A multicomponent psychodynamic treatment for comorbid disorders: a baseline and post-treatment network comparison

Abstract

ABSTRACTThis study aims to assess pre-post change of 439 patients undergoing a multicomponent treatment (psychodynamic psychotherapy complemented with other treatment components) using a novel network methodology targeting symptoms comorbidity. Patients were recruited from seven clinical sites in the Czech Republic. First, the effectiveness of the treatment was assessed traditionally as a pre-post change in wellbeing, depression, and anxiety using a Bayesian mixed model. The Bayesian factors of time effect (pre-post comparison) on the three outcomes indicate evidence in favor of hypotheses suggesting psychotherapy effectiveness. Second, a network analysis of individual items measuring all three outcomes (Gaussian Graphical Model) was conducted to compare baseline and post-treatment patients’ networks in global edge strength, the topography of the network, the centrality of nodes, and the clique percolation. The network density represented by global edge strength was not affected by the treatment. Nevertheless, the network structure changed in a more qualitative manner into a clearer and separated set of node communities, potentially showing a reduction in comorbidity. The central position of the depression node community in the patients’ self-reported outcome assessment network was replaced by anxiety and wellbeing node communities after treatment.