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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 χ=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 χ=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.

journal_name

Med Care

journal_title

Medical care

authors

Busch AB,Neelon B,Zelevinsky K,He Y,Normand SL

doi

10.1097/MLR.0b013e3182422aec

subject

Has Abstract

pub_date

2012-04-01 00:00:00

pages

311-9

issue

eissn

0025-7079

issn

1537-1948

journal_volume

pub_type

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Accurately predicting bipolar disorder mood outcomes: implications for the use of electronic databases.