ArticleJournal of clinical neurophysiology : official publication of the American Electroencephalographic Society2019
Seizure Prediction Models in the Neonatal Intensive Care Unit.
Article in Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Scalp high-frequency activity differentiates neonates with seizures from healthy neonates and indicates postneonatal epilepsy risk.Epilepsia · 2026Article
- AES ILAE North America Symposium: Guiding Evidence-Based Epilepsy Management.Epilepsy currents · 2026Article
- Epilepsy prediction models for children and adolescents: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Short-horizon neonatal seizure prediction using EEG-based deep learning.PLOS digital health · 2025Article
- Observational
- Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.Epilepsia · 2025Article
- Treating Seizures and Improving Newborn Outcomes for Infants with Hypoxic-Ischemic Encephalopathy.Clinics in perinatology · 2024Review
- Scalp high-frequency oscillations differentiate neonates with seizures from healthy neonates.Epilepsia open · 2023Article
- Quantitative EEG in the neonatal intensive care unit: Current application and future promise.Annals of the Child Neurology Society · 2023Review
- Neuromonitoring in neonatal critical care part I: neonatal encephalopathy and neonates with possible seizures.Pediatric research · 2023Review
- Leveraging electronic medical record-embedded standardised electroencephalogram reporting to develop neonatal seizure prediction models: a retrospective cohort study.The Lancet. Digital health · 2023Article
- Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic-ischemic encephalopathy.Epilepsia · 2023Article
- Current and Future Uses of Continuous EEG in the NICU.Frontiers in pediatrics · 2021Review
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
purposeConventional video-EEG monitoring is required to diagnose seizures accurately in neonates. This tool is resource-intense and has limited availability in many centers. Seizure prediction models could help allocate resources by improving efficiency in which conventional video-EEG monitoring is used to detect subclinical seizures. The aim of this retrospective study was to create a neonate-specific seizure prediction model using clinical characteristics and EEG background findings.
methodsWe conducted a 3-year retrospective study of all consecutive neonates who underwent conventional video-EEG monitoring at a tertiary care pediatric hospital. Variables including age, EEG indication, high-risk clinical characteristics, and EEG background informed seizure prediction models based on a multivariable logistic regression model. A Cox proportional hazard regression model was used to construct time to first EEG seizure.
resultsPrediction models with clinical variables or background EEG features alone versus combined clinical and background EEG features were created from 210 neonates who met inclusion criteria. The combined clinical and EEG model had a higher area under the curve for combined sensitivity and specificity to 83.0% when compared to the clinical model (76.4%) or EEG model (66.2%). The same trend of higher sensitivity of the combined model was found for time to seizure outcome.
conclusionsWhile both clinical and EEG background features were predictive of neonatal seizures, the combination improved overall prediction of seizure occurrence and prediction of time to first seizure as compared with prediction models based solely on clinical or EEG features alone. With prospective validation, this model may improve efficiency of patient-oriented EEG monitoring.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.