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Guerrero-Aranda A, Alvarado-Rodríguez FJ, Enríquez-Zaragoza A, Carmona-Huerta J, González-Garrido AA. Assessment of Classical and Non-Classical Quantitative Electroencephalographic Measures in Patients with Substance Use Disorders. Clin EEG Neurosci 2024; 55:296-304. [PMID: 37849312 DOI: 10.1177/15500594231208245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
Background: People diagnosed with substance use disorders (SUDs) are at risk for impairment of brain function and structure. However, physicians still do not have any clinical biomarker of brain impairment that helps diagnose or treat these patients when needed. The most common method to study these patients is the classical electroencephalographic (EEG) analyses of absolute and relative powers, but this has limited individual clinical applicability. Other non-classical measures such as frequency band ratios and entropy show promise in these patients. Therefore, there is a need to expand the use of quantitative (q)EEG beyond classical measures in clinical populations. Our aim is to assess a group of classical and non-classical qEEG measures in a population with SUDs. Methods: We selected 56 non-medicated and drug-free adult patients (30 males) diagnosed with SUDs and admitted to Rehabilitation Clinics. According to qualitative EEG findings, patients were divided into four groups. We estimated the absolute and relative powers and calculated the entropy, and the alpha/(delta + theta) ratio. Results: Our findings showed a significant variability of absolute and relative powers among patients with SUDs. We also observed a decrease in the EEG-based entropy index and alpha/(theta + delta) ratio, mainly in posterior regions, in the patients with abnormal qualitative EEG. Conclusions: Our findings support the view that the power spectrum is not a reliable biomarker on an individual level. Thus, we suggest shifting the approach from the power spectrum toward other potential methods and designs that may offer greater clinical possibilities.
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Affiliation(s)
- Alioth Guerrero-Aranda
- University Center "Los Valles", University of Guadalajara, Ameca, México
- Department of EEG and Brain Mapping, Teleeg, México
| | | | | | - Jaime Carmona-Huerta
- University Center of Health Sciences, University of Guadalajara, Guadalajara, México
- Jalisco Institute of Mental Health, Salme, México
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Guerrero-Aranda A, Ramírez-Ponce E, Ramos-Quezada O, Paredes O, Guzmán-Quezada E, Genel-Espinoza A, Romo-Vazquez R, Vélez-Pérez H. Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns. Front Hum Neurosci 2023; 17:1274834. [PMID: 37915754 PMCID: PMC10616594 DOI: 10.3389/fnhum.2023.1274834] [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: 08/08/2023] [Accepted: 09/21/2023] [Indexed: 11/03/2023] Open
Abstract
A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.
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Affiliation(s)
- Alioth Guerrero-Aranda
- Depto. de Ciencias de la Salud, Centro Universitario de Los Valles, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
- Clínica de Epilepsia, Hospital “Country 2000, ” Guadalajara, Jalisco, Mexico
| | - Evelin Ramírez-Ponce
- Depto. de Bioingeniería Traslacional, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - Oscar Ramos-Quezada
- Depto. de Bioingeniería Traslacional, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - Omar Paredes
- Depto. de Bioingeniería Traslacional, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
- Mecatrónica, Instituto Tecnológico y de Estudios Superiores de Monterrey, Escuela de Ingenierías y Ciencias (ITESM) Campus Guadalajara, Zapopan, Mexico
| | - Erick Guzmán-Quezada
- Depto. de Ciencias Computacionales, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
- Depto. de Electromecánica, Universidad Autónoma de Guadalajara, Zapopan, Jalisco, Mexico
| | | | - Rebeca Romo-Vazquez
- Depto. de Bioingeniería Traslacional, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
| | - Hugo Vélez-Pérez
- Depto. de Bioingeniería Traslacional, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico
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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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Alkhachroum A, Ganesan SL, Koren JP, Kromm J, Massad N, Reyes RA, Miller MR, Roh D, Agarwal S, Park S, Claassen J. Quantitative EEG-Based Seizure Estimation in Super-Refractory Status Epilepticus. Neurocrit Care 2022; 36:897-904. [PMID: 34791594 PMCID: PMC9987776 DOI: 10.1007/s12028-021-01395-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/04/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND The objective of this study was to evaluate the accuracy of seizure burden in patients with super-refractory status epilepticus (SRSE) by using quantitative electroencephalography (qEEG). METHODS EEG recordings from 69 patients with SRSE (2009-2019) were reviewed and annotated for seizures by three groups of reviewers: two board-certified neurophysiologists using only raw EEG (gold standard), two neurocritical care providers with substantial experience in qEEG analysis (qEEG experts), and two inexperienced qEEG readers (qEEG novices) using only a qEEG trend panel. RESULTS Raw EEG experts identified 35 (51%) patients with seizures, accounting for 2950 seizures (3,126 min). qEEG experts had a sensitivity of 93%, a specificity of 61%, a false positive rate of 6.5 per day, and good agreement (κ = 0.64) between both qEEG experts. qEEG novices had a sensitivity of 98.5%, a specificity of 13%, a false positive rate of 15 per day, and fair agreement (κ = 0.4) between both qEEG novices. Seizure burden was not different between the qEEG experts and the gold standard (3,257 vs. 3,126 min), whereas qEEG novices reported higher burden (6066 vs. 3126 min). CONCLUSIONS Both qEEG experts and novices had a high sensitivity but a low specificity for seizure detection in patients with SRSE. qEEG could be a useful tool for qEEG experts to estimate seizure burden in patients with SRSE.
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Affiliation(s)
- Ayham Alkhachroum
- Department of Neurology, Columbia University and NewYork Presbyterian Hospital, New York, NY, USA
- Department of Neurology, University of Miami, Miami, FL, USA
- Department of Neurology, Jackson Memorial Hospital, Miami, FL, USA
| | - Saptharishi Lalgudi Ganesan
- Children's Hospital of Western Ontario, London Health Sciences Centre, London, ON, Canada
- Department of Paediatrics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - Julie Kromm
- Departments of Critical Care Medicine and Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Nina Massad
- Department of Neurology, Columbia University and NewYork Presbyterian Hospital, New York, NY, USA
| | - Renz A Reyes
- Department of Neurology, Columbia University and NewYork Presbyterian Hospital, New York, NY, USA
| | - Michael R Miller
- Children's Hospital of Western Ontario, London Health Sciences Centre, London, ON, Canada
- Department of Paediatrics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - David Roh
- Department of Neurology, Columbia University and NewYork Presbyterian Hospital, New York, NY, USA
| | - Sachin Agarwal
- Department of Neurology, Columbia University and NewYork Presbyterian Hospital, New York, NY, USA
| | - Soojin Park
- Department of Neurology, Columbia University and NewYork Presbyterian Hospital, New York, NY, USA
| | - Jan Claassen
- Department of Neurology, Columbia University and NewYork Presbyterian Hospital, New York, NY, USA.
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Prendergast E, Mills MG, Kurz J, Goldstein J, Pardo AC. Implementing Quantitative Electroencephalogram Monitoring by Nurses in a Pediatric Intensive Care Unit. Crit Care Nurse 2022; 42:32-40. [PMID: 35362080 DOI: 10.4037/ccn2022680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BACKGROUND Nonconvulsive seizures occur frequently in pediatric intensive care unit patients and can be impossible to detect clinically without electroencephalogram monitoring. Quantitative electroencephalography uses mathematical signal analysis to compress data, monitoring trends over time. Nonneurologists can identify seizures with quantitative electroencephalography, but data on its use in the clinical setting are limited. LOCAL PROBLEM Bedside quantitative electroencephalography was implemented and nurses received education on its use for seizure detection. This quality improvement project aimed to describe the time between nurses' recognition of electrographic seizures and seizure treatment. METHODS Education was provided in phases over several months. Retrospective medical record review evaluated quantitative electroencephalograms and medication interventions from September 2019 through March 2020. A bedside form was used to measure nurses' use of quantitative electroencephalograms, change recognition, clinician notification, and seizure treatment. A nurse survey evaluated the education after implementation. RESULTS Data included 44 electroencephalograms from 30 pediatric intensive care unit patients aged 18 years or less with electroencephalogram monitoring durations of 4 hours or longer. Nurses monitored quantitative electroencephalograms in 73% of cases, documented at least 1 change in the quantitative electroencephalogram display in 28% of these cases, and contacted the neurocritical care team in 78% of cases in which they documented a change. Seizure treatment was initiated in response to the nursing call in 1 patient. Time to treatment was approximately 20 minutes. CONCLUSIONS An education program for quantitative electroencephalogram interpretation by nurse providers is feasible yet complex, requiring multiple reeducation cycles.
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Affiliation(s)
- Erica Prendergast
- Erica Prendergast is a pediatric neurocritical care nurse practitioner, Ruth D. & Ken M. Davee Pediatric Neurocritical Care Program at Ann & Robert H. Lurie Children's Hospital of Chicago, Illinois
| | - Michele Grimason Mills
- Michele Grimason Mills is a neurocritical care nurse practitioner, Ruth D. & Ken M. Davee Pediatric Neurocritical Care Program at Ann & Robert H. Lurie Children's Hospital of Chicago
| | - Jonathan Kurz
- Jonathan Kurz is an assistant professor of pediatrics and neurology, Northwestern University Feinberg School of Medicine, Chicago, and a pediatric neurologist, Ruth D. & Ken M. Davee Pediatric Neuro-critical Care Program at Ann & Robert H. Lurie Children's Hospital of Chicago
| | - Joshua Goldstein
- Joshua Goldstein is an associate professor of pediatrics and neurology, Northwestern University Feinberg School of Medicine, and a pediatric neurologist and epileptologist, Ruth D. & Ken M. Davee Pediatric Neurocritical Care Program at Ann & Robert H. Lurie Children's Hospital of Chicago
| | - Andrea C Pardo
- Andrea C. Pardo is an associate professor of pediatrics and neurology, Northwestern University Feinberg School of Medicine, and a pediatric neurologist and Director of the Ruth D. & Ken M. Davee Pediatric Neurocritical Care Program at Ann & Robert H. Lurie Children's Hospital of Chicago
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Munjal NK, Bergman I, Scheuer ML, Genovese CR, Simon DW, Patterson CM. Quantitative Electroencephalography (EEG) Predicting Acute Neurologic Deterioration in the Pediatric Intensive Care Unit: A Case Series. J Child Neurol 2022; 37:73-79. [PMID: 34816755 PMCID: PMC8691173 DOI: 10.1177/08830738211053908] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Introduction: Continuous neurologic assessment in the pediatric intensive care unit is challenging. Current electroencephalography (EEG) guidelines support monitoring status epilepticus, vasospasm detection, and cardiac arrest prognostication, but the scope of brain dysfunction in critically ill patients is larger. We explore quantitative EEG in pediatric intensive care unit patients with neurologic emergencies to identify quantitative EEG changes preceding clinical detection. Methods: From 2017 to 2020, we identified pediatric intensive care unit patients at a single quaternary children's hospital with EEG recording near or during acute neurologic deterioration. Quantitative EEG analysis was performed using Persyst P14 (Persyst Development Corporation). Included features were fast Fourier transform, asymmetry, and rhythmicity spectrograms, "from-baseline" patient-specific versions of the above features, and quantitative suppression ratio. Timing of quantitative EEG changes was determined by expert review and prespecified quantitative EEG alert thresholds. Clinical detection of neurologic deterioration was defined pre hoc and determined through electronic medical record documentation of examination change or intervention. Results: Ten patients were identified, age 23 months to 27 years, and 50% were female. Of 10 patients, 6 died, 1 had new morbidity, and 3 had good recovery; the most common cause of death was cerebral edema and herniation. The fastest changes were on "from-baseline" fast Fourier transform spectrograms, whereas persistent changes on asymmetry spectrograms and suppression ratio were most associated with morbidity and mortality. Median time from first quantitative EEG change to clinical detection was 332 minutes (interquartile range: 201-456 minutes). Conclusion: Quantitative EEG is potentially useful in earlier detection of neurologic deterioration in critically ill pediatric intensive care unit patients. Further work is required to quantify the predictive value, measure improvement in outcome, and automate the process.
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Affiliation(s)
- Neil K. Munjal
- UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Ira Bergman
- UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
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Continuous Electroencephalographic Training for Neuroscience Intensive Care Unit Nurses: A Feasibility Study. J Neurosci Nurs 2021; 52:245-250. [PMID: 32740316 DOI: 10.1097/jnn.0000000000000535] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Use of continuous electroencephalographic (cEEG) monitoring has more than doubled at our institution for the last 4 years. Although intensive care unit cEEG is reviewed remotely by board-certified epileptologists every 4 to 6 hours, there are inherent delays between occurrence, recognition, and treatment of epileptiform activity. Neuroscience intensive care unit (NSICU) nurses are uniquely positioned to monitor cEEG in real time yet do not receive formal training. The purpose of this study was to evaluate the effectiveness of an education program to teach nurses to monitor cEEG, identify a burst suppression pattern, and measure the duration of suppression. METHODS We performed a retrospective analysis of pretest and posttest data. All NSICU nurses (40) were invited to complete the pretest (PT-0), with 25 participating. Learning style/preference, demographics, comfort with cEEG, and knowledge of EEG fundamentals were assessed. A convenience cohort of NSICU nurses (13) were selected to undergo EEG training. Posttests evaluating EEG fundamental knowledge were completed immediately after training (PT-1), at 3 months (PT-3), and at 6 months (PT-6). The cohort also completed a burst suppression module after the training, which assessed ability to quantify the duration of suppression. RESULTS Mean cohort test scores significantly improved after the training (P < .001). All nurses showed improvement in test scores, and 76.9% passed PT-1 (a score of 80% or higher). Reported mean comfort level with EEG also significantly improved after the training (P = .001). There was no significant difference between mean cohort scores between PT-1, PT-3, and PT-6 (all 88.6%; P = 1.000). Mean cohort score from the bust suppression module was 73%, with test scores ranging from 31% to 93%. CONCLUSIONS NSICU nurses can be taught fundamentals of cEEG, to identify a burst suppression pattern, and to quantify the duration of suppression. Further research is needed to determine whether this knowledge can be translated into clinical competency and affect patient care.
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Kromm J, Fiest KM, Alkhachroum A, Josephson C, Kramer A, Jette N. Structure and Outcomes of Educational Programs for Training Non-electroencephalographers in Performing and Screening Adult EEG: A Systematic Review. Neurocrit Care 2021; 35:894-912. [PMID: 33591537 DOI: 10.1007/s12028-020-01172-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 12/01/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVE To qualitatively and quantitatively summarize curricula, teaching methods, and effectiveness of educational programs for training bedside care providers (non-experts) in the performance and screening of adult electroencephalography (EEG) for nonconvulsive seizures and other patterns. METHODS PRISMA methodological standards were followed. MEDLINE, EMBASE, Cochrane, CINAHL, WOS, Scopus, and MedEdPORTAL databases were searched from inception until February 26, 2020 with no restrictions. Abstract and full-text review was completed in duplicate. Studies were included if they were original research; involved non-experts performing, troubleshooting, or screening adult EEG; and provided qualitative descriptions of curricula and teaching methods and/or quantitative assessment of non-experts (vs gold standard EEG performance by neurodiagnostic technologists or interpretation by neurophysiologists). Data were extracted in duplicate. A content analysis and a meta-narrative review were performed. RESULTS Of 2430 abstracts, 35 studies were included. Sensitivity and specificity of seizure identification varied from 38 to 100% and 65 to 100% for raw EEG; 40 to 93% and 38 to 95% for quantitative EEG, and 95 to 100% and 65 to 85% for sonified EEG, respectively. Non-expert performance of EEG resulted in statistically significant reduced delay (86 min, p < 0.0001; 196 min, p < 0.0001; 667 min, p < 0.005) in EEG completion and changes in management in approximately 40% of patients. Non-experts who were trained included physicians, nurses, neurodiagnostic technicians, and medical students. Numerous teaching methods were utilized and often combined, with instructional and hands-on training being most common. CONCLUSIONS Several different bedside providers can be educated to perform and screen adult EEG, particularly for the purpose of diagnosing nonconvulsive seizures. While further rigorous research is warranted, this review demonstrates several potential bridges by which EEG may be integrated into the care of critically ill patients.
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Affiliation(s)
- Julie Kromm
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Room 04112, Foothills Medical Centre, McCaig Tower, 3134 Hospital Drive NW, Calgary, Alberta, T2N 5A1, Canada. .,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada. .,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada.
| | - Kirsten M Fiest
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Room 04112, Foothills Medical Centre, McCaig Tower, 3134 Hospital Drive NW, Calgary, Alberta, T2N 5A1, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Ayham Alkhachroum
- Neurocritical Care Division, Miller School of Medicine, University of Miami, Miami, USA
| | - Colin Josephson
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Andreas Kramer
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Room 04112, Foothills Medical Centre, McCaig Tower, 3134 Hospital Drive NW, Calgary, Alberta, T2N 5A1, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Nathalie Jette
- Department of Neurology, Icahn School of Medicine, Mount Sinai, New York, USA
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Grippo A, Amantini A. Continuous EEG on the intensive care unit: Terminology standardization of spectrogram patterns will improve the clinical utility of quantitative EEG. Clin Neurophysiol 2020; 131:2281-2283. [DOI: 10.1016/j.clinph.2020.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 07/03/2020] [Indexed: 11/30/2022]
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10
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Singla S, Garcia GE, Rovenolt GE, Soto AL, Gilmore EJ, Hirsch LJ, Blumenfeld H, Sheth KN, Omay SB, Struck AF, Westover MB, Kim JA. Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury. Curr Neurol Neurosci Rep 2020; 20:42. [PMID: 32715371 DOI: 10.1007/s11910-020-01060-4] [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] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Acute brain injury (ABI) is a broad category of pathologies, including traumatic brain injury, and is commonly complicated by seizures. Electroencephalogram (EEG) studies are used to detect seizures or other epileptiform patterns. This review seeks to clarify EEG findings relevant to ABI, explore practical barriers limiting EEG implementation, discuss strategies to leverage EEG monitoring in various clinical settings, and suggest an approach to utilize EEG for triage. RECENT FINDINGS Current literature suggests there is an increased morbidity and mortality risk associated with seizures or patterns on the ictal-interictal continuum (IIC) due to ABI. Further, increased use of EEG is associated with better clinical outcomes. However, there are many logistical barriers to successful EEG implementation that prohibit its ubiquitous use. Solutions to these limitations include the use of rapid EEG systems, non-expert EEG analysis, machine learning algorithms, and the incorporation of EEG data into prognostic models.
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Affiliation(s)
- Shobhit Singla
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Gabriella E Garcia
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Grace E Rovenolt
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Alexandria L Soto
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Emily J Gilmore
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Lawrence J Hirsch
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Hal Blumenfeld
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Kevin N Sheth
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - S Bulent Omay
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA
| | - Aaron F Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Jennifer A Kim
- Department of Neurology, Yale University, Box 208018, 15 York Street
- LLCI Room 1004B, New Haven, CT, 06520, USA.
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