Can electroencephalograms provide guidance for the withdrawal of antiepileptic drugs: A meta-analysis. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Tang, Liwei; Xiao, Zheng
• Is Part Of: Clinical neurophysiology. Volume 128:Issue 2(2017:Feb.)
• Page Start: 297
• Page End: 302
• Publication Date: 2017-02
• Subjects: Epilepsy -- Withdrawal -- Electroencephalogram -- EEG -- Relapse -- Recurrence; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2016.11.024
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Abnormal EEG recordings prior to AED withdrawal predicted a high risk of relapse. Paroxysmal, slowing, spike and wave activities on EEG indicated an increased risk of relapse. Nonparoxysmal, spike and focal abnormalities on EEG showed lower predictive values. Abstract: Objective: The discontinuation of antiepileptic drugs (AEDs) is an important treatment decision for epilepsy patients who have been seizure-free for 2 years or longer. Some patients experience seizures relapse after AED withdrawal. The prognostic value of electroencephalograms (EEGs) for seizure relapse following AED withdrawal is controversial. To our knowledge, this is the first meta-analysis to address whether EEG data can be used to guide the discontinuation of AEDs. Method: We performed a meta-analysis of cohort studies that reported original EEG data from before AED withdrawal and recurrence after AED-withdrawal. The quality of each study was assessed using the Newcastle–Ottawa Scale. Results: Fifteen studies including a total of 2349 participants were included in this meta-analysis. This meta-analysis of 15 studies demonstrates that an abnormal electroencephalogram was a predictor of the risk of relapse. Additionally, paroxysmal, slowing, spike and wave activities on electroencephalograms were associated with increased risk of relapse. Conclusion: We reveal that abnormal EEG records, particularly paroxysmal abnormalities, before AED withdrawal predicted a high risk of relapse. Slowing and spike and wave activities also exhibited moderate predictive values. Significance: Our findings suggest that, EEGs might be an important prognostic tool for antiepileptic drug reduction.