Treatment profiles and trajectories surrounding the diagnosis of major depressive disorder: a research protocol for a Danish register-based study.

Abstract

Background: Major depressive disorder (MDD) is a prevalent illness that causes significant suffering and expenses at the personal and societal levels. The disorder is subject to heterogeneity reflected by diverse clinical phenotypes and assorted responses to treatment. Research on MDD treatments have focused on one treatment at a time, however many patients receive several different treatments. Considering the number of available treatment options, we hypothesize that it is possible to identify clinically meaningful groups of patients based on their psychiatric treatment. The objective of this study is therefore to identify psychiatric treatment profiles and trajectories of patients with major depressive disorder and, for the identified profiles and trajectories, to assess clinical and sociodemographic factors. Method: The study will be a population-based register study of patients with major depressive disorder in the Danish National Patient Register between 2011 and 2015. Using latent class analyses, we will identify homogenous groups of patients based on their psychiatric treatment patterns. These patterns constitute psychiatric treatment profiles which will be identified at six time-intervals, from 1.5 years before to 3 years after diagnosis of major depressive disorder. By cross-tabulating the identified treatment profiles, we will establish psychiatric treatment trajectories. Patients sharing profiles and trajectories will be characterized. Discussion: Identification of psychiatric treatment profiles and trajectories based on an unsupervised learning algorithm have the potential to reveal hidden patterns of psychiatric treatment. This will potentially pave the way for future studies of treatment combinations and a larger insight into the different courses of treatment. Furthermore, the assessment of clinical and sociodemographic factors may indicate different patient characteristics across treatment profiles and trajectories.