Translating Science Into Service: Lessons Learned From the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study

摘要

Objective: The purpose of this review is to summarize lessons learned from, and limitations of, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial, focusing on measurement-based care.Data Sources: PubMed and MEDLINE were searched from 1980 through 2006 using terms such as depression, major depressive disorder, augmentation, switching, measurement-based care, and remission. Other relevant articles were identified by checking reference lists of the identified studies.Study Selection: A total of 60 studies were initially identified, which resulted in 34 studies used in this review. The salient criteria used for selection of studies centered on whether results had implications for clinical practice and provided lessons that could be learned and practically applied to real-life settings.Data Extraction: Data were extracted from the STAR*D trial and associated studies that were pertinent to everyday problems encountered by mental health professionals in the community: determination of whether the optimum strategy for a particular patient involves “augmentation” or “switching” of a patient's medication.Data Synthesis: Measurement-based care is essential in order to identify the two thirds of patients who do not achieve remission with the first treatment strategy. Timely changes in antidepressant therapy can improve outcomes.Conclusions: The STAR*D trial underscores the importance of measurement-based care in identifying patients who may not have achieved remission with an initial antidepressant, enabling alternative options such as augmentation or switching to be prescribed to meet this ultimate goal of therapy.