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MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
Published on: August 11, 2015
Treatment resistant depression: Socio-demographic characteristics, comorbidity and treatment patterns from the All of
Katherine L Forthman1, Rayus Kuplicki1, Wesley K Thompson1
1Laureate Institute for Brain Research, Oxley College of Health and Natural Sciences, University of Tulsa, 6655 S Yale Ave, Tulsa, OK 74136-3326, United States of America.
Objective:
This study used the All of Us (AoU) cohort to investigate treatment-resistant depression (TRD) and its demographic associations, comorbidities and antidepressant treatment patterns.
Methods:
Electronic health records were used to classify major depressive disorder participants as treatment resistant. Logistic regressions determined socio-demographic and comorbidity differences between participants with TRD and treatment-responsive depression. Sankey diagrams visualized differences in treatment patterns.
Results:
13.92 % of the depression population was characterized as TRD. The TRD population was 19.10 % of the medicated depression population. Compared to treatment-responsive depression individuals, those with TRD had lower education (Odds Ratio (OR) = 1.10 [1.03, 1.16]), were unemployed (OR = 1.35 [1.27, 1.43]) or in lower income brackets (OR < 35 k = 1.10 [1.03, 1.18]), and were more likely to be between ages 35-44 (OR = 1.15 [1.07, 1.24]). TRD relative to treatment-responsive depression individuals were more likely to have comorbidity with anxiety disorders (ORs ranging from 1.85 to 2.56 for various forms), Post-Traumatic Stress Disorder (OR = 2.10) and substance use disorders (ORs from 1.36 to 3.12). A Sankey diagram revealed heterogeneity in treatment among TRD and treatment-responsive depression.
Conclusion:
This study finds that TRD is especially prevalent among younger individuals, those with lower income, the unemployed, and individuals with comorbid anxiety or substance use disorders. This underscores the importance of integrating socio-demographic and clinical characteristics into targeted strategies aimed at identifying high-risk populations and guiding more effective treatment approaches.
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