Continuous Video Electroencephalography (EEG) for Event Characterization in Critically Ill Children

Objective To determine features of paroxysmal events and background electroencephalographic (EEG) abnormalities associated with electroclinical seizures in critically ill children who undergo continuous video EEG to characterize clinical events. Methods This is a prospective study of critically ill children from July 2016 to October 2018. Non-neonates with continuous video EEG indication to characterize a clinical event were included. Patients with continuous video EEG to assess for subclinical seizures due to unexplained encephalopathy and those whose event of concern were not captured on continuous video EEG were excluded. The event to be characterized was taken from documented descriptions of health care providers and classified as motor, ocular, orobuccal, autonomic, and other. In patients with more than 1 component to their paroxysmal event, the events were classified as motor plus and nonmotor plus. Results One hundred patients met inclusion and exclusion criteria, with electroclinical seizures captured in 30% (30/100). The most common event to be characterized was an autonomic event in 32% (32/100). Asymmetry and epileptiform discharges were associated with electroclinical seizures (odds ratio [OR] 2.7, 95% confidence interval [CI] 1.1-6.5, P = .03; and OR 12.5, 95% CI 4.4-35.6, P < .0001). Autonomic events alone, particularly unexplained vital sign changes, were not associated with electroclinical seizures (OR 0.3, 95% CI 0.11-0.93, P = .03). Conclusions Isolated autonomic events are unlikely to be electroclinical seizures. Details of the paroxysmal events in question can help decide which patient will benefit most from continuous video EEG based on institutional resources.

• Publication year

  2022

• Venue

  Journal of Child Neurology

• Publication date

  2022-05-29

• Fields of study

  Medicine

• Identifiers
  DOI 10.1177/08830738221096014PMID 35635225
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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