A review of quetiapine in combination with antidepressant therapy in patients with depression

Ella J Daly, Madhukar H TrivediMood Disorders Program, Department of Psychiatry, University of Texas Southwestern Medical School, Dallas, TX, USABackground: Atypical antipsychotics are increasingly used in the treatment of a broad spectrum of psychiatric disorders. There is evidence that in addition to treating the positive and negative symptoms of schizophrenia, as well as mania in bipolar disorder, these agents may have a potential role to play in the treatment of depressive disorders. In the following article we review the literature regarding the role of atypical antipsychotics, and specifically, quetiapine, in the treatment of major depressive disorder.Materials and methods: In March 2007 the authors performed a Medline search (English-language) using the keywords quetiapine and depression, revealing a total of 47 articles published. We also looked for cross-references in the published articles, obtained data-on-file from AstraZeneca Pharmaceutical L.P., and included abstracts presented at conferences and recent meetings.Results: From our review we found that there is increasing literature supporting the efficacy of add-on quetiapine in the treatment of major depressive disorder.Conclusion: There is a need, however, for further well-designed, adequately powered, randomized, controlled trials to confirm this finding, specifically in unipolar depression.Keywords: depression, adjunctive treatment, atypical antipsychotics, quetiapin

Men and women from the STRIDE clinical trial: An assessment of stimulant abstinence symptom severity at residential treatment entry

Background and Objectives Gender‐specific factors associated with stimulant abstinence severity were examined in a stimulant abusing or dependent residential treatment sample (N = 302). Method Bivariate statistics tested gender differences in stimulant abstinence symptoms, measured by participant‐reported experiences of early withdrawal. Multivariate linear regression examined gender and other predictors of stimulant abstinence symptom severity. Results Women compared to men reported greater stimulant abstinence symptom severity. Anxiety disorders and individual anxiety‐related abstinence symptoms accounted for this difference. African American race/ethnicity was predictive of lower stimulant abstinence severity. Discussion and Conclusions Women were more sensitive to anxiety‐related stimulant withdrawal symptoms. Scientific Significance Clinics that address anxiety‐related abstinence symptoms, which more commonly occur in women, may improve treatment outcome. (Am J Addict 2015;XX:XX –XX

Phase II Proof-of-Concept Trial of the Orexin Receptor Antagonist Filorexant (MK-6096) in Patients with Major Depressive Disorder.

BackgroundWe evaluated the orexin receptor antagonist filorexant (MK-6096) for treatment augmentation in patients with major depressive disorder.MethodsWe conducted a 6-week, double-blind, placebo-controlled, parallel-group, Phase II, proof-of-concept study. Patients with major depressive disorder (partial responders to ongoing antidepressant therapy) were randomized 1:1 to once-daily oral filorexant 10 mg or matching placebo.ResultsDue to enrollment challenges, the study was terminated early, resulting in insufficient statistical power to detect a prespecified treatment difference; of 326 patients planned, 129 (40%) were randomized and 128 took treatment. There was no statistically significant difference in the primary endpoint of change from baseline to week 6 in Montgomery Asberg Depression Rating Scale total score; the estimated treatment difference for filorexant-placebo was -0.7 (with negative values favoring filorexant) (P=.679). The most common adverse events were somnolence and suicidal ideation.ConclusionsThe interpretation of the results is limited by the enrollment, which was less than originally planned, but the available data do not suggest efficacy of orexin receptor antagonism with filorexant for the treatment of depression. (Clinical Trial Registry: clinicaltrials.gov: NCT01554176)

Temporal Multi-Step Predictive Modeling of Remission in Major Depressive Disorder Using Early Stage Treatment Data; Star*D Based Machine Learning Approach

BACKGROUND: Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes. METHODS: Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step. RESULTS: Our model showed significant prediction performance across the three treatment steps, At step 1, Model accuracy was 66 %; sensitivity-65 %, specificity-67 %, positive predictive value (PPV)-65.5 %, and negative predictive value (NPV)-66.6 %. At step 2, model accuracy was 71.3 %, sensitivity-74.3 %, specificity-69 %, PPV-64.5 %, and NPV-77.9 %. At step 3, accuracy reached 84.6 %; sensitivity-69 %, specificity-88.8 %, PPV-67 %, and NPV-91.1 %. Across all three steps, the early Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) scores were key elements in predicting the final treatment outcome. The model also identified key sociodemographic factors that predicted treatment remission at different steps. LIMITATIONS: The retrospective design, lack of replication in an independent dataset, and the use of a complete case analysis model in our analysis. CONCLUSIONS: This proof-of-concept study showed that using early treatment data, multi-step temporal prediction of depressive symptom remission results in clinically useful accuracy rates. Whether these predictive models are generalizable deserves further study

Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity

Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71‐channel EEG recorded from 35 healthy adults at two sessions (1‐week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal‐to‐noise ratio, participant‐level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait‐multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low‐variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component‐based identification of spectral activity (CSD/eLORETA‐fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.Magnitude of frontal theta (rostral ACC eLORETA source amplitude) and posterior alpha (spectral components of scalp current source density) at rest have been considered candidate EEG biomarkers of depression outcomes. Given inconsistent findings, we examined the discriminant and convergent validity of these measures in healthy adults. Unlike theta, two distinct alpha components constituted reliable, convergent, and discriminant biometrics. While results have marked implications for clinical utility, we make several recommendations for improving the psychometric properties of resting frontal theta.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153675/1/psyp13483.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153675/2/psyp13483_am.pd

The Concise Health Risk Tracking - Self-Report (CHRT-SR)—A measure of suicidal risk: Performance in adolescent outpatients

Article describes how the Concise Health Risk Tracking Self-Report (CHRT-SR) assesses the risk of suicidal behavior. The authors report its psychometric properties in a representative sample of adolescent outpatients