CNS neuroscience & therapeutics · 2022
A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy.
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Ma Jiayi, Wang Zhiyan, Cheng Tungyang, Hu Yingbing, Qin Xiaoya, Wang Wen, Yu Guojing, Liu Qingzhu, Ji Taoyun, Xie Han, Zha Daqi, Wang Shuang, Yang Zhixian, Liu Xiaoyan, Cai Lixin, Jiang Yuwu, Hao Hongwei, Wang Jing, Li Luming, Wu Ye
Аннотация
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.
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.