Citation

Cepeda MS, Reps J, Ryan P. Depress. Anxiety 2018; 35(7): 668-673.

Affiliation

Department of Epidemiology, Janssen Research and Development, Titusville, Florida.

Copyright

(Copyright © 2018, John Wiley and Sons)

DOI

10.1002/da.22774

PMID

29786922

Abstract

BACKGROUND: Treatment for depressive disorders often requires subsequent interventions. Patients who do not respond to antidepressants have treatment-resistant depression (TRD). Predicting who will develop TRD may help healthcare providers make more effective treatment decisions. We sought to identify factors that predict TRD in a real-world setting using claims databases.

METHODS: A retrospective cohort study was conducted in a US claims database of adult subjects with newly diagnosed and treated depression with no mania, dementia, and psychosis. The index date was the date of antidepressant dispensing. The outcome was TRD, defined as having at least three distinct antidepressants or one antidepressant and one antipsychotic within 1 year after the index date. Predictors were age, gender, medical conditions, medications, and procedures 1 year before the index date.

RESULTS: Of 230,801 included patients, 10.4% developed TRD within 1 year. TRD patients at baseline were younger; 10.87% were between 18 and 19 years old versus 7.64% in the no-TRD group, risk ratio (RR) = 1.42 (95% confidence interval [CI] 1.37-1.48). TRD patients were more likely to have an anxiety disorder at baseline than non-TRD patients, RR = 1.38 (95% CI 1.35-1.14). At 3.68, fatigue had the highest RR (95% CI 3.18-4.25). TRD patients had substance use disorders, psychiatric conditions, insomnia, and pain more often at baseline than non-TRD patients.

CONCLUSION: Ten percent of subjects newly diagnosed and treated for depression developed TRD within a year. They were younger and suffered more frequently from fatigue, substance use disorders, anxiety, psychiatric conditions, insomnia, and pain than non-TRD patients.

© 2018 Wiley Periodicals, Inc.

Language: en

Keywords

epidemiology; observational studies; prediction; real world evidence; risk factors