Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors

Xiao Liu; Stephen J Read

doi:10.3389/fpsyt.2024.1349576

Development of a multivariate prediction model for antidepressant resistant depression using reward-related predictors

Front Psychiatry. 2024 Mar 25:15:1349576. doi: 10.3389/fpsyt.2024.1349576. eCollection 2024.

Authors

Xiao Liu¹, Stephen J Read¹

Affiliation

¹ Department of Psychology, University of Southern California, Los Angeles, CA, United States.

Abstract

Introduction: Individuals with depression who do not respond to two or more courses of serotonergic antidepressants tend to have greater deficits in reward processing and greater internalizing symptoms, yet there is no validated self-report method to determine the likelihood of treatment resistance based on these symptom dimensions.

Methods: This online case-control study leverages machine learning techniques to identify differences in self-reported anhedonia and internalizing symptom profiles of antidepressant non-responders compared to responders and healthy controls, as an initial proof-of-concept for relating these indicators to medication responsiveness. Random forest classifiers were used to identify a subset from a set of 24 reward predictors that distinguished among serotonergic medication resistant, non-resistant, and non-depressed individuals recruited online (N = 393). Feature selection was implemented to refine model prediction and improve interpretability.

Results: Accuracies for full predictor models ranged from .54 to .71, while feature selected models retained 3-5 predictors and generated accuracies of .42 to .70. Several models performed significantly above chance. Sensitivity for non-responders was greatest after feature selection when compared to only responders, reaching .82 with 3 predictors. The predictors retained from feature selection were then explored using factor analysis at the item level and cluster analysis of the full data to determine empirically driven data structures.

Discussion: Non-responders displayed 3 distinct symptom profiles along internalizing dimensions of anxiety, anhedonia, motivation, and cognitive function. Results should be replicated in a prospective cohort sample for predictive validity; however, this study demonstrates validity for using a limited anhedonia and internalizing self-report instrument for distinguishing between antidepressant resistant and responsive depression profiles.

Keywords: SSRI; anhedonia; antidepressants; depression; internalizing; machine learning; reward; treatment-resistant.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the Department of Psychology, University of Southern California.