Optimization of Treatment Algorithms for Clinical Depression: Glutamate Antagonists and Transcranial Magnetic Stimulation as Case Examples

Current treatment approaches for depression are still largely based on trial and error, necessitating adequate guidance for sequential treatment selection and maintenance. Issues surrounding the adequate implementation and integration of evidence-based treatment approaches, particularly as they relate to novel approaches and combination strategies, remain an important concern.

Treatment guidelines and algorithms have been associated with improved outcomes. Utilization of measurement-based care (MBC) provides a simple and effective way to optimize personalized evidence-based medical care based on current treatment guidelines. Because current treatments lead to only modest outcomes, incorporating the use of novel treatments is desirable. However, ways in which promising treatments can effectively and appropriately be incorporated into treatment algorithms requires careful assessment of risks and benefits.

Utilization of treatment guidelines and algorithms in practice has been fairly slow to occur, although MBC approaches have aided in better adoption of evidence-based clinical practice guidelines into standard care. A balance must be struck between adopting insufficiently studied novel treatments and rapid dissemination of evidence-based treatments via treatment guidelines and algorithms.

MBC is critical to personalization of evidence-based treatment selection and optimization of treatment outcomes. Incorporation of biomarker data into treatment selection and maintenance will be critical to improve personalization of treatment in the future.