COMPARATIVE EFFECTIVENESS OF AUGMENTATION, SWITCHING, AND DOSE ESCALATION AFTER INADEQUATE ANTIDEPRESSANT RESPONSE IN MEDICARE BENEFICIARIES
Author(s)
Tim C. Lai, MSc1, Jingjing Qian, PhD1, Cherry W. Jackson, PharmD2, Kimberly B. Garza, MBA, PharmD, PhD1, Jingyi Zheng, PhD3, Richard H. Chapman, MS, PhD4, Surachat Ngorsuraches, PhD1;
1Auburn University, Health Outcomes Research and Policy, College of Pharmacy, Auburn, AL, USA, 2Auburn University, Pharmacy Practice, College of Pharmacy, Auburn, AL, USA, 3Auburn University, Mathematics and Statistics, College of Science and Mathematics, Auburn, AL, USA, 4Center for Innovation & Value Research, Alexandria, VA, USA
1Auburn University, Health Outcomes Research and Policy, College of Pharmacy, Auburn, AL, USA, 2Auburn University, Pharmacy Practice, College of Pharmacy, Auburn, AL, USA, 3Auburn University, Mathematics and Statistics, College of Science and Mathematics, Auburn, AL, USA, 4Center for Innovation & Value Research, Alexandria, VA, USA
OBJECTIVES: To evaluate the comparative effectiveness of augmentation, switching, or dose escalation for inadequate treatment response among patients living with depression.
METHODS: We conducted a retrospective cohort study using data from the Medicare Current Beneficiary Survey (MCBS; 2016-2022). We identified beneficiaries with depression who had both survey responses and medication prescription event data for >=6 months prior to outcome assessment. Patients diagnosed with bipolar, schizophrenia, psychotic, or psychoactive substance-related disorders were excluded. The primary outcome was the incidence of remission, defined as a Patient Health Questionnaire (PHQ-8) score <5. Treatment strategies were based on medication use patterns at 3 months (index) compared to 6months (baseline) preceding the PHQ assessment. Patients were classified into: (1) dose escalation; (2) switching; or (3) augmentation. We estimated remission using augmented inverse probability weighting (AIPW), with bootstrapping applied to estimate 95% confidence intervals (CI).
RESULTS: A total of 216 eligible patients were included in the analysis. Most participants were female (72%), non-Hispanic White (86%), and held a high school diploma or equivalent (54%). Baseline characteristics, including sex, age, income, area of residence, marital status, and the Charlson Comorbidity Index score, were similar across groups. The observed remission rate for the total sample was 44%. Based on AIPW estimation, the probability of remission was 47% (95% CI: 37-60%) for augmentation, 44% (95% CI: 37-54%) for dose escalation, and 43% (95% CI: 36-51%) for switching. However, the numerical differences in remission between groups were not statistically significant.
CONCLUSIONS: Less than half of the sample had attained remission, underscoring a significant unmet clinical need. The lack of intergroup differences may be due to the small sample size; therefore, future studies with larger samples are needed. Given the absence of statistically significant differences, subsequent treatment selection should prioritize factors such as minimizing adverse events and optimizing adherence.
METHODS: We conducted a retrospective cohort study using data from the Medicare Current Beneficiary Survey (MCBS; 2016-2022). We identified beneficiaries with depression who had both survey responses and medication prescription event data for >=6 months prior to outcome assessment. Patients diagnosed with bipolar, schizophrenia, psychotic, or psychoactive substance-related disorders were excluded. The primary outcome was the incidence of remission, defined as a Patient Health Questionnaire (PHQ-8) score <5. Treatment strategies were based on medication use patterns at 3 months (index) compared to 6months (baseline) preceding the PHQ assessment. Patients were classified into: (1) dose escalation; (2) switching; or (3) augmentation. We estimated remission using augmented inverse probability weighting (AIPW), with bootstrapping applied to estimate 95% confidence intervals (CI).
RESULTS: A total of 216 eligible patients were included in the analysis. Most participants were female (72%), non-Hispanic White (86%), and held a high school diploma or equivalent (54%). Baseline characteristics, including sex, age, income, area of residence, marital status, and the Charlson Comorbidity Index score, were similar across groups. The observed remission rate for the total sample was 44%. Based on AIPW estimation, the probability of remission was 47% (95% CI: 37-60%) for augmentation, 44% (95% CI: 37-54%) for dose escalation, and 43% (95% CI: 36-51%) for switching. However, the numerical differences in remission between groups were not statistically significant.
CONCLUSIONS: Less than half of the sample had attained remission, underscoring a significant unmet clinical need. The lack of intergroup differences may be due to the small sample size; therefore, future studies with larger samples are needed. Given the absence of statistically significant differences, subsequent treatment selection should prioritize factors such as minimizing adverse events and optimizing adherence.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
CO96
Topic
Clinical Outcomes
Topic Subcategory
Comparative Effectiveness or Efficacy