Abstract
AIMS: To predict the progression of children with self-limited epilepsy with centrotemporal spikes (SeLECTS) to epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS). METHODS: We conducted a retrospective analysis of early clinical and electroencephalography (EEG) data. Clinical parameters included demographic and epilepsy-related characteristics. EEG were qualitatively (localization, lateralization, synchrony, non-Rolandic discharges, nondipole spikes, multiple spikes, focal slow-wave activity) and quantitatively (spike-wave index [SWI], spike-wave frequency [SWF], power spectral density [PSD], phase-locking value [PLV], phase lag index [PLI], weighted phase lag index [wPLI], characteristic path length [CPL], clustering coefficient [CC], small-worldness [Sigma]) analyzed. A logistic regression-based prediction model was further formulated and evaluated. RESULTS: This study included 50 children with seizure-free typical SeLECTS and 76 who developed EE-SWAS. Multivariable logistic regression revealed that early EEG features-SWF, relative PSD in the alpha band, wPLI and CPL in the delta band-were associated with the risk of encephalopathic transformation. The model demonstrated good performance with an area under the curve of 0.817 (95% confidence interval 0.736-0.898). The model showed a good fit and clinical benefit. CONCLUSION: Initial quantitative EEG characteristics of SeLECTS can predict the development of EE-SWAS, suggesting distinct disease characteristics and pathogeneses in children at risk of encephalopathic transformation.