients who had encephalopathy with TWs, EEG source imaging of the first negative and the positive phases of the TW was performed. Three different approaches were used: equivalent current dipoles, a distributed source model, and a recently described spatial filtration method for visualizing EEG in source space. RESULTS Equivalent current dipole models failed to provide valid solutions. The distributed source model and the spatial filtration method suggested that TWs were generated by large, bilateral cortical networks, invariably involving the anterior frontal and the temporo-polar areas. CONCLUSIONS Source imaging localized TWs to anterior frontal and temporo-frontal structures. Involvement of these regions is consistent with the typical pathophysiological changes of altered consciousness and cognitive changes observed in patients with TW encephalopathy.
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Affiliation(s)
- Mustafa Aykut Kural
- Departments of Clinical Neurophysiology and
- Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Martin Fabricius
- Department of Clinical Neurophysiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Jakob Christensen
- Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Peter W Kaplan
- Department of Neurology, Johns Hopkins Bayview Medical Center, Baltimore, Maryland, U.S.A.; and
| | - Sándor Beniczky
- Departments of Clinical Neurophysiology and
- Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark
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Scheuer ML, Wilson SB, Antony A, Ghearing G, Urban A, Bagić AI. Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset. J Clin Neurophysiol 2021; 38:439-447. [PMID: 32472781 PMCID: PMC8404956 DOI: 10.1097/wnp.0000000000000709] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE To compare the seizure detection performance of three expert humans and two computer algorithms in a large set of epilepsy monitoring unit EEG recordings. METHODS One hundred twenty prolonged EEGs, 100 containing clinically reported EEG-evident seizures, were evaluated. Seizures were marked by the experts and algorithms. Pairwise sensitivity and false-positive rates were calculated for each human-human and algorithm-human pair. Differences in human pairwise performance were calculated and compared with the range of algorithm versus human performance differences as a type of statistical modified Turing test. RESULTS A total of 411 individual seizure events were marked by the experts in 2,805 hours of EEG. Mean, pairwise human sensitivities and false-positive rates were 84.9%, 73.7%, and 72.5%, and 1.0, 0.4, and 1.0/day, respectively. Only the Persyst 14 algorithm was comparable with humans-78.2% and 1.0/day. Evaluation of pairwise differences in sensitivity and false-positive rate demonstrated that Persyst 14 met statistical noninferiority criteria compared with the expert humans. CONCLUSIONS Evaluating typical prolonged EEG recordings, human experts had a modest level of agreement in seizure marking and low false-positive rates. The Persyst 14 algorithm was statistically noninferior to the humans. For the first time, a seizure detection algorithm and human experts performed similarly.
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Affiliation(s)
- Mark L. Scheuer
- Persyst Development Corporation, Solana Beach, California, U.S.A
| | - Scott B. Wilson
- Persyst Development Corporation, Solana Beach, California, U.S.A
| | - Arun Antony
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, U.S.A.; and
| | - Gena Ghearing
- Department of Neurology, University of Iowa, Iowa City, Iowa, U.S.A
| | - Alexandra Urban
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, U.S.A.; and
| | - Anto I. Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, U.S.A.; and
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Beniczky S, Asadi-Pooya AA, Perucca E, Rubboli G, Tartara E, Meritam Larsen P, Ebrahimi S, Farzinmehr S, Rampp S, Sperling MR. A web-based algorithm to rapidly classify seizures for the purpose of drug selection. Epilepsia 2021; 62:2474-2484. [PMID: 34420206 DOI: 10.1111/epi.17039] [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: 05/04/2021] [Revised: 07/22/2021] [Accepted: 08/02/2021] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To develop and validate a pragmatic algorithm that classifies seizure types, to facilitate therapeutic decision-making. METHODS Using a modified Delphi method, five experts developed a pragmatic classification of nine types of epileptic seizures or combinations of seizures that influence choice of medication, and constructed a simple algorithm, freely available on the internet. The algorithm consists of seven questions applicable to patients with seizure onset at the age of 10 years or older. Questions to screen for nonepileptic attacks were added. Junior physicians, nurses, and physician assistants applied the algorithm to consecutive patients in a multicenter prospective validation study (ClinicalTrials.gov identifier: NCT03796520). The reference standard was the seizure classification by expert epileptologists, based on all available data, including electroencephalogram (EEG), video-EEG monitoring, and neuroimaging. In addition, physicians working in underserved areas assessed the feasibility of using the web-based algorithm in their clinical setting. RESULTS A total of 262 patients were assessed, of whom 157 had focal, 51 had generalized, and 10 had unknown onset epileptic seizures, and 44 had nonepileptic paroxysmal events. Agreement between the algorithm and the expert classification was 83.2% (95% confidence interval = 78.6%-87.8%), with an agreement coefficient (AC1) of .82 (95% confidence interval = .77-.87), indicating almost perfect agreement. Thirty-two health care professionals from 14 countries evaluated the feasibility of the web-based algorithm in their clinical setting, and found it applicable and useful for their practice (median = 6.5 on 7-point Likert scale). SIGNIFICANCE The web-based algorithm provides an accurate classification of seizure types, which can be used for selecting antiseizure medications in adolescents and adults.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Ali A Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.,Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Emilio Perucca
- Division of Clinical and Experimental Pharmacology, Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.,Clinical Trial Center, Istituto Neurologico Nazionale a Carattere Scientific Mondino Foundation Pavia, Pavia, Italy
| | - Guido Rubboli
- Department of Neurology, Danish Epilepsy Center, Dianalund, Denmark.,University of Copenhagen, Copenhagen, Denmark
| | - Elena Tartara
- Regional Epilepsy Center, IRCCS Mondino Foundation Pavia, Pavia, Italy
| | | | - Saqar Ebrahimi
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Somayeh Farzinmehr
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany.,Department of Neurosurgery, University Hospital Halle, Halle, Germany
| | - Michael R Sperling
- Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
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Prospective evaluation of interrater agreement between EEG technologists and neurophysiologists. Sci Rep 2021; 11:13406. [PMID: 34183718 PMCID: PMC8238944 DOI: 10.1038/s41598-021-92827-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 06/16/2021] [Indexed: 11/22/2022] Open
Abstract
We aim to prospectively investigate, in a large and heterogeneous population, the electroencephalogram (EEG)-reading performances of EEG technologists. A total of 8 EEG technologists and 5 certified neurophysiologists independently analyzed 20-min EEG recordings. Interrater agreement (IRA) for predefined EEG pattern identification between EEG technologists and neurophysiologits was assessed using percentage of agreement (PA) and Gwet-AC1. Among 1528 EEG recordings, the PA [95% confidence interval] and interrater agreement (IRA, AC1) values were as follows: status epilepticus (SE) and seizures, 97% [96–98%], AC1 kappa = 0.97; interictal epileptiform discharges, 78% [76–80%], AC1 = 0.63; and conclusion dichotomized as “normal” versus “pathological”, 83.6% [82–86%], AC1 = 0.71. EEG technologists identified SE and seizures with 99% [98–99%] negative predictive value, whereas the positive predictive values (PPVs) were 48% [34–62%] and 35% [20–53%], respectively. The PPV for normal EEGs was 72% [68–76%]. SE and seizure detection were impaired in poorly cooperating patients (SE and seizures; p < 0.001), intubated and older patients (SE; p < 0.001), and confirmed epilepsy patients (seizures; p = 0.004). EEG technologists identified ictal features with few false negatives but high false positives, and identified normal EEGs with good PPV. The absence of ictal features reported by EEG technologists can be reassuring; however, EEG traces should be reviewed by neurophysiologists before taking action.
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Rossetti AO, Schindler K, Sutter R, Rüegg S, Zubler F, Novy J, Oddo M, Warpelin-Decrausaz L, Alvarez V. Continuous vs Routine Electroencephalogram in Critically Ill Adults With Altered Consciousness and No Recent Seizure: A Multicenter Randomized Clinical Trial. JAMA Neurol 2021; 77:1225-1232. [PMID: 32716479 PMCID: PMC7385681 DOI: 10.1001/jamaneurol.2020.2264] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Question In patients with acute consciousness impairment and no recent seizures, does continuous electroencephalogram (cEEG) correlate with reduced mortality compared with repeated routine EEG (rEEG)? Findings In this pragmatic, multicenter randomized clinical trial analyzing 364 adults, cEEG translated into a higher rate of seizures/status epilepticus detection and antiseizure treatment modifications but did not improve mortality compared with rEEG. Meaning Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources. Importance In critically ill patients with altered consciousness, continuous electroencephalogram (cEEG) improves seizure detection, but is resource-consuming compared with routine EEG (rEEG). It is also uncertain whether cEEG has an effect on outcome. Objective To assess whether cEEG is associated with reduced mortality compared with rEEG. Design, Setting, and Participants The pragmatic multicenter Continuous EEG Randomized Trial in Adults (CERTA) was conducted between 2017 and 2018, with follow-up of 6 months. Outcomes were assessed by interviewers blinded to interventions.The study took place at 4 tertiary hospitals in Switzerland (intensive and intermediate care units). Depending on investigators’ availability, we pragmatically recruited critically ill adults having Glasgow Coma Scale scores of 11 or less or Full Outline of Responsiveness score of 12 or less, without recent seizures or status epilepticus. They had cerebral (eg, brain trauma, cardiac arrest, hemorrhage, or stroke) or noncerebral conditions (eg, toxic-metabolic or unknown etiology), and EEG was requested as part of standard care. An independent physician provided emergency informed consent. Interventions Participants were randomized 1:1 to cEEG for 30 to 48 hours vs 2 rEEGs (20 minutes each), interpreted according to standardized American Clinical Neurophysiology Society guidelines. Main Outcomes and Measures Mortality at 6 months represented the primary outcome. Secondary outcomes included interictal and ictal features detection and change in therapy. Results We analyzed 364 patients (33% women; mean [SD] age, 63 [15] years). At 6 months, mortality was 89 of 182 in those with cEEG and 88 of 182 in those with rEEG (adjusted relative risk [RR], 1.02; 95% CI, 0.83-1.26; P = .85). Exploratory comparisons within subgroups stratifying patients according to age, premorbid disability, comorbidities on admission, deeper consciousness reduction, and underlying diagnoses revealed no significant effect modification. Continuous EEG was associated with increased detection of interictal features and seizures (adjusted RR, 1.26; 95% CI, 1.08-1.15; P = .004 and 3.37; 95% CI, 1.63-7.00; P = .001, respectively) and more frequent adaptations in antiseizure therapy (RR, 1.84; 95% CI, 1.12-3.00; P = .01). Conclusions and Relevance This pragmatic trial shows that in critically ill adults with impaired consciousness and no recent seizure, cEEG leads to increased seizure detection and modification of antiseizure treatment but is not related to improved outcome compared with repeated rEEG. Pending larger studies, rEEG may represent a valid alternative to cEEG in centers with limited resources. Trial Registration ClinicalTrials.gov Identifier: NCT03129438
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Affiliation(s)
- Andrea O Rossetti
- Department of Neurology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Kaspar Schindler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Raoul Sutter
- Clinic for Intensive Care Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.,Department of Neurology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Stephan Rüegg
- Department of Neurology, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Frédéric Zubler
- Sleep-Wake-Epilepsy-Center, Department of Neurology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Jan Novy
- Department of Neurology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Mauro Oddo
- Department of Intensive Care Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Loane Warpelin-Decrausaz
- Clinical Trial Unit, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Vincent Alvarez
- Department of Neurology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.,Department of Neurology, Hôpital du Valais, Sion, Switzerland
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Touchard C, Cartailler J, Vellieux G, de Montmollin E, Jaquet P, Wanono R, Reuter J, Para M, Bouadma L, Timsit JF, d'Ortho MP, Kubis N, Rouvel Tallec A, Sonneville R. Simplified frontal EEG in adults under veno-arterial extracorporeal membrane oxygenation. Ann Intensive Care 2021; 11:76. [PMID: 33987690 PMCID: PMC8119573 DOI: 10.1186/s13613-021-00854-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/12/2021] [Indexed: 12/04/2022] Open
Abstract
Background EEG-based prognostication studies in intensive care units often rely on a standard 21-electrode montage (stdEEG) requiring substantial human, technical, and financial resources. We here evaluate whether a simplified 4-frontal electrode montage (4-frontEEG) can detect EEG patterns associated with poor outcomes in adult patients under veno-arterial extracorporeal membrane oxygenation (VA-ECMO). Methods We conducted a reanalysis of EEG data from a prospective cohort on 118 adult patients under VA-ECMO, in whom EEG was performed on admission to intensive care. EEG patterns of interest included background rhythm, discontinuity, reactivity, and the Synek’s score. They were all reassessed by an intensivist on a 4-frontEEG montage, whose analysis was then compared to an expert’s interpretation made on stdEEG recordings. The main outcome measure was the degree of correlation between 4-frontEEG and stdEEG montages to identify EEG patterns of interest. The performance of the Synek scores calculated on 4-frontEEG and stdEEG montage to predict outcomes (i.e., 28-day mortality and 90-day Rankin score \documentclass[12pt]{minimal}
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\begin{document}$${\ge {4}}$$\end{document}≥4) was investigated in a secondary exploratory analysis. Results The detection of EEG patterns using 4-frontEEG was statistically similar to that of stdEEG for background rhythm (Spearman rank test, ρ = 0.66, p < 0.001), discontinuity (Cohen’s kappa, \documentclass[12pt]{minimal}
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\begin{document}$$\kappa$$\end{document}κ = 0.955), reactivity (\documentclass[12pt]{minimal}
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\begin{document}$$\kappa$$\end{document}κ = 0.739) and the Synek’s score (ρ = 0.794, p < 0.001). Using the Synek classification, we found similar performances between 4-frontEEG and stdEEG montages in predicting 28-day mortality (AUC 4-frontEEG 0.71, AUC stdEEG 0.68) and for 90-day poor neurologic outcome (AUC 4-frontEEG 0.71, AUC stdEEG 0.66). An exploratory analysis confirmed that the Synek scores determined by 4 or 21 electrodes were independently associated with 28-day mortality and poor 90-day functional outcome. Conclusion In adult patients under VA-ECMO, a simplified 4-frontal electrode EEG montage interpreted by an intensivist, detected common EEG patterns associated with poor outcomes, with a performance similar to that of a standard EEG montage interpreted by expert neurophysiologists. This simplified montage could be implemented as part of a multimodal evaluation for bedside prognostication. Supplementary Information The online version contains supplementary material available at 10.1186/s13613-021-00854-0.
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Affiliation(s)
- Cyril Touchard
- Department of Anesthesiology and Intensive Care, APHP, Lariboisière-Saint Louis Hospitals, 75010, Paris, France
| | - Jérôme Cartailler
- Department of Anesthesiology and Intensive Care, APHP, Lariboisière-Saint Louis Hospitals, 75010, Paris, France.,Inserm, UMRS-942, Paris Diderot University, Paris, France
| | - Geoffroy Vellieux
- Université de Paris, NeuroDiderot, Inserm, 75019, Paris, France.,Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Etienne de Montmollin
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Pierre Jaquet
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Ruben Wanono
- Université de Paris, NeuroDiderot, Inserm, 75019, Paris, France.,Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Jean Reuter
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Marylou Para
- Department of Cardiac Surgery, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Lila Bouadma
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Jean-François Timsit
- Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Marie-Pia d'Ortho
- Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Nathalie Kubis
- Laboratory for Vascular Translational Science, INSERM UMR1148, Team 6, Université de Paris, 75018, Paris, France.,Department of Clinical Physiology, APHP, Lariboisière - Saint Louis hospitals, DMU DREAM, 75010, Paris, France
| | - Anny Rouvel Tallec
- Université de Paris, NeuroDiderot, Inserm, 75019, Paris, France.,Department of Clinical Physiology, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France
| | - Romain Sonneville
- Laboratory for Vascular Translational Science, INSERM UMR1148, Team 6, Université de Paris, 75018, Paris, France. .,Department of Intensive Care Medicine and Infectious Diseases, AP-HP, Bichat-Claude Bernard Hospital, 75018, Paris, France.
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Caroyer S, Depondt C, Rikir E, Mavroudakis N, Peluso L, Silvio Taccone F, Legros B, Gaspard N. Assessment of a standardized EEG reactivity protocol after cardiac arrest. Clin Neurophysiol 2021; 132:1687-1693. [PMID: 34049028 DOI: 10.1016/j.clinph.2021.03.047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 02/02/2021] [Accepted: 03/08/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE Reactivity assessment during EEG might provide important prognostic information in post-anoxic coma. It is still unclear how best to perform reactivity testing and how it might be affected by hypothermia. Our primary aim was to determine and compare the effectiveness, inter-rater reliability and prognostic value of different types of stimulus for EEG reactivity testing, using a standardized stimulation protocol and standardized definitions. Our secondary aims were to assess the effect of hypothermia on these measures, and to determine the prognostic value of a simplified sequence with the three most efficient stimuli. METHODS Prospective single-center cohort of post-anoxic comatose patients admitted to the intensive care unit of an academic medical center between January 1, 2016 and December 31, 2018 and receiving continuous EEG monitoring (CEEG). Reactivity was assessed using standardized definitions and standardized sequence of stimuli: auditory (mild noise and loud noise), tactile (shaking), nociceptive (nostril tickling, trapezius muscle squeezing, endotracheal tube suctioning), and visual (passive eye opening). Gwet's AC1 and percent agreement (PA) were used to measure inter-rater agreement (IRA). Ability to predict favorable neurological outcome (defined as a Cerebral Performance Category of 1 to 2: no disability to moderate disability) was measured with sensitivity (Se), specificity (Sp), accuracy, and odds ratio [OR]. These were calculated for each stimulus type and at the level of the entire sequence comprising all the stimuli. RESULTS One-hundred and fifteen patients were included and 242 EEG epochs were analyzed. Loud noise, shaking and trapezius muscle squeezing most frequently elicited EEG reactivity (42%, 38% and 38%, respectively) but were all inferior to the entire sequence, which elicited reactivity in 58% cases. The IRA for reactivity to individual stimuli varied from moderate to good (AC1:58-69%; PA:56-68%) and was the highest for loud noise (AC1:69%; PA:68%), trapezius muscle squeezing (AC1:67%; PA:65%) and passive eye opening (AC1:68%; PA:64%). Mild (odds ratio [OR]:11.0; Se:70% and Sp:86%) and loud noises (OR:27.0; Se:73% and Sp:75%), and trapezius muscle squeezing (OR:15.3; Se:76% and Sp:83%) during hypothermia had the best predictive value for favorable neurological outcome, although each was inferior to the whole sequence (OR:60.2; Se:91% and Sp:73%). A simplified sequence of loud noise, shaking and trapezius muscle squeezing had the same performance for predicting neurological outcome as the entire sequence. Hypothermia did not significantly affect the effectiveness of stimulation, but IRA was slightly better during hypothermia, for all stimuli. Similarly, the predictive value was higher during hypothermia than during normothermia. CONCLUSIONS Despite a standardized stimulation protocol and standardized definitions, the IRA of EEG reactivity testing in post-anoxic comatose patients was only good at best (AC1 < 70%), and its predictive value for neurological outcome remained imperfect, in particular with Sp values < 90%. While no single stimulus appeared superior to others, a full sequence using all stimuli or a simplified sequence comprising loud noise, shaking and trapezius muscle squeezing had the best combination of IRA and predictive value. SIGNIFICANCE This study stresses the necessity to use multiple stimulus types to improve the predictive value of reactivity testing in post-anoxic coma and confirms that it is not affected by hypothermia.
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Affiliation(s)
- Sarah Caroyer
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Chantal Depondt
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Estelle Rikir
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Nicolas Mavroudakis
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Lorenzo Peluso
- Department of Intensive Care, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Fabio Silvio Taccone
- Department of Intensive Care, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Benjamin Legros
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium
| | - Nicolas Gaspard
- Department of Neurology, Université Libre de Bruxelles-Hôpital Erasme, Brussels, Belgium; Yale University Comprehensive Epilepsy Center and Computational Neurophysiology Laboratory New Haven, CT, USA.
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Peluso L, Gaspard N. Electroencephalography in post-cardiac arrest patients: a matter of timing? Minerva Anestesiol 2021; 87:637-639. [PMID: 33938681 DOI: 10.23736/s0375-9393.21.15715-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Lorenzo Peluso
- Department of Intensive Care, Cliniques Universitaires de Bruxelles - Erasme Hospital, Brussels, Belgium -
| | - Nicolas Gaspard
- Department of Neurology, Cliniques Universitaires de Bruxelles - Erasme Hospital, Brussels, Belgium.,Department of Neurology, Yale University Medical School, New Haven, CT, USA
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Lin L, Al‐Faraj A, Ayub N, Bravo P, Das S, Ferlini L, Karakis I, Lee JW, Mukerji SS, Newey CR, Pathmanathan J, Abdennadher M, Casassa C, Gaspard N, Goldenholz DM, Gilmore EJ, Jing J, Kim JA, Kimchi EY, Ladha HS, Tobochnik S, Zafar S, Hirsch LJ, Westover MB, Shafi MM. Electroencephalographic Abnormalities are Common in COVID-19 and are Associated with Outcomes. Ann Neurol 2021; 89:872-883. [PMID: 33704826 PMCID: PMC8104061 DOI: 10.1002/ana.26060] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 03/08/2021] [Accepted: 03/08/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. METHODS We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. RESULTS Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio [HR] 4.07 [1.44-11.51], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03-0.33] vs 0.43 [0.36-0.49]). INTERPRETATION This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes. ANN NEUROL 2021;89:872-883.
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Affiliation(s)
- Lu Lin
- Beth Israel Deaconess Medical Center, Department of NeurologyHarvard Medical SchoolBostonMA
| | | | - Neishay Ayub
- Massachusetts General Hospital, Department of NeurologyHarvard Medical SchoolBostonMA
| | - Pablo Bravo
- Department of NeurologyYale UniversityNew HavenCT
| | - Sudeshna Das
- Massachusetts General Hospital, Department of NeurologyHarvard Medical SchoolBostonMA
| | - Lorenzo Ferlini
- Hôspital Erasme, Département de NeurologieUniversité Libre de BruxellesBruxellesBelgium
| | | | - Jong Woo Lee
- Brigham and Women's Hospital, Department of NeurologyHarvard Medical SchoolBoston, MA
| | - Shibani S. Mukerji
- Massachusetts General Hospital, Department of NeurologyHarvard Medical SchoolBostonMA
| | | | | | | | - Charles Casassa
- Beth Israel Deaconess Medical Center, Department of NeurologyHarvard Medical SchoolBostonMA
| | - Nicolas Gaspard
- Department of NeurologyYale UniversityNew HavenCT
- Hôspital Erasme, Département de NeurologieUniversité Libre de BruxellesBruxellesBelgium
| | - Daniel M. Goldenholz
- Beth Israel Deaconess Medical Center, Department of NeurologyHarvard Medical SchoolBostonMA
| | | | - Jin Jing
- Massachusetts General Hospital, Department of NeurologyHarvard Medical SchoolBostonMA
| | | | - Eyal Y. Kimchi
- Massachusetts General Hospital, Department of NeurologyHarvard Medical SchoolBostonMA
| | | | - Steven Tobochnik
- Brigham and Women's Hospital, Department of NeurologyHarvard Medical SchoolBoston, MA
| | - Sahar Zafar
- Massachusetts General Hospital, Department of NeurologyHarvard Medical SchoolBostonMA
| | | | - M. Brandon Westover
- Massachusetts General Hospital, Department of NeurologyHarvard Medical SchoolBostonMA
| | - Mouhsin M. Shafi
- Beth Israel Deaconess Medical Center, Department of NeurologyHarvard Medical SchoolBostonMA
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Pharmacotherapy for Nonconvulsive Seizures and Nonconvulsive Status Epilepticus. Drugs 2021; 81:749-770. [PMID: 33830480 DOI: 10.1007/s40265-021-01502-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/13/2021] [Indexed: 12/22/2022]
Abstract
Most seizures in critically ill patients are nonconvulsive. A significant number of neurological and medical conditions can be complicated by nonconvulsive seizures (NCSs) and nonconvulsive status epilepticus (NCSE), with brain infections, hemorrhages, global hypoxia, sepsis, and recent neurosurgery being the most prominent etiologies. Prolonged NCSs and NCSE can lead to adverse neurological outcomes. Early recognition requires a high degree of suspicion and rapid and appropriate duration of continuous electroencephalogram (cEEG) monitoring. Although high quality research evaluating treatment with antiseizure medications and long-term outcome is still lacking, it is probable that expeditious pharmacological management of NCSs and NCSE may prevent refractoriness and further neurological injury. There is limited evidence on pharmacotherapy for NCSs and NCSE, although a few clinical trials encompassing both convulsive and NCSE have demonstrated similar efficacy of different intravenous (IV) antiseizure medications (ASMs), including levetiracetam, valproate, lacosamide and fosphenytoin. The choice of specific ASMs lies on tolerability and safety since critically ill patients frequently have impaired renal and/or hepatic function as well as hematological/hemodynamic lability. Treatment frequently requires more than one ASM and occasionally escalation to IV anesthetic drugs. When multiple ASMs are required, combining different mechanisms of action should be considered. There are several enteral ASMs that could be used when IV ASM options have been exhausted. Refractory NCSE is not uncommon, and its treatment requires a very judicious selection of ASMs aiming at reducing seizure burden along with management of the underlying condition.
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Du L, Zheng K, Feng L, Cao Y, Niu Z, Song Z, Liu Z, Liu X, Xiang X, Zhou Q, Xiong H, Chen F, Zhang G, Ma Q. The first national survey on practices of neurological prognostication after cardiac arrest in China, still a lot to do. Int J Clin Pract 2021; 75:e13759. [PMID: 33098255 DOI: 10.1111/ijcp.13759] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 10/04/2020] [Indexed: 02/05/2023] Open
Abstract
AIMS To investigate current awareness and practices of neurological prognostication in comatose cardiac arrest (CA) patients. METHODS An anonymous questionnaire was distributed to 1600 emergency physicians in 75 hospitals which were selected randomly from China between January and July 2018. RESULTS 92.1% respondents fulfilled the survey. The predictive value of brain stem reflex, motor response and myoclonus was confirmed by 63.5%, 44.6% and 31.7% respondents, respectively. Only 30.7% knew that GWR value < 1.1 indicated poor prognosis and only 8.1% know the most commonly used SSEP N20. Status epilepticus, burst suppression and suppression were considered to predict poor outcome by only 35.0%, 27.4% and 20.9% respondents, respectively. Only 46.7% knew NSE and only 24.7% knew S-100. Only a few respondents knew that neurological prognostication should be performed later than 72 hours from CA either in TTM or non-TTM patients. In practice, the most commonly used method was clinical examination (85.4%). Only 67.9% had used brain CT for prognosis and 18.4% for MRI. NSE (39.6%) was a little more widely used than S-100β (18.0%). However, SSEP (4.4%) and EEG (11.4%) were occasionally performed. CONCLUSIONS Neurological prognostication in CA survivors had not been well understood and performed by emergency physicians in China. They were more likely to use clinical examination rather than objective tools, especially SSEP and EEG, which also illustrated that multimodal approach was not well performed in practice.
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Affiliation(s)
- Lanfang Du
- Emergency Department, The Peking University Third Hospital, Beijing, China
| | - Kang Zheng
- Emergency Department, The Peking University Third Hospital, Beijing, China
| | - Lu Feng
- Emergency Department, The Peking University Third Hospital, Beijing, China
| | - Yu Cao
- Emergency Department, West China Hospital, Chengdu City, China
| | - Zhendong Niu
- Emergency Department, West China Hospital, Chengdu City, China
| | - Zhenju Song
- Emergency Department, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhi Liu
- Emergency Department, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xiaowei Liu
- Emergency Department, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xudong Xiang
- Emergency Department, Second Xiangya Hospital, Central South University, Changsha, China
| | - Qidi Zhou
- Emergency Department, Peking University Shenzhen Hospital, Shenzhen City, China
| | - Hui Xiong
- Emergency Department, Peking University First Hospital, Beijing, China
| | - Fengying Chen
- Emergency Department, The Affiliated Hospital of Innor Mongolia Medical University, Huherhaote City, China
| | - Guoqiang Zhang
- Emergency Department, China-Japan Friendship Hospital, Beijing, China
| | - Qingbian Ma
- Emergency Department, The Peking University Third Hospital, Beijing, China
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Abstract
Continuous video-EEG (cEEG, lasting hours to several days) is increasingly used in ICU patients, as it is more sensitive than routine video-EEG (rEEG, lasting 20-30 min) to detect seizures or status epilepticus, and allows more frequent changes in therapeutic regimens. However, cEEG is more resource-consuming, and its relationship to outcome compared to repeated rEEG has only been formally assessed very recently in a randomized controlled trial, which did not show any significant difference in terms of long-term mortality or functional outcome. Awaiting more refined trials, it seems therefore that using repeated rEEG in ICU patients may represent a reasonable alternative in resource-limited settings. Prolonged EEG has been used recently in patients with severe COVID-19 infection, the proportion of seizures seems albeit relatively low, and similar to ICU patients with medical conditions. As in any case a timely EEG recording is recommended in the ICU, r ecent technical developments may ease its use in clinical practice.
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Affiliation(s)
- Andrea O Rossetti
- Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland -
| | - Jong W Lee
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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46
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Ahrens S, Twanow JD, Vidaurre J, Gedela S, Moore-Clingenpeel M, Ostendorf AP. Electroencephalography Technologist Inter-rater Agreement and Interpretation of Pediatric Critical Care Electroencephalography. Pediatr Neurol 2021; 115:66-71. [PMID: 33333462 PMCID: PMC7856064 DOI: 10.1016/j.pediatrneurol.2020.10.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/11/2020] [Accepted: 10/27/2020] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Electroencephalography (EEG) technologists commonly screen continuous EEG. Until now, the inter-rater agreement or sensitivity for important EEG findings has been unknown in this group. METHODS Twenty-nine EEG technologists and three clinical neurophysiologists interpreted 90 five-minute samples of pediatric critical care EEG. Inter-rater agreement was examined with Cohen's kappa and Fleiss' kappa for EEG findings. A gold-standard consensus agreement was developed for examining sensitivity and specificity for seizures or discontinuity. Kruskal-Wallis tests with Benjamani-Hochberg corrections for multiple comparisons were utilized to examine associations between correct scoring and certification status and years of experience. RESULTS Aggregate agreement was moderate for seizures and fair for EEG background continuity among EEG technologists. Individual agreement for seizures and continuity varied from slight to substantial. For individual EEG technologists, sensitivity for seizures ranged from 44 to 93% and sensitivity for continuity ranged from 81 to 100%. Raters with Certified Long Term Monitoring credentials were more likely to identify seizures correctly. SIGNIFICANCE This is the first study to evaluate inter-rater agreement and interpretation correctness among EEG technologists interpreting pediatric critical care EEG. EEG technologists demonstrated better aggregate agreement for seizure detection than other EEG findings, yet individual results and internal consistency varied widely. These data provide important insight into the common practice of utilizing EEG technologists for screening critical care EEG.
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Affiliation(s)
- Stephanie Ahrens
- Division of Neurology, Department of Pediatrics, The Ohio State University and Nationwide Children's Hospital, Columbus, Ohio.
| | - Jaime D Twanow
- Division of Neurology, Department of Pediatrics, The Ohio State University and Nationwide Children's Hospital, Columbus, Ohio
| | - Jorge Vidaurre
- Division of Neurology, Department of Pediatrics, The Ohio State University and Nationwide Children's Hospital, Columbus, Ohio
| | - Satyanarayana Gedela
- Division of Neurology, Department of Pediatrics, Emory University and Children's Healthcare of Atlanta, Atlanta, Georgia
| | - Melissa Moore-Clingenpeel
- Division of Critical Care Medicine, Department of Pediatrics, Biostatistics Core, The Research Institute, The Ohio State University and Nationwide Children's Hospital, Columbus, Ohio
| | - Adam P Ostendorf
- Division of Neurology, Department of Pediatrics, The Ohio State University and Nationwide Children's Hospital, Columbus, Ohio
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Hirsch LJ, Fong MW, Leitinger M, LaRoche SM, Beniczky S, Abend NS, Lee JW, Wusthoff CJ, Hahn CD, Westover MB, Gerard EE, Herman ST, Haider HA, Osman G, Rodriguez-Ruiz A, Maciel CB, Gilmore EJ, Fernandez A, Rosenthal ES, Claassen J, Husain AM, Yoo JY, So EL, Kaplan PW, Nuwer MR, van Putten M, Sutter R, Drislane FW, Trinka E, Gaspard N. American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2021 Version. J Clin Neurophysiol 2021; 38:1-29. [PMID: 33475321 PMCID: PMC8135051 DOI: 10.1097/wnp.0000000000000806] [Citation(s) in RCA: 360] [Impact Index Per Article: 120.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Lawrence J. Hirsch
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - Michael W.K. Fong
- Westmead Comprehensive Epilepsy Unit, Westmead Hospital, University of Sydney, Sydney, Australia
| | - Markus Leitinger
- Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria
| | - Suzette M. LaRoche
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund and Aarhus University Hospital, Aarhus, Denmark
| | - Nicholas S. Abend
- Departments of Neurology and Pediatrics, The Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A
| | - Jong Woo Lee
- Brigham and Women’s Hospital, Boston, Massachusetts, U.S.A
| | | | - Cecil D. Hahn
- Division of Neurology, The Hospital for Sick Children, and Department of Pediatrics, University of Toronto, Toronto, Canada
| | | | - Elizabeth E. Gerard
- Comprehensive Epilepsy Center, Department of Neurology, Northwestern University, Chicago, Illinois, U.S.A
| | | | - Hiba Arif Haider
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Gamaleldin Osman
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, U.S.A
| | - Andres Rodriguez-Ruiz
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, U.S.A
| | - Carolina B. Maciel
- Division of Neurocritical Care, Department of Neurology, University of Florida, Gainesville, Florida, U.S.A
| | - Emily J. Gilmore
- Comprehensive Epilepsy Center, Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, U.S.A
| | - Andres Fernandez
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania, U.S.A
| | - Eric S. Rosenthal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, U.S.A
| | - Jan Claassen
- Neurocritical Care, Department of Neurology, Columbia University, New York, New York, U.S.A
| | - Aatif M. Husain
- Department of Medicine (Neurology), Duke University Medical Center, and Veterans Affairs Medical Center, Durham, North Carolina, U.S.A
| | - Ji Yeoun Yoo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A
| | - Elson L. So
- Division of Epilepsy, Mayo Clinic, Rochester, Minnesota, U.S.A
| | - Peter W. Kaplan
- Department of Neurology, Johns Hopkins University School of Medicine, Johns Hopkins Bayview Medical Center, Baltimore, Maryland, U.S.A
| | - Marc R. Nuwer
- Department of Neurology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California, U.S.A
| | - Michel van Putten
- Medisch Spectrum Twente and University of Twente, Enschede, The Netherlands
| | - Raoul Sutter
- Medical Intensive Care Units and Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Frank W. Drislane
- Department of Neurology, Harvard Medical School, and Comprehensive Epilepsy Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts, U.S.A
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria
| | - Nicolas Gaspard
- Department of Neurology, Université Libre de Bruxelles, Hôpital Erasme, Brussels, Belgium
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Kommentar zur Intensiv-EEG-Klassifikation der Amerikanischen Gesellschaft
für Klinische Neurophysiologie. KLIN NEUROPHYSIOL 2020. [DOI: 10.1055/a-1250-6275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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49
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Beniczky S, Rampp S, Asadi-Pooya AA, Rubboli G, Perucca E, Sperling MR. Optimal choice of antiseizure medication: Agreement among experts and validation of a web-based decision support application. Epilepsia 2020; 62:220-227. [PMID: 33280100 DOI: 10.1111/epi.16763] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/26/2020] [Accepted: 10/27/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Optimal choice of antiseizure medication (ASM) depends on seizure type, syndrome, age, gender, comorbidities and co-medications. There are no fixed rules on how to weigh these factors; choices are subjective and experience-driven. We investigated agreement among experts in selecting ASM as monotherapy and used their prevailing choices to validate a web-based decision-support application. METHODS Twenty-four international experts, blinded to the app, selected the optimal ASM for 25 individual patient-cases covering a wide variation of seizure types and other factors influencing ASM selection. The app ranked ASMs in order of likely appropriateness for each case. In a second step, experts rated anonymously the choices of the app. RESULTS Of the 25 patient-cases (age 13-74 years), 13 were female, 18 (72%) had comorbidities, six (24%) were on contraceptives, and 13 (52%) had other co-medications. The median number of experts who selected the same ASM for a given case was 15 (62.5%) and interquartile range (IQR) 13-18 (54%-75%). Gwet's agreement coefficient among experts was 0.38 (95% confidence interval [CI] 0.32-0.44), corresponding to a "fair" agreement. Agreement between the app and the prevailing expert choice for each case was 0.48 (95% CI 0.29-0.67), corresponding to a "moderate" beyond chance agreement. The percent agreement between the highest ranked selections of the app and the expert selections was 73% (95% CI 64%-82%). Ninety-five percent of the experts considered that no incorrect or potentially harmful ASMs were ranked highest by the app, and most experts strongly agreed with the app's selections. SIGNIFICANCE This app, now validated by experts, provides an objective, reproducible method for selecting ASM that accounts for relevant clinical features. It is freely available at: https://epipick.org.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre (Member of the ERN EpiCARE), Dianalund, Denmark.,Department of Clinical Medicine, Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus University, Aarhus, Denmark
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany.,Department of Neurosurgery, University Hospital Halle (Saale), Halle (Saale), Germany
| | - Ali A Asadi-Pooya
- Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.,Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Guido Rubboli
- Department of Neurology, Danish Epilepsy Centre (Member of the ERN EpiCARE), University of Copenhagen, Copenhagen, Denmark
| | - Emilio Perucca
- Division of Clinical and Experimental Pharmacology, Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy.,Clinical Trial Center, IRCCS Mondino Foundation (Member of the ERN EpiCARE), Pavia, Italy
| | - Michael R Sperling
- Department of Neurology, Jefferson Comprehensive Epilepsy Center, Thomas Jefferson University, Philadelphia, PA, USA
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50
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Jing J, d'Angremont E, Ebrahim S, Tabaeizadeh M, Ng M, Herlopian A, Dauwels J, Brandon Westover M. Rapid annotation of seizures and interictal-ictal-injury continuum EEG patterns. J Neurosci Methods 2020; 347:108956. [PMID: 33099261 DOI: 10.1016/j.jneumeth.2020.108956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 09/16/2020] [Accepted: 09/18/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Manual annotation of seizures and interictal-ictal-injury continuum (IIIC) patterns in continuous EEG (cEEG) recorded from critically ill patients is a time-intensive process for clinicians and researchers. In this study, we evaluated the accuracy and efficiency of an automated clustering method to accelerate expert annotation of cEEG. NEW METHOD We learned a local dictionary from 97 ICU patients by applying k-medoids clustering to 592 features in the time and frequency domains. We utilized changepoint detection (CPD) to segment the cEEG recordings. We then computed a bag-of-words (BoW) representation for each segment. We further clustered the segments by affinity propagation. EEG experts scored the resulting clusters for each patient by labeling only the cluster medoids. We trained a random forest classifier to assess validity of the clusters. RESULTS Mean pairwise agreement of 62.6% using this automated method was not significantly different from interrater agreements using manual labeling (63.8%), demonstrating the validity of the method. We also found that it takes experts using our method 5.31 ± 4.44 min to label the 30.19 ± 3.84 h of cEEG data, more than 45 times faster than unaided manual review, demonstrating efficiency. COMPARISON WITH EXISTING METHODS Previous studies of EEG data labeling have generally yielded similar human expert interrater agreements, and lower agreements with automated methods. CONCLUSIONS Our results suggest that long EEG recordings can be rapidly annotated by experts many times faster than unaided manual review through the use of an advanced clustering method.
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Affiliation(s)
- Jin Jing
- Massachusetts General Hospital, Boston, MA, United States; Nanyang Technological University, Singapore, Singapore
| | | | - Senan Ebrahim
- Massachusetts General Hospital, Boston, MA, United States
| | | | - Marcus Ng
- University of Manitoba, Winnipeg, MB, Canada
| | - Aline Herlopian
- Yale University School of Medicine, New Haven, CT, United States
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