ArticleSeizure2015
Development and validation of a seizure prediction model in critically ill children.
Article in Seizure, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 41 citations in OpenAlex.
- Association of EEG characteristics with outcomes following pediatric ICU cardiac arrest: A secondary analysis of the ICU-RESUScitation trial.Resuscitation · 2024Trial
- Late-Onset Findings During Extended EEG Monitoring Are Rare in Critically Ill Children.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2025Article
- Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2023Article
- Real-time seizure detection in paediatric intensive care patients: the RESET child brain protocol.BMJ open · 2022Article
- Multimodal monitoring including early EEG improves stratification of brain injury severity after pediatric cardiac arrest.Resuscitation · 2021Observational
- Machine learning models to predict electroencephalographic seizures in critically ill children.Seizure · 2021Observational
- Prediction of EEG Seizures in Critically Ill Children.Pediatric neurology briefs · 2020Article
- EEG monitoring duration to identify electroencephalographic seizures in critically ill children.Neurology · 2020Observational
- Article
- Early EEG Features for Outcome Prediction After Cardiac Arrest in Children.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2019Observational
- Seizure Prediction Models in the Neonatal Intensive Care Unit.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2019Article
- Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy.Communications in nonlinear science & numerical simulation · 2018Article
- Risk Factors for Seizures Among Young Children Monitored With Continuous Electroencephalography in Intensive Care Unit: A Retrospective Study.Frontiers in pediatrics · 2018Article
- Electrographic status epilepticus in children with critical illness: Epidemiology and outcome.Epilepsy & behavior : E&B · 2015Review
- How much does it cost to identify a critically ill child experiencing electrographic seizures?Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors at 12 institutions in 1 country.
Funding
Abstract
purposeElectrographic seizures are common in encephalopathic critically ill children, but identification requires continuous EEG monitoring (CEEG). Development of a seizure prediction model would enable more efficient use of limited CEEG resources. We aimed to develop and validate a seizure prediction model for use among encephalopathic critically ill children.
methodWe developed a seizure prediction model using a retrospectively acquired multi-center database of children with acute encephalopathy without an epilepsy diagnosis, who underwent clinically indicated CEEG. We performed model validation using a separate prospectively acquired single center database. Predictor variables were chosen to be readily available to clinicians prior to the onset of CEEG and included: age, etiology category, clinical seizures prior to CEEG, initial EEG background category, and inter-ictal discharge category.
resultsThe model has fair to good discrimination ability and overall performance. At the optimal cut-off point in the validation dataset, the model has a sensitivity of 59% and a specificity of 81%. Varied cut-off points could be chosen to optimize sensitivity or specificity depending on available CEEG resources.
conclusionDespite inherent variability between centers, a model developed using multi-center CEEG data and few readily available variables could guide the use of limited CEEG resources when applied at a single center. Depending on CEEG resources, centers could choose lower cut-off points to maximize identification of all patients with seizures (but with more patients monitored) or higher cut-off points to reduce resource utilization by reducing monitoring of lower risk patients (but with failure to identify some patients with seizures).
Indexed as
Identifiers
What OpenQuestion holds
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.