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Fung FW, Carpenter JL, Chapman KE, Gallentine W, Giza CC, Goldstein JL, Hahn CD, Loddenkemper T, Matsumoto JH, Press CA, Riviello JJ, Abend NS. Survey of Pediatric ICU EEG Monitoring-Reassessment After a Decade. J Clin Neurophysiol 2024; 41:458-472. [PMID: 36930237 PMCID: PMC10504411 DOI: 10.1097/wnp.0000000000001006] [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: 03/18/2023] Open
Abstract
PURPOSE In 2011, the authors conducted a survey regarding continuous EEG (CEEG) utilization in critically ill children. In the interim decade, the literature has expanded, and guidelines and consensus statements have addressed CEEG utilization. Thus, the authors aimed to characterize current practice related to CEEG utilization in critically ill children. METHODS The authors conducted an online survey of pediatric neurologists from 50 US and 12 Canadian institutions in 2022. RESULTS The authors assessed responses from 48 of 62 (77%) surveyed institutions. Reported CEEG indications were consistent with consensus statement recommendations and included altered mental status after a seizure or status epilepticus, altered mental status of unknown etiology, or altered mental status with an acute primary neurological condition. Since the prior survey, there was a 3- to 4-fold increase in the number of patients undergoing CEEG per month and greater use of written pathways for ICU CEEG. However, variability in resources and workflow persisted, particularly regarding technologist availability, frequency of CEEG screening, communication approaches, and electrographic seizure management approaches. CONCLUSIONS Among the surveyed institutions, which included primarily large academic centers, CEEG use in pediatric intensive care units has increased with some practice standardization, but variability in resources and workflow were persistent.
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Affiliation(s)
- France W Fung
- Departments of Pediatrics and Neurology, Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - Jessica L Carpenter
- Departments of Pediatrics and Neurology, University of Maryland School of Medicine, Baltimore, Maryland, U.S.A
| | - Kevin E Chapman
- Division of Neurology, Phoenix Children's Hospital and University of Arizona School of Medicine Phoenix, Arizona, U.S.A
| | - William Gallentine
- Division of Neurology, Stanford University and Lucile Packard Children's Hospital, Palo Alto, California, U.S.A
| | - Christopher C Giza
- Division of Neurology, Department of Pediatrics, Mattel Children's Hospital and UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, U.S.A
| | - Joshua L Goldstein
- Division of Neurology, Children's Memorial Hospital and Northwestern University Feinberg School of Medicine, Chicago, Illinois, U.S.A
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children and University of Toronto, Toronto, U.S.A
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, U.S.A.; and
| | - Joyce H Matsumoto
- Division of Neurology, Department of Pediatrics, Mattel Children's Hospital and UCLA Brain Injury Research Center, Department of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, U.S.A
| | - Craig A Press
- Departments of Pediatrics and Neurology, Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - James J Riviello
- Division of Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, U.S.A
| | - Nicholas S Abend
- Departments of Pediatrics and Neurology, Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A
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Fung FW, Parikh DS, Donnelly M, Jacobwitz M, Topjian AA, Xiao R, Abend NS. EEG Monitoring in Critically Ill Children: Establishing High-Yield Subgroups. J Clin Neurophysiol 2024; 41:305-311. [PMID: 36893385 PMCID: PMC10492893 DOI: 10.1097/wnp.0000000000000995] [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: 03/11/2023] Open
Abstract
PURPOSE Continuous EEG monitoring (CEEG) is increasingly used to identify electrographic seizures (ES) in critically ill children, but it is resource intense. We aimed to assess how patient stratification by known ES risk factors would impact CEEG utilization. METHODS This was a prospective observational study of critically ill children with encephalopathy who underwent CEEG. We calculated the average CEEG duration required to identify a patient with ES for the full cohort and subgroups stratified by known ES risk factors. RESULTS ES occurred in 345 of 1,399 patients (25%). For the full cohort, an average of 90 hours of CEEG would be required to identify 90% of patients with ES. If subgroups of patients were stratified by age, clinically evident seizures before CEEG initiation, and early EEG risk factors, then 20 to 1,046 hours of CEEG would be required to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation and EEG risk factors present in the initial hour of CEEG required only 20 (<1 year) or 22 (≥1 year) hours of CEEG to identify a patient with ES. Conversely, patients with no clinically evident seizures before CEEG initiation and no EEG risk factors in the initial hour of CEEG required 405 (<1 year) or 1,046 (≥1 year) hours of CEEG to identify a patient with ES. Patients with clinically evident seizures before CEEG initiation or EEG risk factors in the initial hour of CEEG required 29 to 120 hours of CEEG to identify a patient with ES. CONCLUSIONS Stratifying patients by clinical and EEG risk factors could identify high- and low-yield subgroups for CEEG by considering ES incidence, the duration of CEEG required to identify ES, and subgroup size. This approach may be critical for optimizing CEEG resource allocation.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphi||a, Pennsylvania, U.S.A
- Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.; and
| | - Rui Xiao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, U.S.A
- Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A.; and
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, U.S.A
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Fung FW, Parikh DS, Walsh K, Fitzgerald MP, Massey SL, Topjian AA, Abend NS. Late-Onset Findings During Extended EEG Monitoring Are Rare in Critically Ill Children. J Clin Neurophysiol 2024:00004691-990000000-00131. [PMID: 38687298 DOI: 10.1097/wnp.0000000000001083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024] Open
Abstract
PURPOSE Electrographic seizures (ES) are common in critically ill children undergoing continuous EEG (CEEG) monitoring, and previous studies have aimed to target limited CEEG resources to children at highest risk of ES. However, previous studies have relied on observational data in which the duration of CEEG was clinically determined. Thus, the incidence of late occurring ES is unknown. The authors aimed to assess the incidence of ES for 24 hours after discontinuation of clinically indicated CEEG. METHODS This was a single-center prospective study of nonconsecutive children with acute encephalopathy in the pediatric intensive care unit who underwent 24 hours of extended research EEG after the end of clinical CEEG. The authors assessed whether there were new findings that affected clinical management during the extended research EEG, including new-onset ES. RESULTS Sixty-three subjects underwent extended research EEG. The median duration of the extended research EEG was 24.3 hours (interquartile range 24.0-25.3). Three subjects (5%) had an EEG change during the extended research EEG that resulted in a change in clinical management, including an increase in ES frequency, differential diagnosis of an event, and new interictal epileptiform discharges. No subjects had new-onset ES during the extended research EEG. CONCLUSIONS No subjects experienced new-onset ES during the 24-hour extended research EEG period. This finding supports observational data that patients with late-onset ES are rare and suggests that ES prediction models derived from observational data are likely not substantially underrepresenting the incidence of late-onset ES after discontinuation of clinically indicated CEEG.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Kathleen Walsh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Mark P Fitzgerald
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Shavonne L Massey
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA; and
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
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Coleman K, Fung FW, Topjian A, Abend NS, Xiao R. Optimizing EEG monitoring in critically ill children at risk for electroencephalographic seizures. Seizure 2024; 117:244-252. [PMID: 38522169 DOI: 10.1016/j.seizure.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/06/2024] [Accepted: 03/19/2024] [Indexed: 03/26/2024] Open
Abstract
OBJECTIVE Strategies are needed to optimally deploy continuous EEG monitoring (CEEG) for electroencephalographic seizure (ES) identification and management due to resource limitations. We aimed to construct an efficient multi-stage prediction model guiding CEEG utilization to identify ES in critically ill children using clinical and EEG covariates. METHODS The largest prospective single-center cohort of 1399 consecutive children undergoing CEEG was analyzed. A four-stage model was developed and trained to predict whether a subject required additional CEEG at the conclusion of each stage given their risk of ES. Logistic regression, elastic net, random forest, and CatBoost served as candidate methods for each stage and were evaluated using cross validation. An optimal multi-stage model consisting of the top-performing stage-specific models was constructed. RESULTS When evaluated on a test set, the optimal multi-stage model achieved a cumulative specificity of 0.197 and cumulative F1 score of 0.326 while maintaining a high minimum cumulative sensitivity of 0.938. Overall, 11 % of test subjects with ES were removed from the model due to a predicted low risk of ES (falsely negative subjects). CEEG utilization would be reduced by 32 % and 47 % compared to performing 24 and 48 h of CEEG in all test subjects, respectively. We developed a web application called EEGLE (EEG Length Estimator) that enables straightforward implementation of the model. CONCLUSIONS Application of the optimal multi-stage ES prediction model could either reduce CEEG utilization for patients at lower risk of ES or promote CEEG resource reallocation to patients at higher risk for ES.
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Affiliation(s)
- Kyle Coleman
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, United States
| | - France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, United States; Department of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, United States
| | - Alexis Topjian
- Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, United States
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, United States; Department of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, United States; Department of Anesthesia and Critical Care, University of Pennsylvania Perelman School of Medicine, United States; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, United States
| | - Rui Xiao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, United States; Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, United States.
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Fung FW, Parikh DS, Donnelly M, Xiao R, Topjian AA, Abend NS. Electrographic Seizure Characteristics and Electrographic Status Epilepticus Prediction. J Clin Neurophysiol 2024:00004691-990000000-00117. [PMID: 38194638 PMCID: PMC11231061 DOI: 10.1097/wnp.0000000000001068] [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] [Indexed: 01/11/2024] Open
Abstract
PURPOSE We aimed to characterize electrographic seizures (ES) and electrographic status epilepticus (ESE) and determine whether a model predicting ESE exclusively could effectively guide continuous EEG monitoring (CEEG) utilization in critically ill children. METHODS This was a prospective observational study of consecutive critically ill children with encephalopathy who underwent CEEG. We used descriptive statistics to characterize ES and ESE, and we developed a model for ESE prediction. RESULTS ES occurred in 25% of 1,399 subjects. Among subjects with ES, 23% had ESE, including 37% with continuous seizures lasting >30 minutes and 63% with recurrent seizures totaling 30 minutes within a 1-hour epoch. The median onset of ES and ESE occurred 1.8 and 0.18 hours after CEEG initiation, respectively. The optimal model for ESE prediction yielded an area under the receiver operating characteristic curves of 0.81. A cutoff selected to emphasize sensitivity (91%) yielded specificity of 56%. Given the 6% ESE incidence, positive predictive value was 11% and negative predictive value was 99%. If the model were applied to our cohort, then 53% of patients would not undergo CEEG and 8% of patients experiencing ESE would not be identified. CONCLUSIONS ESE was common, but most patients with ESE had recurrent brief seizures rather than long individual seizures. A model predicting ESE might only slightly improve CEEG utilization over models aiming to identify patients at risk for ES but would fail to identify some patients with ESE. Models identifying ES might be more advantageous for preventing ES from evolving into ESE.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, U.S.A
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, U.S.A
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, U.S.A
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, U.S.A
| | - Rui Xiao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, U.S.A
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, U.S.A.; and
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, U.S.A
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, U.S.A
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, U.S.A
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, U.S.A
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, U.S.A
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, U.S.A
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Benedetti GM, Guerriero RM, Press CA. Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG. Neurocrit Care 2023; 39:618-638. [PMID: 36949358 PMCID: PMC10033183 DOI: 10.1007/s12028-023-01686-5] [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: 07/28/2022] [Accepted: 01/30/2023] [Indexed: 03/24/2023]
Abstract
Critically ill children with acute neurologic dysfunction are at risk for a variety of complications that can be detected by noninvasive bedside neuromonitoring. Continuous electroencephalography (cEEG) is the most widely available and utilized form of neuromonitoring in the pediatric intensive care unit. In this article, we review the role of cEEG and the emerging role of quantitative EEG (qEEG) in this patient population. cEEG has long been established as the gold standard for detecting seizures in critically ill children and assessing treatment response, and its role in background assessment and neuroprognostication after brain injury is also discussed. We explore the emerging utility of both cEEG and qEEG as biomarkers of degree of cerebral dysfunction after specific injuries and their ability to detect both neurologic deterioration and improvement.
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Affiliation(s)
- Giulia M Benedetti
- Division of Pediatric Neurology, Department of Neurology, Seattle Children's Hospital and the University of Washington School of Medicine, Seattle, WA, USA.
- Division of Pediatric Neurology, Department of Pediatrics, C.S. Mott Children's Hospital and the University of Michigan, 1540 E Hospital Drive, Ann Arbor, MI, 48109-4279, USA.
| | - Rejéan M Guerriero
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Craig A Press
- Departments of Neurology and Pediatric, Children's Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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Fung FW, Fan J, Parikh DS, Vala L, Donnelly M, Jacobwitz M, Topjian AA, Xiao R, Abend NS. Validation of a Model for Targeted EEG Monitoring Duration in Critically Ill Children. J Clin Neurophysiol 2023; 40:589-599. [PMID: 35512186 PMCID: PMC9582115 DOI: 10.1097/wnp.0000000000000940] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Continuous EEG monitoring (CEEG) to identify electrographic seizures (ES) in critically ill children is resource intense. Targeted strategies could enhance implementation feasibility. We aimed to validate previously published findings regarding the optimal CEEG duration to identify ES in critically ill children. METHODS This was a prospective observational study of 1,399 consecutive critically ill children with encephalopathy. We validated the findings of a multistate survival model generated in a published cohort ( N = 719) in a new validation cohort ( N = 680). The model aimed to determine the CEEG duration at which there was <15%, <10%, <5%, or <2% risk of experiencing ES if CEEG were continued longer. The model included baseline clinical risk factors and emergent EEG risk factors. RESULTS A model aiming to determine the CEEG duration at which a patient had <10% risk of ES if CEEG were continued longer showed similar performance in the generation and validation cohorts. Patients without emergent EEG risk factors would undergo 7 hours of CEEG in both cohorts, whereas patients with emergent EEG risk factors would undergo 44 and 36 hours of CEEG in the generation and validation cohorts, respectively. The <10% risk of ES model would yield a 28% or 64% reduction in CEEG hours compared with guidelines recommending CEEG for 24 or 48 hours, respectively. CONCLUSIONS This model enables implementation of a data-driven strategy that targets CEEG duration based on readily available clinical and EEG variables. This approach could identify most critically ill children experiencing ES while optimizing CEEG use.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Jiaxin Fan
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Rui Xiao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
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8
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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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Harrar DB, Sun LR, Segal JB, Lee S, Sansevere AJ. Neuromonitoring in Children with Cerebrovascular Disorders. Neurocrit Care 2023; 38:486-503. [PMID: 36828980 DOI: 10.1007/s12028-023-01689-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/31/2023] [Indexed: 02/26/2023]
Abstract
BACKGROUND Cerebrovascular disorders are an important cause of morbidity and mortality in children. The acute care of a child with an ischemic or hemorrhagic stroke or cerebral sinus venous thrombosis focuses on stabilizing the patient, determining the cause of the insult, and preventing secondary injury. Here, we review the use of both invasive and noninvasive neuromonitoring modalities in the care of pediatric patients with arterial ischemic stroke, nontraumatic intracranial hemorrhage, and cerebral sinus venous thrombosis. METHODS Narrative review of the literature on neuromonitoring in children with cerebrovascular disorders. RESULTS Neuroimaging, near-infrared spectroscopy, transcranial Doppler ultrasonography, continuous and quantitative electroencephalography, invasive intracranial pressure monitoring, and multimodal neuromonitoring may augment the acute care of children with cerebrovascular disorders. Neuromonitoring can play an essential role in the early identification of evolving injury in the aftermath of arterial ischemic stroke, intracranial hemorrhage, or sinus venous thrombosis, including recurrent infarction or infarct expansion, new or recurrent hemorrhage, vasospasm and delayed cerebral ischemia, status epilepticus, and intracranial hypertension, among others, and this, is turn, can facilitate real-time adjustments to treatment plans. CONCLUSIONS Our understanding of pediatric cerebrovascular disorders has increased dramatically over the past several years, in part due to advances in the neuromonitoring modalities that allow us to better understand these conditions. We are now poised, as a field, to take advantage of advances in neuromonitoring capabilities to determine how best to manage and treat acute cerebrovascular disorders in children.
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Affiliation(s)
- Dana B Harrar
- Division of Neurology, Children's National Hospital, George Washington University School of Medicine, Washington, DC, USA.
| | - Lisa R Sun
- Divisions of Pediatric Neurology and Vascular Neurology, Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - J Bradley Segal
- Division of Child Neurology, Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Sarah Lee
- Division of Child Neurology, Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Arnold J Sansevere
- Division of Neurology, Children's National Hospital, George Washington University School of Medicine, Washington, DC, USA
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10
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Koster LK, Zamyadi R, Yan L, Payne ET, McBain KL, Dunkley BT, Hahn CD. Brain network properties of clinical versus subclinical seizures among critically ill children. Clin Neurophysiol 2023; 149:33-41. [PMID: 36878028 DOI: 10.1016/j.clinph.2023.02.160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 01/16/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023]
Abstract
OBJECTIVE Electrographic seizures are common among critically ill children, and have been associated with worse outcomes. Despite their often-widespread cortical representation, most of these seizures remain subclinical, a phenomenon which remains poorly understood. We compared the brain network properties of clinical versus subclinical seizures to gain insight into their relative potential deleterious effects. METHODS Functional connectivity (phase lag index) and graph measures (global efficiency and clustering coefficients) were computed for 2178 electrographic seizures recorded during 48-hours of 19-channel continuous EEG monitoring obtained in 20 comatose children. Frequency-specific group differences in clinical versus subclinical seizures were analyzed using a non-parametric ANCOVA, adjusting for age, sex, medication exposure, treatment intensity and seizures per subject. RESULTS Clinical seizures demonstrated greater functional connectivity than subclinical seizures at alpha frequencies, but less connectivity than subclinical seizures at delta frequencies. Clinical seizures also demonstrated significantly higher median global efficiency than subclinical seizures (p < 0.01), and significantly higher median clustering coefficients across all electrodes at alpha frequencies. CONCLUSIONS Clinical expression of seizures correlates with greater alpha synchronization of distributed brain networks. SIGNIFICANCE The stronger global and local alpha-mediated functional connectivity observed during clinical seizures may indicate greater pathological network recruitment. These observations motivate further studies to investigate whether the clinical expression of seizures may influence their potential to cause secondary brain injury.
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Affiliation(s)
- Laura K Koster
- Division of Neurology, The Hospital for Sick Children and Department of Paediatrics, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Rouzbeh Zamyadi
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Luowei Yan
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada
| | - Eric T Payne
- Department of Pediatrics, Section of Neurology, Alberta Children's Hospital and University of Calgary, Calgary, Canada
| | - Kristin L McBain
- MAP Centre for Urban Health Solutions, Unity Health Toronto, Toronto, Canada
| | - Benjamin T Dunkley
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada; Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children and Department of Paediatrics, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, Canada.
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11
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Sang T, Wang Y, Wu Y, Guan Q, Yang Z. VEEG monitoring and electrographic seizures in 232 pediatric patients in ICU at a tertiary hospital in China. Front Neurol 2022; 13:957465. [PMID: 36504668 PMCID: PMC9726868 DOI: 10.3389/fneur.2022.957465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/07/2022] [Indexed: 11/24/2022] Open
Abstract
Objectives To investigate neonatal electroencephalography (EEG) background activity and electrographic seizures in patients in the pediatric intensive care unit (PICU) who underwent bedside video-electroencephalography (vEEG) monitoring. Methods A total of 232 pediatric patients admitted or transferred to PICU that underwent vEEG monitoring were retrospectively enrolled in this study, and electrographic status epilepticus was observed after vEEG monitoring. Results The median age was 1.56 years [95% confidence interval (CI) = 1.12-2.44]. Electrographic seizures occurred in 88 patients (37.9%), out of which 36 cases (40.9%) had electrographic status epilepticus. Prior epileptic encephalopathy diagnosis [odds ratio (OR) = 6.57, 95% CI = 1.91-22.59, p = 0.003], interictal epileptiform discharges (OR = 46.82, 95%CI = 5.31-412.86, p = 0.0005), slow disorganized EEG background (OR = 11.92, 95%CI = 1.31-108.71, p = 0.028), and burst-suppression EEG background (OR = 23.64, 95%CI = 1.71-327.57, p = 0.018) were the risk factors for electrographic seizures' occurrence. Of the 232 patients, the condition of 179 (77.2%) patients improved and they were discharged, 34 cases (14.7%) were withdrawn, and 18 cases (7.8%) died. The in-hospital death rate was 47.6% (10 in 21 cases) in patients with attenuated/featureless, compared to 0/23 with normal EEG background. Conclusions Electrographic status epilepticus occurs in more than one-third of patients with electrographic seizures. vEEG is an efficient method to determine electrographic seizures in children. Abnormal EEG background activity is associated with both electrographic seizures' occurrence and unfavorable in-hospital outcomes.
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12
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The Role of Electroencephalography in the Prognostication of Clinical Outcomes in Critically Ill Children: A Review. CHILDREN 2022; 9:children9091368. [PMID: 36138677 PMCID: PMC9497701 DOI: 10.3390/children9091368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/30/2022] [Accepted: 09/02/2022] [Indexed: 11/16/2022]
Abstract
Electroencephalography (EEG) is a neurologic monitoring modality that allows for the identification of seizures and the understanding of cerebral function. Not only can EEG data provide real-time information about a patient’s clinical status, but providers are increasingly using these results to understand short and long-term prognosis in critical illnesses. Adult studies have explored these associations for many years, and now the focus has turned to applying these concepts to the pediatric literature. The aim of this review is to characterize how EEG can be utilized clinically in pediatric intensive care settings and to highlight the current data available to understand EEG features in association with functional outcomes in children after critical illness. In the evaluation of seizures and seizure burden in children, there is abundant data to suggest that the presence of status epilepticus during illness is associated with poorer outcomes and a higher risk of mortality. There is also emerging evidence indicating that poorly organized EEG backgrounds, lack of normal sleep features and lack of electrographic reactivity to clinical exams portend worse outcomes in this population. Prognostication in pediatric critical illness must be informed by the comprehensive evaluation of a patient’s clinical status but the utilization of EEG may help contribute to this assessment in a meaningful way.
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Bozarth XL, Ko PY, Bao H, Abend NS, Watson RS, Qu P, Dervan LA, Morgan LA, Wainwright M, McGuire JK, Novotny E. Use of Continuous EEG Monitoring and Short-Term Outcomes in Critically Ill Children. J Pediatr Intensive Care 2022. [DOI: 10.1055/s-0042-1749433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022] Open
Abstract
AbstractThis study aimed to compare short-term outcomes at pediatric intensive care unit (PICU) discharge in critically ill children with and without continuous electroencephalography (cEEG) monitoring. We retrospectively compared 234 patients who underwent cEEG with 2294 patients without cEEG. Propensity score matching was used to compare patients with seizures and status epilepticus between cEEG and historical cohorts. The EEG cohort had higher in-hospital mortality, worse Pediatric Cerebral Performance Category (PCPC) scores, and greater PCPC decline at discharge. In patients with status epilepticus, the PCPC decline was higher in the cEEG cohort. PCPC decline at PICU discharge was associated with cEEG monitoring in patients with status epilepticus.
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Affiliation(s)
- Xiuhua Liang Bozarth
- Division of Pediatric Neurology, Department of Neurology, University of Washington, Seattle, Washington, United States
| | - Pin-Yi Ko
- Division of Pediatric Neurology, Department of Neurology, University of Washington, Seattle, Washington, United States
| | - Hao Bao
- Biostatistics, Epidemiology, Econometrics and Programming Core, Seattle Children's Research Institute, Washington, United States
| | - Nicholas S. Abend
- Division of Neurology, Departments of Neurology and Pediatrics, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - R Scott Watson
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, Washington, United States
- Center for Child Health, Behavior, and Development, Seattle Children's Research Institute, Seattle, Washington, United States
| | - Pingping Qu
- Biostatistics, Epidemiology, Econometrics and Programming Core, Seattle Children's Research Institute, Washington, United States
| | - Leslie A. Dervan
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, Washington, United States
| | - Lindsey A. Morgan
- Division of Pediatric Neurology, Department of Neurology, University of Washington, Seattle, Washington, United States
| | - Mark Wainwright
- Division of Pediatric Neurology, Department of Neurology, University of Washington, Seattle, Washington, United States
| | - John K. McGuire
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Washington, Seattle, Washington, United States
| | - Edward Novotny
- Division of Pediatric Neurology, Department of Neurology, University of Washington, Seattle, Washington, United States
- Center for Integrative Brain Research, Seattle Children's Research Institute, Washington, United States
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14
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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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15
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Baum U, Katrin Baum A, Deike R, Feistner H, Markgraf B, Hinrichs H, Robra BP, Neumann T. Feasibility assessment of patient-controlled EEG home-monitoring: More results from the HOMEONE study. Clin Neurophysiol 2022; 140:12-20. [DOI: 10.1016/j.clinph.2022.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/07/2022] [Accepted: 04/21/2022] [Indexed: 11/29/2022]
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16
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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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17
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Abstract
Routine electroencephalogram (EEG) has many limitations, especially the inability to capture reported habitual events in question. A prolonged EEG with synchronized video (video-EEG) overcomes some of these limitations by improving the sensitivity, specificity and the diagnostic yield by attempting to record the habitual events when they are frequent and when indicated. Video-EEG is employed commonly for the diagnosis and classification of epilepsy/epilepsy syndromes, to distinguish between seizures and seizures mimickers, for pre-surgical evaluation and in the management of critically ill children. The duration of recording would vary depending on the indication and frequency of events. Ambulatory EEG is another cost effective and convenient alternative in certain circumstances. However, availability of the machines and expertise, accessibility, affordability and labor intensive nature of the procedure limit widespread use in India. This review explores the role of video-EEG in the management of children with epileptic and non-epileptic paroxysmal events with respect to routine clinical practice in India.
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Affiliation(s)
- Lakshminarayanan Kannan
- Department of Neurology and Epileptology, Advanced Center for Epilepsy, Gleneagles Global Health City, Perumbakkam, Chennai, 600100, India.
| | - Puneet Jain
- Epilepsy Program, Division of Neurology, Department of Pediatrics, University of Toronto, Hospital for Sick Children, Toronto, ON, M5G1X8, Canada
| | - Dinesh Nayak
- Department of Neurology and Epileptology, Advanced Center for Epilepsy, Gleneagles Global Health City, Perumbakkam, Chennai, 600100, India
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18
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Butler E, Mills N, J P Alix J, Hart AR. Knowledge and attitudes of critical care providers towards neurophysiological monitoring, seizure diagnosis, and treatment. Dev Med Child Neurol 2021; 63:976-983. [PMID: 33913148 DOI: 10.1111/dmcn.14907] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/25/2021] [Indexed: 12/19/2022]
Abstract
AIM To explore the attitudes of paediatric intensive care unit (PICU) health care professionals towards diagnosis and neurophysiological monitoring of seizures. METHOD This study used an explanatory sequential mixed-methods approach, interconnecting quantitative and qualitative features, comprising questionnaires and interviews, with equal weighting between stages, of health care professionals working in UK PICUs. Interview data were analysed using thematic analysis and triangulated with questionnaire data. RESULTS Seventy-two questionnaires were returned: 49 out of 60 (71.0%) of respondents reported that seizures were extremely hard or somewhat hard to diagnose in a critically ill child, and 81.2% had seen misdiagnosis occur. Thematic analysis revealed two main themes: (1) feeling out of control when faced with 'grey areas'; and (2) regaining control, which compromised three subthemes: aggressive intervention, accurate diagnosis, and eschewing diagnosis. INTERPRETATION Health care professionals find accurate diagnosis of seizures difficult, particularly in sedated/paralysed children and those with chronic neurological disorders. They report they would like better educational opportunities on discriminating between epileptic and non-epileptic events to improve their confidence. Professionals want routine neurophysiological monitoring that can be applied and interpreted at the bedside throughout the day to regain a sense of control over their patient, direct treatment appropriately, and, potentially, improve outcomes, but report appropriate training and peer review are essential if it is to be introduced into routine care. What this study adds Paediatric intensive care unit (PICU) staff feel out of control when faced with diagnosing seizures. Neurophysiological monitoring is wanted to help diagnosis and treatment. Amplitude-integrated electroencephalography is the preferred, pragmatic tool by PICU staff.
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Affiliation(s)
- Evie Butler
- University of Sheffield Medical School, Sheffield, UK
| | - Nicholas Mills
- Department of Paediatric Intensive Care Unit, Sheffield Children's NHS Foundation Trust, Sheffield, UK
| | - James J P Alix
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Anthony R Hart
- Department of Paediatric and Neonatal Neurology, Ryegate Children's Centre, Sheffield Children's NHS Foundation Trust, Sheffield, UK
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Benson A, Shahwan A. Monitoring the frequency and duration of epileptic seizures: "A journey through time". Eur J Paediatr Neurol 2021; 33:168-178. [PMID: 34120833 DOI: 10.1016/j.ejpn.2021.05.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 01/19/2021] [Accepted: 05/25/2021] [Indexed: 11/28/2022]
Abstract
Seizure monitoring plays an undeniably important role in diagnosing and managing epileptic seizures. Establishing the frequency and duration of seizures is crucial for assessing the burden of this chronic neurological disease, selecting treatment methods, determining how frequently these methods are applied, and informing short and long-term therapeutic decisions. Over the years, seizure monitoring tools and methods have evolved and become increasingly sophisticated; from home seizure diaries to EEG monitoring to cutting-edge responsive neurostimulation systems. In this article, the various methods of seizure monitoring are reviewed.
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Affiliation(s)
- Ailbhe Benson
- Department of Clinical Neurophysiology & Neurology, CHI at Temple Street, Dublin, Ireland.
| | - Amre Shahwan
- Department of Clinical Neurophysiology & Neurology, CHI at Temple Street, Dublin, Ireland.
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20
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Abstract
INTRODUCTION Evidence for continuous EEG monitoring in the pediatric intensive care unit (PICU) is increasing. However, 24/7 access to EEG is not routinely available in most centers, and clinical management is often informed by more limited EEG resources. The experience of EEG was reviewed in a tertiary PICU where 24/7 EEG cover is unavailable. METHODS Retrospective EEG and clinical review of 108 PICU patients. Correlations were carried out between EEG and clinical variables including mortality. The role of EEG in clinical decision making was documented. RESULTS One hundred ninety-six EEGs were carried out in 108 PICU patients over 2.5 years (434 hours of recording). After exclusion of 1 outlying patient with epileptic encephalopathy, 136 EEGs (median duration, 65 minutes; range, 20 minutes to 4 hours 40 minutes) were included. Sixty-two patients (57%) were less than 12 months old. Seizures were detected in 18 of 107 patients (17%); 74% of seizures were subclinical; 72% occurred within the first 30 minutes of recording. Adverse EEG findings were associated with high mortality. Antiepileptic drug use was high in the studied population irrespective of EEG seizure detection. Prevalence of epileptiform discharges and EEG seizures diminished with increasing levels of sedation. CONCLUSIONS EEG provides important diagnostic information in a large proportion of PICU patients. In the absence of 24/7 EEG availability, empirical antiepileptic drug utilization is high.
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Alterations in coordinated EEG activity precede the development of seizures in comatose children. Clin Neurophysiol 2021; 132:1505-1514. [PMID: 34023630 DOI: 10.1016/j.clinph.2021.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 02/21/2021] [Accepted: 03/12/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children. METHODS In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring. RESULTS Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results. CONCLUSIONS These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures. SIGNIFICANCE If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.
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22
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Machine learning models to predict electroencephalographic seizures in critically ill children. Seizure 2021; 87:61-68. [PMID: 33714840 DOI: 10.1016/j.seizure.2021.03.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/23/2020] [Accepted: 03/02/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To determine whether machine learning techniques would enhance our ability to incorporate key variables into a parsimonious model with optimized prediction performance for electroencephalographic seizure (ES) prediction in critically ill children. METHODS We analyzed data from a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy who underwent clinically-indicated continuous EEG monitoring (CEEG). We implemented and compared three state-of-the-art machine learning methods for ES prediction: (1) random forest; (2) Least Absolute Shrinkage and Selection Operator (LASSO); and (3) Deep Learning Important FeaTures (DeepLIFT). We developed a ranking algorithm based on the relative importance of each variable derived from the machine learning methods. RESULTS Based on our ranking algorithm, the top five variables for ES prediction were: (1) epileptiform discharges in the initial 30 minutes, (2) clinical seizures prior to CEEG initiation, (3) sex, (4) age dichotomized at 1 year, and (5) epileptic encephalopathy. Compared to the stepwise selection-based approach in logistic regression, the top variables selected by our ranking algorithm were more informative as models utilizing the top variables achieved better prediction performance evaluated by prediction accuracy, AUROC and F1 score. Adding additional variables did not improve and sometimes worsened model performance. CONCLUSION The ranking algorithm was helpful in deriving a parsimonious model for ES prediction with optimal performance. However, application of state-of-the-art machine learning models did not substantially improve model performance compared to prior logistic regression models. Thus, to further improve the ES prediction, we may need to collect more samples and variables that provide additional information.
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Non-neurophysiologist Physicians and Nurses Can Detect Subclinical Seizures in Children Using a Panel of Quantitative EEG Trends and a Seizure Detection Algorithm. J Clin Neurophysiol 2020; 39:453-458. [PMID: 33417383 DOI: 10.1097/wnp.0000000000000812] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE This study evaluated the sensitivity of nonconvulsive seizure detection by non-neurophysiologist physicians and nurses using a panel of quantitative EEG (QEEG) trends in the setting of a pediatric intensive care unit. METHODS Forty-five 1-hour QEEG epochs were obtained retrospectively from 10 patients admitted to the McMaster Children's Hospital pediatric intensive care unit, which included 184 electrographic seizures. Each epoch constituted 4 QEEG trends, a seizure probability marker, automated seizure detector, rhythmicity spectrograms, and amplitude-integrated EEG. Six pediatric residents and 5 pediatric intensive care unit nurses analyzed the epochs for possible seizures after a 15-minute power point presentation. This was compared with the gold standard of a board-certified epileptologist interpreting the conventional EEG data for seizures. RESULTS Sensitivity of seizure detection for pediatric residents and intensive care unit nurses were 0.90. The specificity was 0.87 and 0.89, respectively. The interrater agreement among the pediatric residents was moderate with a kappa (κ) value of 0.45 (confidence interval: 0.41-0.49), and among the nurses were moderate with a κ value of 0.59 (confidence interval: 0.54-0.63). A post hoc analysis involving 2 neurophysiologists demonstrated a sensitivity of 0.90 and a specificity of 0.93 (confidence interval: 0.90-0.96) for seizure detection and a substantial interrater agreement of κ = 0.76 (confidence interval: 0.61-0.91). CONCLUSIONS A panel of QEEG trends can be used by non-neurophysiologists in a pediatric critical care setting to detect nonconvulsive seizures with a reasonable accuracy, which may expedite subclinical seizure identification and timely intervention.
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EEG Assessment in a 2-Year-Old Child with Prolonged Disorders of Consciousness: 3 Years' Follow-up. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8826238. [PMID: 33293944 PMCID: PMC7718066 DOI: 10.1155/2020/8826238] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 10/21/2020] [Accepted: 10/31/2020] [Indexed: 11/25/2022]
Abstract
A 2-year-old girl, diagnosed with traumatic brain injury and epilepsy following car trauma, was followed up for 3 years (a total of 15 recordings taken at 0, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 19, 26, and 35 months). There is still no clear guidance on the diagnosis, treatment, and prognosis of children with disorders of consciousness. At each appointment, recordings included the child's height, weight, pediatric Glasgow Coma Scale (pGCS), Coma Recovery Scale-Revised (CRS-R), Gesell Developmental Schedule, computed tomography or magnetic resonance imaging, electroencephalogram, frequency of seizures, oral antiepileptic drugs, stimulation with subject's own name (SON), and median nerve electrical stimulation (MNS). Growth and development were deemed appropriate for the age of the child. The pGCS and Gesell Developmental Schedule provided a comprehensive assessment of consciousness and mental development; the weighted Phase Lag Index (wPLI ) in the β-band (13–25 Hz) can distinguish unresponsive wakefulness syndrome from minimally conscious state and confirm that the SON and MNS were effective. The continuous increase of delta-band power indicates a poor prognosis. Interictal epileptiform discharges (IEDs) have a cumulative effect and seizures seriously affect the prognosis.
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Abstract
After convulsive status epilepticus, patients of all ages may have ongoing EEG seizures identified by continuous EEG monitoring. Furthermore, high EEG seizure exposure has been associated with unfavorable neurobehavioral outcomes. Thus, recent guidelines and consensus statements recommend many patients with persisting altered mental status after convulsive status epilepticus undergo continuous EEG monitoring. This review summarizes the available epidemiologic data and related recommendations provided by recent guidelines and consensus statements.
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Fung FW, Parikh DS, Jacobwitz M, Vala L, Donnelly M, Wang Z, Xiao R, Topjian AA, Abend NS. Validation of a model to predict electroencephalographic seizures in critically ill children. Epilepsia 2020; 61:2754-2762. [PMID: 33063870 DOI: 10.1111/epi.16724] [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: 06/30/2020] [Revised: 09/20/2020] [Accepted: 09/21/2020] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but identification requires extensive resources for continuous electroencephalographic monitoring (CEEG). In a previous study, we developed a clinical prediction rule using three clinical variables (age, acute encephalopathy category, clinically evident seizure[s] prior to CEEG initiation) and two electroencephalographic (EEG) variables (EEG background category and interictal discharges within the first 30 minutes of EEG) to identify patients at high risk for ESs for whom CEEG might be essential. In the current study, we aimed to validate the ES prediction model using an independent cohort. METHODS The prospectively acquired validation cohort consisted of 314 consecutive critically ill children treated in the Pediatric Intensive Care Unit of a quaternary care referral hospital with acute encephalopathy undergoing clinically indicated CEEG. We calculated test characteristics using the previously developed prediction model in the validation cohort. As in the generation cohort study, we selected a 0.10 cutpoint to emphasize sensitivity. RESULTS The incidence of ESs in the validation cohort was 22%. The generation and validation cohorts were alike in most clinical and EEG characteristics. The ES prediction model was well calibrated and well discriminating in the validation cohort. The model had a sensitivity of 90%, specificity of 37%, positive predictive value of 28%, and negative predictive value of 93%. If applied, the model would limit 31% of patients from undergoing CEEG while failing to identify 10% of patients with ESs. The model had similar performance characteristics in the generation and validation cohorts. SIGNIFICANCE A model employing five readily available clinical and EEG variables performed well when validated in a new consecutive cohort. Implementation would substantially reduce CEEG utilization, although some patients with ESs would not be identified. This model may serve a critical role in targeting limited CEEG resources to critically ill children at highest risk for ESs.
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Affiliation(s)
- France W Fung
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Departments Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Darshana S Parikh
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Marin Jacobwitz
- Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Zi Wang
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Rui Xiao
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nicholas S Abend
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Departments Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Division of Neurology, Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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Katyal N, Singh I, Narula N, Idiculla PS, Premkumar K, Beary JM, Nattanmai P, Newey CR. Continuous Electroencephalography (CEEG) in Neurological Critical Care Units (NCCU): A Review. Clin Neurol Neurosurg 2020; 198:106145. [PMID: 32823186 DOI: 10.1016/j.clineuro.2020.106145] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/20/2020] [Accepted: 08/07/2020] [Indexed: 12/17/2022]
Affiliation(s)
- Nakul Katyal
- University of Missouri, Department of Neurology, 5 Hospital Drive, CE 540, United States.
| | - Ishpreet Singh
- University of Missouri, Department of Neurology, 5 Hospital Drive, CE 540, United States.
| | - Naureen Narula
- Staten Island University Hospital, Department of Pulmonary- critical Care Medicine, 475 Seaview Avenue Staten Island, NY, 10305, United States.
| | - Pretty Sara Idiculla
- University of Missouri, Department of Neurology, 5 Hospital Drive, CE 540, United States.
| | - Keerthivaas Premkumar
- University of Missouri, Department of biological sciences, Columbia, MO 65211, United States.
| | - Jonathan M Beary
- A. T. Still University, Department of Neurobehavioral Sciences, Kirksville, MO, United States.
| | - Premkumar Nattanmai
- University of Missouri, Department of Neurology, 5 Hospital Drive, CE 540, United States.
| | - Christopher R Newey
- Cleveland clinic Cerebrovascular center, 9500 Euclid Avenue, Cleveland, OH 44195, United States.
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Ghossein J, Alnaji F, Webster RJ, Bulusu S, Pohl D. Continuous EEG in a Pediatric Intensive Care Unit: Adherence to Monitoring Criteria and Barriers to Adequate Implementation. Neurocrit Care 2020; 34:519-528. [PMID: 32696100 DOI: 10.1007/s12028-020-01053-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 07/09/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Subclinical seizures are common in critically ill children and are best detected by continuous EEG (cEEG) monitoring. Timely detection of seizures requires pediatric intensive care unit (PICU) physicians to identify patients at risk of seizures and request cEEG monitoring. A recent consensus statement from the American Clinical Neurophysiology Society (ACNS) outlines the indications for cEEG monitoring in critically ill patients. However, adherence to these cEEG monitoring criteria among PICU physicians is unknown. Our project had two goals: 1. To assess adherence to cEEG monitoring indications and barriers toward their implementation; 2. To improve compliance with the ACNS cEEG monitoring criteria in our PICU. METHODS This is a single-institution study. A total of 234 PICU admissions (183 unique patients) were studied. A 6-month retrospective chart review identified PICU patients meeting ACNS criteria for cEEG monitoring, and patients for whom monitoring was requested. This was followed by an 8-week quality improvement project. During this mentorship period, a didactic 15-min lecture and summary handouts regarding the ACNS indications for cEEG monitoring were provided to all PICU physicians. Requests for cEEG monitoring during the mentorship period were compared to baseline adherence to cEEG monitoring recommendations, and barriers toward timely cEEG monitoring were assessed. RESULTS Nearly every fifth PICU patient met cEEG monitoring indications, and prevalences of patients meeting those indications were similar in the retrospective and the prospective mentorship period (18% vs. 19%). Almost all patients (98%) requiring cEEG as per ACNS criteria met the indication for monitoring already at the time of their PICU admission. During the retrospective period, 23% of patients meeting ACNS criteria had a request for cEEG monitoring, which increased to 83% during the mentorship period. The median delay to cEEG initiation was 16.7 h during the mentorship period, largely due to limited hours of EEG technician availability. Electrographic seizures were identified in 36% of patients monitored, all within the first 120 min of cEEG recording. The majority (79%) of cEEGs informed clinical management. CONCLUSIONS A brief teaching intervention supplemented by pictographic handouts significantly increased adherence to cEEG monitoring recommendations, and cEEGs guided clinical management. However, there were long delays to cEEG initiation. In order to promptly recognize subclinical seizures in critically ill children, we strongly advocate for a routine screening for cEEG monitoring indications as part of the PICU admission process, and a care model allowing for cEEG initiation around-the-clock.
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Affiliation(s)
- Jamie Ghossein
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Fuad Alnaji
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Children's Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada.,CHEO Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada
| | - Richard J Webster
- CHEO Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada
| | - Srinivas Bulusu
- Children's Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada
| | - Daniela Pohl
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada. .,Children's Hospital of Eastern Ontario, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada. .,CHEO Research Institute, 401 Smyth Road, Ottawa, ON, K1H 8L1, Canada.
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Fung FW, Fan J, Vala L, Jacobwitz M, Parikh DS, Donnelly M, Topjian AA, Xiao R, Abend NS. EEG monitoring duration to identify electroencephalographic seizures in critically ill children. Neurology 2020; 95:e1599-e1608. [PMID: 32690798 DOI: 10.1212/wnl.0000000000010421] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 04/10/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVES To determine the optimal duration of continuous EEG monitoring (CEEG) for electrographic seizure (ES) identification in critically ill children. METHODS We performed a prospective observational cohort study of 719 consecutive critically ill children with encephalopathy. We evaluated baseline clinical risk factors (age and prior clinically evident seizures) and emergent CEEG risk factors (epileptiform discharges and ictal-interictal continuum patterns) using a multistate survival model. For each subgroup, we determined the CEEG duration for which the risk of ES was <5% and <2%. RESULTS ES occurred in 184 children (26%). Patients achieved <5% risk of ES after (1) 6 hours if ≥1 year without prior seizures or EEG risk factors; (2) 1 day if <1 year without prior seizures or EEG risks; (3) 1 day if ≥1 year with either prior seizures or EEG risks; (4) 2 days if ≥1 year with prior seizures and EEG risks; (5) 2 days if <1 year without prior seizures but with EEG risks; and (6) 2.5 days if <1 year with prior seizures regardless of the presence of EEG risks. Patients achieved <2% risk of ES at the same durations except patients without prior seizures or EEG risk factors would require longer CEEG (1.5 days if <1 year of age, 1 day if ≥1 year of age). CONCLUSIONS A model derived from 2 baseline clinical risk factors and emergent EEG risk factors would allow clinicians to implement personalized strategies that optimally target limited CEEG resources. This would enable more widespread use of CEEG-guided management as a potential neuroprotective strategy. CLINICALTRIALSGOV IDENTIFIER NCT03419260.
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Affiliation(s)
- France W Fung
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia.
| | - Jiaxin Fan
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Lisa Vala
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Marin Jacobwitz
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Darshana S Parikh
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Maureen Donnelly
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Alexis A Topjian
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Rui Xiao
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Nicholas S Abend
- From the Department of Anesthesia and Critical Care Medicine (D.S.P., A.A.T.), Department of Pediatrics, Division of Neurology (F.W.F., M.J., D.S.P., N.S.A.), and Department of Neurodiagnostics (L.V., M.D., N.S.A.), Children's Hospital of Philadelphia; and Departments of Neurology (N.S.A., F.W.F.), Pediatrics (N.S.A., F.W.F.), Anesthesia and Critical Care (A.A.T., N.S.A.), and Biostatistics, Epidemiology and Informatics (J.F., R.X., N.S.A.), University of Pennsylvania Perelman School of Medicine, Philadelphia
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Granum LK, Bush WW, Williams DC, Stecker MM, Weaver CE, Werre SR. Prevalence of electrographic seizure in dogs and cats undergoing electroencephalography and clinical characteristics and outcome for dogs and cats with and without electrographic seizure: 104 cases (2009-2015). J Am Vet Med Assoc 2020; 254:967-973. [PMID: 30938610 DOI: 10.2460/javma.254.8.967] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
OBJECTIVE To determine the prevalence of electrographic seizure (ES) and electrographic status epilepticus (ESE) in dogs and cats that underwent electroencephalography (EEG) because of suspected seizure activity and to characterize the clinical characteristics, risk factors, and in-hospital mortality rates for dogs and cats with ES or ESE. DESIGN Retrospective case series. ANIMALS 89 dogs and 15 cats. PROCEDURES Medical records of dogs and cats that underwent EEG at a veterinary neurology service between May 2009 and April 2015 were reviewed. Electrographic seizure was defined as ictal discharges that evolved in frequency, duration, or morphology and lasted at least 10 seconds, and ESE was defined as ES that lasted ≥ 10 minutes. Patient signalment and history, physical and neurologic examination findings, diagnostic test results, and outcome were compared between patients with and without ES or ESE. RESULTS Among the 104 patients, ES and ESE were diagnosed in 21 (20%) and 12 (12%), respectively. Seventeen (81%) patients with ES had no or only subtle signs of seizure activity. The in-hospital mortality rate was 48% and 50% for patients with ES and ESE, respectively, compared with 19% for patients without ES or ESE. Risk factors for ES and ESE included young age, overt seizure activity within 8 hours before EEG, and history of cluster seizures. CONCLUSIONS AND CLINICAL REVELANCE Results indicated that ES and ESE were fairly common in dogs and cats with suspected seizure activity and affected patients often had only subtle clinical signs. Therefore, EEG is necessary to detect patients with ES and ESE.
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Griffith JL, Tomko ST, Guerriero RM. Continuous Electroencephalography Monitoring in Critically Ill Infants and Children. Pediatr Neurol 2020; 108:40-46. [PMID: 32446643 DOI: 10.1016/j.pediatrneurol.2020.04.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 12/15/2022]
Abstract
Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.
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Affiliation(s)
- Jennifer L Griffith
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri.
| | - Stuart T Tomko
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Réjean M Guerriero
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
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Fung FW, Jacobwitz M, Parikh DS, Vala L, Donnelly M, Fan J, Xiao R, Topjian AA, Abend NS. Development of a model to predict electroencephalographic seizures in critically ill children. Epilepsia 2020; 61:498-508. [PMID: 32077099 DOI: 10.1111/epi.16448] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/23/2020] [Accepted: 01/23/2020] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. METHODS We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. RESULTS The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES. SIGNIFICANCE A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jiaxin Fan
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rui Xiao
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Current Status of Continuous Electroencephalographic Monitoring in Critically Ill Children. Pediatr Neurol 2019; 101:11-17. [PMID: 31493974 DOI: 10.1016/j.pediatrneurol.2019.07.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 06/13/2019] [Accepted: 07/26/2019] [Indexed: 11/21/2022]
Abstract
The utilization of continuous electroencephalographic monitoring in critical care units has increased significantly, and several consensus statements and guidelines have been published. The use of critical care electroencephalographic monitoring has become a standard of care in many centers in the United States and other countries. The most common indication is to detect electrographic seizures and status epilepticus. Other indications include monitoring treatment efficacy in patients with electrographic seizures and status epilepticus, evaluating the degree of disturbance of function in patients with encephalopathy, monitoring brain function in patients treated with sedation and neuromuscular blocking agents, and event characterization. The urgent initiation of critical care electroencephalographic monitoring is recommended in certain clinical populations, but varies among institutions. The consensus among neurologists is to start treatment after identifying electrographic seizures or electrographic status epilepticus with or without clinical signs. However, the optimal treatment of nonconvulsive and electrographic-only seizures remains controversial. Critical care electroencephalographic monitoring has significant impact on clinical management, but there is lack of clear evidence that treatment guided by critical care electroencephalographic monitoring leads to improvement of clinical and neurodevelopmental outcome. There are substantial discrepancies among institutions on personnel and technical support used for critical care electroencephalographic monitoring. The optimal critical care electroencephalographic monitoring team should include electroencephalographers with experience in critical care electroencephalographic monitoring interpretation and appropriately trained technologists certified in electroencephalography by the American Board of Registration of Electroencephalographic and Evoked Potential Technologists specializing in critical care electroencephalographic monitoring or long-term monitoring.
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Electrographic seizure burden and outcomes following pediatric status epilepticus. Epilepsy Behav 2019; 101:106409. [PMID: 31420288 DOI: 10.1016/j.yebeh.2019.07.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Accepted: 07/04/2019] [Indexed: 12/11/2022]
Abstract
Pediatric status epilepticus carries a substantial risk for morbidity and mortality, but the relationship between seizure burden, treatment, and outcome remains incompletely understood. This review summarizes the evidence linking seizure burden and outcomes among critically ill children in the intensive care unit (ICU), a population in whom accurate quantification of seizure burden is possible using continuous electroencephalographic monitoring. Several high-quality observational studies among critically ill children have reported an association between higher seizure burden and worse outcome, even after adjusting for potential confounders such as age, etiology, and illness severity. Although these studies support the hypothesis that seizures contribute to brain injury and worsen outcome, a causal link between seizures and outcome remains to be proven. The relationship between seizures and outcome is likely complex, and dependent on factors such as etiology, preexisting neurological disability, medication exposure, and possibly individual genetic factors. Studies attempting to define this complex relationship will need to measure and account for these factors in their analyses. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".
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Fung FW, Jacobwitz M, Vala L, Parikh D, Donnelly M, Xiao R, Topjian AA, Abend NS. Electroencephalographic seizures in critically ill children: Management and adverse events. Epilepsia 2019; 60:2095-2104. [PMID: 31538340 DOI: 10.1111/epi.16341] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2019] [Revised: 08/27/2019] [Accepted: 08/27/2019] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Guidelines recommend that encephalopathic critically ill children undergo continuous electroencephalographic (CEEG) monitoring for electrographic seizure (ES) identification and management. However, limited data exist on antiseizure medication (ASM) safety for ES treatment in critically ill children. METHODS We performed a single-center prospective observational study of encephalopathic critically ill children undergoing CEEG. Clinical and EEG features and ASM utilization patterns were evaluated. We determined the incidence, types, and risk factors for adverse events associated with ASM administration. RESULTS A total of 472 consecutive critically ill children undergoing CEEG were enrolled. ES occurred in 131 children (28%). Clinicians administered ASM to 108 children with ES (82%). ES terminated after the initial ASM in 38% of patients who received one ASM, after the second ASM in 35% of patients who received two ASMs, after the third ASM in 50% of patients who received three ASMs, and after the fourth ASM in 53% of patients who received four ASMs. Thirty patients (28%) received anesthetic infusions for ES management. Adverse events occurred in 18 patients (17%). Adverse effects were expected and resolved in all patients, and they were generally serious (in 15 patients) and definitely related (in 12 patients). Adverse events were rare in patients with acute symptomatic seizures requiring only one to two ASMs for treatment, but were more common in children with epilepsy, ictal-interictal continuum EEG patterns, or patients requiring more extensive ASM management. SIGNIFICANCE ES ceased after one ASM in only 38% of critically ill children but ceased after two ASMs in 73% of critically ill children. Thus, ES management was often accomplished with readily available medications, but optimization of multistep ES management strategies might be beneficial. Adverse events were rare and manageable in children with acute symptomatic seizures requiring only one to two ASMs for treatment. Future studies are needed to determine whether management of acute symptomatic ES improves neurobehavioral outcomes.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia,, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia,, Philadelphia, PA, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia,, Philadelphia, PA, USA
| | - Darshana Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia,, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia,, Philadelphia, PA, USA
| | - Rui Xiao
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia,, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia,, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Neumann T, Baum AK, Baum U, Deike R, Feistner H, Scholz M, Hinrichs H, Robra BP. Assessment of the technical usability and efficacy of a new portable dry-electrode EEG recorder: First results of the HOME ONE study. Clin Neurophysiol 2019; 130:2076-2087. [PMID: 31541985 DOI: 10.1016/j.clinph.2019.08.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 07/04/2019] [Accepted: 08/14/2019] [Indexed: 11/24/2022]
Abstract
OBJECTIVES The HOME project is intended to provide evidence of diagnostic and therapeutic yield of a patient-controlled EEG home-monitoring for neurological outpatients. METHODS This study evaluated the technical and practical usability and efficacy of a new portable dry-electrode EEG recorder in comparison to conventional EEG devices based on technical assessments and inter-rater comparisons of EEG record examinations of office-based practitioners and two experienced neurologists. RESULTS The technical assessment was based on channel-wise comparisons of band power values derived from power spectra as observed in two recording modalities. Slight yet significant differences were observed only in the Delta-frequency band (1.5-4 Hz). The fraction of automatically detected artifact segments was larger in the new portable recordings than in conventional recordings (20% vs. 11%, median). Overall, 93% of raters' stated diagnostic findings gathered from conventional devices were concordant with stated diagnostic findings gathered from the new portable device. CONCLUSION The new EEG device was shown to have technical comparability to and a high concordance rate of diagnostic findings with conventional EEG devices. SIGNIFICANCE The new portable dry-electrode EEG device is suitable to meet the HOME projects' goal of establishing a patient-controlled EEG home-monitoring in the routine care of neurological outpatients. TRIAL REGISTRATION DRKS DRKS00012685. Registered 09 August 2017, retrospectively registered.
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Affiliation(s)
- Thomas Neumann
- University Department of Neurology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany; Chair in Empirical Economics, Faculty of Economics and Management, Otto-von-Guericke-University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany; Forschungscampus STIMULATE, Otto-von-Guericke-University Magdeburg, Sandtorstraße 23, 39106 Magdeburg, Germany.
| | - Anne Katrin Baum
- University Department of Neurology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany.
| | - Ulrike Baum
- University Department of Neurology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany.
| | - Renate Deike
- University Department of Neurology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany.
| | - Helmut Feistner
- University Department of Neurology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany.
| | - Michael Scholz
- University Department of Neurology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany.
| | - Hermann Hinrichs
- University Department of Neurology, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany; Leibniz Institute for Neurobiology, Brenneckestraße 6, 39118 Magdeburg, Germany; German Center for Neurodegenerative Diseases, Site Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany; Forschungscampus STIMULATE, Otto-von-Guericke-University Magdeburg, Sandtorstraße 23, 39106 Magdeburg, Germany.
| | - Bernt-Peter Robra
- Institute of Social Medicine and Health Economics, Otto-von-Guericke-University Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany.
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Araki T. Pediatric Neurocritical Care. Neurocrit Care 2019. [DOI: 10.1007/978-981-13-7272-8_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Continuous EEG in Pediatric Critical Care: Yield and Efficiency of Seizure Detection. J Clin Neurophysiol 2018; 34:421-426. [PMID: 28430674 DOI: 10.1097/wnp.0000000000000379] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Our goal was to define the duration of continuous EEG (cEEG) monitoring needed to adequately capture electrographic seizures and EEG status epilepticus in the pediatric intensive care unit using clinical and background EEG features. METHODS Retrospective study of patients aged 1 month to 21 years admitted to a tertiary pediatric intensive care unit and undergoing cEEG (>3 hours). Clinical data collected included admission diagnosis, EEG background features, and time variables including time to first seizure after initiation of cEEG. RESULTS Four hundred fourteen patients aged 4.2 (0.75-11.3) years (median, interquartile range) were included. With a median duration of 21 (16-42.2) hours of cEEG monitoring, we identified electrographic seizure or EEG status epilepticus in 25% of subjects. We identified three features that could improve the efficiency of cEEG resources and provide a decision-making framework: (1) clinical history of acute encephalopathy is not predictive of detecting electrographic seizure or EEG status epilepticus, whereas a history of status epilepticus or seizures is; (2) normal EEG background or absence of epileptiform discharges in the initial 24 hours of recording informs the decision to discontinue cEEG; (3) failure to record electrographic ictal events within the first 4 to 6 hours of monitoring may be sufficient to predict the absence of subsequent ictal events. CONCLUSIONS Individualized monitoring plans are necessary to increase seizure detection yield while improving resource utilization. A strategy using information from the clinical history, initial EEG background, and the first 4 to 6 hours of recording may be effective in determining the necessary duration of cEEG monitoring in the pediatric intensive care unit.
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Sánchez Fernández I, Sansevere AJ, Gaínza-Lein M, Kapur K, Loddenkemper T. Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit. J Child Neurol 2018; 33:546-553. [PMID: 29756499 DOI: 10.1177/0883073818773230] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The aim of this study was to evaluate the performance of models predicting in-hospital mortality in critically ill children undergoing continuous electroencephalography (cEEG) in the intensive care unit (ICU). We evaluated the performance of machine learning algorithms for predicting mortality in a database of 414 critically ill children undergoing cEEG in the ICU. The area under the receiver operating characteristic curve (AUC) in the test subset was highest for stepwise selection/elimination models (AUC = 0.82) followed by least absolute shrinkage and selection operator (LASSO) and support vector machine with linear kernel (AUC = 0.79), and random forest (AUC = 0.71). The explanatory models had the poorest discriminative performance (AUC = 0.63 for the model without considering etiology and AUC = 0.45 for the model considering etiology). Using few variables and a relatively small number of patients, machine learning techniques added information to explanatory models for prediction of in-hospital mortality.
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Affiliation(s)
- Iván Sánchez Fernández
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,2 Department of Child Neurology, Hospital Sant Joan de Déu, Universidad de Barcelona, Passeig de Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Arnold J Sansevere
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marina Gaínza-Lein
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,3 Facultad de Medicina, Universidad Austral de Chile, Edificio de Ciencias Biomédicas, Campus Isla Teja s/n, UACh, Valdivia, Chile
| | - Kush Kapur
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tobias Loddenkemper
- 1 Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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Outcomes following electrographic seizures and electrographic status epilepticus in the pediatric and neonatal ICUs. Curr Opin Neurol 2018; 30:156-164. [PMID: 28118303 DOI: 10.1097/wco.0000000000000425] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Increasing recognition of electrographic seizures and electrographic status epilepticus in critically ill neonates and children has highlighted the importance of identifying their potential contributions to neurological outcomes to guide optimal management. RECENT FINDINGS Recent studies in children and neonates have found an independent association between increasing seizure burden and worse short-term and long-term outcomes, even after adjusting for other important contributors to outcome such as seizure cause and illness severity. The risk of worse neurological outcome has been shown to increase above a seizure burden threshold of 12-13 min/h, which is considerably lower than the conventional definition of status epilepticus of 30 min/h. Randomized controlled trials in neonates have demonstrated that electroencephalography-targeted therapy can successfully reduce seizure burden, but due to their small size these trials have not been able to demonstrate that more aggressive electroencephalography-targeted treatment of both subclinical and clinical seizures results in improved outcome. SUMMARY Despite mounting evidence for an independent association between increasing seizure burden and worse outcome, further study is needed to determine whether early seizure identification and aggressive antiseizure treatment can improve neurodevelopmental outcomes.
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Vlachy J, Jo M, Li Q, Ayer T, Keskinocak P, Swann J, Olson L, Vats A. Risk Factors for Seizures Among Young Children Monitored With Continuous Electroencephalography in Intensive Care Unit: A Retrospective Study. Front Pediatr 2018; 6:303. [PMID: 30374434 PMCID: PMC6196272 DOI: 10.3389/fped.2018.00303] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 09/27/2018] [Indexed: 12/22/2022] Open
Abstract
Objective: cEEG is an emerging technology for which there are no clear guidelines for patient selection or length of monitoring. The purpose of this study was to identify subgroups of pediatric patients with high incidence of seizures. Study Design: We conducted a retrospective study on 517 children monitored by cEEG in the intensive care unit (ICU) of a children's hospital. The children were stratified using an age threshold selection method. Using regression modeling, we analyzed significant risk factors for increased seizure risk in younger and older children. Using two alternative correction procedures, we also considered a relevant comparison group to mitigate selection bias and to provide a perspective for our findings. Results: We discovered an approximate risk threshold of 14 months: below this threshold, the seizure risk increases dramatically. The older children had an overall seizure rate of 18%, and previous seizures were the only significant risk factor. In contrast, the younger children had an overall seizure rate of 45%, and the seizures were significantly associated with hypoxic-ischemic encephalopathy (HIE; p = 0.007), intracranial hemorrhage (ICH; p = 0.005), and central nervous system (CNS) infection (p = 0.02). Children with HIE, ICH, or CNS infection accounted for 61% of all seizure patients diagnosed through cEEG under 14 months. Conclusions: An extremely high incidence of seizures prevails among critically ill children under 14 months, particularly those with HIE, ICH, or CNS infection.
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Affiliation(s)
- Jan Vlachy
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Mingyoung Jo
- Children's Healthcare of Atlanta, Atlanta, GA, United States
| | - Qing Li
- Johns Hopkins Applied Physics Lab, Baltimore, MD, United States
| | - Turgay Ayer
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Pinar Keskinocak
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Julie Swann
- Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States
| | - Larry Olson
- Children's Healthcare of Atlanta, Atlanta, GA, United States.,Departments of Pediatrics and Neurology, Emory University, Atlanta, GA, United States
| | - Atul Vats
- Children's Healthcare of Atlanta, Atlanta, GA, United States.,Department of Pediatrics, Emory University, Atlanta, GA, United States
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Sánchez Fernández I, Sansevere AJ, Gaínza-Lein M, Buraniqi E, Tasker RC, Loddenkemper T. Time to continuous electroencephalogram in repeated admissions to the pediatric intensive care unit. Seizure 2017; 54:19-26. [PMID: 29182970 DOI: 10.1016/j.seizure.2017.11.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 11/14/2017] [Accepted: 11/20/2017] [Indexed: 10/18/2022] Open
Abstract
PURPOSE Describe timing from intensive care unit (ICU) admission to initiation of continuous electroencephalogram (cEEG) in repeated ICU admissions. METHOD We performed a retrospective observational study in pediatric patients who underwent repeated ICU admissions with cEEG from 2011 to 2013. The main outcome measure was time from ICU admission to cEEG. RESULTS There were 41 patients (54% males) with at least 2 ICU admissions with cEEG (median (p25-p75) age at first admission: 3.3 (0.3-8.4) years, at second admission: 3.9 (1.1-9.4) years), 7 patients (57% males, 9.9 (2.9-11.5) years) with at least 3 ICU admissions, and 5 patients (60% males, 10.1 (4-10.5) years) with at least 4 ICU admissions. One patient had 21 ICU admissions. The median (p25-p75) time from ICU admission to cEEG was not different during the first and second ICU admissions [10.7 (1.9-22.9) hours versus 13 (0.2-36.7) hours; p=0.908]. Among patients with electrographic seizures on first admission, time to cEEG was not different during the first and second admissions [7.9 (0.5-23.4) hours versus 14.5 (-2 to 44.5) hours; p=0.636]. Among patients with status epilepticus during the first admission, time to cEEG was not different between the first and second admissions [15.3 (9-79) hours versus 40.7 (19.3-42.6) hours; p=0.75]. CONCLUSIONS The time from ICU admission to the initiation of cEEG did not decrease in second or subsequent ICU admissions, even in patients with seizures or status epilepticus on the first admission.
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Affiliation(s)
- Iván Sánchez Fernández
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Child Neurology, Hospital Sant Joan de Déu, Universidad de Barcelona, Spain
| | - Arnold J Sansevere
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marina Gaínza-Lein
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Facultad de Medicina, Universidad Austral de Chile, Valdivia, Chile
| | - Ersida Buraniqi
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert C Tasker
- Division of Critical Care, Departments of Neurology, Anesthesiology, Perioperative and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Fangsaad T, Assawabumrungkul S, Visudtibhan A. Clinical course and long-term outcome in children with alteration of consciousness underwent continuous EEG monitoring: A prospective observational study in Thailand. J Clin Neurosci 2017; 47:93-96. [PMID: 29097134 DOI: 10.1016/j.jocn.2017.10.063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2017] [Revised: 10/12/2017] [Accepted: 10/22/2017] [Indexed: 10/18/2022]
Abstract
The study aims to explore the clinical course and long-term outcome in children with altered consciousness who underwent cEEG monitoring. A prospective observational study was conducted in neonatal and pediatric intensive care units from 1 September 2014 through 31 March 2017. Standard 10-20 cEEG monitoring was applied. Twenty children were included in this study. Their ages ranged from 1 day to 142.7 months (median age 40.6 months). Continuous EEG was commenced from 5 h to 5 days after the onset of alteration of consciousness (median 40.2 h). The range of EEG monitoring duration was 6.7-256.3 h (mean 50.4 h). Four patients (20%) had preexisting neurological diseases, which were 2 epilepsy, adrenoleukodystrophy and unknown cause of brain atrophy. Eleven patients (55%) had principle neurological diagnosis and the others 9 (45%) had systemic illnesses. Sixteen patients (80%) had clinical seizures prior to the commencement of cEEG monitoring. Fifteen patients (75%) received antiepileptic drugs before cEEG monitoring. NCSE was diagnosed in 25%. Comparison of patients' characteristics and long-term outcome between the NCSE and non NCSE groups, there was statistical significance between the two groups only with respect to epileptiform discharges. The patients are being follow up for a period of 24 months after the end of cEEG monitoring. The outcome of patients divided into those with a favorable outcome and those with poor outcome according to modified Rankin scale. The patients with isoelectric EEG background had relatively poorer outcomes than those with normal or slow background activities. The overall mortality rate for the entire population was 15%.
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Affiliation(s)
- Thitiporn Fangsaad
- Department of Pediatrics, Bhumubol Adulyadej Hospital, Bangkok, Thailand.
| | | | - Anannit Visudtibhan
- Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol, Bangkok, Thailand
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Gaínza-Lein M, Sánchez Fernández I, Loddenkemper T. Use of EEG in critically ill children and neonates in the United States of America. J Neurol 2017; 264:1165-1173. [PMID: 28503704 DOI: 10.1007/s00415-017-8510-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 05/04/2017] [Accepted: 05/06/2017] [Indexed: 01/06/2023]
Abstract
The objective of the study was to estimate the proportion of patients who receive an electroencephalogram (EEG) among five common indications for EEG monitoring in the intensive care unit: traumatic brain injury (TBI), extracorporeal membrane oxygenation (ECMO), cardiac arrest, cardiac surgery and hypoxic-ischemic encephalopathy (HIE). We performed a retrospective cross-sectional descriptive study utilizing the Kids' Inpatient Database (KID) for the years 2010-2012. The KID is the largest pediatric inpatient database in the USA and it is based on discharge reports created by hospitals for billing purposes. We evaluated the use of electroencephalogram (EEG) or video-electroencephalogram in critically ill children who were mechanically ventilated. The KID database had a population of approximately 6,000,000 pediatric admissions. Among 22,127 admissions of critically ill children who had mechanical ventilation, 1504 (6.8%) admissions had ECMO, 9201 (41.6%) TBI, 4068 (18.4%) HIE, 2774 (12.5%) cardiac arrest, and 4580 (20.7%) cardiac surgery. All five conditions had a higher proportion of males, with the highest (69.8%) in the TBI group. The mortality rates ranged from 7.02 to 39.9% (lowest in cardiac surgery and highest in ECMO). The estimated use of EEG was 1.6% in cardiac surgery, 4.1% in TBI, 7.2% in ECMO, 8.2% in cardiac arrest, and 12.1% in HIE, with an overall use of 5.8%. Among common indications for EEG monitoring in critically ill children and neonates, the estimated proportion of patients actually having an EEG is low.
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Affiliation(s)
- Marina Gaínza-Lein
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Facultad de Medicina, Universidad Austral de Chile, Valdivia, Chile
| | - Iván Sánchez Fernández
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Child Neurology, Hospital Sant Joan de Déu, Universidad de Barcelona, Barcelona, Spain
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Pont-Thibodeau GD, Sanchez SM, Jawad AF, Nadkarni VM, Berg RA, Abend NS, Topjian AA. Seizure Detection by Critical Care Providers Using Amplitude-Integrated Electroencephalography and Color Density Spectral Array in Pediatric Cardiac Arrest Patients. Pediatr Crit Care Med 2017; 18:363-369. [PMID: 28234810 PMCID: PMC5380542 DOI: 10.1097/pcc.0000000000001099] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
OBJECTIVES Determine the accuracy and confidence of critical care medicine providers to identify seizures using amplitude-integrated electroencephalography versus amplitude-integrated electroencephalography combined with color density spectral array electroencephalography (aEEG + CDSA). DESIGN Tutorial and questionnaire. SETTING PICU. SUBJECTS Pediatric critical care providers (attendings, fellows, and nurses). INTERVENTIONS A standardized powerpoint tutorial on amplitude-integrated electroencephalography and color density spectral array followed by classification of 100 amplitude-integrated electroencephalography images and 100 amplitude-integrated electroencephalography combined with color density spectral array as displaying seizures or not displaying seizures. MEASUREMENTS AND MAIN RESULTS Electroencephalography tracings were obtained from children monitored with continuous electroencephalography after cardiac arrest. The gold standard for seizure identification was continuous electroencephalography interpretation by a pediatric electroencephalographer. The same electroencephalography tracings were used to generate images containing only amplitude-integrated electroencephalography or aEEG + CDSA. Twenty-three critical care medicine providers underwent a 30-minute tutorial on amplitude-integrated electroencephalography and color density spectral array interpretation. They were then asked to determine if there were seizures on 100 amplitude-integrated electroencephalography images and 100 aEEG + CDSA. Amplitude-integrated electroencephalography seizure detection sensitivity was 77% (95% CI, 73%-80%), specificity of 65% (95% CI, 62%-67%), negative predictive value of 88% (95% CI, 86%-90%), and positive predictive value of 46% (95% CI, 43%-49%). For aEEG + CDSA, sensitivity was 77% (95% CI, 74%-81%), specificity of 68% (95% CI, 66%-71%), negative predictive value of 89% (95% CI, 87%-90%), and positive predictive value of 49% (95% CI, 46%-52%). Sensitivity for status epilepticus detection was 77% (95% CI, 71%-82%) with amplitude-integrated electroencephalography and 75% (95% CI, 69%-81%) with aEEG + CDSA. The addition of color density spectral array to amplitude-integrated electroencephalography did not improve seizure detection. However, 87% of critical care medicine providers qualitatively felt that combining both modalities increased their ability to detect seizures. CONCLUSIONS Amplitude-integrated electroencephalography and aEEG + CDSA offer reasonable sensitivity and negative predictive value for seizure detection by critical care medicine providers. aEEG + CDSA did not improve seizure detection over amplitude-integrated electroencephalography alone although critical care medicine providers felt more confident using both tools combined. Amplitude-integrated electroencephalography and color density spectral array require further evaluation as a tool for screening for seizures and should only be used in conjunction with professional continuous electroencephalography review.
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Affiliation(s)
- Geneviève Du Pont-Thibodeau
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
| | - Sarah M. Sanchez
- Department of Pediatrics, The Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
| | - Abbas F. Jawad
- Department of Pediatrics, The Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
| | - Vinay M. Nadkarni
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
| | - Robert A. Berg
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
| | - Nicholas S. Abend
- Departments of Neurology and Pediatrics, The Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
| | - Alexis A. Topjian
- Department of Anesthesiology and Critical Care Medicine, The Children’s Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States
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Could EEG Monitoring in Critically Ill Children Be a Cost-effective Neuroprotective Strategy? J Clin Neurophysiol 2016; 32:486-94. [PMID: 26057408 DOI: 10.1097/wnp.0000000000000198] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
PURPOSE Electrographic status epilepticus (ESE) in critically ill children is associated with unfavorable functional outcomes, but identifying candidates for ESE management requires resource-intense EEG monitoring. A cost-effectiveness analysis was performed to estimate how much ESE identification and management would need to improve patient outcomes to make EEG monitoring strategies a good value. METHODS A decision tree was created to examine the relationships among variables important to deciding whether to perform EEG monitoring. Variable costs were estimated from their component parts, outcomes were estimated in quality-adjusted life-years, and incremental cost-effectiveness ratios were calculated to compare the relative values using four alternative EEG monitoring strategies that varied by monitoring duration. RESULTS Forty-eight hours of EEG monitoring would be worth its cost if ESE identification and management improved patient outcomes by ≥7%. If ESE identification and management improved patient outcomes by 3% to 6%, then 24 or 48 hours of EEG monitoring would be worth the cost depending on how much decision makers were willing to pay per quality-adjusted life-year gained. If ESE identification and management improved outcomes by as little as 3%, then 24 hours of EEG monitoring would be worth the cost. CONCLUSIONS EEG monitoring has the potential to be cost-effective if ESE identification and management improves patient outcomes by as little as 3%.
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Williams RP, Banwell B, Berg RA, Dlugos DJ, Donnelly M, Ichord R, Kessler SK, Lavelle J, Massey SL, Hewlett J, Parker A, Topjian A, Abend NS. Impact of an ICU EEG monitoring pathway on timeliness of therapeutic intervention and electrographic seizure termination. Epilepsia 2016; 57:786-95. [PMID: 26949220 PMCID: PMC4862885 DOI: 10.1111/epi.13354] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/12/2016] [Indexed: 12/27/2022]
Abstract
OBJECTIVES We aimed to determine whether implementation of a structured multidisciplinary electroencephalography (EEG) monitoring pathway improved the timeliness of administration of antiseizure medication in response to electrographic seizures in encephalopathic critically ill children. METHODS A multidisciplinary team developed a pathway to standardize EEG monitoring and seizure management in encephalopathic critically ill children, aiming to decrease the time from electrographic seizure onset to antiseizure medication administration. Data were collected to inform the team of improvement opportunities, which were then provided by an institutional pathway, staff education, and streamlined communication. Measurements were obtained before and after pathway implementation to assess for improvement. RESULTS We collected data on 41 patients before and 21 after pathway implementation. There were no differences between the baseline and pathway groups in demographic characteristics, acute encephalopathy etiologies, or antiseizure medications utilized. The median duration [interquartile range, IQR] from seizure onset to antiseizure medication administration was shorter for patients treated with the pathway (64 min [50, 101]) compared to patients treated prior to pathway implementation (139 min [71, 189]; p = 0.0006). The median [IQR] interval from seizure onset to antiseizure medication order was shorter for the pathway group (31 min [20, 49]) than the baseline group (71 min [33, 131]; p = 0.003). The median [IQR] interval from antiseizure medication order to administration was shorter for the pathway group (30 min [19, 40]) than the baseline group (40 min [17, 68]) (p = 0.047). Seizure termination was more likely to occur following initial antiseizure medication administration in the pathway than baseline group (67% vs. 27%, p = 0.002). SIGNIFICANCE Implementation of the pathway resulted in a significant reduction in the duration between electrographic seizure onset and antiseizure medication administration, and a significant increase in the rate of electrographic seizure termination following an initial antiseizure medication. Further study is needed to determine whether these changes are associated with improved outcomes.
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Affiliation(s)
- Ryan P. Williams
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Department of Neurology, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Brenda Banwell
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Department of Neurology, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Robert A. Berg
- Department of Critical Care Medicine, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Dennis J. Dlugos
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Department of Neurology, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | | | - Rebecca Ichord
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Department of Neurology, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Sudha Kilaru Kessler
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Department of Neurology, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Jane Lavelle
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Shavonne L. Massey
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Department of Neurology, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Jennifer Hewlett
- Department of Pharmacy Services, The Children’s Hospital of Philadelphia
| | - Allison Parker
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Alexis Topjian
- Department of Critical Care Medicine, The Children’s Hospital of Philadelphia and the University of Pennsylvania
| | - Nicholas S. Abend
- Department of Pediatrics, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Department of Neurology, The Children’s Hospital of Philadelphia and the University of Pennsylvania
- Neurodiagnostics, The Children’s Hospital of Philadelphia
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Consensus statement on continuous EEG in critically ill adults and children, part I: indications. J Clin Neurophysiol 2016; 32:87-95. [PMID: 25626778 DOI: 10.1097/wnp.0000000000000166] [Citation(s) in RCA: 380] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
INTRODUCTION Critical Care Continuous EEG (CCEEG) is a common procedure to monitor brain function in patients with altered mental status in intensive care units. There is significant variability in patient populations undergoing CCEEG and in technical specifications for CCEEG performance. METHODS The Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society developed expert consensus recommendations on the use of CCEEG in critically ill adults and children. RECOMMENDATIONS The consensus panel recommends CCEEG for diagnosis of nonconvulsive seizures, nonconvulsive status epilepticus, and other paroxysmal events, and for assessment of the efficacy of therapy for seizures and status epilepticus. The consensus panel suggests CCEEG for identification of ischemia in patients at high risk for cerebral ischemia; for assessment of level of consciousness in patients receiving intravenous sedation or pharmacologically induced coma; and for prognostication in patients after cardiac arrest. For each indication, the consensus panel describes the patient populations for which CCEEG is indicated, evidence supporting use of CCEEG, utility of video and quantitative EEG trends, suggested timing and duration of CCEEG, and suggested frequency of review and interpretation. CONCLUSION CCEEG has an important role in detection of secondary injuries such as seizures and ischemia in critically ill adults and children with altered mental status.
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How much does it cost to identify a critically ill child experiencing electrographic seizures? J Clin Neurophysiol 2016; 32:257-64. [PMID: 25626776 DOI: 10.1097/wnp.0000000000000170] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVES Electrographic seizures in critically ill children may be identified by continuous EEG monitoring. We evaluated the cost effectiveness of 4 electrographic seizure identification strategies (no EEG monitoring and EEG monitoring for 1 hour, 24 hours, or 48 hours). METHODS We created a decision tree to model the relationships among variables from a societal perspective. To provide input for the model, we estimated variable costs directly related to EEG monitoring from their component parts, and we reviewed the literature to estimate the probabilities of outcomes. We calculated incremental cost-effectiveness ratios to identify the trade-off between cost and effectiveness at different willingness-to-pay values. RESULTS Our analysis found that the preferred strategy was EEG monitoring for 1 hour, 24 hours, and 48 hours if the decision maker was willing to pay <$1,666, $1,666-$22,648, and >$22,648 per critically ill child identified with electrographic seizures, respectively. The 48-hour strategy only identified 4% more children with electrographic seizures at substantially higher cost. Sensitivity analyses found that all 3 strategies were acceptable at lower willingness-to-pay values when children with higher electrographic seizure risk were monitored. CONCLUSIONS The results of this study support monitoring of critically ill children for 24 hours because the cost to identify a critically ill child with electrographic seizures is modest. Further study is needed to predict better which children may benefit from 48 hours of EEG monitoring because the costs are much higher.
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Wilson CA. Continuous electroencephalogram detection of non-convulsive seizures in the pediatric intensive care unit: review of the utility and impact on management and outcomes. Transl Pediatr 2015; 4:283-9. [PMID: 26835390 PMCID: PMC4728999 DOI: 10.3978/j.issn.2224-4336.2015.10.02] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Non-convulsive seizures (NCS) are common among critically ill children with acute encephalopathy. Continuous electroencephalogram (CEEG) monitoring is an indispensable tool to detect NCS, which is essential to guiding management and assessing prognosis. Risk factors for NCS are highest in pediatric intensive care unit (PICU) patients with altered mental status (AMS) and a recently witnessed clinical seizure, acute changes on neuroimaging, and/or interictal abnormalities on CEEG. Screening for at least 24 hours in at risk pediatric populations is ideal, but around half of NCS may be detected within the first hour. Rapid treatment of prolonged seizures or status epilepticus is critical, as higher seizure burdens have been associated with poorer outcomes in critically ill children. This review integrates current information on critically ill children with AMS and the use of CEEGs, risk factors for NCS, duration of CEEG monitoring, and how the detection of NCS impacts management and outcomes.
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Affiliation(s)
- Carey A Wilson
- Department of Child Neurology, University of Utah School of Medicine, UT 84113, USA
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