Conference Coverage

Seizure predictors may help guide EEG monitoring in kids




SEATTLE– Two sets of simple clinical factors can help physicians identify which critically ill children with brain conditions are at highest risk for subclinical seizures and therefore most likely to benefit from continuous electroencephalographic (EEG) monitoring, according to a pair of studies reported at the annual meeting of the American Epilepsy Society.

Up to a third of patients with brain conditions in critical care units have seizures that are clinically silent, evident only on an EEG, Dr. Nicholas S. Abend, an assistant professor of neurology and pediatrics at the Children’s Hospital of Philadelphia, commented in a press briefing.

Dr. Nicholas S. Abend

Dr. Nicholas S. Abend

“There are more and more data that these seizures are associated with worse outcomes for patients – worse functional outcomes, lower quality of life later, and a higher risk for later epilepsy. And that’s led to a lot of interest in trying to monitor more and more patients in order to identify these seizures and treatment to manage them,” he said. But many hospitals have limited equipment and staff for continuous EEG monitoring and therefore face tough decisions in allocating these resources.

In the first study, Dr. Abend and his colleagues developed and validated a model to predict electrographic seizures in critically ill children with acute encephalopathic conditions.

They developed the model using retrospectively collected data from 336 consecutive children at multiple centers participating in the Critical Care EEG Monitoring Research Consortium. They then validated the model using prospectively collected data from 222 children at a single center, the Children’s Hospital of Philadelphia. Overall, about a third of the children had subclinical seizures.

“When we looked at predictors of seizures, we tried to keep it to things that a clinician would know pretty easily right on admission – not detailed data that might not be available until way later because they usually have to decide at the beginning, ‘Am I going to do EEG monitoring?’ ” Dr. Abend explained. The final model thus had five basic factors: age, etiology, clinically evident seizures before continuous EEG monitoring, initial EEG background pattern, and interictal epileptiform discharges.

Results presented in a poster session showed that the model had an area under the receiver operating characteristic curve of 0.845 in the development cohort and 0.799 in the validation cohort for identifying children who were experiencing subclinical seizures.

Intensive care units can select a model cutoff value based on the resources they have available for continuous EEG monitoring, noted Dr. Abend, who disclosed that he had no relevant conflicts of interest. “So, a center with very limited resources might decide, we only want to do this for really high-risk patients; a center with lots of resources could decide, we are going to apply things much more broadly, because we have EEG machines and we have EEG readers.”


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