Development of a Baseline Battery to Optimize Depression Treatment Assignment: Balancing Breadth and Brevity
Abstract
BackgroundFirst-line treatments for depression – psychotherapy and antidepressant medications – are equally effective on average, but individual response varies widely. Predicting which treatment will work best for a given patient is a challenge and requires a comprehensive yet feasible assessment of baseline characteristics. This paper describes the development of a baseline battery for a precision treatment trial designed to guide optimal treatment allocation for adults with depression in primary care in India.MethodsWe used a stepwise multimethod approach to develop the battery. We updated a prior review to identify constructs associated with differential treatment response, conducted an expert survey (220 invited; 80 responses) to refine the list, and selected measurement tools based on brevity, validity, and scalability. We then applied semantic analysis to identify redundancies, piloted the battery with 200 primary care patients, and used exploratory factor analysis and machine learning to optimize item selection.ResultsThe final battery included 68 constructs covering clinical, psychological, cognitive, socioeconomic, and biological domains across multiple assessment modalities (questionnaires, neurocognitive tasks, and biological markers). Pilot testing revealed comprehension challenges, participant fatigue, and low-variability items. Statistical techniques reduced the total items by over 25% while retaining coverage of all targeted constructs. This reduced administration time from more than three hours to approximately two.ConclusionThe OptimizeD battery provides a novel, scalable framework for identifying personalized treatment strategies in primary care. The trial will evaluate which variables best predict optimal treatment assignment, informing future integration of predictive tools into routine care in low-resource settings.
Related articles
Related articles are currently not available for this article.