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Cerebral function monitoring in paediatric intensive care: useful features for predicting outcome
D Murdoch-Eaton1, M Darowski, J Livingston
1Department of Paediatric Neurology, Leeds General Infirmary, UK. d.g.murdoch-eaton@leeds.ac.uk
Insights
Bedside electroencephalogram (EEG) monitoring in critically ill children can predict neurological outcomes. Specific EEG patterns, like seizure activity and background suppression, indicate poor prognosis, aiding clinical decision-making.
Area of Science:
- Pediatric Neurology
- Critical Care Medicine
- Neurophysiology
Background:
- Assessing neurological integrity in critically ill children presents significant clinical challenges.
- Cerebral function analyzing monitors (CFAM) offer a potential solution for continuous bedside neurological assessment.
Purpose of the Study:
- To evaluate the predictive value of electroencephalogram (EEG) activity recorded via a bedside CFAM.
- To correlate EEG findings with long-term neurological outcomes in pediatric intensive care unit (PICU) patients.
Main Methods:
- Monitored EEG activity in 108 children (2 weeks to 16 years) at risk for cerebral abnormalities using a CFAM.
- Analyzed EEG features: background activity (amplitude, frequencies, symmetry) and seizure activity.
- Correlated EEG data with clinical neurological outcomes assessed one year post-monitoring.
Main Results:
- Suppression of background EEG activity was observed in 75% of children who died.
- Seizures were detected in 68% of children with poor neurological outcomes; 65% of deceased children had prolonged seizures.
- Absence of seizures and superimposed fast EEG activity during benzodiazepine infusion predicted good outcomes.
Conclusions:
- Specific EEG features, such as background suppression and seizure activity, are significant predictors of neurological outcomes in critically ill children.
- CFAM-detected cerebral activity changes provide valuable, readily available information for bedside decision-making in the ICU.
- Combining multiple predictive EEG features offers high specificity and positive predictive value for poor neurological outcomes.
Abstract:
Neurological integrity in sick children is difficult to assess clinically. The aim of this study was to determine the predictive value of EEG activity recorded with a bedside EEG analysing monitor in an intensive care unit. EEG activity was monitored in 108 children (age range 2 weeks to 16 years, median 1.7 years) considered at risk for cerebral abnormalities with a cerebral function analysing monitor (CFAM). Recordings were evaluated for features of background EEG activity including mean amplitude, frequencies, and symmetry. Electrical seizure activity was quantified if present. Predictive value of the EEG features was evaluated relative to the clinical neurological outcome after one year. Asymmetrical recordings were not seen in any child with a normal outcome. Suppression of background activity was seen in 75% of the children who died. Seizures were present in 68% of children with a poor outcome. Seventeen of the 32 children (65%) who died had prolonged seizures. Absence of seizures and the presence of superimposed fast EEG activity in response to benzodiazepine infusions correlated with good outcome. A combination of two or more predictive EEG features demonstrated >90% specificity and positive predictive likelihood of poor outcome. EEG features provide information about the functional cerebral integrity of sick children. Changes in cerebral activity detected by the CFAM aid decision making by providing such information readily at the bedside in intensive care.