Pattern Analysis of Response to Acute Fluoxetine Treatment in the Prediction of Relapse in Children and Adolescents

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2008-09-12

Authors

Eggertsen, Ann Stevens Airy

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Abstract

Major depressive disorder (MDD) is increasingly recognized as a common and serious affliction among children and adolescents. Antidepressant drug trials aimed at addressing the problem frequently encounter problems in establishing drug efficacy due to the prevalence of placebo effects that are especially prominent in this population, and likewise, placebo responding clouds clinical decisions regarding which patients will benefit from continuation treatment to prevent relapse. Using the method of pattern analysis, Quitkin and his colleagues (1984, 1987) have shown that in the acute treatment of depressed adults, true drug benefits are characterized by delayed and persistent improvement, whereas placebo effects tend to occur early and not persist. The present study extends this method to the pediatric population by examining the relationship between Quitkin response patterns during acute fluoxetine treatment and subsequent risk for relapse during randomized placebocontrolled continuation. A total of 168 children age 7 to 18 meeting DSM criteria for MDD first entered 12 weeks of acute treatment on open-label fluoxetine 10.40 mgs with frequent assessment. Using patient response patterns derived from sequential CGI improvement ratings during this period, patients were identified as either true drug or placebo pattern responders in the manner of Quitkin. After 12 weeks, 102 acute responders were randomized to 6 months of continuation treatment on fluoxetine or placebo and monitored for relapse. True drug responders showed an enhanced and very robust fluoxetine-placebo treatment effect (significantly fewer relapses on fluoxetine), whereas placebo pattern responders showed no significant treatment effect. Pattern analysis was also investigated in the larger, multivariate context of predicting risk for depressive relapse in young patients. Cox proportional hazards regression modeling showed continuation treatment and gender to be strong predictors, with the interaction of pattern X treatment falling just short of significance (p = .07). Overall results of this study suggest that pattern analysis can be useful in drug studies for pediatric depression and contribute to the prediction of relapse.

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