Observational studyNeurology2019
Quantitative EEG predicts outcomes in children after cardiac arrest.
Observational study in Neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.Resuscitation · 2024Pooled it
- Prediction of neurological outcome after pediatric cardiac arrest using heart rate variability and machine learning.Resuscitation · 2026Article
- Pediatric cardiac arrest outcome prediction using data-driven machine learning of early quantitative electroencephalogram (qEEG) features.Resuscitation · 2025Article
- Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.Scientific reports · 2025Article
- Background EEG Suppression Ratio for Early Detection of Cerebral Injury in Pediatric Cardiac Arrest.Neurocritical care · 2024Article
- Review of Noninvasive Neuromonitoring Modalities in Children II: EEG, qEEG.Neurocritical care · 2023Review
- Initial study on quantitative electroencephalographic analysis of bioelectrical activity of the brain of children with fetal alcohol spectrum disorders (FASD) without epilepsy.Scientific reports · 2023Article
- Observational
- Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.IEEE transactions on bio-medical engineering · 2022Article
- Multimodal Neurologic Monitoring in Children With Acute Brain Injury.Pediatric neurology · 2022Review
- State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.JMIR medical informatics · 2022Review
- Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method.Frontiers in neurology · 2022Article
- Using EEG in Resource-Limited Areas: Comparing Qualitative and Quantitative Interpretation Methods in Cerebral Malaria.Pediatric neurology · 2022Article
- Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks.Resuscitation · 2021Article
- EEG markers predictive of epilepsy risk in pediatric cerebral malaria - A feasibility study.Epilepsy & behavior : E&B · 2020Article
- IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit.IEEE journal of biomedical and health informatics · 2020Article
- Neurological Prognostication in Children After Cardiac Arrest.Pediatric neurology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveTo determine whether quantitative EEG (QEEG) features predict neurologic outcomes in children after cardiac arrest.
methodsWe performed a single-center prospective observational study of 87 consecutive children resuscitated and admitted to the pediatric intensive care unit after cardiac arrest. Full-array conventional EEG data were obtained as part of clinical management. We computed 8 QEEG features from 5-minute epochs every hour after return of circulation. We developed predictive models utilizing random forest classifiers trained on patient age and 8 QEEG features to predict outcome. The features included SD of each EEG channel, normalized band power in alpha, beta, theta, delta, and gamma wave frequencies, line length, and regularity function scores. We measured outcomes using Pediatric Cerebral Performance Category (PCPC) scores. We evaluated the models using 5-fold cross-validation and 1,000 bootstrap samples.
resultsThe best performing model had a 5-fold cross-validation accuracy of 0.8 (0.88 area under the receiver operating characteristic curve). It had a positive predictive value of 0.79 and a sensitivity of 0.84 in predicting patients with favorable outcomes (PCPC score of 1-3). It had a negative predictive value of 0.8 and a specificity of 0.75 in predicting patients with unfavorable outcomes (PCPC score of 4-6). The model also identified the relative importance of each feature. Analyses using only frontal electrodes did not differ in prediction performance compared to analyses using all electrodes.
conclusionsQEEG features can standardize EEG interpretation and predict neurologic outcomes in children after cardiac arrest.
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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.