ArticleResuscitation2025
Pediatric cardiac arrest outcome prediction using data-driven machine learning of early quantitative electroencephalogram (qEEG) features.
Article in Resuscitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Prediction of Neurological Outcome and Mortality in Cardiac Arrest Patients: An Explainable Machine Learning Study Integrating HRV, EEG, and Clinical Features.Neurocritical care · 2026Article
- Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.Pediatric cardiology · 2026Review
- Prediction of neurological outcome after pediatric cardiac arrest using heart rate variability and machine learning.Resuscitation · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
aimsHypoxic-ischemic brain injury drives poor outcomes after pediatric cardiac arrest, highlighting the need for early prognostication. This study evaluates whether machine learning models using a high-dimensional set of quantitative EEG (qEEG) features improve prediction of unfavorable neurologic outcome compared to a previously studied 7-feature model. We also assessed performance stability over time and the added value of clinical variables.
methodsSingle-center retrospective cohort study of children aged 3 months to 18 years who experienced cardiac arrest and received EEG monitoring within 24 h post-arrest. Patients with pre-arrest Pediatric Cerebral Performance Category (PCPC) >3 were excluded. Unfavorable outcome was defined as death or PCPC ≥4 at hospital discharge or 30 days post-arrest. We extracted 164 qEEG features and trained models using three established algorithms. Performance was evaluated using area under the ROC curve (AUROC).
resultsSeventy patients were included (median age 7.0 years, IQR 1.5-11.5); 53 % had unfavorable outcomes. Models using 164 qEEG features outperformed the 7-feature model: LASSO [0.81 (95 % CI: 0.69-0.91) vs 0.45 (0.31-0.58)] and Random Forest [0.80 (0.67-0.90) vs 0.65 (0.50-0.78)]. Adding clinical variables did not improve performance. AUROCs were stable across 6-h epochs from 6 to 24 h. Higher phase locking value, fractal exponent, and coherence were associated with better outcomes; higher delta power and suppression ratio variability were associated with worse outcomes.
conclusionsData-driven models using 164 qEEG features accurately predicted neurologic outcomes after pediatric cardiac arrest, with stable performance over time. Future work includes external validation to assess generalizability.
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.