Clinical characteristics and treatment outcome in a representative sample of depressed inpatients - Findings from the Munich Antidepressant Response Signature (MARS) project
ABSTRACT Depression is a common and often difficult-to-treat clinical condition with a high rate of patients showing insufficient treatment response and persistence of symptoms. We report the characteristics of a representative sample of depressed inpatients participating in the Munich Antidepressant Response Signature (MARS) project. Eight hundred and forty-two inpatients admitted to a psychiatric hospital for treatment of a major depressive episode, recurrent or bipolar depression were thoroughly characterized with respect to demographic factors, clinical history, and the degree of HPA-axis dysregulation evaluated by means of combined dex/CRH tests, and the predictive value of these factors for treatment outcome is investigated. 80.8% of patients responded to treatment (i.e., improvement in symptom severity of at least 50%) and 57.9% reached remission (i.e., near absence of residual depressive symptoms) at discharge after a mean treatment period of 11.8 weeks. Regression analysis identified early partial response (within 2 weeks) as the most important positive predictor for achieving remission. Previous ineffective treatment trials in the current episode and presence of a migration background are potent negative predictors for treatment outcome. In addition, remitters were characterized by a more pronounced normalization of an initially dysregulated HPA-axis. We could show that a large majority of inpatients suffering from depression benefits from antidepressant treatment during hospitalization. However, a considerable number of patients failed to achieve remission. We demonstrated that this subgroup can be characterized by a set of demographic, clinical and neuroendocrine variables allowing to predict unfavorable outcome at an early stage of treatment.
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- "An early response to pharmacotherapy has consistently been found to be favourable (Andreescu et al., 2008; Henkel et al., 2009; Hennings et al., 2009), along with existing employment, marital status, and not living alone (Trivedi et al., 2005; Rush et al., 2008; Hennings et al., 2009). Shorter duration of symptoms with less hospitalisations and a negative family history are all related to an increased rate of pharmacotherapy response (Trivedi et al., 2005; Hennings et al., 2009). "
ABSTRACT: Treatment for depression is not effective in all patients and it is therefore important to identify factors that can be used to tailor treatments. One potential factor is insomnia. Several repetitive transcranial magnetic stimulation (rTMS) studies have reported on this symptom, however, they did not take into account the presence of hypersomnia or that insomnia was related to their outcome measure. Our aim was to investigate whether baseline sleep disruption was related to rTMS treatment response. We pooled data from four clinical trials using rTMS to treat depression, including 139 subjects in data analysis. Insomnia was measured using the Hamilton Depression Rating Scale (HamD) sleep questions and hypersomnia from the Beck Depression Inventory (BDI). To reduce the possible impact of insomnia on our treatment response outcome we created an adjusted HamD score which omitted sleep items. Sleep disturbances were common in our study: 66% had insomnia and 38% hypersomnia. Using regression analysis with our adjusted HamD score we found no relation between baseline insomnia or hypersomnia and rTMS treatment response. Our data are consistent with previous studies; however, this is the first rTMS study to our knowledge that has attempted to dissociate baseline insomnia from the HamD outcome measure and to report no relationship between hypersomnia and rTMS outcome.05/2013; DOI:10.1016/j.psychres.2013.04.028
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- "Obtaining smaller item intercepts after 1 month of treatment would be expected, as this indicates that depression was endorsed at lower levels after this 1-month time point. This finding is consistent with research demonstrating a reduction in depressive symptoms after inpatient mental health treatment (e.g., Hennings et al., 2009; Schneider et al., 2005), in contrast to findings showing limited evidence of improvement in the full spectrum of treatments for depression (Pettit et al., 2009). Noteworthy, however, was that factor loadings increased over the course of treatment. "
ABSTRACT: Research has not investigated changes in the symptom structure of depression over the course of mental health treatment. In the present study, 1025 psychiatric inpatients were recruited and assessed for depression symptom severity using the Beck Depression Inventory-II (BDI-II) at admission and after 1 month of treatment. A three-factor BDI-II model was tested using confirmatory factor analysis and fit reasonably well at both time points. Measurement invariance testing results demonstrated that factor loadings increased, indicating that the meaning of the three underlying depression dimensions changed through treatment. However, observed variable intercepts and residual error variances decreased significantly after 1 month of treatment, reflecting decreases in symptom severity as well as measurement error. Thus, depressive symptom severity decreased over the course of treatment, and the underlying factor structure of depression improved in fit after treatment. Implications for changes to the structure of depression symptoms and in the clinical practice of tracking depression over time are discussed.The Journal of nervous and mental disease 04/2013; 201(5). DOI:10.1097/NMD.0b013e31828e1004 · 1.81 Impact Factor
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- ". Furthermore, neuroimaging studies that combine cognitive assessments with functional neuroimaging have potential for predicting antidepressant treatment response . Combination of clinical and neuroendocrine features also improves prediction of treatment outcome . The multifactorial nature of depressive illness suggests that a multifactorial approach will provide the most accurate predictions of treatment outcome. "
ABSTRACT: Identifying biological and clinical markers of treatment response in depression is an area of intense research that holds promise for increasing the efficiency and efficacy of resolving a major depressive episode and preventing future episodes. Collateral benefits include decreased healthcare costs and increased workplace productivity. Despite research advances in many areas, efforts to identify biomarkers have not revealed any consistently validated candidates. Studies of clinical characteristics, genetic, neuroimaging, and various biochemical markers have all shown promise in discrete studies, but these findings have not translated into a personalized medicine approach to treating individual patients in the clinic. We propose that an integrated study of a range of biomarker candidates from across different modalities is required. Furthermore, advanced mathematical modeling and pattern recognition methods are required to detect important biological signatures associated with treatment outcome. Through an informatics-based integration of the various clinical, molecular and imaging parameters that are known to be important in the pathophysiology of depression, it becomes possible to encompass the complexity of contributing factors and phenotypic presentations of depression, and identify the key signatures of treatment response.Current pharmaceutical design 06/2012; 18(36). DOI:10.2174/138161212803523635 · 3.29 Impact Factor