Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine

AimsTo predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the clinical and electroencephalographic (EEG) factors that affect outcomes. MethodsRetrospective analysis of PWEs treated with levetiracetam for 3years identified 22 patients who were SF and 24 who were not. Before starting levetiracetam, 11 clinical factors and four EEG features (sample entropy of , , , ) were identified. Overall, 80% of each the two groups were chosen to establish a support vector machine (SVM) model with 5-fold cross-validation, hold-out validation and jack-knife validation. The other 20% were used to predict the efficacy of levetiracetam. The mean impact value (MIV) algorithm was used to rank the relativity between factors and outcomes. ResultsCompared with SF patients, not SF patients displayed a specific decrease in EEG sample entropy in band from the F4 channel, band from Fp2 and F8 channels, band from C3 channel (P<0.05). The SVM model based on the clinical and EEG features yielded 72.2% accuracy of 5-fold cross-validation, 75.0% accuracy of jack-knife validation, 67.7% accuracy of hold-out validation in the training set and had a high prediction accuracy of 90% in test set (sensitivity was 100%, area under the receiver operating characteristic curve was 0.96). The feature of band from Fp2 weighs heavily in the prediction model according to the mean impact value algorithm. ConclusionsThe efficacy of levetiracetam on newly diagnosed PWEs could be predicted using an SVM model, which could guide antiepileptic drug selection.