Using a machine learning approach to investigate factors associated with treatment-resistant depression among adults with chronic non-cancer pain conditions and major depressive disorder

Abstract

Objective

Presence of chronic non-cancer pain conditions (CNPC) among adults with major depressive disorder (MDD) may reduce benefits of antidepressant therapy, thereby increasing the possibility of treatment resistance. This study sought to investigate factors associated with treatment-resistant depression (TRD) among adults with MDD and CNPC using machine learning approaches.

Methods

This retrospective cohort study was conducted using a US claims database which included adults with newly diagnosed MDD and CNPC (January 2007–June 2017). TRD was identified using a clinical staging algorithm for claims data. Random forest (RF), a machine learning method, and logistic regression was used to identify factors associated with TRD. Initial model development included 42 known and/or probable factors that may be associated with TRD. The final refined model included 20 factors.

Results

Included in the sample were 23,645 patients (73% female mean age: 55 years; 78% with ≥2 CNPC, and 91% with joint pain/arthritis). Overall, 11.4% adults (N = 2684) met selected criteria for TRD. The five leading factors associated with TRD were the following: mental health specialist visits, polypharmacy (≥5 medications), psychotherapy use, anxiety, and age. Cross-validated logistic regression model indicated that those with TRD were younger, more likely to have anxiety, mental health specialist visits, polypharmacy, and psychotherapy use with adjusted odds ratios (AORs) ranging from 1.93 to 1.27 (all ps < .001).

Conclusion

Machine learning identified several factors that warrant further investigation and may serve as potential targets for clinical intervention to improve treatment outcomes in patients with TRD and CNPC.

Keywords:

• Treatment-resistant depression
• chronic non-cancer pain conditions
• major depressive disorder
• antidepressants
• leading factors
• machine learning

Transparency

Declaration of funding

Nothing declared. Research reported in this publication was partially supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number U54GM104942-03. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Declaration of financial/other relationships

The authors have disclosed that they have no significant relationships with or financial interests in any commercial companies related to this study or article. D. S. and S. M. have disclosed that they were employees at West Virginia University at the time of the study. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Data availability statement

This retrospective database study used commercial claims data from the Optum Clinformatics Data Mart (Eden Prairie, MN). The claims data that support the findings of this study are from a proprietary administrative claims database and are not publicly available. However, summary data tables are available from the authors upon reasonable request.