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Veciana de Las Heras M, Sala-Padro J, Pedro-Perez J, García-Parra B, Hernández-Pérez G, Falip M. Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice. Brain Sci 2024; 14:939. [PMID: 39335433 PMCID: PMC11430096 DOI: 10.3390/brainsci14090939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 08/22/2024] [Accepted: 09/11/2024] [Indexed: 09/30/2024] Open
Abstract
The electroencephalogram (EEG) is a cornerstone tool for the diagnosis, management, and prognosis of selected patient populations. EEGs offer significant advantages such as high temporal resolution, real-time cortical function assessment, and bedside usability. The quantitative EEG (qEEG) added the possibility of long recordings being processed in a compressive manner, making EEG revision more efficient for experienced users, and more friendly for new ones. Recent advancements in commercially available software, such as Persyst, have significantly expanded and facilitated the use of qEEGs, marking the beginning of a new era in its application. As a result, there has been a notable increase in the practical, real-world utilization of qEEGs in recent years. This paper aims to provide an overview of the current applications of qEEGs in daily neurological emergencies and ICU practice, and some elementary principles of qEEGs using Persyst software in clinical settings. This article illustrates basic qEEG patterns encountered in critical care and adopts the new terminology proposed for spectrogram reporting.
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Affiliation(s)
- Misericordia Veciana de Las Heras
- Neurology Service, Neurophysiology Department, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Jacint Sala-Padro
- Neurology Service, Epilepsy Unit, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Jordi Pedro-Perez
- Neurology Service, Neurophysiology Department, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Beliu García-Parra
- Neurology Service, Neurophysiology Department, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Guillermo Hernández-Pérez
- Neurology Service, Epilepsy Unit, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
| | - Merce Falip
- Neurology Service, Epilepsy Unit, Hospital Universitari de Bellvitge-IDIBELL, Universitat de Barcelona, 08908 L'Hospitalet de Llobregat, Barcelona, Spain
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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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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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Janmohamed M, Nhu D, Shakathreh L, Gonen O, Kuhlman L, Gilligan A, Tan CW, Perucca P, O'Brien TJ, Kwan P. Comparison of Automated Spike Detection Software in Detecting Epileptiform Abnormalities on Scalp-EEG of Genetic Generalized Epilepsy Patients. J Clin Neurophysiol 2023:00004691-990000000-00110. [PMID: 37934089 DOI: 10.1097/wnp.0000000000001039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023] Open
Abstract
PURPOSE Despite availability of commercial EEG software for automated epileptiform detection, validation on real-world EEG datasets is lacking. Performance evaluation of two software packages on a large EEG dataset of patients with genetic generalized epilepsy was performed. METHODS Three epileptologists labelled IEDs manually of EEGs from three centres. All Interictal epileptiform discharge (IED) markings predicted by two commercial software (Encevis 1.11 and Persyst 14) were reviewed individually to assess for suspicious missed markings and were integrated into the reference standard if overlooked during manual annotation during a second phase. Sensitivity, precision, specificity, and F1-score were used to assess the performance of the software packages against the adjusted reference standard. RESULTS One hundred and twenty-five routine scalp EEG recordings from different subjects were included (total recording time, 310.7 hours). The total epileptiform discharge reference count was 5,907 (including spikes and fragments). Encevis demonstrated a mean sensitivity for detection of IEDs of 0.46 (SD 0.32), mean precision of 0.37 (SD 0.31), and mean F1-score of 0.43 (SD 0.23). Using the default medium setting, the sensitivity of Persyst was 0.67 (SD 0.31), with a precision of 0.49 (SD 0.33) and F1-score of 0.51 (SD 0.25). Mean specificity representing non-IED window identification and classification was 0.973 (SD 0.08) for Encevis and 0.968 (SD 0.07) for Persyst. CONCLUSIONS Automated software shows a high degree of specificity for detection of nonepileptiform background. Sensitivity and precision for IED detection is lower, but may be acceptable for initial screening in the clinical and research setting. Clinical caution and continuous expert human oversight are recommended with all EEG recordings before a diagnostic interpretation is provided based on the output of the software.
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Affiliation(s)
- Mubeen Janmohamed
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Duong Nhu
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Lubna Shakathreh
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Ofer Gonen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Levin Kuhlman
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Amanda Gilligan
- Neurosciences Clinical Institute, Epworth Healthcare Hospital, Melbourne, Victoria, Australia
| | - Chang Wei Tan
- Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | - Piero Perucca
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, The Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Epilepsy Research Centre, Department of Medicine (Austin Health), The University of Melbourne, Melbourne, Victoria, Australia; and
- Bladin-Berkovic Comprehensive Epilepsy Program, Department of Neurology, Austin Health, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
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Benedetti GM, Morgan LA, Sansevere AJ, Harrar DB, Guerriero RM, Wainwright MS, LaRovere KL, Kielian A, Ganesan SL, Press CA. The Spectrum of Quantitative EEG Utilization Across North America: A Cross-Sectional Survey. Pediatr Neurol 2023; 141:1-8. [PMID: 36731228 DOI: 10.1016/j.pediatrneurol.2022.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 11/16/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023]
Abstract
BACKGROUND Continuous electroencephalography (cEEG) is commonly used for neuromonitoring in pediatric intensive care units (PICU); however, there are barriers to real-time interpretation of EEG data. Quantitative EEG (qEEG) transforms the EEG signal into time-compressed graphs, which can be displayed at the bedside. A survey was designed to understand current PICU qEEG use. METHODS An electronic survey was sent to the Pediatric Neurocritical Care Research Group and Pediatric Status Epilepticus Research Group, and intensivists in 16 Canadian PICUs. Questions addressed demographics, qEEG acquisition and storage, clinical use, and education. RESULTS Fifty respondents from 39 institutions completed the survey (response rate 53% [39 of 74 institutions]), 76% (37 of 50) from the United States and 24% (12 of 50) from Canada. Over half of the institutions (22 of 39 [56%]) utilize qEEG in their ICUs. qEEG use was associated with having a neurocritical care (NCC) service, ≥200 NCC consults/year, ≥1500 ICU admissions/year, and ≥4 ICU EEGs/day (P < 0.05 for all). Nearly all users (92% [24 of 26]) endorsed that qEEG enhanced care of children with acute neurological injury. Lack of training in qEEG was identified as a common barrier [85% (22 of 26)]. Reviewing and reporting of qEEG was not standard at most institutions. Training was required by 14% (three of 22) of institutions, and 32% (seven of 22) had established curricula. CONCLUSIONS ICU qEEG was used at more than half of the institutions surveyed, but review, reporting, and application of this tool remained highly variable. Although providers identify qEEG as a useful tool in patient management, further studies are needed to define clinically meaningful pediatric trends, standardize reporting, and enhance educate bedside providers.
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Affiliation(s)
- Giulia M Benedetti
- Division of Pediatric Neurology, Department of Neurology, University of Washington School of Medicine, Seattle, Washington
| | - Lindsey A Morgan
- Division of Pediatric Neurology, Department of Neurology, University of Washington School of Medicine, Seattle, Washington
| | - Arnold J Sansevere
- Department of Neurology, Children's National Hospital and Departments of Neurology and Pediatrics, George Washington University School of Medicine, Washington, District of Columbia
| | - Dana B Harrar
- Department of Neurology, Children's National Hospital and Departments of Neurology and Pediatrics, George Washington University School of Medicine, Washington, District of Columbia
| | - Réjean M Guerriero
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University School of Medicine, St. Louis, Missouri
| | - Mark S Wainwright
- Division of Pediatric Neurology, Department of Neurology, University of Washington School of Medicine, Seattle, Washington
| | - Kerri L LaRovere
- Department of Neurology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts
| | - Agnieszka Kielian
- Department of Neurology, Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts
| | - Saptharishi Lalgudi Ganesan
- Paediatric Critical Care Medicine, Children's Hospital of Western Ontario, London Health Sciences Centre, London, Ontario, Canada; Department of Paediatrics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada
| | - Craig A Press
- Departments of Neurology and Pediatrics, Children's Hospital of Philadelphia and Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
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Ganguly TM, Ellis CA, Tu D, Shinohara RT, Davis KA, Litt B, Pathmanathan J. Seizure Detection in Continuous Inpatient EEG: A Comparison of Human vs Automated Review. Neurology 2022; 98:e2224-e2232. [PMID: 35410905 PMCID: PMC9162163 DOI: 10.1212/wnl.0000000000200267] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 02/08/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The aim of this work was to test the accuracy of Persyst commercially available automated seizure detection in critical care EEG by comparing automated seizure detections to human review in a manually reviewed cohort and on a large scale. METHODS Automated seizure detections (Persyst versions 12 and 13) were compared to human review in a pilot cohort of 229 seizures from 85 EEG records and then in an expanded cohort of 7,924 EEG records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for individual seizures (pilot cohort) and for entire records (pilot and expanded cohorts). We assessed EEG features associated with the accuracy of automated seizure detections. RESULTS In the pilot cohort, accuracy of automated detection for individual seizures was modest (sensitivity 0.50, PPV 0.60). At the record level (did the recording contain seizures or not?), sensitivity was higher (pilot cohort 0.78, expanded cohort 0.91), PPV was low (pilot cohort 0.40, expanded cohort 0.08), and NPV was high (pilot cohort 0.88, expanded cohort 0.97). Different software versions (version 12 vs 13) performed similarly. Sensitivity was higher for records containing focal-onset seizures compared to generalized-onset seizures (0.93 vs 0.85, p = 0.012). DISCUSSION In critical care continuous EEG recordings, automated detection of individual seizures had rates of both false negatives and false positives that bring into question its utility as a seizure alarm in clinical practice. At the level of entire EEG records, the absence of automated detections accurately predicted EEG records without true seizures. The true value of Persyst automated seizure detection appears to lie in triaging of low-risk EEGs. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that an automated seizure detection program cannot accurately identify EEG records that contain seizures.
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Affiliation(s)
| | | | | | | | | | | | - Jay Pathmanathan
- From the Department of Neurology (T.M.G., C.A.E., K.A.D., B.L., J.P.), and Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center of Excellence (D.T., R.T.S.), Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania; Department of Biostatistics, Epidemiology, & Informatics (D.T., R.T.S.) and Center for Biomedical Image Computing and Analytics (R.T.S.), University of Pennsylvania, Philadelphia.
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Effect of Early Nutritional Assessment and Nutritional Support on Immune Function and Clinical Prognosis of Critically Ill Children. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:7100238. [PMID: 35035853 PMCID: PMC8759854 DOI: 10.1155/2022/7100238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 11/29/2021] [Accepted: 12/09/2021] [Indexed: 11/18/2022]
Abstract
The aim of this study was to study the effect of early nutritional assessment and nutritional support on immune function and clinical prognosis of critically ill children. 90 critically ill children at the same level of severity admitted to the pediatric intensive care unit (PICU) of our hospital (June 2019-June 2020) were chosen as the research objects and were equally separated into the experimental group and the control group by the random number table method. The children in the control group were admitted to the PICU according to the routine process, and the nutritional support was provided to the malnourished ones. After admission to the PICU, the children in the experimental group were given nutritional assessment, nutritional risk screening, and nutritional support according to the screening results. The PICU stay time and total hospitalization time of the experimental group were obviously shorter than those of the control group (P < 0.05), the hospitalization expenses of the experimental group were obviously lower than those of the control group (P < 0.05), the clinical outcomes and immune function of the experimental group were obviously better than those of the control group (P < 0.05), and the nutrition indicators of the experimental group were obviously higher than those of the control group (P < 0.05). Early nutritional assessment and nutritional support can effectively improve the immune function and reduce the incidence of adverse clinical outcomes of critically ill children, which are worthy of clinical application and promotion.
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Lalgudi Ganesan S, Hahn CD. Spectrograms for Seizure Detection in Critically Ill Children. J Clin Neurophysiol 2022; 39:195-206. [PMID: 34510096 DOI: 10.1097/wnp.0000000000000868] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
SUMMARY Electrographic seizures are common in critically ill children and a significant proportion of these seizures are nonconvulsive. There is an association between electrographic seizures and neurophysiological disturbances, worse short- and long-term neurologic outcomes, and mortality in critically ill patients. In this context, timely diagnosis and treatment of electrographic seizures in critically ill children becomes important. However, most institutions lack the resources to support round-the-clock or frequent review of continuous EEG recordings causing significant delays in seizure diagnosis. Given the current gaps in review of continuous EEG across institutions globally, use of visually simplified, time-compressed quantitative EEG trends such as spectrograms has the potential to enhance timeliness of seizure diagnosis and treatment in critically ill children.
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Affiliation(s)
- Saptharishi Lalgudi Ganesan
- Paediatric Critical Care Medicine, Children's Hospital of Western Ontario, London Health Sciences Centre, London, ON, Canada
- Department of Paediatrics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Children's Health Research Institute, London, ON, Canada
| | - Cecil D Hahn
- Division of Paediatric Neurology, Department of Paediatrics, The Hospital for Sick Children, Toronto, ON, Canada; and
- Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Huang YC, Chao YC, Lee IC. Syndromic and non-syndromic etiologies causing neonatal hypocalcemic seizures. Front Endocrinol (Lausanne) 2022; 13:998675. [PMID: 36440223 PMCID: PMC9685421 DOI: 10.3389/fendo.2022.998675] [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] [Received: 07/20/2022] [Accepted: 10/26/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND The diagnosis of neonatal hypocalcemic seizures (HS) in newborns is made based on clinical signs and serum calcium level. Their etiology is broad and diverse, and timely detection and initiation of treatment is essential. METHODS We retrospectively reviewed 1029 patients admitted to the neonatal intensive care unit. Neonatal HS were diagnosed in 16 patients, and we compared etiologies and clinical outcomes, including clinical seizures and neurodevelopment at least over 1 year old. RESULTS The etiologies can be broadly categorized into 5 syndromic and 11 non-syndromic neonatal HS. Syndromic neonatal HS included 3 Digeorge syndrome, 1 Kleefstra syndrome and 1 Alström syndrome. Non-syndromic neonatal HS included 8 vitamin D deficiency, 1 hypoparathyroidism, and 2 hypoxic-ischemic encephalopathy. Patients with syndromic neonatal HS were found to have worse clinical outcomes than those with nonsyndromic HS. In eight patients with vitamin D deficiency, neurodevelopment was normal. Five of five patients (100%) with syndromic HS used two or more antiseizure drugs. However, among patients with non-syndromic neonatal HS, only one of 11 (9.1%) used more than one drug (p = 0.001). CONCLUSION This finding highlighted that syndromic hypocalcemic seizures in newborns have worse neurodevelopmental outcomes and are more often difficult to manage, and would benefit from a genetic diagnostic approach.
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Affiliation(s)
- Yi-Chieh Huang
- Division of Pediatric Neurology, Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Yin-Chi Chao
- Division of Pediatric Neurology, Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Inn-Chi Lee
- Division of Pediatric Neurology, Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
- Institute of Medicine, School of Medicine, Chung Shan Medical University, Taichung, Taiwan
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Abstract
SUMMARY Traditional review of EEG for seizure detection requires time and the expertise of a trained neurophysiologist; therefore, it is time- and resource-intensive. Quantitative EEG (qEEG) encompasses a variety of methods to make EEG review more efficient and allows for nonexpert review. Literature supports that qEEG is commonly used by neurophysiologists and nonexperts in clinical practice. In this review, the different types of qEEG trends and spectrograms used for seizure detection in adults, from basic concepts to clinical applications, are discussed. The merits and drawbacks of the most common qEEG trends are detailed. The authors detail the retrospective literature on qEEG sensitivity, specificity, and false alarm rate as interpreted by experts and nonexperts alike. Finally, the authors discuss the future of qEEG as a useful screening tool and speculate on the trajectory of future investigations in the field.
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11
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McCredie VA. Sonification of Seizures: Music to Our Ears. Crit Care Med 2021; 48:1383-1385. [PMID: 32826490 DOI: 10.1097/ccm.0000000000004483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Victoria A McCredie
- Interdepartmental Division of Critical Care Medicine, University of Toronto; Department of Critical Care Medicine Toronto Western Hospital University Health Network; and Krembil Research Institute, Toronto Western Hospital, Toronto, ON, Canada
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Bruns N, Felderhoff‐Müser U, Dohna‐Schwake C. aEEG as a useful tool for neuromonitoring in critically ill children - Current evidence and knowledge gaps. Acta Paediatr 2021; 110:1132-1140. [PMID: 33210762 DOI: 10.1111/apa.15676] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 11/06/2020] [Accepted: 11/16/2020] [Indexed: 12/28/2022]
Abstract
AIM Amplitude-integrated electroencephalography (aEEG) is used in children beyond neonatal age, but systematic investigations have been lacking. This mini-review summarised aEEG studies on children aged one month to 18 years, evaluated the usefulness of aEEG and identified knowledge gaps or limitations. METHODS We searched the PubMed database for articles published in English up to September 2020, and 23 papers were identified. RESULTS aEEG was frequently used to compensate for the absence of continuous full-channel EEG monitoring, particularly for detecting seizures. Interpreting background patterns was based on neonatal classifications, as reference values for older infants and children are lacking. It is possible that aEEG could predict outcomes after paediatric cardiac arrests and other conditions. Gaps in our knowledge exist with regard to normal values in healthy children and the effects of sedation on aEEG background patterns in children. CONCLUSION The main application of aEEG was detecting and treating paediatric seizures. Further research should determine reference values and investigate the potential to predict outcome after critical events or in acute neurological disease. It is likely that aEEG will play a role in paediatric critical care in the future.
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Affiliation(s)
- Nora Bruns
- Department of Paediatrics I University Hospital Essen University of Duisburg‐Essen Essen Germany
| | - Ursula Felderhoff‐Müser
- Department of Paediatrics I University Hospital Essen University of Duisburg‐Essen Essen Germany
| | - Christian Dohna‐Schwake
- Department of Paediatrics I University Hospital Essen University of Duisburg‐Essen Essen Germany
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Koren J, Hafner S, Feigl M, Baumgartner C. Systematic analysis and comparison of commercial seizure-detection software. Epilepsia 2021; 62:426-438. [PMID: 33464580 DOI: 10.1111/epi.16812] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVE To determine if three different commercially available seizure-detection software packages (Besa 2.0, Encevis 1.7, and Persyst 13) accurately detect seizures with high sensitivity, high specificity, and short detection delay in epilepsy patients undergoing long-term video-electroencephalography (EEG) monitoring (VEM). METHODS Comparison of sensitivity (detection rate), specificity (false alarm rate), and detection delay of three commercially available seizure-detection software packages in 81 randomly selected patients with epilepsy undergoing long-term VEM. RESULTS Detection rates on a per-patient basis were not significantly different between Besa (mean 67.6%, range 0-100%), Encevis (77.8%, 0-100%) and Persyst (81%, 0-100%; P = .059). False alarm rate (per hour) was significantly different between Besa (mean 0.7/h, range 0.01-6.2/h), Encevis (0.2/h, 0.01-0.5/h), and Persyst (0.9/h, 0.04-6.5/h; P < .001). Detection delay was significantly different between Besa (mean 30 s, range 0-431 s), Encevis (25 s, 2-163 s), and Persyst (20 s, 0-167 s; P = .007). Kappa statistics showed moderate to substantial agreement between the reference standard and each seizure-detection software (Besa: 0.47, 95% confidence interval [CI] 0.36-0.59; Encevis: 0.59, 95% CI 0.47-0.7; Persyst: 0.63, 95% CI 0.51-0.74). SIGNIFICANCE Three commercially available seizure-detection software packages showed similar, reasonable sensitivities on the same data set, but differed in false alarm rates and detection delay. Persyst 13 showed the highest detection rate and false alarm rate with the shortest detection delay, whereas Encevis 1.7 had a slightly lower sensitivity, the lowest false alarm rate, and longer detection delay.
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Affiliation(s)
- Johannes Koren
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria.,Department of Neurology, Clinic Hietzing, Vienna, Austria
| | | | - Moritz Feigl
- Department of Medicine I, Institute of Cancer Research, Medical University of Vienna, Austria.,Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
| | - Christoph Baumgartner
- Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Vienna, Austria.,Department of Neurology, Clinic Hietzing, Vienna, Austria.,Medical Faculty, Sigmund Freud University, Vienna, Austria
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Lee IC, Hong SY, Weng YH, Chen YT. Amplitude Integrated Electroencephalography and Continuous Electroencephalography Monitoring Is Crucial in High-Risk Infants and Their Findings Correlate With Neurodevelopmental Outcomes. Front Pediatr 2021; 9:691764. [PMID: 34414144 PMCID: PMC8369262 DOI: 10.3389/fped.2021.691764] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/08/2021] [Indexed: 11/13/2022] Open
Abstract
Background: To evaluate seizure diagnosis in sick infants in the neonatal intensive care unit (NICU) based on electroencephalography (EEG) monitoring combined with amplitude integrated electroencephalography (aEEG). Methods: We retrospectively reviewed EEG and aEEG findings and determined their correlations with neurodevelopmental outcomes at the age of >1 year in 65 patients with diagnosed seizures, encephalopathy, or both. Results: Seizure identification rate was 43.1%. The rate in nonstructural groups (hypocalcemic, hypoglycemic, and genetic seizures) was 71.4%, which was higher (p < 0.05) than the rate of 35.3% of structural brain lesion group [hypoxic-ischemic encephalopathy (HIE) and congenital brain structural malformation]. The aEEG background correlating with neurodevelopmental outcomes had 70.0% positive prediction value (PPV), 65.5%% negative prediction value (NPV), 67.7% specificity, and 67.9% sensitivity (p < 0.005). The aEEG background strongly (PPV, 93.8%; p < 0.005) correlated with the outcomes in HIE. For genetic seizures, the detected rate was high. The ictal recordings for the nonstructural seizures revealed downflected on the aEEG background initially, which differed from the structural lesion. Conclusions: EEG monitoring combined with aEEG can detect seizures, facilitating early treatment. EEG changes during seizures could exhibit delta-theta waves with or without clinical seizures in patients with brain lesions. In non-structural etiologies (hypocalcemic and KCNQ2 seizures), aEEG initially exhibited lower background during seizures that could aid in differentiating these EEG changes from those of other etiologies. The aEEG background was correlated with neurodevelopmental outcome and exhibited high PPV but not NPV in neonatal HIE.
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Affiliation(s)
- Inn-Chi Lee
- Division of Pediatric Neurology, Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan.,Institute of Medicine, School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Syuan-Yu Hong
- Division of Pediatrics Neurology, Department of Pediatrics, Children's Hospital, China Medical University, Taichung, Taiwan
| | - Yi-Ho Weng
- Division of Pediatric Neurology, Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Yi-Ting Chen
- Division of Pediatric Neurology, Department of Pediatrics, Chung Shan Medical University Hospital, Taichung, Taiwan
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