RESUMO
Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.
Assuntos
Estimulação Encefálica Profunda/métodos , Depressão/prevenção & controle , Depressão/reabilitação , Transtorno Depressivo Maior/terapia , Eletroconvulsoterapia , Encéfalo , Resultado do Tratamento , Transtorno Depressivo Maior/fisiopatologia , Estimulação Magnética Transcraniana/métodos , Estimulação Transcraniana por Corrente ContínuaRESUMO
BACKGROUND: Better understanding of the heterogeneity of treatment responses could help to improve care for adolescents with depression. We analysed data from a clinical trial to assess whether specific symptom clusters responded differently to various treatments. METHODS: For this secondary analysis, we used data from the Treatment for Adolescents with Depression Study (TADS), in which 439 US adolescents aged 12-17 with a DSM-IV diagnosis of major depressive disorder and a minimum score of 45 on the Children's Depression Rating Scale-Revised (CDRS-R) were randomly assigned (1:1:1:1) to treatment with fluoxetine, cognitive behavioural therapy (CBT), fluoxetine plus CBT, or pill placebo. Our analysis focuses on the acute phase of the trial (ie, the first 12 weeks). Groups of co-occurring symptoms were established by clustering scores for each CDRS-R item at baseline with Ward's method, with Euclidean distances for hierarchical agglomerative clustering. We then used a linear mixed-effects model to investigate the relationship between symptom clusters and treatment efficacy, with the sum of symptom scores within each cluster as the dependent measure. As fixed effects, we entered cluster, time, and treatment assignment, with all two-way and three-way interactions, into the model. The random effect providing better fit was established to be a by-subject random slope for cluster based on improvement in the Schwarz-Bayesian information criterion. OUTCOMES: We identified two symptom clusters: cluster 1 comprised depressed mood, difficulty having fun, irritability, social withdrawal, sleep disturbance, impaired schoolwork, excessive fatigue, and low self-esteem, and cluster 2 comprised increased appetite, physical complaints, excessive weeping, decreased appetite, excessive guilt, morbid ideation, and suicidal ideation. For cluster 1 symptoms, CDRS-R scores were reduced by 5·8 points (95% CI 2·8-8·9) in adolescents treated with fluoxetine plus CBT, and by 4·1 points (1·1-7·1) in those treated with fluoxetine, compared with those given placebo. For cluster 2 symptoms, no significant differences in improvements in CDRS-R scores were detected between the active treatment and placebo groups. INTERPRETATION: Response to fluoxetine and CBT among adolescents with depression is heterogeneous. Clinicians should consider clinical profile when selecting therapeutic modality. The contrast in response patterns between symptom clusters could provide opportunities to improve treatment efficacy by gearing the development of new therapies towards the resolution of specific symptoms. FUNDING: Conselho Nacional de Desenvolvimento Científico e Tecnológico.
Assuntos
Terapia Cognitivo-Comportamental/métodos , Transtorno Depressivo Maior/terapia , Fluoxetina/uso terapêutico , Inibidores Seletivos de Recaptação de Serotonina/uso terapêutico , Adolescente , Teorema de Bayes , Criança , Terapia Combinada , Manual Diagnóstico e Estatístico de Transtornos Mentais , Feminino , Humanos , Masculino , Escalas de Graduação Psiquiátrica , Resultado do Tratamento , Estados UnidosRESUMO
Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.