Cohen SE, Zantvoord JB, Wezenberg BN, Daams JG, Bockting CLH, Denys D, van Wingen GA. Electroencephalography for predicting antidepressant treatment success: A systematic review and meta-analysis.
J Affect Disord 2023;
321:201-207. [PMID:
36341804 DOI:
10.1016/j.jad.2022.10.042]
[Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022]
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 systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only prediction studies that used cross-validation. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity. Furthermore, we analyzed prediction success for separate antidepressant interventions.
RESULTS
15 studies with 12 individual patient samples and a total of 479 patients were included. Research methods varied considerably between studies. Meta-analysis of results from this heterogeneous set of studies 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.
LIMITATIONS
Sample sizes were relatively small and no study used external validation, increasing the risk of overestimation of accuracy.
CONCLUSIONS
Electroencephalography 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.
PROSPERO REGISTRATION NUMBER
CRD42021268169.
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