Perceived Stress and Cognitive Vulnerability Mediate the Effects of Personality Disorder Comorbidity on Treatment Outcome in Major Depressive Disorder: A Path Analysis Study

Harvard University, Cambridge, Massachusetts, United States
Journal of Nervous & Mental Disease (Impact Factor: 1.69). 10/2007; 195(9):729-37. DOI: 10.1097/NMD.0b013e318142cbd5
Source: PubMed


Although personality disorder (PD) comorbidity has been associated with poor treatment outcome in major depressive disorder (MDD), little is known about mechanisms mediating this link. Converging evidence suggests that maladaptive cognitive patterns, particularly in interaction with stressors, might lead to poor treatment outcome in MDD subjects with PD pathology. The goal of this study was to test the role of PD comorbidity, cognitive vulnerability, and perceived stress in treatment outcome in MDD. Three hundred eighty-four MDD outpatients were enrolled in an 8-week open-label treatment of fluoxetine. Structural equation modeling and path analyses revealed that the effect of PD vulnerability on treatment outcome was fully mediated by increased pretreatment cognitive vulnerability and depression severity, which led to increased stress perception after treatment and poorer antidepressant response. Depressogenic cognitions might be continuously activated by chronic distress in MDD subjects reporting axis II pathology, leading to stress exacerbation and eventually poorer treatment outcome.

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Available from: Lee Baer, Oct 07, 2015
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    • "Pizzagalli, 2011) and psychosocial variables (e.g. Candrian et al. 2007) with depression treatment response , subtyping distinctions based on empirically derived symptom profiles have been disappointing because of profile instability (Hasler & Northoff, 2011; Baumeister & Parker, 2012; van Loo et al. 2012). However, an alternative approach to symptom-based subtyping, given the desire to predict treatment response and course of illness, would be to define subtypes using recursive partitioning (Strobl et al. 2009; Zhang & Singer, 2010) and related machine learning methods (van der Laan & Rose, 2011; James et al. 2013) that search for synergistic associations of baseline measures with subsequent outcomes. "
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    ABSTRACT: Background: Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question. Method: Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes. Results: Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors. Conclusions: Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.
    Psychological Medicine 07/2014; 44(15):1-14. DOI:10.1017/S0033291714000993 · 5.94 Impact Factor
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    • "Poorer treatment responses have been associated with older age, longer duration of current depressive episode, a history of multiple episodes, melancholic or psychotic features (McGrath et al., 2008; Kilts et al., 2009), and low compliance (Weiss et al., 1997). Converging evidence suggests that maladaptive behavioral patterns, particularly in interaction with stressors, ineffective utilization of medical treatment, and noneffective coping styles, might lead to poor treatment outcomes in MDD patients (Candrian et al., 2007; Leskelä et al., 2009). However, few reliable predictors have been found and most findings of antidepressant treatment prediction have not been replicated. "
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    ABSTRACT: There is growing evidence that individual differences among patients with major depressive disorder (MDD) on psychological and demographic measures may predict the therapeutic response to selective serotonin reuptake inhibitors (SSRIs). In this retrospective chart review, 108 outpatients with current major depressive episodes were treated with citalopram, paroxetine, or fluvoxamine. The Hamilton Depression Rating Scale and the Minnesota Multiphasic Personality Inventory-2 were administered before and after 8 weeks of SSRIs treatment. Clinical response was defined as a 50% or greater decrease in the 17-item Hamilton Depression Rating Scale total score (final visit minus baseline). This naturalistic short-term follow-up outcome study demonstrates that among depressive outpatients who responded to an 8-week trial, 57.4% achieved a good response to SSRIs. Statistical analysis showed that SSRI treatment may be 3.03 times more advantageous for MDD outpatients who are younger than 39 years. The patients with an elevated score of above 66T on the Social Introversion Minnesota Multiphasic Personality Inventory-2 scale are approximately 0.37 times as likely to be SSRI responders as are patients with a Social Introversion score less than 66T. Thus, it seems that in MDD outpatient age is the strongest predictor of response to SSRIs.
    International clinical psychopharmacology 03/2012; 27(3):134-41. DOI:10.1097/YIC.0b013e3283524d5c · 2.46 Impact Factor
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    • "The poorer treatment outcomes of some depressive subtypes is partly explained by the patients' level of negative or dysfunctional cognitions.33 Depressed patients' interpretation of negative events also may increase the likelihood of maintaining depression and of poor response to medication.34,35 In the midst of an episode of MDD, ineffective treatment trials may constitute a specific stressor that, interpreted in a negative context, could combine with dysfunctional attitudes to result in increasingly resistant depression in some patients. "
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    ABSTRACT: Current treatment of Major Depressive Disorder utilizes a trial-and-error sequential treatment strategy that results in delays in achieving response and remission for a majority of patients. Protracted ineffective treatment prolongs patient suffering and increases health care costs. In addition, long and unsuccessful antidepressant trials may diminish patient expectations, reinforce negative cognitions, and condition patients not to respond during subsequent antidepressant trials, thus contributing to further treatment resistance. For these reasons, it is critical to identify reliable predictors of antidepressant treatment response that can be used to shorten or eliminate lengthy and ineffective trials. Research on possible endophenotypic as well as genomic predictors has not yet yielded reliable predictors. The most reliable predictors identified thus far are symptomatic and physiologic characteristics of patients that emerge early in the course of treatment. We propose here the term "response endophenotypes" (REs) to describe this class of predictors, defined as latent measurable symptomatic or neurobiologic responses of individual patients that emerge early in the course of treatment, and which carry strong predictive power for individual patient outcomes. Use of REs constitutes a new paradigm in which medication treatment trials that are likely to be ineffective could be stopped within 1 to 2 weeks and other medication more likely to be effective could be started. Data presented here suggest that early changes in symptoms, quantitative electroencephalography, and gene expression could be used to construct effective REs. We posit that this new paradigm could lead to earlier recovery from depressive illness and ultimately produce profound health and economic benefits.
    Dialogues in clinical neuroscience 12/2009; 11(4):435-46.
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