Accurately Predicting Bipolar Disorder Mood Outcomes—Implications for the Use of Electronic Medical Records

BACKGROUND: Monitoring mental health treatment outcomes for populations requires an understanding as to which patient information is needed in electronic format and is feasible to obtain in routine care. OBJECTIVE: To examine whether bipolar disorder outcomes can be accurately predicted and how much clinical detail is needed to do so. RESEARCH DESIGN, DATA SOURCES, AND PARTICIPANTS: Longitudinal study of bipolar disorder patients treated during 2000 to 2004 in the 19-site Systematic Treatment Enhancement Program for Bipolar Disorder observational study arm (N=3168). Clinical data were obtained at baseline and quarterly for over 1 year. We fit a "gold standard" longitudinal random-effects regression model using a detailed clinical information and estimated the area under the receiver operating characteristic curve (AUC) to predict accuracy using a validation sample. The model was then modified to include patient characteristics feasible in routinely collected electronic data (eg, administrative data). We compared the AUCs for the "limited-detail" and gold standard models, testing for differences between the AUCs using the validation sample. MEASURE: Remission, defined as Montgomery-Asberg Depression Rating Scale score