ArticleFrontiers in computational neuroscience2026
EEG criticality as a prognostic tool for functional outcomes in sedated pediatric intensive care patients.
Article in Frontiers in computational neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: Predicting meaningful recovery in sedated patients remains a major challenge in the pediatric intensive care unit (PICU) due to the lack of reliable, behavior-independent prognostic markers for children. Criticality of electroencephalography (EEG) signals reflects the brain's dynamic balance between order and chaos and capacity for information processing. The objective of this study was to assess the association between criticality-related EEG features and the functional outcomes of sedated PICU patients. Methods: A multi-center, retrospective cohort observational study was conducted with 32 patients admitted to two PICUs in urban Canada between 2014 and 2024, 5-18 years of age, 14 females. Patients were admitted with mixed etiology: acute seizure (28%), acute brain injures (22%), and systemic illness (50%). All patients received a clinically indicated EEG while exposed to an inhibitory anesthetic (midazolam, propofol, dexmedetomidine with a GABAergic sedative). Nine EEG features were calculated from three categories of criticality-related measures (entropy, fractal and complexity); five spectral EEG features and three patient demographic features were extracted from the databases. Patient outcomes were assessed with the Glasgow Outcome Scale-Extended three months post-injury. All features were statistically compared between good and poor recovery groups, and several machine learning models were trained with different combinations of features to predict patient outcome. Results: A threshold between lower and upper severe disability (GOS-E = 4) optimized classification of recovery. Criticality-related and spectral EEG features differed significantly between good and poor recovery groups, and EEG features predicted patient recovery above and beyond patient demographics, with a maximal predictive accuracy of 87% and AUC of 0.92. Patients with good outcomes exhibited greater EEG complexity, entropy and fractal patterns compared to those with poor outcomes. Conclusion: This study demonstrates the prognostic potential of EEG spectral and criticality-related features in predicting outcomes for sedated PICU patients.
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