Context Antidepressant medications represent the best established treatment for major depressive disorder, but there is little evidence that they have a specific pharmacological effect relative to pill placebo for patients with less severe depression.Objective To estimate the relative benefit of medication vs placebo across a wide range of initial symptom severity in patients diagnosed with depression.Data Sources PubMed, PsycINFO, and the Cochrane Library databases were searched from January 1980 through March 2009, along with references from meta-analyses and reviews.Study Selection Randomized placebo-controlled trials of antidepressants approved by the Food and Drug Administration in the treatment of major or minor depressive disorder were selected. Studies were included if their authors provided the requisite original data, they comprised adult outpatients, they included a medication vs placebo comparison for at least 6 weeks, they did not exclude patients on the basis of a placebo washout period, and they used the Hamilton Depression Rating Scale (HDRS). Data from 6 studies (718 patients) were included. Data ExtractionIndividual patient-level data were obtained from study authors. ResultsMedication vs placebo differences varied substantially as a function of baseline severity. Among patients with HDRS scores below 23, Cohen d effect sizes for the difference between medication and placebo were estimated to be less than 0.20 (a standard definition of a small effect). Estimates of the magnitude of the superiority of medication over placebo increased with increases in baseline depression severity and crossed the threshold defined by the National Institute for Clinical Excellence for a clinically significant difference at a baseline HDRS score of 25. ConclusionsThe magnitude of benefit of antidepressant medication compared with placebo increases with severity of depression symptoms and may be minimal or nonexistent, on average, in patients with mild or moderate symptoms. For patients with very severe depression, the benefit of medications over placebo is substantial.

BackgroundAdvances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations.ObjectiveTo illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison.MethodData from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units.ResultsFor 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their “Optimal” treatment versus those assigned to their “Non-optimal” treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17—1.01).ConclusionsThis approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.

A recent randomized controlled trial found nearly equivalent response rates for antidepressant medications and cognitive therapy in a sample of moderate-to-severely depressed outpatients. In this article, we seek to identify the variables that were associated with response across both treatments as well as variables that predicted superior response in one treatment over the other. The sample consisted of 180 depressed outpatients: 60 of whom were randomly assigned to cognitive therapy; 120 were assigned to antidepressant medications. Treatment was provided for 16 weeks. Chronic depression, older age, and lower intelligence each predicted relatively poor response across both treatments. Three prescriptive variables were identified: marriage, unemployment, and having experienced a greater number of recent life events predicted superior response to cognitive therapy compared to antidepressant medications. Thus, six markers of treatment outcome were identified, each of which might be expected to carry considerable clinical utility. The three prognostic variables identify subgroups that might benefit from alternative treatment strategies; the three prescriptive variables identify groups who appear to respond particularly well to cognitive therapy.

Diffusion tensor imaging (DTI) studies consistently reported abnormalities in fractional anisotropy (FA) and radial diffusivity (RD), measures of the integrity of white matter (WM), in bipolar disorder (BD), that may reflect underlying pathophysiologic processes. There is, however, a pressing need to identify peripheral measures that are related to these WM measures, to help identify easily-obtainable peripheral biomarkers of BD. Given the high lipid content of axonal membranes and myelin sheaths, and that elevated serum levels of lipid peroxidation are reported in BD, these serum measures may be promising peripheral biomarkers of underlying WM abnormalities in BD. We used DTI and probabilistic tractography to compare FA and RD in ten prefrontal-centered WM tracts, 8 of which are consistently shown to have abnormal FA (and/or RD) in BD, and also examined serum lipid peroxidation (lipid hydroperoxides, LPH and 4-hydroxy-2-nonenal, 4-HNE), in 24 currently euthymic BD adults (BDE)and 19 age- and gender- matched healthy adults (CONT). There was a significant effect of group upon FA in these a priori WM tracts (BDECONT:F[1,41]=10.3;p=0.003), and a significant between-group difference in LPH (BDE>CONT:t[40]=2.4;p=0.022), but not 4-HNE. Multivariate multiple regression analyses revealed that LPH variance explained, respectively, 59% and 51% of the variance of FA and RD across all study participants. This is the first study to examine relationships between measures of WM integrity and peripheral measures of lipid peroxidation. Our findings suggest that serum LPH may be useful in the development of a clinically-relevant, yet easily obtainable and inexpensive, peripheral biomarkers of BD.

Although current American Psychiatric Association treatment guidelines state that 'cognitive behavioral therapy may be more effective than other treatments for depressed individuals with personality disorders', 1 this statement appears to be largely based on a misunderstanding of data from the Treatment of Depression Collaborative Research Program.2,3 The results from this project did not reveal a personality disorder6treatment interaction, but rather a non-significant trend whereby people with no comorbid personality disorder responded more poorly to cognitive therapy than did people with personality disorder. Subsequent studies have not supported the claim that the presence of personality disorder predicts favourable response to cognitive therapy. 4 Moreover, the conclusions from two recent meta-analyses reflect the controversy regarding whether comorbid personality pathology affects response to treatment for depression. 5,6 One reported that people with depression and comorbid personality disorder experienced poorer response when receiving either cognitive therapy or pharmacotherapy. 6 The other, which included only trials of antidepressant medication, reported no difference in response as a function of personality pathology. 5 We present data drawn from a multi-site randomised controlled trial comparing cognitive therapy and paroxetine for individuals diagnosed with moderate-to-severe depression. 7,8 We focus on whether the presence of comorbid personality disorder predicts differential response to cognitive therapy and pharmacotherapy, and we explore the effect of comorbid personality disorder on relapse once treatment is terminated. MethodThe sample characteristics, treatment protocols and main treatment outcome findings have been reported elsewhere.7,8 Briefly, the sample consisted of 240 out-patients with depression (measured using the Structured Clinical Interview for DSM-IV Diagnosis) 9 who registered a score of 20 or higher on the modified 17-item version of the Hamilton Rating Scale for Depression (HRSD).10 Personality pathology was assessed at intake using the Structured Clinical Interview for DSM-III-R Personality Disorders.11 Among the entire sample, 48% of individuals met criteria for at least one comorbid personality disorder. As was done in the Treatment of Depression Collaborative Research Program, participants with antisocial (n=3) and schizotypal (n=1) personality disorders were excluded from the trial.2 Those meeting criteria for borderline personality disorder (n=8) were also excluded. The treatments under investigation were judged to be either ill-suited or too brief for individuals with depression comorbid with any of these three disorders. The distributions of personality disorders (shown in the online table DS1) were similar between the treatment arms and resemble those found in other samples of out-patients with depression. 12 The institutional review boards of the University of Pennsylvania and Vanderbilt University approved the study's protocols. All participants provided written in...

A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

We identify difficulties researchers encounter in psychotherapy process-outcome investigations, and we describe several limitations of the popular “variance accounted for” approach to understanding the effects of psychotherapy. Using data simulations, we show how the expected correlation between an excellent measure of therapy quality and outcome would be surprisingly small (approximately .25) under conditions likely to be common in psychotherapy research. Even when we modeled conditions designed to increase the likelihood that strong process-outcome relationships would be observed, we found that the expected correlations were still only in the modest range (.38 – .51). We discuss the implications of our analysis for the interpretation of process-outcome findings as well as for design considerations in future investigations.

No abstract