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.
Subject(s)
Depressive Disorder, Major , Humans , Depressive Disorder, Major/diagnosis , Depressive Disorder, Major/drug therapy , Antidepressive Agents/therapeutic use , Treatment Outcome , Electroencephalography , Sample SizeABSTRACT
BACKGROUND: Oxidative stress is a state of imbalance between reactive oxidants and anti-oxidants. Oxidative stress and a disrupted redox regulation in the brain might contribute to the pathophysiology of psychotic disorders and could serve as interesting new targets for clinical intervention. Advanced glycation end products (AGEs) in the skin can be measured non-invasively and indicate cumulative oxidative stress. AIM: To investigate cross-sectional and longitudinal differences in AGE-levels in patients with recent onset psychosis (patients) and healthy controls (controls). To investigate association of AGE-levels and brain volume in psychosis. METHOD: An autofluorescence measurement of AGEs in the skin was performed in patients and controls. AGEs were compared in patients and controls. Furthermore, the association between AGEs and volumes of the amygdala, hippocampus and total cortical gray matter was investigated in patients. RESULTS: AGEs in the skin were elevated by 15% (or 0.66 standard deviations) in patients (n = 86) compared to controls (n = 135) (p < 0.001). An indication of a higher AGE-accumulation rate (p = 0.07) was found in patients (n = 66) compared to controls (n = 160). We found a negative association between AGEs in the skin and hippocampus volume (standardized beta= 0.27; p = 0.03) in patients (n = 46). CONCLUSION: Findings of a high level of AGEs in the skin indicate excessive oxidative stress in patients with recent onset psychosis.