Electroencephalography for predicting antidepressant treatment success: a systematic review and meta-analysis

Abstract Background Patients suffering from major depressive disorder (MDD) regularly experience non-response to treatment for their depressive episode. Personalized clinical decision making could shorten depressive episodes and reduce patient suffering. Although no clinical tools are currently available, machine learning analysis of electroencephalography (EEG) shows promise in treatment response prediction. Methods With a pre-registered systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only validated and therefore more generalizable prediction studies. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity and. Furthermore, we analyzed prediction success for separate antidepressant interventions. PROSPERO registration number: CRD42021268169ResultsA meta-analysis of 15 studies with 12 individual patient samples including 479 patients resulted in an area under the curve of 0.91, a sensitivity of 83% (95% CI 74-89%), and a specificity of 86% (95% CI 81-90%). Classification performance did not significantly differ between treatments. Although studies were all internally validated, no externally validated studies have been reported. We found substantial risk of bias caused by methodological shortcomings such as non-independent feature selection, though performance of non-biased studies was comparable. LimitationsWe could not compare accuracy of different EEG approaches due to divergent processing techniques. Sample sizes were relatively small, increasing the risk of overestimation of accuracy. ConclusionsElectroencephalography can predict the response to antidepressant treatment with high accuracy. However, future studies with more rigorous validation are needed to produce a clinical tool to guide interventions in MDD.