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- W2028045658 abstract "The article by Perlis ( 1 Perlis R.H. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol Psychiatry. 2013; 74 (7–14) Abstract Full Text Full Text PDF PubMed Scopus (113) Google Scholar ) addresses a question of major importance for treatment matching of patients with major depressive disorder (MDD): Can we identify patients seeking treatment for depression that are unlikely to respond to currently available treatments? The ability to prospectively identify patient subgroups based on their predicted outcomes especially early in the course of treatment is likely to reduce the inordinate delays in identifying the correct treatment for patients, reduce morbidity and mortality, shorten suffering, reduce rates of treatment dropouts, and likely reduce the cost burden for the patient and society ( 2 Fava M. Nelson J.C. Thase M.E. Trivedi M.H. Easing the burden of treatment-resistant depression. J Clin Psychiatry. 2009; 70: 273-280 Crossref PubMed Scopus (2) Google Scholar , 3 Rush A.J. Trivedi M.H. Wisniewski S.R. Nierenberg A.A. Stewart J.W. Warden D. et al. Acute and longer-term outcomes in depressed outpatients who required one or several treatment steps: A STAR*D report. Am J Psychiatry. 2006; 163: 1905-1917 Crossref PubMed Scopus (1904) Google Scholar ). The author also suggests that early identification of treatment resistant depression (TRD) could direct patients to more complicated treatments more rapidly. The author used data available from the STAR*D sample and developed a model that is designed to address these issues." @default.
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- W2028045658 date "2013-07-01" @default.
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- W2028045658 title "Modeling Predictors, Moderators and Mediators of Treatment Outcome and Resistance in Depression" @default.
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- W2028045658 doi "https://doi.org/10.1016/j.biopsych.2013.05.009" @default.
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