Drishti Shah1, Wanhong Zheng2, Lindsay Allen3, Wenhui Wei1,4, Traci LeMasters1, Suresh Madhavan5, Usha Sambamoorthi1,5. 1. Department of Pharmaceutical Systems and Policy, School of Pharmacy, West Virginia University, Morgantown, WV, USA. 2. Department of Behavioral Medicine and Psychiatry, West Virginia University, Morgantown, WV, USA. 3. Health Policy, Management, and Leadership Department, School of Public Health, West Virginia University, Morgantown, WV, USA. 4. Regeneron Pharmaceuticals, Tarrytown, NY, USA. 5. University of North Texas Health Sciences Center, College of Pharmacy, TX, USA.
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.
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.
Entities:
Keywords:
Treatment-resistant depression; antidepressants; chronic non-cancer pain conditions; leading factors; machine learning; major depressive disorder
Authors: Paul Greenberg; Patricia K Corey-Lisle; Howard Birnbaum; Maryna Marynchenko; Ami Claxton Journal: Pharmacoeconomics Date: 2004 Impact factor: 4.981
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