ORIGINAL RESEARCH article
Front. Hum. Neurosci.
Sec. Brain Health and Clinical Neuroscience
This article is part of the Research TopicAdvances in epilepsy research: exploring biomarkers, brain stimulation, and neurosurgical interventions - Volume IIView all 4 articles
Predicting Progression from SeLECTS with SWAS to EE-SWAS: Risk Factor Identification and Model Development
Provisionally accepted
- Children‘s Hospital of Chongqing Medical University, Chongqing, China
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To identify early risk factors and develop a predictive model for progression from self-limited epilepsy with centrotemporal spikes (SeLECTS) with spike-and-wave activation in sleep (SWAS) to epileptic encephalopathy with spike-and-wave activation in sleep (EE-SWAS). From an initial cohort of pediatric cases with a spike-and-wave index (SWI) >50%, we identified 77 patients with SeLECTS. After a follow-up period of at least two years, 33 patients progressed to EE-SWAS, while 36 showed no progression. We performed a comprehensive analysis of baseline clinical characteristics and electroencephalogram (EEG) findings in both cohorts to identify risk factors for cognitive regression or stagnation. Based on these factors, we developed a predictive model, which was subsequently validated using data from an independent campus. Multivariate logistic analysis revealed that long spike-and-wave clusters, high-amplitude spike-and-wave ( SW ) with secondary generalization and young age of first seizure were risk factors for the progression of cognitive regression or stagnation. These risk factors were used to construct a prediction model via a nomogram. Internal (C index, 0.932) and external (C index, 0.934) validation showed that the model could predict the risk of progression from SeLECTS with SWAS to EE-SWAS. Prolonged spike-and-wave clusters, high-amplitude SW with secondary generalization, and younger age at first seizure were identified as key risk factors for cognitive regression or stagnation in SeLECTS patients with SWAS progressing to EE-SWAS.Based on these findings, we developed a predictive model to aid clinicians in early risk stratification and therapeutic decision-making.
Keywords: Selects, EE-SWAS, Prediction model, SWAS, risk factor
Received: 07 Jul 2025; Accepted: 09 Dec 2025.
Copyright: © 2025 Hu, Luo, Deng, Xie, Ma, Si-Qi, Yuan and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ping Yuan
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