Tracking medication changes to assess outcomes in comparative effectiveness research: A bipolar CHOICE study JOURNAL OF AFFECTIVE DISORDERS Reilly-Harrington, N. A., Sylvia, L. G., Rabideau, D. J., Gold, A. K., Deckersbach, T., Bowden, C. L., Bobo, W. V., Singh, V., Calabrese, J. R., Shelton, R. C., Friedman, E. S., Thase, M. E., Kamali, M., Tohen, M., McInnis, M. G., McElroy, S. L., Ketter, T. A., Kocsis, J. H., Kinrys, G., Nierenberg, A. A. 2016; 205: 159-164

Abstract

Comparative effectiveness research uses multiple tools, but lacks outcome measures to assess large electronic medical records and claims data. Aggregate changes in medications in response to clinical need may serve as a surrogate outcome measure. We developed the Medication Recommendation Tracking Form (MRTF) to record the frequency, types, and reasons for medication adjustments in order to calculate Necessary Clinical Adjustments (NCAs), medication adjustments to reduce symptoms, maximize treatment response, or address problematic side effects.The MRTF was completed at every visit for 482 adult patients in Bipolar CHOICE, a 6-month randomized comparative effectiveness trial.Responders had significantly fewer NCAs compared to non-responders. NCAs predicted subsequent response status such that every additional NCA during the previous visit decreased a patient's odds of response by approximately 30%. Patients with more severe symptoms had a greater number of NCAs at the subsequent visit. Patients with a comorbid anxiety disorder demonstrated a significantly higher rate of NCAs per month than those without a comorbid anxiety disorder. Patients with greater frequency, intensity, and interference of side effects had higher rates of NCAs. Participants with fewer NCAs reported a higher quality of life and decreased functional impairment.The MRTF has not been examined in community clinic settings and did not predict response more efficiently than the Clinical Global Impression-Bipolar Version (CGI-BP).The MRTF is a feasible proxy of clinical outcome, with implications for clinical training and decision-making. Analyses of big data could use changes in medications as a surrogate outcome measure.

View details for DOI 10.1016/j.jad.2016.07.007

View details for Web of Science ID 000385440900021

View details for PubMedID 27449548