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220923 r ||| eng |
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|a Peterson, Kim
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|a Evidence brief: The comparative effectiveness, harms, and cost-effectiveness of pharmacogenomics-guided antidepressant treatment versus usual care for major depressive disorder
|h Elektronische Ressource
|c prepared for Department of Veterans Affairs, Veterans Health Administration, Quality Enhancement Research Initiative, Health Services Research & Development Service ; prepared by Evidence-based Synthesis Program (ESP) Coordinating Center, Portland VA Health Care System ; investigators, Kim Peterson, Eric Dieperink, Lauren Ferguson, Johanna Anderson, Mark Helfand
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|a Comparative effectiveness, harms, and cost-effectiveness of pharmacogenomics-guided antidepressant treatment versus usual care for major depressive disorder
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|a Washington, DC
|b Department of Veterans Affairs, Health Services Research & Development Service
|c 2016, May 2016
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|a 1 PDF file (i, 31 pages)
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|a Includes bibliographical references
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|a Dieperink, Eric
|e [author]
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|a Ferguson, Lauren
|e [author]
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|a Anderson, Johanna
|e [author]
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|a United States
|b Department of Veterans Affairs
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|a Portland VA Medical Center
|b Evidence-based Synthesis Program Center
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|a Quality Enhancement Research Initiative (U.S.)
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|a eng
|2 ISO 639-2
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|b NCBI
|a National Center for Biotechnology Information
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|a Evidence-based synthesis program
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|a At head of title: QUERI.
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|u https://www.ncbi.nlm.nih.gov/books/NBK384610
|3 Volltext
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|a 610
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|a Antidepressants are a mainstay of treatment for Major Depressive Disorder (MDD). To guide the choice of antidepressants, clinicians have typically taken a "trial and error" approach, informed by various clinical factors thought to be associated with variable treatment response. But rates of remission are low and variable, with approximately 11-30% of patients remitting, even after one year of antidepressant treatment. As a result, there is intense interest in identifying additional factors that could help clinicians optimize the effectiveness of available treatments. Genetic variation has long been explored as another potential contributor to individual differences in antidepressant treatment outcome. Whether using genetic information can help predict how an individual might respond to a particular antidepressant - referred to as 'pharmacogenomics' - is of great interest for further advancing precision medicine efforts
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