Does diagnostic information contribute to predicting functional decline in long-term care

Amy K. Rosen, Boston University
Jeanne Wu
Bei-Hung Chang
Dan R. Berlowitz, Bedford Veterans Affairs Medical Center
Arlene S. Ash, University of Massachusetts Medical School
Mark A. Moskowitz, Boston University

Abstract

BACKGROUND: Compared with the acute-care setting, use of risk-adjusted outcomes in long-term care is relatively new. With the recent development of administrative databases in long-term care, such uses are likely to increase.

OBJECTIVES: The objective of this study was to determine the contribution of ICD-9-CM diagnosis codes from administrative data in predicting functional decline in long-term care.

RESEARCH DESIGN: We used a retrospective sample of 15,693 long-term care residents in VA facilities in 1996.

METHODS: We defined functional decline as an increase of > or =2 in the activities of daily living (ADL) summary score from baseline to semiannual assessment. A base regression model was compared to a full model enhanced with ICD-9-CM codes. We calculated validated measures of model performance in an independent cohort.

RESULTS: The full model fit the data significantly better than the base model as indicated by the likelihood ratio test (chi2 = 179, df = 11, P <0.001). The full model predicted decline more accurately than the base model (R2 = 0.06 and 0.05, respectively) and discriminated better (c statistics were 0.70 and 0.68). Observed and predicted risks of decline were similar within deciles between the 2 models, suggesting good calibration. Validated R2 statistics were 0.05 and 0.04 for the full and base models; validated c statistics were 0.68 and 0.66.

CONCLUSIONS: Adding specific diagnostic variables to administrative data modestly improves the prediction of functional decline in long-term care residents. Diagnostic information from administrative databases may present a cost-effective alternative to chart abstraction in providing the data necessary for accurate risk adjustment.