Investigating the Role of Maintenance TMS Protocols for Major Depression: Systematic Review and Future Perspectives for Personalized Interventions.
Giacomo D'AndreaGianluca MancusiMaria Chiara SantovitoCarlotta MarrangoneFabrizio MartinoMario SantorelliAndrea MiuliFrancesco Di CarloMaria Salvina SignorelliMassimo ClericiMauro PettorrusoGiovanni MartinottiPublished in: Journal of personalized medicine (2023)
Repetitive Transcranial Magnetic Stimulation (rTMS) has been approved by the FDA as an effective intervention for Treatment-Resistant Depression (TRD). However, there is little evidence about maintenance protocol necessity. The aim of this systematic review is to identify, characterize, and evaluate the current maintenance TMS protocols for MDD and TRD patients who have received acute treatment. A literature search was conducted following the PRISMA guidelines of 2015 on PubMed, Scopus, and Web of Science databases for publications up to March 2022. Fourteen articles were included. High protocol heterogeneity was observed. Most studies highlighted significant efficacy of maintenance protocols in decreasing relapse risk, suggesting that administering two or fewer stimulations per month is ineffective in sustaining an antidepressant effect or in reducing the risk of relapse in responder patients. The risk of relapse was most pronounced after five months from the acute treatment. Maintenance TMS appears to be a resourceful strategy to maintain acute antidepressant treatment effects, significantly reducing relapse risk. The ease of administering and the ability to monitor treatment adherence should be considered when evaluating the future use of maintenance TMS protocols. Further studies are needed to clarify the clinical relevance of overlapping acute TMS effects with maintenance protocols and to evaluate their long-term effectiveness.
Keyphrases
- transcranial magnetic stimulation
- systematic review
- high frequency
- randomized controlled trial
- end stage renal disease
- meta analyses
- depressive symptoms
- type diabetes
- peritoneal dialysis
- chronic kidney disease
- adipose tissue
- machine learning
- combination therapy
- prognostic factors
- intensive care unit
- artificial intelligence
- patient reported outcomes
- metabolic syndrome
- free survival
- big data
- physical activity
- clinical practice