Depression symptom clusters and their predictive value for treatment outcomes: results from an individual patient data meta-analysis of duloxetine trials

Abstract

We evaluated individual patient data from phase II to IV clinical trials of duloxetine in major depressive disorder (MDD) (34 studies, 13,887 patients). Our goal was to identify clusters of patients with similar depressive symptom patterns at baseline, as measured by the 17-item Hamilton Depression Rating Scale (HAMD-17), and to investigate their respective predictive value of outcomes as measured by the HAMD-17 total score. Five clusters were identified at baseline: 1) "Lack of insight"; 2) "Sleep/sexual/somatic"; 3) "Typical MDD"; 4) "Gastrointestinal/weight loss"; and 5) "Mild MDD". However, it should be noted that cluster descriptors are not mutually exclusive. Analyses of the HAMD-17 total score results over time were performed using the 18 randomized placebo and/or actively controlled studies representing 6723 patients. At the end of acute treatment (ranging from 4 to 36 weeks), different levels of effect sizes for active therapy (64.5% duloxetine) vs. placebo were detected by cluster. In 3 out of 5 clusters (representing about 80% of the patients), the effect size was significantly different from 0, in favor of active therapy. The effect size was largest in those clusters with severe somatic symptoms ("Sleep/sexual/somatic" cluster [-0.4170], and "Gastrointestinal/weight loss" cluster [-0.338]). In conclusion, our cluster analysis identified 5 clinically relevant MDD patient clusters with specific mean treatment outcomes. Identification of MDD clusters may help to improve outcomes by adapting MDD treatment to particular clinical profiles.

Keywords: Cluster analysis; Depression; Duloxetine; Hamilton Depression Rating Scale (HAMD); Major depressive disorder (MDD).