Predicting functional impairment in euthymic patients with mood disorder: A 5-year follow-up

Abstract

Major Depressive Disorder and Bipolar Disorder are psychiatric disorders associated with psychosocial impairment. Despite clinical improvement, functional complaints usually remain, mainly impairing occupational and cognitive performance. The aim of this study was to use machine learning techniques to predict functional impairment in patients with mood disorders. For that, analyzes were performed using a population-based cohort study. Participants diagnosed with a mood disorder at baseline and reassessed were considered (n = 282). Random forest (RF) with previous recursive feature selection and LASSO algorithms were applied to a training set with imputed data by bagged trees resulting in two main models. Following recursive feature selection, 25 variables were retained. The RF model had the best performance compared to LASSO. The most important variables in predicting functional impairment were sexual abuse, severity of depressive, anxiety, and somatic symptoms, physical neglect, emotional abuse, and physical abuse. The model demonstrated acceptable performance to predict functional impairment. However, our sample is composed of young participants and the model may not generalize to older individuals with mood disorders. More studies are needed in this direction. The presented calculator has clinical, sociodemographic, and environmental data, demonstrating that it is possible to use such information to predict functional performance.