1
|
Montazeri S, Nevalainen P, Metsäranta M, Stevenson NJ, Vanhatalo S. Clinical outcome prediction with an automated EEG trend, Brain State of the Newborn, after perinatal asphyxia. Clin Neurophysiol 2024; 162:68-76. [PMID: 38583406 DOI: 10.1016/j.clinph.2024.03.007] [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: 07/14/2023] [Revised: 02/13/2024] [Accepted: 03/11/2024] [Indexed: 04/09/2024]
Abstract
OBJECTIVE To evaluate the utility of a fully automated deep learning -based quantitative measure of EEG background, Brain State of the Newborn (BSN), for early prediction of clinical outcome at four years of age. METHODS The EEG monitoring data from eighty consecutive newborns was analyzed using the automatically computed BSN trend. BSN levels during the first days of life (a of total 5427 hours) were compared to four clinical outcome categories: favorable, cerebral palsy (CP), CP with epilepsy, and death. The time dependent changes in BSN-based prediction for different outcomes were assessed by positive/negative predictive value (PPV/NPV) and by estimating the area under the receiver operating characteristic curve (AUC). RESULTS The BSN values were closely aligned with four visually determined EEG categories (p < 0·001), as well as with respect to clinical milestones of EEG recovery in perinatal Hypoxic Ischemic Encephalopathy (HIE; p < 0·003). Favorable outcome was related to a rapid recovery of the BSN trend, while worse outcomes related to a slow BSN recovery. Outcome predictions with BSN were accurate from 6 to 48 hours of age: For the favorable outcome, the AUC ranged from 95 to 99% (peak at 12 hours), and for the poor outcome the AUC ranged from 96 to 99% (peak at 12 hours). The optimal BSN levels for each PPV/NPV estimate changed substantially during the first 48 hours, ranging from 20 to 80. CONCLUSIONS We show that the BSN provides an automated, objective, and continuous measure of brain activity in newborns. SIGNIFICANCE The BSN trend discloses the dynamic nature that exists in both cerebral recovery and outcome prediction, supports individualized patient care, rapid stratification and early prognosis.
Collapse
Affiliation(s)
- Saeed Montazeri
- BABA Center, Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland.
| | - Päivi Nevalainen
- BABA Center, Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Clinical Neurophysiology, Epilepsia Helsinki, Full Member of ERN Epicare, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Marjo Metsäranta
- Department of Pediatrics, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland; Department of Clinical Neurophysiology, Epilepsia Helsinki, Full Member of ERN Epicare, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| |
Collapse
|
2
|
Tuiskula A, Pospelov AS, Nevalainen P, Montazeri S, Metsäranta M, Haataja L, Stevenson N, Tokariev A, Vanhatalo S. Quantitative EEG features during the first day correlate to clinical outcome in perinatal asphyxia. Pediatr Res 2024:10.1038/s41390-024-03235-y. [PMID: 38745028 DOI: 10.1038/s41390-024-03235-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 03/21/2024] [Accepted: 03/24/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVE To assess whether computational electroencephalogram (EEG) measures during the first day of life correlate to clinical outcomes in infants with perinatal asphyxia with or without hypoxic-ischemic encephalopathy (HIE). METHODS We analyzed four-channel EEG monitoring data from 91 newborn infants after perinatal asphyxia. Altogether 42 automatically computed amplitude- and synchrony-related EEG features were extracted as 2-hourly average at very early (6 h) and early (24 h) postnatal age; they were correlated to the severity of HIE in all infants, and to four clinical outcomes available in a subcohort of 40 newborns: time to full oral feeding (nasogastric tube NGT), neonatal brain MRI, Hammersmith Infant Neurological Examination (HINE) at three months, and Griffiths Scales at two years. RESULTS At 6 h, altogether 14 (33%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.61, p < 0.05), and one feature correlated to NGT ([r]= 0.50). At 24 h, altogether 13 (31%) EEG features correlated significantly to the HIE grade ([r]= 0.39-0.56), six features correlated to NGT ([r]= 0.36-0.49) and HINE ([r]= 0.39-0.61), while no features correlated to MRI or Griffiths Scales. CONCLUSIONS Our results show that the automatically computed measures of early cortical activity may provide outcome biomarkers for clinical and research purposes. IMPACT The early EEG background and its recovery after perinatal asphyxia reflect initial severity of encephalopathy and its clinical recovery, respectively. Computational EEG features from the early hours of life show robust correlations to HIE grades and to early clinical outcomes. Computational EEG features may have potential to be used as cortical activity biomarkers in early hours after perinatal asphyxia.
Collapse
Affiliation(s)
- Anna Tuiskula
- Department of Pediatrics, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
| | - Alexey S Pospelov
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Päivi Nevalainen
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurophysiology, Children's Hospital, HUS Diagnostic Center, and Epilepsia Helsinki, full member of ERN EpiCare University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Saeed Montazeri
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Marjo Metsäranta
- Department of Pediatrics, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Leena Haataja
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Nathan Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Anton Tokariev
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
- Department of Clinical Neurophysiology, Children's Hospital, HUS Diagnostic Center, and Epilepsia Helsinki, full member of ERN EpiCare University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| |
Collapse
|
3
|
Chahine A, Chenouard A, Joram N, Berthomieu L, Du Pont-Thibodeau G, Leclere B, Liet JM, Maminirina P, Leclair-Visonneau L, Breinig S, Bourgoin P. Continuous Amplitude-Integrated Electroencephalography During Neonatal and Pediatric Extracorporeal Membrane Oxygenation. J Clin Neurophysiol 2023; 40:317-324. [PMID: 34387276 DOI: 10.1097/wnp.0000000000000890] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Early prognostication of neurologic outcome in neonates and children supported with extra-corporeal membrane oxygenation (ECMO) is challenging. Amplitude-integrated EEG (aEEG) offers the advantages of continuous monitoring and 24-hours availability at the bedside for intensive care unit providers. The objective of this study was to describe the early electrophysiological background patterns of neonates and children undergoing ECMO and their association with neurologic outcomes. METHODS This was a retrospective review of neonates and children undergoing ECMO and monitored with aEEG. Amplitude-integrated EEG was summarized as an aEEG background score determined within the first 24 hours of ECMO and divided in 3-hour periods. Screening for electrical seizures was performed throughout the full ECMO duration. Neurologic outcome was defined by the Pediatric Cerebral Performance Category score at hospital discharge. RESULTS Seventy-three patients (median age 79 days [8-660], median weight 4.78 kg [3.24-10.02]) were included in the analysis. Thirty-two patients had a favorable neurologic outcome and 41 had an unfavorable neurologic outcome group at hospital discharge. A 24-hour aEEG background score >17 was associated with an unfavorable outcome with a sensitivity of 44%, a specificity of 97%, a positive predictive value of 95%, and a negative predictive value of 57%. In multivariate analysis, 24-hour aEEG background score was associated with unfavorable outcome (hazard ratio, 6.1; p = 0.001; 95% confidence interval, 2.31-16.24). The presence of seizures was not associated with neurologic outcome at hospital discharge. CONCLUSIONS Continuous aEEG provides accurate neurologic prognostication in neonates and children supported with ECMO. Early aEEG monitoring may help intensive care unit providers to guide clinical care and family counseling.
Collapse
Affiliation(s)
- Adela Chahine
- Pediatric Intensive Care Unit, University Hospital, Toulouse, France
| | - Alexis Chenouard
- Pediatric Intensive Care Unit, University Hospital, Nantes, France
| | - Nicolas Joram
- Pediatric Intensive Care Unit, University Hospital, Nantes, France
| | - Lionel Berthomieu
- Pediatric Intensive Care Unit, University Hospital, Toulouse, France
| | | | - Brice Leclere
- Department of Medical Evaluation and Epidemiology, Nantes University Hospital, Nantes, France
| | - Jean-Michel Liet
- Pediatric Intensive Care Unit, University Hospital, Nantes, France
| | | | | | - Sophie Breinig
- Pediatric Intensive Care Unit, University Hospital, Toulouse, France
| | - Pierre Bourgoin
- Pediatric Intensive Care Unit and Pediatric Cardiac Anesthesia, University Hospital, Nantes, France
| |
Collapse
|
4
|
Bruns N, Schara-Schmidt U, Dohna-Schwake C. [Pediatric neurocritical care]. DER NERVENARZT 2023; 94:75-83. [PMID: 36645451 DOI: 10.1007/s00115-022-01424-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/06/2022] [Indexed: 01/17/2023]
Abstract
Pediatric neurocritical care requires multidisciplinary expertise for the care of critically ill children. Approximately 14-16% of critically ill children in pediatric intensive care suffer from a primary neurological disease, whereby cardiac arrest and severe traumatic brain injury play major roles in Europe. The short-term goal of interventions in the pediatric intensive care unit is to stabilize vital functions, whereas the overarching goal is to achieve survival without neurological damage that enables fulfillment of the individual developmental physiological potential. For this reason, evidence-based methods for brain monitoring during the acute phase and recovery are necessary, which can be performed clinically or with technical devices. This applies to critically ill children with primary neurological diseases and for all children at risk for secondary neurological insults. Patients with diseases of the peripheral nervous system are also treated in pediatric intensive care medicine. In these patients, the primary aim frequently consists of bridging the time until recovery after acute deterioration, for example during an infection. In these patients, monitoring the cerebral function can be especially challenging, because due to the underlying disease the results of the examination cannot be interpreted in the same way as for previously neurologically healthy children. This article summarizes the complexity of pediatric neurocritical care by presenting examples of diagnostic and therapeutic approaches in the context of various neurological diseases that can be routinely encountered in the pediatric intensive care unit and can only be successfully treated by multidisciplinary teams.
Collapse
Affiliation(s)
- Nora Bruns
- Zentrum für Kinder- und Jugendmedizin, Klinik für Kinderheilkunde I (Neonatologie, Pädiatrische Intensivmedizin, Neuropädiatrie), Universitätsklinikum Essen, Hufelandstr. 55, 45147, Essen, Deutschland.
- Center for Translational and Behavioral Sciences (TNBS), Universitätsklinikum Essen, Hufelandstr. 55, 45147, Essen, Deutschland.
| | - Ulrike Schara-Schmidt
- Zentrum für Kinder- und Jugendmedizin, Klinik für Kinderheilkunde I (Neonatologie, Pädiatrische Intensivmedizin, Neuropädiatrie), Universitätsklinikum Essen, Hufelandstr. 55, 45147, Essen, Deutschland
- Center for Translational and Behavioral Sciences (TNBS), Universitätsklinikum Essen, Hufelandstr. 55, 45147, Essen, Deutschland
| | - Christian Dohna-Schwake
- Zentrum für Kinder- und Jugendmedizin, Klinik für Kinderheilkunde I (Neonatologie, Pädiatrische Intensivmedizin, Neuropädiatrie), Universitätsklinikum Essen, Hufelandstr. 55, 45147, Essen, Deutschland
- Center for Translational and Behavioral Sciences (TNBS), Universitätsklinikum Essen, Hufelandstr. 55, 45147, Essen, Deutschland
| |
Collapse
|
5
|
Moghadam SM, Airaksinen M, Nevalainen P, Marchi V, Hellström-Westas L, Stevenson NJ, Vanhatalo S. An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation. Lancet Digit Health 2022; 4:e884-e892. [PMID: 36427950 DOI: 10.1016/s2589-7500(22)00196-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 10/06/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Electroencephalogram (EEG) monitoring is recommended as routine in newborn neurocritical care to facilitate early therapeutic decisions and outcome predictions. EEG's larger-scale implementation is, however, hindered by the shortage of expertise needed for the interpretation of spontaneous cortical activity, the EEG background. We developed an automated algorithm that transforms EEG recordings to quantified interpretations of EEG background and provides simple intuitive visualisations in patient monitors. METHODS In this method-development and proof-of-concept study, we collected visually classified EEGs from infants recovering from birth asphyxia or stroke. We used unsupervised learning methods to explore latent EEG characteristics, which guided the supervised training of a deep learning-based classifier. We assessed the classifier performance using cross-validation and an external validation dataset. We constructed a novel measure of cortical function, brain state of the newborn (BSN), from the novel EEG background classifier and a previously published sleep-state classifier. We estimated clinical utility of the BSN by identification of two key items in newborn brain monitoring, the onset of continuous cortical activity and sleep-wake cycling, compared with the visual interpretation of the raw EEG signal and the amplitude-integrated (aEEG) trend. FINDINGS We collected 2561 h of EEG from 39 infants (gestational age 35·0-42·1 weeks; postnatal age 0-7 days). The external validation dataset included 105 h of EEG from 31 full-term infants. The overall accuracy of the EEG background classifier was 92% in the whole cohort (95% CI 91-96; range 85-100 for individual infants). BSN trend values were closely related to the onset of continuous EEG activity or sleep-wake cycling, and BSN levels showed robust difference between aEEG categories. The temporal evolution of the BSN trends showed early diverging trajectories in infants with severely abnormal outcomes. INTERPRETATION The BSN trend can be implemented in bedside patient monitors as an EEG interpretation that is intuitive, transparent, and clinically explainable. A quantitative trend measure of brain function might harmonise practices across medical centres, enable wider use of brain monitoring in neurocritical care, and might facilitate clinical intervention trials. FUNDING European Training Networks Funding Scheme, the Academy of Finland, Finnish Pediatric Foundation (Lastentautiensäätiö), Aivosäätiö, Sigrid Juselius Foundation, HUS Children's Hospital, HUS Diagnostic Center, National Health and Medical Research Council of Australia.
Collapse
Affiliation(s)
- Saeed Montazeri Moghadam
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland.
| | - Manu Airaksinen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland
| | - Päivi Nevalainen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Viviana Marchi
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Fondazione Stella Maris Foundation, Pisa, Italy
| | | | - Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, HUS imaging, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Department of Physiology, University of Helsinki, Helsinki, Finland
| |
Collapse
|
6
|
Greve S, Löffelhardt VT, Della Marina A, Felderhoff-Müser U, Dohna-Schwake C, Bruns N. The impact of age and electrode position on amplitude-integrated EEGs in children from 1 month to 17 years of age. Front Neurol 2022; 13:952193. [PMID: 36090865 PMCID: PMC9452771 DOI: 10.3389/fneur.2022.952193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 08/03/2022] [Indexed: 11/13/2022] Open
Abstract
AimAmplitude-integrated electroencephalography (aEEG) is used to monitor electrocortical activity in critically ill children but age-specific reference values are lacking. We aimed to assess the impact of age and electrode position on aEEG amplitudes and derive normal values for pediatric aEEGs from neurologically healthy children.MethodsNormal EEGs from awake children aged 1 month to 17 years (213 female, 237 male) without neurological disease or neuroactive medication were retrospectively converted into aEEGs. Two observers manually measured the upper and lower amplitude borders of the C3 – P3, C4 – P4, C3 – C4, P3 – P4, and Fp1 – Fp2 channels of the 10–20 system. Percentiles (10th, 25th, 50th, 75th, 90th) were calculated for each age group (<1 year, 1 year, 2–5 years, 6–9 years, 10–13 years, 14–17 years).ResultsAmplitude heights and curves differed between channels without sex-specific differences. During the first 2 years of life, upper and lower amplitudes of all but the Fp1–Fp2 channel increased and then declined until 17 years. The decline of the upper Fp1–Fp2 amplitude began at 4 years, while the lower amplitude declined from the 1st year of life.ConclusionsaEEG interpretation must account for age and electrode positions but not for sex in infants and children.
Collapse
Affiliation(s)
- Sandra Greve
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Verena Tamara Löffelhardt
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Adela Della Marina
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- C-TNBS, Centre for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Ursula Felderhoff-Müser
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- C-TNBS, Centre for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christian Dohna-Schwake
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Nora Bruns
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- C-TNBS, Centre for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- *Correspondence: Nora Bruns
| |
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|
8
|
Beck J, Grosjean C, Bednarek N, Loron G. Amplitude-Integrated EEG Monitoring in Pediatric Intensive Care: Prognostic Value in Meningitis before One Year of Age. CHILDREN 2022; 9:children9050668. [PMID: 35626845 PMCID: PMC9140190 DOI: 10.3390/children9050668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/30/2022] [Accepted: 05/01/2022] [Indexed: 11/16/2022]
Abstract
Pediatric morbidity from meningitis remains considerable. Preventing complications is a major challenge to improve neurological outcome. Seizures may reveal the meningitis itself or some complications of this disease. Amplitude-integrated electroencephalography (aEEG) is gaining interest for the management of patients with acute neurological distress, beyond the neonatal age. This study aimed at evaluating the predictive value of aEEG monitoring during the acute phase in meningitis among a population of infants hospitalized in the pediatric intensive care unit (PICU), and at assessing the practicability of the technique. AEEG records of 25 infants younger than one year of age hospitalized for meningitis were retrospectively analyzed and correlated to clinical data and outcome. Recording was initiated, on average, within the first six hours for n = 18 (72%) patients, and overall quality was considered as good. Occurrence of seizure, of status epilepticus, and the background pattern were significantly associated with unfavorable neurological outcomes. AEEG may help in the management and prognostic assessment of pediatric meningitis. It is an easily achievable, reliable technique, and allows detection of subclinical seizures with minimal training. However, it is important to consider the limitations of aEEG, and combinate it with conventional EEG for the best accuracy.
Collapse
Affiliation(s)
- Jonathan Beck
- Department of Neonatology, Reims University Hospital Alix de Champagne, 51100 Reims, France; (J.B.); (C.G.); (N.B.)
- CReSTIC EA 3804 UFR Sciences Exactes et Naturelles, Campus Moulin de la Housse, Université de Reims Champagne Ardenne, 51100 Reims, France
| | - Cecile Grosjean
- Department of Neonatology, Reims University Hospital Alix de Champagne, 51100 Reims, France; (J.B.); (C.G.); (N.B.)
| | - Nathalie Bednarek
- Department of Neonatology, Reims University Hospital Alix de Champagne, 51100 Reims, France; (J.B.); (C.G.); (N.B.)
- CReSTIC EA 3804 UFR Sciences Exactes et Naturelles, Campus Moulin de la Housse, Université de Reims Champagne Ardenne, 51100 Reims, France
| | - Gauthier Loron
- Department of Neonatology, Reims University Hospital Alix de Champagne, 51100 Reims, France; (J.B.); (C.G.); (N.B.)
- CReSTIC EA 3804 UFR Sciences Exactes et Naturelles, Campus Moulin de la Housse, Université de Reims Champagne Ardenne, 51100 Reims, France
- Correspondence:
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Taran S, Ahmed W, Pinto R, Bui E, Prisco L, Hahn CD, Englesakis M, McCredie VA. Educational initiatives for electroencephalography in the critical care setting: a systematic review and meta-analysis. Can J Anaesth 2021; 68:1214-1230. [PMID: 33709264 PMCID: PMC7952081 DOI: 10.1007/s12630-021-01962-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 01/18/2021] [Accepted: 01/18/2021] [Indexed: 11/22/2022] Open
Abstract
PURPOSE We systematically reviewed existing critical care electroencephalography (EEG) educational programs for non-neurologists, with the primary goal of reporting the content covered, methods of instruction, overall duration, and participant experience. Our secondary goals were to assess the impact of EEG programs on participants' core knowledge, and the agreement between non-experts and experts for seizure identification. SOURCE Major databases were searched from inception to 30 August 2020. Randomized controlled trials, cohort studies, and descriptive studies were all considered if they reported an EEG curriculum for non-neurologists in a critical care setting. Data were presented thematically for the qualitative primary outcome and a meta-analysis using a random effects model was performed for the quantitative secondary outcomes. PRINCIPAL FINDINGS Twenty-nine studies were included after reviewing 7,486 citations. Twenty-two studies were single centre, 17 were from North America, and 16 were published after 2016. Most EEG studies were targeted to critical care nurses (17 studies), focused on processed forms of EEG with amplitude-integrated EEG being the most common (15 studies), and were shorter than one day in duration (24 studies). In pre-post studies, EEG programs significantly improved participants' knowledge of tested material (standardized mean change, 1.79; 95% confidence interval [CI], 0.86 to 2.73). Agreement for seizure identification between non-experts and experts was moderate (Cohen's kappa = 0.44; 95% CI, 0.27 to 0.60). CONCLUSIONS It is feasible to teach basic EEG to participants in critical care settings from different clinical backgrounds, including physicians and nurses. Brief training programs can enable bedside providers to recognize high-yield abnormalities such as non-convulsive seizures.
Collapse
Affiliation(s)
- Shaurya Taran
- Interdepartmental Division of Critical Care Medicine, Department of Medicine, Li Ka Shing Knowledge Institute, University of Toronto, 204 Victoria Street, 4th Floor Room 411, Toronto, ON, M5B 1T8, Canada.
| | - Wael Ahmed
- Department of Critical Care Medicine, Sunnybrook Health Sciences Center, Toronto, ON, Canada
| | - Ruxandra Pinto
- Department of Critical Care Medicine, Sunnybrook Health Sciences Center, Toronto, ON, Canada
| | - Esther Bui
- Division of Neurology, University Health Network, Toronto, ON, Canada
| | - Lara Prisco
- Neurosciences Intensive Care Unit, John Radcliffe Hospital, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children, and Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Marina Englesakis
- Library and Information Services, University Health Network, Toronto, ON, Canada
| | - Victoria A McCredie
- Interdepartmental Division of Critical Care Medicine, Department of Medicine, Li Ka Shing Knowledge Institute, University of Toronto, 204 Victoria Street, 4th Floor Room 411, Toronto, ON, M5B 1T8, Canada
- Department of Critical Care Medicine, Sunnybrook Health Sciences Center, Toronto, ON, Canada
- Division of Critical Care Medicine, Department of Medicine, University Health Network, Toronto, ON, Canada
- Krembil Research Institute, University Health Network, Toronto, ON, Canada
| |
Collapse
|
11
|
Sinkin MV, Talypov AE, Yakovlev AA, Kordonskaya OO, Teplyshova AM, Trifonov IS, Guekht AB, Krylov VV. [Long-term EEG monitoring in patients with acute traumatic brain injury]. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:62-67. [PMID: 34184480 DOI: 10.17116/jnevro202112105162] [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: 11/18/2022]
Abstract
OBJECTIVE To investigate the informativeness of long-term scalp EEG monitoring in patients with acute traumatic brain injury (TBI). MATERIAL AND METHODS The informativity of long-term EEG monitoring (LTM) was performed in 60 patients with acute severe TBI. Odd ratios (OR) of unfavorable outcome and non-convulsive status epilepticus (NCSE) among clinical, neurophysiological and radiological features were calculated. RESULTS EEG features of the unfavorable outcome are: slowing of the dominant background rhythm below q range (OR 3.5, CI 1.2-10.7), absence of frontal-occipital gradient (OR 10.2, CI 1.89-10.12), absence of reactivity (OR 8.75, CI 2.14-35.7), absence of variability (OR 6.25, CI 1.72-22.6) and absence of NREM sleep, stage 2 (OR 5.8, CI 1.79-18.91). Clinical features associated with the unfavorable outcome are: a decrease in GCS score (OR 1.25, CI 1.07-1.47), TBI severity (OR 2.46, CI 1.16-5.18), axial dislocation (OR 4.45, CI 1.08-18.29). ORs for NCSE are significant for the following EEG features: presence of rhythmic and periodic patterns (RPP) (OR 11.92, CI 1.37-103.39), stimulus induced RPP (OR 23.14, CI 2.56-209.34), "plus" modifier (OR 4.11, CI 1.13-14.91) and electrographic evolution (OR 13.05, CI 3.59-47.39). Background rhythm slowing below q range reduces NCSE probability (OR 3.33, CI 1.09-10). CONCLUSION Long-term EEG monitoring is an informative tool for prognosis of outcome and diagnosis of NCSE in patients with severe TBI. The risk of NCSE increases with Marshall score but NCSE is not associated with poor outcome that requires an individual selection of intensive care.
Collapse
Affiliation(s)
- M V Sinkin
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia.,Evdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia
| | - A E Talypov
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia
| | - A A Yakovlev
- Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, Russia.,Soloviev Scientific and Practical Psychoneurological Center, Moscow, Russia
| | - O O Kordonskaya
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia.,Federal Center of Brain and Neurotechnology, Moscow, Russia
| | | | - I S Trifonov
- Evdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia
| | - A B Guekht
- Soloviev Scientific and Practical Psychoneurological Center, Moscow, Russia.,Pirogov Russian National Research Medical University, Moscow, Russia
| | - V V Krylov
- Sklifosovsky Research Institute of Emergenscy Medicine, Moscow, Russia.,Evdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Löffelhardt VT, Della Marina A, Greve S, Müller H, Felderhoff-Müser U, Dohna-Schwake C, Bruns N. Characterization of aEEG During Sleep and Wakefulness in Healthy Children. Front Pediatr 2021; 9:773188. [PMID: 35127587 PMCID: PMC8814596 DOI: 10.3389/fped.2021.773188] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/31/2021] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Interpretation of amplitude-integrated EEG (aEEG) is hindered by lacking knowledge on physiological background patterns in children. The aim of this study was to find out whether aEEG differs between wakefulness and sleep in children. METHODS Forty continuous full-channel EEGs (cEEG) recorded during the afternoon and overnight in patients <18 years of age without pathologies or only solitary interictal epileptiform discharges were converted into aEEGs. Upper and lower amplitudes of the C3-C4, P3-P4, C3-P3, C4-P4, and Fp1-Fp2 channels were measured during wakefulness and sleep by two investigators and bandwidths (BW) calculated. Sleep states were assessed according to the American Academy of Sleep Medicine. Median and interquartile ranges (IQR) were calculated to compare the values of amplitudes and bandwidth between wakefulness and sleep. RESULTS Median age was 9.9 years (IQR 6.1-14.7). All patients displayed continuous background patterns. Amplitudes and BW differed between wakefulness and sleep with median amplitude values of the C3-C4 channel 35 μV (IQR: 27-49) for the upper and 13 μV (10-19) for the lower amplitude. The BW was 29 μV (21-34). During sleep, episodes with high amplitudes [upper: 99 μV (71-125), lower: 35 μV (25-44), BW 63 μV (44-81)] corresponded to sleep states N2-N3. High amplitude-sections were interrupted by low amplitude-sections, which became the longer toward the morning [upper amplitude: 39 μV (30-51), lower: 16 μV (11-20), BW 23 μV (19-31)]. Low amplitude-sections were associated with sleep states REM, N1, and N2. With increasing age, amplitudes and bandwidths declined. CONCLUSION aEEGs in non-critically ill children displayed a wide range of amplitudes and bandwidths. Amplitudes were low during wakefulness and light sleep and high during deep sleep. Interpretation of pediatric aEEG background patterns must take into account the state of wakefulness in in clinical practice and research.
Collapse
Affiliation(s)
- Verena T Löffelhardt
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Adela Della Marina
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,TNBS, Centre for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Sandra Greve
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Hanna Müller
- Department of Pediatrics, Neonatology and Pediatric Intensive Care, University of Marburg, Marburg, Germany
| | - Ursula Felderhoff-Müser
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,TNBS, Centre for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Christian Dohna-Schwake
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Nora Bruns
- Department of Pediatrics I, Neonatology, Pediatric Intensive Care Medicine, and Pediatric Neurology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.,TNBS, Centre for Translational Neuro- and Behavioral Sciences, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| |
Collapse
|
14
|
Taran S, Ahmed W, Bui E, Prisco L, Hahn CD, McCredie VA. Educational initiatives and implementation of electroencephalography into the acute care environment: a protocol of a systematic review. Syst Rev 2020; 9:175. [PMID: 32778151 PMCID: PMC7418425 DOI: 10.1186/s13643-020-01439-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 07/29/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Use of electroencephalography (EEG) is currently recommended by the American Clinical Neurophysiology Society for a wide range of indications, including diagnosis of nonconvulsive status epilepticus and evaluation of unexplained disorders of consciousness. Data interpretation usually occurs by expert personnel (e.g., epileptologists, neurophysiologists), with information relayed to the primary care team. However, data cannot always be read in time-sensitive fashion, leading to potential delays in EEG interpretation and patient management. Multiple training programs have recently been described to enable non-experts to rapidly interpret EEG at the bedside. A comprehensive review of these training programs, including the tools used, outcomes obtained, and potential pitfalls, is currently lacking. Therefore, the optimum training program and implementation strategy remain unknown. METHODS We will conduct a systematic review of descriptive studies, case series, cohort studies, and randomized controlled trials assessing training programs for EEG interpretation by non-experts. Our primary objective is to comprehensively review educational programs in this domain and report their structure, patterns of implementation, limitations, and trainee feedback. Our secondary objective will be to compare the performance of non-experts for EEG interpretation with a gold standard (e.g., interpretation by a certified electroencephalographers). Studies will be limited to those performed in acute care settings in both adult and pediatric populations (intensive care unit, emergency department, or post-anesthesia care units). Comprehensive search strategies will be developed for MEDLINE, EMBASE, WoS, CINAHL, and CENTRAL to identify studies for review. The gray literature will be scanned for further eligible studies. Two reviewers will independently screen the search results to identify studies for inclusion. A standardized data extraction form will be used to collect important data from each study. If possible, we will attempt to meta-analyze the quantitative data. If heterogeneity between studies is too high, we will present meaningful quantitative comparisons of secondary outcomes as per the synthesis without meta-analysis (SWiM) reporting guidelines. DISCUSSION We will aim to summarize the current literature in this domain to understand the structure, patterns, and pitfalls of EEG training programs for non-experts. This review is undertaken with a view to inform future education designs, potentially enabling rapid detection of EEG abnormalities, and timely intervention by the treating physician. PROSPERO REGISTRATION Submitted and undergoing review. Registration ID: CRD42020171208 .
Collapse
Affiliation(s)
- Shaurya Taran
- Interdepartmental Division of Critical Care, Li Ka Shing Knowledge Institute, 204 Victoria Street, 4th Floor, Room 411, Toronto, Ontario, M5B 1T8, Canada.
| | - Wael Ahmed
- Department of Critical Care Medicine, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Esther Bui
- Division of Neurology, University Health Network, Toronto, Ontario, Canada
| | - Lara Prisco
- Neurosciences Intensive Care Unit, John Radcliffe Hospital, Oxford, UK.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Cecil D Hahn
- Division of Neurology, The Hospital for Sick Children, and Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Victoria A McCredie
- Interdepartmental Division of Critical Care, Li Ka Shing Knowledge Institute, 204 Victoria Street, 4th Floor, Room 411, Toronto, Ontario, M5B 1T8, Canada.,Department of Critical Care Medicine, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada.,Division of Critical Care Medicine, Department of Medicine, University Health Network, Toronto, Ontario, Canada.,Krembil Research Institute, University Health Network, Toronto, Ontario, Canada
| |
Collapse
|
15
|
Stevenson NJ, Tataranno ML, Kaminska A, Pavlidis E, Clancy RR, Griesmaier E, Roberts JA, Klebermass-Schrehof K, Vanhatalo S. Reliability and accuracy of EEG interpretation for estimating age in preterm infants. Ann Clin Transl Neurol 2020; 7:1564-1573. [PMID: 32767645 PMCID: PMC7480927 DOI: 10.1002/acn3.51132] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVES To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants. METHODS Seven experts estimated the postmenstrual ages (PMA) in a cohort of recordings from preterm infants using cloud-based review software. The FBA was calculated using a machine learning-based algorithm. Error analysis was used to determine the accuracy of PMA assessments and intraclass correlation (ICC) was used to assess agreement between experts. RESULTS EEG recordings from a PMA range 25 to 38 weeks were successfully interpreted. In 179 recordings from 62 infants interpreted by all human readers, there was moderate agreement between experts (aEEG ICC = 0.724; 95%CI:0.658-0.781 and EEG ICC = 0.517; 95%CI:0.311-0.664). In 149 recordings from 61 infants interpreted by all human readers and the FBA algorithm, random and systematic errors in visual interpretation of PMA were significantly higher than the computational FBA estimate. Tracking of maturation in individual infants showed stable FBA trajectories, but the trajectories of the experts' PMA estimate were more likely to be obscured by random errors. The accuracy of visual interpretation of PMA estimation was compromised by neurodevelopmental outcome for both aEEG and EEG review. INTERPRETATION Visual assessment of infant maturity is possible from the EEG or aEEG, with an average of human experts providing the highest accuracy. Tracking PMA of individual infants was hampered by errors in experts' estimates. FBA provided the most accurate maturity assessment and has potential as a biomarker of early outcome.
Collapse
Affiliation(s)
- Nathan J Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Maria-Luisa Tataranno
- Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anna Kaminska
- Department of Clinical Neurophysiology, Necker-Enfants Malades Hospital, APHP, Paris, France.,INSERM U 1141, Neurodiderot, Paris, France
| | - Elena Pavlidis
- Child Neuropsychiatry Service of Carpi, Mental Health Department, AUSL Modena, Carpi, Italy
| | - Robert R Clancy
- Department of Pediatrics (Neurology), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Elke Griesmaier
- Department of Pediatrics (Neonatology), Medical University of Innsbruck, Innsbruck, Austria
| | - James A Roberts
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Katrin Klebermass-Schrehof
- Department of Pediatrics and Adolescent Medicine, Division of Neonatology, Pediatric Intensive Care and Neuropediatrics, Medical University of Vienna, Vienna, Austria
| | - Sampsa Vanhatalo
- BABA Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.,Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| |
Collapse
|