Presentation (CTI)

OBJECTIVES: Frequent switching of initial antidepressant therapy(IAD) due to suboptimal response is a major driver of treatment inefficiency and increased healthcare costs in major depressive disorder(MDD). Understanding predictors of early switching and quantifying associated direct medical costs can inform optimized treatment pathways and value-based care in low- and middle-income settings. Hence, objectives were to identify clinical and treatment-related predictors associated with antidepressant switching and estimate the incremental healthcare costs of premature modification of IAD regimen in MDD patients.
METHODS: A prospective, observational study was conducted in the outpatient psychiatry department of a tertiary-care teaching hospital in India(October 2023-September 2025). Adult patients newly initiated on antidepressant therapy were followed until treatment modification or study completion. Time-to-switch was analyzed using Kaplan-Meier survival analysis and Cox proportional hazards modeling to identify predictors of switch. Direct medical costs, including medication, consultations, and additional interventions, were captured from hospital records and analyzed using generalized linear models.
RESULTS: Among 952 eligible patients, 810 (85%) experienced at least one antidepressant modification during follow-up. The median time to switch was 45 days (95% CI: 35.4-53.9), with 51% of changes occurring within the first 30 days. Predictors significantly associated with higher probability of early switch included younger age (HR 1.45, p<0.01), absence of dose optimization (HR 1.72, p<0.001), and comorbid anxiety (HR 1.58, p<0.05). Patients undergoing early switch incurred mean incremental costs 28% higher than those who maintained the initial regimen (₹6,800 vs ₹5,300 per 3-month period, p=0.02), primarily due to increased outpatient visits and medication adjustments.
CONCLUSIONS: Early switching of antidepressant therapy is highly prevalent and contributes substantially to healthcare costs in MDD management. Identifying modifiable predictors, such as ensuring adequate dose optimization, can improve treatment persistence and economic efficiency. These findings support incorporating predictive markers into clinical decision support systems to enable personalized, cost-effective antidepressant management strategies in real-world care settings.