Management of Late-Life Depression.

Depression is a common disorder in late life that is associated with poor quality of life, increased disability, and increased all-cause mortality. Rates of completed suicide are the highest in older depressed men compared with any other age group. In this age group, depression is often concurrent with medical illness and it can aggravate the course of medical illness. Cognitive impairment is frequently present and may be the result of the depression itself or may be the consequence of a neurodegenerative disorder such as Alzheimer's disease. Evidence-based psychotherapies, antidepressants, and somatic treatments such as electroconvulsive therapy are employed in the treatment of older depressed adults. Treatment may be complicated by the presence of cognitive impairment, other comorbid medical disorders, and medications used to treat these disorders. Certain safety issues such as increased bleeding risk, hyponatremia, decreased bone density and falls may be associated with antidepressant treatment, may be more common in older depressed adults, and their consequences may be more severe in late life. These risks, however, need to be weighed against the hazards of untreated depression. With appropriate care, most older depressed patients can be successfully treated and a positive outcome can have a significant effect on the patient's quality of life.

Application of the Gradient Boosted method in randomised clinical trials: Participant variables that contribute to depression treatment efficacy of duloxetine, SSRIs or placebo.

BACKGROUND: Randomised, placebo-controlled trials of treatments for depression typically collect outcomes data but traditionally only analyse data to demonstrate efficacy and safety. Additional post-hoc statistical techniques may reveal important insights about treatment variables useful when considering inter-individual differences amongst depressed patients. This paper aims to examine the Gradient Boosted Model (GBM), a statistical technique that uses regression tree analyses and can be applied to clinical trial data to identify and measure variables that may influence treatment outcomes. METHODS: GBM was applied to pooled data from 12 randomised clinical trials of 4987 participants experiencing an acute depressive episode who were treated with duloxetine, an SSRI or placebo to predict treatment remission. Additional analyses were conducted on the same dataset using the logistic regression model for comparison between these two methods. RESULTS: With GBM, there were noticeable differences between treatments when identifying which and to what extent variables were associated with remission. A single logistic regression only revealed a decreasing or increasing relationship between predictors and remission while GBM was able to reveal a complex relationship between predictors and remission. LIMITATIONS: These analyses were conducted post-hoc utilising clinical trials databases. The criteria for constructing the analyses data were based on the characteristics of the clinical trials. CONCLUSIONS: GBM can be used to identify and quantify patient variables that predict remission with specific treatments and has greater flexibility than the logistic regression model. GBM may provide new insights into inter-individual differences in treatment response that may be useful for selecting individualised treatments. TRIAL REGISTRATION: IMPACT clinical trial number 3327; IMPACT clinical trial number 4091; IMPACT clinical trial number 4689; IMPACT clinical trial number 4298; NCT00071695; NCT00062673; NCT00036335; NCT00067912; NCT00073411; NCT00489775; NCT00536471; NCT00666757 (note that trials with IMPACT numbers predate mandatory clinical trial registration requirements).

Predictors of relapse in patients with major depressive disorder in a 52-week, fixed dose, double blind, randomized trial of selegiline transdermal system (STS).

OBJECTIVE: We investigated patient and disease characteristics predictive of relapse of MDD during a 52-week placebo controlled trial of selegiline transdermal system (STS) to identify patient characteristics relevant for STS treatment. METHOD: After 10 weeks of open-label stabilization with STS, 322 remitted patients with MDD were randomized to 52-weeks of double-blind treatment with STS (6 mg/24h) or placebo (PLB). Relapse was defined as Hamilton Depression Rating Scale (HAMD-17) score of ≥ 14 and a CGI-S score of ≥ 3 with at least 2-point increase from the beginning of the double blind phase on 2 consecutive visits. Cox's proportional hazards regression was used to examine the effect of potential predictors (age, sex, age at onset of first MDD, early response pattern, number of previous antidepressant trials, severity of index episode, number of previous episodes, melancholic features, atypical features and anxious feature) on outcome. Exploratory analyses examined additional clinical variables (medical history, other psychiatric history, and individual items of HAM-D 28) on relapse. RESULTS: For all predictor variables analyzed, treatment Hazard Ratio (HR=0.48~0.54) was significantly in favor of STS (i.e., lower relapse risk than PLB). Age of onset was significantly predictive of relapse. Type, duration, and severity of depressive episodes, previous antidepressant trials, or demographic variables did not predict relapse. In additional exploratory analysis, eating disorder history and suicidal ideation were significant predictors of relapse after controlling for the effect of treatment in individual predictor analysis. CONCLUSIONS: While age of onset, eating disorder history and suicidal ideation were significant predictors, the majority of clinical and demographic variables were not predictive of relapse. Given the post-hoc nature of analysis, the findings need confirmation from a prospective study. It appears that selegiline transdermal system was broadly effective in preventing relapse across different subtypes and symptoms clusters of MDD.