Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

Abstract

Objective: Reducing unsuccessful treatment trials could improve depression treatment. Quantitative analysis of the electro-encephalogram (QEEG) might predict treatment response, and is being commercially marketed for this purpose. The authors sought to (1) quantify the reliability of QEEG for response prediction in depressive illness and (2) identify methodological limitations of the available evidence. Method: The authors performed a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio (DOR). Raters also judged each article on indicators of good research practice. Results: In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed sensitivity 0.72 (0.67–0.76), specificity 0.68 (0.63–0.73), log(DOR) 1.89 (1.56–2.21), and area under the receiver-operator curve 0.76 (0.71–0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias (arcsine asymmetry test, t=6.33, p=2.64e-8). Most studies did not use ideal practices. Conclusions: QEEG does not appear clinically reliable for predicting depression treatment response due to under-reporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of prior findings. Until these limitations are remedied, QEEG is not recommended for guiding psychiatric treatment selection.

What are the chances for personalised treatment with antidepressants? Detection of patient-by-treatment interaction with a variance ratio meta-analysis

“…Second, and closely related to the above argument, even after decades of massive research efforts there is no evidence of robust neurobiological and genetic predictors of differential treatment response in depression 6–9 32. Biostatistics professor Dr Stephen Senn once stated: ‘Unless patient by treatment interaction exists, it is pointless looking for gene by treatment interactions’ 33.…”

“…Despite substantial research efforts, no predictors of treatment success with ADs were found that were robust and reliable enough for use in clinical practice 6–9. Thus, much of the variance of the treatment outcome remains unexplained so far.…”

Identification of clinical features and biomarkers that may inform a personalized approach to rTMS for depression

“…Overall, while there are a number of promising EEG metrics associated with positive rTMS treatment outcomes in depressed patients, currently there is insufficient data for deployment of any of them in a standard clinical setting. Indeed, results of a recent meta-analysis underscore the fact that there is presently insufficient evidence to recommend use of EEG for guiding rTMS or other psychiatric treatment decisions at the present time [60]. As noted by the authors, several factors characterizing the currently published body of EEG biomarker studies may contribute, including under-publication of negative results, lack of out of sample validation, and insufficient direct replication of previous findings.…”

Technology, psychiatry, and answering the ultimate question

“…Some medical entrepreneurs are trying to change that. In this month's issue, we cover a paper appearing in the American Journal of Psychiatry by Widge et al 1 examining the usefulness of quantitative electroencephalography (QEEG) for predicting treatment response in depression. This technology, which utilizes complex mathematical algorithms to analyze EEG waveforms, has been around for many years, but the advent of readily available powerful computers has led to a recent increase in efforts to market this approach to clinicians.…”

“…But as Widge et al (and Zeier et al, with regard to pharmacogenetics) have shown, we're not there yet, and the “use of commercial or research‐grade QEEG methods in routine clinical practice would not be a wise use of health care dollars.” 1 Given the many potential demands on those scarce dollars, in psychiatry as well as in the rest of medicine, it's incumbent on all of us to be cautious in welcoming these new technologies into the clinic until their validity, and cost/benefit utility, are clear.…”