Quantitative EEG improves prediction of Sturge-Weber syndrome in infants with port-wine birthmark. Issue 10 (October 2021) - Gill, Ryan E.; Tang, Bohao; Smegal, Lindsay; Adamek, Jack H.; McAuliffe, Danielle; Lakshmanan, Balaji M.; Srivastava, Siddharth; Quain, Angela M.; Sebold, Alison J.; Lin, Doris D.M.; Kossoff, Eric H.; Caffo, Brian; et al.
Highlights: Quantitative EEG helps predict which infants with facial port-wine birthmark are most likely to develop Sturge-Weber syndrome. Risk prediction is critical for emerging presymptomatic treatment of infants with port-wine birthmark. Long-term collection of qEEG, clinical EEG, history and neurological exam data allow initial development of a predictive model. Abstract: Objective: Port-wine birthmark (PWB) is a common occurrence in the newborn, and general pediatricians, dermatologists, and ophthalmologists are often called on to make an assessment of risk for Sturge-Weber syndrome (SWS) due to workforce shortages in pediatric neurologists and MRI's low sensitivity for SWS brain involvement in infants. We therefore aimed to develop a quantitative EEG (qEEG) approach to safely screen young infants with PWB for SWS risk and optimal timing of diagnostic MRI. Methods: Forty-eight infants (prior to first birthday) underwent EEG recording. Signal processing methods compared voltage between left and right sides using a previously defined pipeline and diagnostic threshold. In this test sample, we compared sensitivity/specificity of the qEEG metric against MRI performed after the first birthday. We also used likelihood ratio testing to determine whether qEEG adds incremental information beyond topographical extent of PWB, another risk marker of brain involvement. Results: qEEG helped predict SWS risk in the first year of life ( p  = 0.031), with a sensitivity of 50% and a specificity of 81%. It added about 40% incremental information beyond PWB extent alone ( p  = 0.042). Conclusion: qEEG adds information to risk prediction in infants with facial PWB. Significance: qEEG can be used to help determine whether to obtain an MRI in the first year of life. The data collected can assist in developing a predictive model risk calculator that incorporates both PWB extent and qEEG results, which can be validated and then employed in the community.