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Anwar T, Triplett RL, Ahmed A, Glass HC, Shellhaas RA. Treating Seizures and Improving Newborn Outcomes for Infants with Hypoxic-Ischemic Encephalopathy. Clin Perinatol 2024; 51:573-586. [PMID: 39095097 DOI: 10.1016/j.clp.2024.04.013] [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] [Indexed: 08/04/2024]
Abstract
Hypoxic-ischemic encephalopathy is the most common cause of neonatal seizures. Continuous electroencephalographic monitoring is recommended given high rates of subclinical seizures. Prompt diagnosis and treatment of seizures may improve neurodevelopmental outcomes. International League Against Epilepsy guidelines indicate that (1) phenobarbital remains the first-line treatment of neonatal seizures and (2) early discontinuation of antiseizure medications following resolution of acute provoked seizures, and prior to discharge home, is recommended. Long-term follow-up of these infants is necessary to screen for postneonatal epilepsy and support neurodevelopment.
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Affiliation(s)
- Tayyba Anwar
- Department of Neurology, Children's National Hospital, 111 Michigan Avenue Northwest, Washington, DC 20010, USA
| | - Regina L Triplett
- Department of Neurology, Washington University in St Louis, 1 Brookings Drive, Saint Louis, MO 63130, USA
| | - Afaf Ahmed
- Division of Pediatric and Developmental Neurology, Department of Neurology, Washington University in St Louis, 1 Brookings Drive, Saint Louis, MO 63130, USA
| | - Hannah C Glass
- Department of Neurology, University of California San Francisco, 500 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Renée A Shellhaas
- Department of Neurology, Washington University in St Louis, MSC 8091-29-12400, 660 South Euclid Avenue, Saint Louis, MO 63110, USA.
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2
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Proietti J, O'Toole JM, Murray DM, Boylan GB. Advances in Electroencephalographic Biomarkers of Neonatal Hypoxic Ischemic Encephalopathy. Clin Perinatol 2024; 51:649-663. [PMID: 39095102 DOI: 10.1016/j.clp.2024.04.006] [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] [Indexed: 08/04/2024]
Abstract
Electroencephalography (EEG) is a key objective biomarker of newborn brain function, delivering critical, cotside insights to aid the management of encephalopathy. Access to continuous EEG is limited, forcing reliance on subjective clinical assessments. In hypoxia ischaemia, the primary cause of encephalopathy, alterations in EEG patterns correlate with. injury severity and evolution. As HIE evolves, causing secondary neuronal death, EEG can track injury progression, informing neuroprotective strategies, seizure management and prognosis. Despite its value, challenges with interpretation and lack of on site expertise has limited its broader adoption. Technological advances, particularly in digital EEG and machine learning, are enhancing real-time analysis. This will allow EEG to expand its role in HIE diagnosis, management and outcome prediction.
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Affiliation(s)
- Jacopo Proietti
- Department of Engineering for Innovation Medicine, University of Verona, Strada le Grazie, Verona 37134, Italy; INFANT Research Centre, University College Cork, Cork, Ireland
| | - John M O'Toole
- INFANT Research Centre, University College Cork, Cork, Ireland; Cergenx Ltd., Dublin, Ireland
| | - Deirdre M Murray
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics & Child Health, University College Cork, Paediatric Academic Unit, Cork University Hospital, Wilton, Cork, T12 DC4A, Ireland
| | - Geraldine B Boylan
- INFANT Research Centre, University College Cork, Cork, Ireland; Department of Paediatrics & Child Health, University College Cork, Paediatric Academic Unit, Cork University Hospital, Wilton, Cork, T12 DC4A, Ireland.
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Ratcliffe C, Pradeep V, Marson A, Keller SS, Bonnett LJ. Clinical prediction models for treatment outcomes in newly diagnosed epilepsy: A systematic review. Epilepsia 2024; 65:1811-1846. [PMID: 38687193 DOI: 10.1111/epi.17994] [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: 01/30/2024] [Revised: 04/10/2024] [Accepted: 04/10/2024] [Indexed: 05/02/2024]
Abstract
Up to 35% of individuals diagnosed with epilepsy continue to have seizures despite treatment, commonly referred to as drug-resistant epilepsy. Uncontrolled seizures can directly, or indirectly, negatively impact an individual's quality of life. To inform clinical management and life decisions, it is important to be able to predict the likelihood of seizure control. Those likely to achieve seizure control will be able to return sooner to their usual work and leisure activities and require less follow-up, whereas those with a poor prognosis will need more frequent clinical attendance and earlier consideration of epilepsy surgery. This is a systematic review aimed at identifying demographic, clinical, physiological (e.g., electroencephalographic), and imaging (e.g., magnetic resonance imaging) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). MEDLINE and Embase were searched for prediction models of treatment outcomes in patients with NDE. Study characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST (Prediction Model Risk of Bias Assessment Tool) tool. Baseline variables associated with treatment outcomes are reported as prognostic factors. After screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally scored high for susceptibility to bias. Outcomes reported fit broadly into four categories: drug resistance, short-term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics/types, epilepsy history, and age at onset. Antiseizure medication response was often included as a baseline variable, potentially obscuring other factor relationships at baseline. Currently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) practices. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE.
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Affiliation(s)
- Corey Ratcliffe
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
- Department of Neuro Imaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bangalore, India
| | - Vishnav Pradeep
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Anthony Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, UK
- Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Laura J Bonnett
- Department of Health Data Science, University of Liverpool, Liverpool, UK
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Keene JC, Loe ME, Fulton T, Keene M, Morrissey MJ, Tomko SR, Vesoulis ZA, Zempel JM, Ching S, Guerriero RM. A Comparison of Automatically Extracted Quantitative EEG Features for Seizure Risk Stratification in Neonatal Encephalopathy. J Clin Neurophysiol 2024:00004691-990000000-00136. [PMID: 38857366 DOI: 10.1097/wnp.0000000000001067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
PURPOSE Seizures occur in up to 40% of neonates with neonatal encephalopathy. Earlier identification of seizures leads to more successful seizure treatment, but is often delayed because of limited availability of continuous EEG monitoring. Clinical variables poorly stratify seizure risk, and EEG use to stratify seizure risk has previously been limited by need for manual review and artifact exclusion. The goal of this study is to compare the utility of automatically extracted quantitative EEG (qEEG) features for seizure risk stratification. METHODS We conducted a retrospective analysis of neonates with moderate-to-severe neonatal encephalopathy who underwent therapeutic hypothermia at a single center. The first 24 hours of EEG underwent automated artifact removal and qEEG analysis, comparing qEEG features for seizure risk stratification. RESULTS The study included 150 neonates and compared the 36 (23%) with seizures with those without. Absolute spectral power best stratified seizure risk with area under the curve ranging from 63% to 71%, followed by range EEG lower and upper margin, median and SD of the range EEG lower margin. No features were significantly more predictive in the hour before seizure onset. Clinical examination was not associated with seizure risk. CONCLUSIONS Automatically extracted qEEG features were more predictive than clinical examination in stratifying neonatal seizure risk during therapeutic hypothermia. qEEG represents a potential practical bedside tool to individualize intensity and duration of EEG monitoring and decrease time to seizure recognition. Future work is needed to refine and combine qEEG features to improve risk stratification.
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Affiliation(s)
- Jennifer C Keene
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - Maren E Loe
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, Missouri, U.S.A
| | - Talie Fulton
- Washington University in St. Louis, St. Louis, Missouri, U.S.A.; and
| | - Maire Keene
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, Missouri, U.S.A
- Washington University in St. Louis, St. Louis, Missouri, U.S.A.; and
- Division of Newborn Medicine, Department of Pediatrics. Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Michael J Morrissey
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - Stuart R Tomko
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - Zachary A Vesoulis
- Division of Newborn Medicine, Department of Pediatrics. Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - John M Zempel
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, Missouri, U.S.A
| | - Réjean M Guerriero
- Division of Pediatric & Developmental Neurology, Department of Neurology. Washington University in St. Louis, St. Louis, Missouri U.S.A
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Cserpan D, Guidi G, Alessandri B, Fedele T, Rüegger A, Pisani F, Sarnthein J, Ramantani G. Scalp high-frequency oscillations differentiate neonates with seizures from healthy neonates. Epilepsia Open 2023; 8:1491-1502. [PMID: 37702021 PMCID: PMC10690668 DOI: 10.1002/epi4.12827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/02/2023] [Indexed: 09/14/2023] Open
Abstract
OBJECTIVE We aimed to investigate (1) whether an automated detector can capture scalp high-frequency oscillations (HFO) in neonates and (2) whether scalp HFO rates can differentiate neonates with seizures from healthy neonates. METHODS We considered 20 neonates with EEG-confirmed seizures and four healthy neonates. We applied a previously validated automated HFO detector to determine scalp HFO rates in quiet sleep. RESULTS Etiology in neonates with seizures included hypoxic-ischemic encephalopathy in 11 cases, structural vascular lesions in 6, and genetic causes in 3. The HFO rates were significantly higher in neonates with seizures (0.098 ± 0.091 HFO/min) than in healthy neonates (0.038 ± 0.025 HFO/min; P = 0.02) with a Hedge's g value of 0.68 indicating a medium effect size. The HFO rate of 0.1 HFO/min/ch yielded the highest Youden index in discriminating neonates with seizures from healthy neonates. In neonates with seizures, etiology, status epilepticus, EEG background activity, and seizure patterns did not significantly impact HFO rates. SIGNIFICANCE Neonatal scalp HFO can be detected automatically and differentiate neonates with seizures from healthy neonates. Our observations have significant implications for neuromonitoring in neonates. This is the first step in establishing neonatal HFO as a biomarker for neonatal seizures.
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Affiliation(s)
- Dorottya Cserpan
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Greta Guidi
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Beatrice Alessandri
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Tommaso Fedele
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Andrea Rüegger
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
| | - Francesco Pisani
- Department of Human Neurosciences, Child Neurology and Psychiatry UnitSapienza University of RomeRomeItaly
| | - Johannes Sarnthein
- Department of NeurosurgeryUniversity Hospital ZurichZurichSwitzerland
- University of ZurichZurichSwitzerland
| | - Georgia Ramantani
- Department of NeuropediatricsUniversity Children's HospitalZurichSwitzerland
- University of ZurichZurichSwitzerland
- Children's Research CenterUniversity Children's HospitalZurichSwitzerland
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Li L, Deng Y, Chen J, Xie L, Lan X, Hu Y, Hong S, Jiang L. Clinical and electroencephalography characteristics of 45 patients with neonatal seizures. Neurophysiol Clin 2023; 53:102886. [PMID: 37295040 DOI: 10.1016/j.neucli.2023.102886] [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: 10/25/2022] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 06/12/2023] Open
Abstract
OBJECTIVES The aim of our study was to retrospectively research the semiology of neonatal seizures (NSs) based on the 2021 classification scheme of the International League Against Epilepsy, and the relationship between etiology and electroclinical features. METHODS Patients admitted to Children's Hospital of Chongqing Medical University from May 1, 2020 to March 30, 2022 and diagnosed with NSs were included to retrospectively investigate the etiology, seizure characteristics, prognosis, and ictal and interictal video electroencephalography (EEG) characteristics. RESULTS Of the 45 patients, 73.3% had definite etiology. Twenty-seven patients had electro-clinical seizures, of which two had both electro-clinical and electrographic-only seizures. Electrographic-only seizures were reported in 18 patients. The tonic, clonic, and electrographic-only seizures were associated with various etiologies. Both tonic and clonic seizures occurred in acute symptomatic seizures and were associated with neonatal epilepsy. 50% of tonic seizures were related to genetic factors. Among the clonic seizures, 50.0% occurred in acute symptomatic seizures. Epileptic spasms always indicated neonatal epilepsy. There were few patients who experienced automatisms and sequential seizures, and these two seizure types were associated with brain malformation and genetic factors, respectively. Patients with a normal interictal EEG had acute symptomatic seizures. whereas the interictal EEG of patients with neonatal epilepsy mainly showed burst-suppression or multifocal discharges. The ictal EEG recordings were related to seizure semiology. CONCLUSION Seizure semiology and video EEG are suggestive of potential causes but do not provide a definite etiology.
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Affiliation(s)
- Luying Li
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China
| | - Yu Deng
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China
| | - Jin Chen
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China.
| | - Lingling Xie
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China
| | - Xinghui Lan
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China
| | - Yue Hu
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China
| | - Siqi Hong
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China
| | - Li Jiang
- Department of Neurology, Children's Hospital of Chongqing Medical University, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Ministry of Education Key Laboratory of Child Development and Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; National Clinical Research Center for Child Health and Disorders (Chongqing), NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; China International Science and Technology Cooperation Base of Child Development and Critical Disorders, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China; Chongqing Key Laboratory of Pediatrics, NO. 136 Zhongshan 2nd Road, Yuzhong District, Chongqing 400014, China
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McKee JL, Kaufman MC, Gonzalez AK, Fitzgerald MP, Massey SL, Fung F, Kessler SK, Witzman S, Abend NS, Helbig I. Leveraging electronic medical record-embedded standardised electroencephalogram reporting to develop neonatal seizure prediction models: a retrospective cohort study. Lancet Digit Health 2023; 5:e217-e226. [PMID: 36963911 PMCID: PMC10065843 DOI: 10.1016/s2589-7500(23)00004-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/09/2022] [Accepted: 01/06/2023] [Indexed: 03/26/2023]
Abstract
BACKGROUND Accurate prediction of seizures can help to direct resource-intense continuous electroencephalogram (CEEG) monitoring to neonates at high risk of seizures. We aimed to use data from standardised EEG reports to generate seizure prediction models for vulnerable neonates. METHODS In this retrospective cohort study, we included neonates who underwent CEEG during the first 30 days of life at the Children's Hospital of Philadelphia (Philadelphia, PA, USA). The hypoxic ischaemic encephalopathy subgroup included only patients with CEEG data during the first 5 days of life, International Classification of Diseases, revision 10, codes for hypoxic ischaemic encephalopathy, and documented therapeutic hypothermia. In January, 2018, we implemented a novel CEEG reporting system within the electronic medical record (EMR) using common data elements that incorporated standardised terminology. All neonatal CEEG data from Jan 10, 2018, to Feb 15, 2022, were extracted from the EMR using age at the time of CEEG. We developed logistic regression, decision tree, and random forest models of neonatal seizure prediction using EEG features on day 1 to predict seizures on future days. FINDINGS We evaluated 1117 neonates, including 150 neonates with hypoxic ischaemic encephalopathy, with CEEG data reported using standardised templates between Jan 10, 2018, and Feb 15, 2022. Implementation of a consistent EEG reporting system that documents discrete and standardised EEG variables resulted in more than 95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered on the basis of specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of neonates at high risk of seizures. Random forest models incorporating background features performed with classification accuracies of up to 90% (95% CI 83-94) for all neonates and 97% (88-99) for neonates with hypoxic ischaemic encephalopathy; recall (sensitivity) of up to 97% (91-100) for all neonates and 100% (100-100) for neonates with hypoxic ischaemic encephalopathy; and precision (positive predictive value) of up to 92% (84-96) in the overall cohort and 97% (80-99) in neonates with hypoxic ischaemic encephalopathy. INTERPRETATION Using data extracted from the standardised EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of more than 90%. This information, incorporated into routine care, could guide decisions about the necessity of continuing EEG monitoring beyond the first day, thereby improving the allocation of limited CEEG resources. Additionally, this analysis shows the benefits of standardised clinical data collection, which can drive learning health system approaches to personalised CEEG use. FUNDING Children's Hospital of Philadelphia, the Hartwell Foundation, the National Institute of Neurological Disorders and Stroke, and the Wolfson Foundation.
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Affiliation(s)
- Jillian L McKee
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael C Kaufman
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Alexander K Gonzalez
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mark P Fitzgerald
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shavonne L Massey
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - France Fung
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sudha K Kessler
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephanie Witzman
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Nicholas S Abend
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Anesthesia and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; The Epilepsy NeuroGenetics Initiative, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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8
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Barsh GR, Wusthoff CJ. Can electronic medical records predict neonatal seizures? Lancet Digit Health 2023; 5:e175-e176. [PMID: 36963906 DOI: 10.1016/s2589-7500(23)00041-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 02/13/2023] [Indexed: 03/26/2023]
Affiliation(s)
- Gabrielle R Barsh
- Department of Neurology, Division of Child Neurology, Stanford University, Palo Alto, CA 94304, USA
| | - Courtney J Wusthoff
- Department of Neurology, Division of Child Neurology, Stanford University, Palo Alto, CA 94304, USA.
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9
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Pavel AM, O'Toole JM, Proietti J, Livingstone V, Mitra S, Marnane WP, Finder M, Dempsey EM, Murray DM, Boylan GB. Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic-ischemic encephalopathy. Epilepsia 2023; 64:456-468. [PMID: 36398397 PMCID: PMC10107538 DOI: 10.1111/epi.17468] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 10/26/2022] [Accepted: 11/15/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVE To assess if early clinical and electroencephalography (EEG) features predict later seizure development in infants with hypoxic-ischemic encephalopathy (HIE). METHODS Clinical and EEG parameters <12 h of birth from infants with HIE across eight European Neonatal Units were used to develop seizure-prediction models. Clinical parameters included intrapartum complications, fetal distress, gestational age, delivery mode, gender, birth weight, Apgar scores, assisted ventilation, cord pH, and blood gases. The earliest EEG hour provided a qualitative analysis (discontinuity, amplitude, asymmetry/asynchrony, sleep-wake cycle [SWC]) and a quantitative analysis (power, discontinuity, spectral distribution, inter-hemispheric connectivity) from full montage and two-channel amplitude-integrated EEG (aEEG). Subgroup analysis, only including infants without anti-seizure medication (ASM) prior to EEG was also performed. Machine-learning (ML) models (random forest and gradient boosting algorithms) were developed to predict infants who would later develop seizures and assessed using Matthews correlation coefficient (MCC) and area under the receiver-operating characteristic curve (AUC). RESULTS The study included 162 infants with HIE (53 had seizures). Low Apgar, need for ventilation, high lactate, low base excess, absent SWC, low EEG power, and increased EEG discontinuity were associated with seizures. The following predictive models were developed: clinical (MCC 0.368, AUC 0.681), qualitative EEG (MCC 0.467, AUC 0.729), quantitative EEG (MCC 0.473, AUC 0.730), clinical and qualitative EEG (MCC 0.470, AUC 0.721), and clinical and quantitative EEG (MCC 0.513, AUC 0.746). The clinical and qualitative-EEG model significantly outperformed the clinical model alone (MCC 0.470 vs 0.368, p-value .037). The clinical and quantitative-EEG model significantly outperformed the clinical model (MCC 0.513 vs 0.368, p-value .012). The clinical and quantitative-EEG model for infants without ASM (n = 131) had MCC 0.588, AUC 0.832. Performance for quantitative aEEG (n = 159) was MCC 0.381, AUC 0.696 and clinical and quantitative aEEG was MCC 0.384, AUC 0.720. SIGNIFICANCE Early EEG background analysis combined with readily available clinical data helped predict infants who were at highest risk of seizures, hours before they occur. Automated quantitative-EEG analysis was as good as expert analysis for predicting seizures, supporting the use of automated assessment tools for early evaluation of HIE.
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Affiliation(s)
- Andreea M. Pavel
- INFANT Research CentreUniversity College CorkCorkIreland
- Department of Paediatrics and Child HealthUniversity College CorkCorkIreland
| | - John M. O'Toole
- INFANT Research CentreUniversity College CorkCorkIreland
- Department of Paediatrics and Child HealthUniversity College CorkCorkIreland
| | | | - Vicki Livingstone
- INFANT Research CentreUniversity College CorkCorkIreland
- Department of Paediatrics and Child HealthUniversity College CorkCorkIreland
| | | | - William P. Marnane
- INFANT Research CentreUniversity College CorkCorkIreland
- Electrical & Electronic EngineeringSchool of EngineeringUniversity College CorkCorkIreland
| | - Mikael Finder
- Department of Neonatal MedicineKarolinska University HospitalStockholmSweden
- Division of Paediatrics, Department CLINTECKarolinska InstitutetStockholmSweden
| | - Eugene M. Dempsey
- INFANT Research CentreUniversity College CorkCorkIreland
- Department of Paediatrics and Child HealthUniversity College CorkCorkIreland
| | - Deirdre M. Murray
- INFANT Research CentreUniversity College CorkCorkIreland
- Department of Paediatrics and Child HealthUniversity College CorkCorkIreland
| | - Geraldine B. Boylan
- INFANT Research CentreUniversity College CorkCorkIreland
- Department of Paediatrics and Child HealthUniversity College CorkCorkIreland
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10
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El-Dib M, Abend NS, Austin T, Boylan G, Chock V, Cilio MR, Greisen G, Hellström-Westas L, Lemmers P, Pellicer A, Pressler RM, Sansevere A, Tsuchida T, Vanhatalo S, Wusthoff CJ, Wintermark P, Aly H, Chang T, Chau V, Glass H, Lemmon M, Massaro A, Wusthoff C, deVeber G, Pardo A, McCaul MC. Neuromonitoring in neonatal critical care part I: neonatal encephalopathy and neonates with possible seizures. Pediatr Res 2022:10.1038/s41390-022-02393-1. [PMID: 36476747 DOI: 10.1038/s41390-022-02393-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 12/12/2022]
Abstract
The blooming of neonatal neurocritical care over the last decade reflects substantial advances in neuromonitoring and neuroprotection. The most commonly used brain monitoring tools in the neonatal intensive care unit (NICU) are amplitude integrated EEG (aEEG), full multichannel continuous EEG (cEEG), and near-infrared spectroscopy (NIRS). While some published guidelines address individual tools, there is no consensus on consistent, efficient, and beneficial use of these modalities in common NICU scenarios. This work reviews current evidence to assist decision making for best utilization of neuromonitoring modalities in neonates with encephalopathy or with possible seizures. Neuromonitoring approaches in extremely premature and critically ill neonates are discussed separately in the companion paper. IMPACT: Neuromonitoring techniques hold promise for improving neonatal care. For neonatal encephalopathy, aEEG can assist in screening for eligibility for therapeutic hypothermia, though should not be used to exclude otherwise eligible neonates. Continuous cEEG, aEEG and NIRS through rewarming can assist in prognostication. For neonates with possible seizures, cEEG is the gold standard for detection and diagnosis. If not available, aEEG as a screening tool is superior to clinical assessment alone. The use of seizure detection algorithms can help with timely seizures detection at the bedside.
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Affiliation(s)
- Mohamed El-Dib
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Nicholas S Abend
- Departments of Neurology and Pediatrics, Children's Hospital of Philadelphia and the University of Pennsylvania, Philadelphia, PA, USA
| | - Topun Austin
- Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Geraldine Boylan
- INFANT Research Centre & Department of Paediatrics & Child Health, University College Cork, Cork, Ireland
| | - Valerie Chock
- Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - M Roberta Cilio
- Department of Pediatrics, Division of Pediatric Neurology, Cliniques universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Gorm Greisen
- Department of Neonatology, Rigshospitalet, Copenhagen University Hospital & Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Lena Hellström-Westas
- Department of Women's and Children's Health, Uppsala University, and Division of Neonatology, Uppsala University Hospital, Uppsala, Sweden
| | - Petra Lemmers
- Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Adelina Pellicer
- Department of Neonatology, La Paz University Hospital, Madrid, Spain; Neonatology Group, IdiPAZ, Madrid, Spain
| | - Ronit M Pressler
- Department of Clinical Neurophysiology, Great Ormond Street Hospital for Children NHS Trust, and Clinical Neuroscience, UCL- Great Ormond Street Institute of Child Health, London, UK
| | - Arnold Sansevere
- Department of Neurology and Pediatrics, George Washington University School of Medicine and Health Sciences; Children's National Hospital Division of Neurophysiology, Epilepsy and Critical Care, Washington, DC, USA
| | - Tammy Tsuchida
- Department of Neurology and Pediatrics, George Washington University School of Medicine and Health Sciences; Children's National Hospital Division of Neurophysiology, Epilepsy and Critical Care, Washington, DC, USA
| | - Sampsa Vanhatalo
- Department of Clinical Neurophysiology, Children's Hospital, BABA Center, Neuroscience Center/HILIFE, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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11
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Pressler RM, Boylan GB. Translational neonatal seizure research - a reality check. Epilepsia 2022; 63:1874-1879. [PMID: 35524441 DOI: 10.1111/epi.17276] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 04/14/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Ronit M Pressler
- Department of Clinical Neurophysiology, Great Ormond Street Hospital for Children, London, UK.,Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Geraldine B Boylan
- INFANT Research Centre, University College Cork, Ireland.,Department of Paediatrics and Child Health, University College Cork, Cork, Ireland
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12
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Sandoval Karamian AG, Wusthoff CJ. Current and Future Uses of Continuous EEG in the NICU. Front Pediatr 2021; 9:768670. [PMID: 34805053 PMCID: PMC8595393 DOI: 10.3389/fped.2021.768670] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/12/2021] [Indexed: 11/28/2022] Open
Abstract
Continuous EEG (cEEG) is a fundamental neurodiagnostic tool in the care of critically ill neonates and is increasingly recommended. cEEG enhances prognostication via assessment of the background brain activity, plays a role in predicting which neonates are at risk for seizures when combined with clinical factors, and allows for accurate diagnosis and management of neonatal seizures. Continuous EEG is the gold standard method for diagnosis of neonatal seizures and should be used for detection of seizures in high-risk clinical conditions, differential diagnosis of paroxysmal events, and assessment of response to treatment. High costs associated with cEEG are a limiting factor in its widespread implementation. Centralized remote cEEG interpretation, automated seizure detection, and pre-natal EEG are potential future applications of this neurodiagnostic tool.
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Affiliation(s)
| | - Courtney J. Wusthoff
- Division of Child Neurology, Lucile Packard Children's Hospital at Stanford, Palo Alto, CA, United States
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13
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DeLaGarza-Pineda O, Mailo JA, Boylan G, Chau V, Glass HC, Mathur AM, Shellhaas RA, Soul JS, Wusthoff CJ, Chang T. Management of seizures in neonates with neonatal encephalopathy treated with hypothermia. Semin Fetal Neonatal Med 2021; 26:101279. [PMID: 34563467 DOI: 10.1016/j.siny.2021.101279] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Neonatal encephalopathy (NE) is the most common etiology of acute neonatal seizures - about half of neonates treated with therapeutic hypothermia for NE have EEG-confirmed seizures. These seizures are best identified with continuous EEG monitoring, as clinical diagnosis leads to under-diagnosis of subclinical seizures and over-treatment of events that are not seizures. High seizure burden, especially status epilepticus, is thought to augment brain injury. Treatment, therefore, is aimed at minimizing seizure burden. Phenobarbital remains the mainstay of treatment, as it is more effective than levetiracetam and easier to administer than fosphenytoin. Emerging evidence suggests that, for many neonates, it is safe to discontinue the phenobarbital after acute seizures resolve and prior to hospital discharge.
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Affiliation(s)
- Oscar DeLaGarza-Pineda
- Department of Neurology, University Hospital "Dr. Jose E. Gonzalez", Monterrey, Nuevo León, Mexico.
| | - Janette A Mailo
- Neurology & Pediatrics, Stollery Children's Hospital and Glenrose Rehabilitation Hospital University of Alberta, Alberta, Canada.
| | - Geraldine Boylan
- Department of Pediatrics & Child Health University College Cork, Cork, Ireland.
| | - Vann Chau
- Division of Neurology, Hospital for Sick Children and University of Toronto, Toronto, ON, Canada.
| | - Hannah C Glass
- Department of Neurology and Weill Institute for Neuroscience, University of California San Francisco, San Francisco, CA, USA, Department of Pediatrics, UCSF Benioff Children's Hospital, University of California San Francisco, San Francisco, CA, USA, Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, CA, USA.
| | - Amit M Mathur
- Division of Neonatal Perinatal Medicine, Saint Louis University School of Medicine, SSM-Health Cardinal Glennon Children's Hospital, Saint Louis, MO, USA.
| | - Renée A Shellhaas
- Division of Pediatric Neurology, Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA.
| | - Janet S Soul
- Neurology, Harvard Medical School, Boston Children's Hospital, Boston, MA, USA.
| | - Courtney J Wusthoff
- Division of Child Neurology, Division of Pediatrics-Neonatal and Developmental Medicine Stanford Children's Health, Palo Alto, CA, USA.
| | - Taeun Chang
- Neurology & Pediatrics, George Washington University School of Medicine & Health Sciences, Children's National Hospital, Washington, DC, USA.
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14
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Levy RJ, Mayne EW, Sandoval Karamian AG, Iqbal M, Purington N, Ryan KR, Wusthoff CJ. Evaluation of Seizure Risk in Infants After Cardiopulmonary Bypass in the Absence of Deep Hypothermic Cardiac Arrest. Neurocrit Care 2021; 36:30-38. [PMID: 34322828 PMCID: PMC8318326 DOI: 10.1007/s12028-021-01313-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/12/2021] [Indexed: 01/16/2023]
Abstract
Background Guidelines recommend evaluation for electrographic seizures in neonates and children at risk, including after cardiopulmonary bypass (CPB). Although initial research using screening electroencephalograms (EEGs) in infants after CPB found a 21% seizure incidence, more recent work reports seizure incidences ranging 3–12%. Deep hypothermic cardiac arrest was associated with increased seizure risk in prior reports but is uncommon at our institution and less widely used in contemporary practice. This study seeks to establish the incidence of seizures among infants following CPB in the absence of deep hypothermic cardiac arrest and to identify additional risk factors for seizures via a prediction model. Methods A retrospective chart review was completed of all consecutive infants ≤ 3 months who received screening EEG following CPB at a single center within a 2-year period during 2017–2019. Clinical and laboratory data were collected from the perioperative period. A prediction model for seizure risk was fit using a random forest algorithm, and receiver operator characteristics were assessed to classify predictions. Fisher’s exact test and the logrank test were used to evaluate associations between clinical outcomes and EEG seizures. Results A total of 112 infants were included. Seizure incidence was 10.7%. Median time to first seizure was 28.1 h (interquartile range 18.9–32.2 h). The most important factors in predicting seizure risk from the random forest analysis included postoperative neuromuscular blockade, prematurity, delayed sternal closure, bypass time, and critical illness preoperatively. When variables captured during the EEG recording were included, abnormal postoperative neuroimaging and peak lactate were also highly predictive. Overall model accuracy was 90.2%; accounting for class imbalance, the model had excellent sensitivity and specificity (1.00 and 0.89, respectively). Conclusions Seizure incidence was similar to recent estimates even in the absence of deep hypothermic cardiac arrest. By employing random forest analysis, we were able to identify novel risk factors for postoperative seizure in this population and generate a robust model of seizure risk. Further work to validate our model in an external population is needed. Supplementary Information The online version contains supplementary material available at 10.1007/s12028-021-01313-1.
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Affiliation(s)
- Rebecca J Levy
- Division of Child Neurology, Lucile Packard Children's Hospital at Stanford University, Dr Levy 750 Welch Road Suite 317, Palo Alto, CA, USA. .,Division of Medical Genetics, Lucile Packard Children's Hospital at Stanford University, Palo Alto, CA, USA.
| | - Elizabeth W Mayne
- Division of Child Neurology, Lucile Packard Children's Hospital at Stanford University, Dr Levy 750 Welch Road Suite 317, Palo Alto, CA, USA
| | | | - Mehreen Iqbal
- Division of Pediatric Cardiology, Lucile Packard Children's Hospital at Stanford University, Palo Alto, CA, USA
| | - Natasha Purington
- Quantitative Sciences Unit, Department of Medicine, Lucile Packard Children's Hospital at Stanford University, Palo Alto, CA, USA
| | - Kathleen R Ryan
- Division of Pediatric Cardiology, Lucile Packard Children's Hospital at Stanford University, Palo Alto, CA, USA
| | - Courtney J Wusthoff
- Division of Child Neurology, Lucile Packard Children's Hospital at Stanford University, Dr Levy 750 Welch Road Suite 317, Palo Alto, CA, USA.,Division of Pediatrics, Neonatal and Developmental Medicine, Lucile Packard Children's Hospital at Stanford University, Palo Alto, CA, USA
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15
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Macdonald-Laurs E, Sharpe C, Nespeca M, Rismanchi N, Gold JJ, Kuperman R, Wang S, Lee NMD, Michelson DJ, Haas R, Reed P, Davis SL. Does the first hour of continuous electroencephalography predict neonatal seizures? Arch Dis Child Fetal Neonatal Ed 2021; 106:162-167. [PMID: 32928896 DOI: 10.1136/archdischild-2020-318985] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 07/18/2020] [Accepted: 07/26/2020] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Prolonged continuous video-electroencephalography (cEEG) is recommended for neonates at risk of seizures. The cost and expertise required to provide a real-time response to detected seizures often limits its utility. We hypothesised that the first hour of cEEG could predict subsequent seizures. DESIGN AND SETTING Retrospective multicentre diagnostic accuracy study. PATIENTS 266 term neonates at risk of seizure or with suspected seizures. INTERVENTION The first hour of cEEG was graded by expert and novice interpreters as normal, mildly, moderately or severely abnormal; seizures were identified. MAIN OUTCOME MEASURES Association between abnormalities in the first hour of cEEG and the presence of seizures during total cEEG monitoring. RESULTS 50/98 (51%) of neonates who developed seizures had their first seizure in the first hour of cEEG monitoring. The 'time-to-event' risk of seizure from 0 to 96 hours was 0.38 (95% CI 0.32 to 0.44) while the risk in the first hour was 0.19 (95% CI 0.15 to 0.24). cEEG background was normal in 48% of neonates, mildly abnormal in 30%, moderately abnormal in 13% and severely abnormal in 9%. Inter-rater agreement for determination of background was very good (weighted kappa=0.81, 95% CI 0.72 to 0.91). When neonates with seizures during the first hour were excluded, an abnormal background resulted in 2.4 times increased risk of seizures during the subsequent monitoring period (95% CI 1.3 to 4.4, p<0.003) while a severely abnormal background resulted in a sevenfold increased risk (95% CI 3.4 to 14.3, p<0.0001). CONCLUSIONS The first hour of cEEG in at-risk neonates is useful in identifying and predicting whether seizures occur during cEEG monitoring up to 96 hours. This finding enables identification of high-risk neonates who require closer observation.
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Affiliation(s)
- Emma Macdonald-Laurs
- The Department of Paediatric Neurology, Starship Children's Health, Newmarket, New Zealand
| | - Cynthia Sharpe
- The Department of Paediatric Neurology, Starship Children's Health, Newmarket, New Zealand
| | - Mark Nespeca
- The Department of Neurosciences, Rady Children's Hospital San Diego, San Diego, California, USA.,The Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Neggy Rismanchi
- The Department of Neurosciences, Rady Children's Hospital San Diego, San Diego, California, USA.,The Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Jeffrey J Gold
- The Department of Neurosciences, Rady Children's Hospital San Diego, San Diego, California, USA.,The Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Rachel Kuperman
- The Department of Pediatric Neurology, UCSF Benioff Children's Hospital Oakland, Oakland, California, USA
| | - Sonya Wang
- The Department of Neurosciences, Rady Children's Hospital San Diego, San Diego, California, USA.,The Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Ngoc Minh D Lee
- The Department of Neurosciences, Rady Children's Hospital San Diego, San Diego, California, USA.,The Department of Neurosciences, Sharp Mary Birch Hospital for Women and Newborns, San Diego, California, USA
| | - David J Michelson
- Division of Pediatric Neurology, Loma Linda University Medical Center, Loma Linda, California, USA
| | - Richard Haas
- The Department of Neurosciences, Rady Children's Hospital San Diego, San Diego, California, USA.,The Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Peter Reed
- The Department of Paediatric Neurology, Starship Children's Health, Newmarket, New Zealand
| | - Suzanne L Davis
- The Department of Paediatric Neurology, Starship Children's Health, Newmarket, New Zealand
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16
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Cabon S, Porée F, Cuffel G, Rosec O, Geslin F, Pladys P, Simon A, Carrault G. Voxyvi: A system for long-term audio and video acquisitions in neonatal intensive care units. Early Hum Dev 2021; 153:105303. [PMID: 33453631 DOI: 10.1016/j.earlhumdev.2020.105303] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/04/2020] [Accepted: 12/21/2020] [Indexed: 11/18/2022]
Abstract
BACKGROUND In the European Union, 300,000 newborn babies are born prematurely every year. Their care is ensured in Neonatal Intensive Care Units (NICU) where vital signs are constantly monitored. In addition, other descriptors such as motion, facial and vocal activities have been shown to be essential to assess neurobehavioral development. AIM In the scope of the European project Digi-NewB, we aimed to develop and evaluate a new audio-video device designed to non-invasively acquire multi-modal data (audio, video and thermal images), while fitting the wide variety of bedding environment in NICU. METHODS Firstly, a multimodal system and associated software and guidelines to collect data in neonatal intensive care unit were proposed. Secondly, methods for post-evaluation of the acquisition phase were developed, including the study of clinician feedback and a qualitative analysis of the data. RESULTS The deployment of 19 acquisition devices in six French hospitals allowed to record more than 500 newborns of different gestational and postmenstrual ages. After the acquisition phase, clinical feedback was mostly positive. In addition, quality of more than 300 recordings was inspected and showed that 77% of the data is exploitable. In depth, the percentage of sole presence of the newborn was estimated at 62% within recordings. CONCLUSIONS This study demonstrates that audio-video acquisitions are feasible on a large scale in real life in NICU. The experience also allowed us to make a clear observation of the requirements and challenges that will have to be overcome in order to set up audio-video monitoring methods.
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Affiliation(s)
- S Cabon
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France.
| | - F Porée
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France
| | - G Cuffel
- Voxygen, Pleumeur-Bodou F-22560, France
| | - O Rosec
- Voxygen, Pleumeur-Bodou F-22560, France
| | - F Geslin
- CHU Rennes, Rennes F-35000, France
| | - P Pladys
- CHU Rennes, Rennes F-35000, France
| | - A Simon
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France
| | - G Carrault
- Univ Rennes, Inserm, LTSI - UMR 1099, F-35000, France
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17
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Abstract
After convulsive status epilepticus, patients of all ages may have ongoing EEG seizures identified by continuous EEG monitoring. Furthermore, high EEG seizure exposure has been associated with unfavorable neurobehavioral outcomes. Thus, recent guidelines and consensus statements recommend many patients with persisting altered mental status after convulsive status epilepticus undergo continuous EEG monitoring. This review summarizes the available epidemiologic data and related recommendations provided by recent guidelines and consensus statements.
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18
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Nathan M. Seizure Prediction After Neonatal Cardiac Surgery: The Search Continues. Ann Thorac Surg 2020; 111:2048-2049. [PMID: 32891661 DOI: 10.1016/j.athoracsur.2020.06.092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 06/17/2020] [Indexed: 10/23/2022]
Affiliation(s)
- Meena Nathan
- Department of Cardiac Surgery, Boston Children's Hospital, 300 Longwood Ave, Bader 273, Boston, MA 02115; Department of Surgery, Harvard Medical School, Boston, Massachusetts.
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19
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Fung FW, Jacobwitz M, Parikh DS, Vala L, Donnelly M, Fan J, Xiao R, Topjian AA, Abend NS. Development of a model to predict electroencephalographic seizures in critically ill children. Epilepsia 2020; 61:498-508. [PMID: 32077099 DOI: 10.1111/epi.16448] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/23/2020] [Accepted: 01/23/2020] [Indexed: 01/10/2023]
Abstract
OBJECTIVE Electroencephalographic seizures (ESs) are common in encephalopathic critically ill children, but ES identification with continuous electroencephalography (EEG) monitoring (CEEG) is resource-intense. We aimed to develop an ES prediction model that would enable clinicians to stratify patients by ES risk and optimally target limited CEEG resources. We aimed to determine whether incorporating data from a screening EEG yielded better performance characteristics than models using clinical variables alone. METHODS We performed a prospective observational study of 719 consecutive critically ill children with acute encephalopathy undergoing CEEG in the pediatric intensive care unit of a quaternary care institution between April 2017 and February 2019. We identified clinical and EEG risk factors for ES. We evaluated model performance with area under the receiver-operating characteristic (ROC) curve (AUC), validated the optimal model with the highest AUC using a fivefold cross-validation, and calculated test characteristics emphasizing high sensitivity. We applied the optimal operating slope strategy to identify the optimal cutoff to define whether a patient should undergo CEEG. RESULTS The incidence of ES was 26%. Variables associated with increased ES risk included age, acute encephalopathy category, clinical seizures prior to CEEG initiation, EEG background, and epileptiform discharges. Combining clinical and EEG variables yielded better model performance (AUC 0.80) than clinical variables alone (AUC 0.69; P < .01). At a 0.10 cutoff selected to emphasize sensitivity, the optimal model had a sensitivity of 92%, specificity of 37%, positive predictive value of 34%, and negative predictive value of 93%. If applied, the model would limit 29% of patients from undergoing CEEG while failing to identify 8% of patients with ES. SIGNIFICANCE A model employing readily available clinical and EEG variables could target limited CEEG resources to critically ill children at highest risk for ES, making CEEG-guided management a more viable neuroprotective strategy.
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Affiliation(s)
- France W Fung
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marin Jacobwitz
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Darshana S Parikh
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa Vala
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maureen Donnelly
- Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jiaxin Fan
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rui Xiao
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Alexis A Topjian
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Nicholas S Abend
- Department of Pediatrics (Division of Neurology), Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Departments of Neurology and Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Neurodiagnostics, Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Department of Anesthesia & Critical Care, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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Mohamed STM, Oshaiba ZF, Moneim MEHAE, Ibrahim AAEW. Assessment of EEG Changes in Neonatal Sepsis at Al-Zahraa University Hospital’s NIC Unit. OPEN JOURNAL OF PEDIATRICS 2020; 10:493-503. [DOI: 10.4236/ojped.2020.103050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Worden LT, Chinappen DM, Stoyell SM, Gold J, Paixao L, Krishnamoorthy K, Kramer MA, Westover MB, Chu CJ. The probability of seizures during continuous EEG monitoring in high-risk neonates. Epilepsia 2019; 60:2508-2518. [PMID: 31745988 DOI: 10.1111/epi.16387] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 10/17/2019] [Accepted: 10/21/2019] [Indexed: 12/20/2022]
Abstract
OBJECTIVE We evaluated the impact of monitoring indication, early electroencephalography (EEG), and clinical features on seizure risk in all neonates undergoing continuous EEG (cEEG) monitoring following a standardized monitoring protocol. METHODS All cEEGs from unique neonates 34-48 weeks postmenstrual age monitored from 1/2011-10/2017 (n = 291) were included. We evaluated the impact of cEEG monitoring indication (acute neonatal encephalopathy [ANE], suspicious clinical events [SCEs], or other high-risk conditions [OHRs]), age, medication status, and early EEG abnormalities (including the presence of epileptiform discharges and abnormal background continuity, amplitude, asymmetry, asynchrony, excessive sharp transients, and burst suppression) on time to first seizure and overall seizure risk using Kaplan-Meier survival curves and multivariable Cox proportional hazards models. RESULTS Seizures occurred in 28% of high-risk neonates. Discontinuation of monitoring after 24 hours of seizure-freedom would have missed 8.5% of neonates with seizures. Overall seizure risk was lower in neonates monitored for ANE compared to OHR (P = .004) and trended lower compared to SCE (P = .097). The time course of seizure presentation varied by group, where the probability of future seizure was less than 1% after 17 hours of seizure-free monitoring in the SCE group, but required 42 hours in the OHR group, and 73 hours in the ANE group. The presence of early epileptiform discharges increased seizure risk in each group (ANE: adjusted hazard ratio [aHR] 4.32, 95% confidence interval [CI] 1.23-15.13, P = .022; SCE: aHR 10.95, 95% CI 4.77-25.14, P < 1e-07; OHR: aHR 56.90, 95% CI 10.32-313.72, P < 1e-05). SIGNIFICANCE Neonates who undergo cEEG are at high risk for seizures, and risk varies by monitoring indication and early EEG findings. Seizures are captured in nearly all neonates undergoing monitoring for SCE within 24 hours of cEEG monitoring. Neonates monitored for OHR and ANE can present with delayed seizures and require longer durations of monitoring. Early epileptiform discharges are the best early EEG feature to predict seizure risk.
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Affiliation(s)
- Lila T Worden
- Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | | | - Jacquelyn Gold
- Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Luis Paixao
- Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Mark A Kramer
- Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Michael B Westover
- Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Catherine J Chu
- Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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