Factors for predicting response to electroconvulsive therapy (ECT), transcranial magnetic stimulation (TMS) and ketamine in patients with treatment-resistant depression: a systematic review.
International journal of psychiatry in clinical practice – February 07, 2026
Source: PubMed
Summary
Predicting success for severe depression treatments like ECT (electroconvulsive therapy), TMS (transcranial magnetic stimulation), and ketamine is crucial. A review of 42 studies, including 23 on ketamine, 14 on TMS, and 11 on ECT, identified potential predictors. Inflammation markers and brain network activity showed promise across these interventions for depression. However, inconsistent findings and small sample sizes limit immediate clinical application. Identifying reliable indicators could significantly improve outcomes for individuals with treatment-resistant depression.
Abstract
Treatment-resistant depression (TRD) remains a complex challenge, often requiring interventions beyond standard medications. This review explores factors that may predict positive response to electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS) and ketamine-based treatments to help guide clinical decision-making. A systematic review was conducted following PRISMA 2020 guidelines. English-language, peer-reviewed studies were identified through PubMed, Embase and Google Scholar using search terms such as 'treatment-resistant,' 'outcome,' 'prediction,' 'ECT,' 'rTMS,' and 'ketamine.' Studies were included if they examined clinical, biological or imaging predictors of response in adults with TRD. Case reports, reviews, editorials and non-English articles were excluded. A total of 42 studies were selected from 408 screened. Among these, 23 focused on ketamine/esketamine, 14 on rTMS, and 11 on ECT. Predictive factors were grouped into clinical (e.g., symptom profile, illness duration), biological (e.g., IL-6, CRP, BDNF) and imaging (e.g., cingulate cortex activity, connectivity). Inflammation markers and fronto-limbic network findings appeared across treatments, though findings were inconsistent. While some predictors show promise, clinical use remains limited by methodological differences and small sample sizes. Larger studies are required to identify clinically useful predictors. Additionally, for optimal treatment decision-making, comparative studies are necessary.