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Aims Vagus nerve stimulation (VNS) is a neuromodulation therapy for children with drug‐resistant epilepsy (DRE). The efficacy of VNS is heterogeneous. A prediction model is needed to predict the efficacy before implantation. Methods We collected data from children with DRE who underwent VNS implantation and received regular programming for at least 1 year. Preoperative clinical information and scalp video electroencephalography (EEG) were available in 88 children. Synchronization features, including phase lag index (PLI), weighted phase lag index (wPLI), and phase‐locking value (PLV), were compared between responders and non‐responders. We further adapted a support vector machine (SVM) classifier selected from 25 clinical and 18 synchronization features to build a prediction model for efficacy in a discovery cohort (n = 70) and was tested in an independent validation cohort (n = 18). Results In the discovery cohort, the average interictal awake PLI in the high beta band was significantly higher in responders than non‐responders (p < 0.05). The SVM classifier generated from integrating both clinical and synchronization features had the best prediction efficacy, demonstrating an accuracy of 75.7%, precision of 80.8% and area under the receiver operating characteristic (AUC) of 0.766 on 10‐fold cross‐validation. In the validation cohort, the prediction model demonstrated an accuracy of 61.1%. Conclusion This study established the first prediction model integrating clinical and baseline synchronization features for preoperative VNS responder screening among children with DRE. With further optimization of the model, we hope to provide an effective and convenient method for identifying responders before VNS implantation. A support vector machine prediction model integrating clinical and baseline synchronization features was established for preoperative screening of responders to vagus nerve stimulation among children with drug‐resistant epilepsy.

Aims To predict the vagus nerve stimulation (VNS) efficacy for pediatric drug‐resistant epilepsy (DRE) patients, we aim to identify preimplantation biomarkers through clinical features and electroencephalogram (EEG) signals and thus establish a predictive model from a multi‐modal feature set with high prediction accuracy. Methods Sixty‐five pediatric DRE patients implanted with VNS were included and followed up. We explored the topological network and entropy features of preimplantation EEG signals to identify the biomarkers for VNS efficacy. A Support Vector Machine (SVM) integrated these biomarkers to distinguish the efficacy groups. Results The proportion of VNS responders was 58.5% (38/65) at the last follow‐up. In the analysis of parieto‐occipital α band activity, higher synchronization level and nodal efficiency were found in responders. The central‐frontal θ band activity showed significantly lower entropy in responders. The prediction model reached an accuracy of 81.5%, a precision of 80.1%, and an AUC (area under the receiver operating characteristic curve) of 0.838. Conclusion Our results revealed that, compared to nonresponders, VNS responders had a more efficient α band brain network, especially in the parieto‐occipital region, and less spectral complexity of θ brain activities in the central‐frontal region. We established a predictive model integrating both preimplantation clinical and EEG features and exhibited great potential for discriminating the VNS responders. This study contributed to the understanding of the VNS mechanism and improved the performance of the current predictive model. The long‐term efficacy of VNS was assessed among 65 pediatric patients. Presurgical EEG analysis shows that VNS responders exhibit higher nodal efficiency in parietal‐occipital EEG α activity and lower entropy in central‐frontal EEG θ activity. The SVM model with clinical and EEG features for VNS efficacy shows high accuracy.