Accurately Predicting Bipolar Disorder Mood Outcomes

Implications for the use of Electronic Databases

Abstract

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<5 and Young Mania Rating Scale score <4.

Results: 

The gold standard models had baseline AUC=0.80 (95% confidence interval=0.74 to 0.86) and 0.75(0.64 to 0.86) at 1-year follow-up. The predicted accuracies of the limited-detail model were lower at baseline [AUC=0.67(0.60 to 0.75)]; correlated test χ²=14.25, P=0.002] and not statistically different from the gold standard model at 1 year [AUC=0.67(0.54-0.80); correlated test χ²=2.88, P=0.090].

Conclusions: 

Future work is needed to develop clinically accurate and feasible models to predict bipolar disorder outcomes. Clinically detailed and limited models performed similarly for shorter-term prediction at 1-year; however, there is room for improvement in prediction accuracy.