Development of a baseline battery to optimize depression treatment assignment in primary care: Balancing breadth and brevity.

BACKGROUND

Psychotherapy and antidepressant medications are effective treatments for depression on average, yet individual response varies widely. Predicting which treatment is most effective for a given patient requires a comprehensive yet feasible assessment of baseline characteristics. This paper describes the development of a baseline battery to guide treatment allocation between behavioral activation and fluoxetine for adults with depression in primary care in India.

METHODS

We used a six-step, 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 applied semantic analysis to identify redundancies, piloted the battery with 200 primary care patients, and used advanced statistical methods to optimize item selection.

RESULTS

The final battery comprised 68 constructs spanning clinical, psychological, cognitive, socioeconomic, and biological domains, assessed through questionnaires, tasks, and biomarkers. Pilot testing revealed comprehension challenges, participant fatigue, and low-variability items. Data-driven item reduction removed more than 25% of items while retaining full construct coverage, reducing administration time from over three hours to approximately two.

CONCLUSION

This study shows how a structured, data-driven development process can yield a scientifically robust baseline assessment battery capturing a broad range of candidate treatment moderators. The battery will undergo further refinement following the results of the OptimizeD trial, which will identify the subset of baseline characteristics that meaningfully predict differential treatment response. While not intended as an implementation-ready clinical tool, the development framework presented here may help guide other researchers conducting similar precision treatment studies, particularly in resource-constrained settings.