Abstract
BackgroundTreatment-resistant depression affects about 30% of individuals with major depressive disorder. Deep brain stimulation is an investigational intervention for treatment-resistant depression with varied results. We undertook this meta-analysis to synthesize outcome data across trial designs, anatomical targets, and institutions to better establish efficacy and side-effect profiles. MethodsWe conducted a systematic PubMed review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Seven randomized controlled trials (n = 198) and 8 open-label trials (n = 77) were included spanning 2009 to 2020. Outcome measures included Hamilton Depression Rating Scale or Montgomery–Åsberg Depression Rating Scale scores, as well as response and remission rates over time. Outcomes were tracked at the last follow-up and quantified as a time course using model-based network meta-analysis. Linear mixed models were fit to individual patient data to identify covariates. ResultsDeep brain stimulation achieved 47% improvement in long-term depression scale scores, with an estimated time to reach 50% improvement of around 23 months. There were no significant subgroup effects of stimulation target, time of last follow-up, sex, age of disease onset, or duration of disease, but open-label trials showed significantly greater treatment effects than randomized controlled trials. Long-term (12–60 month) response and remission rates were 48% and 35%, respectively. The time course of improvement with active stimulation could not be adequately distinguished from that with sham stimulation, when available. ConclusionsDeep brain stimulation produces significant chronic improvement in symptoms of treatment-resistant depression. However, the limited sham-controlled data do not demonstrate significant improvement over placebo. Future advancements in stimulation optimization and careful blinding and placebo schemes are important next steps for this therapy.
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More From: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
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