Does diagnostic information contribute to predicting functional decline in long-term care?
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Updated: Oct 16, 2026

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
Published on: September 30, 2020
1Center for Health Quality, Outcomes and Economic Research, Bedford VAMC, Massachusetts 01730, USA. akrosen@bu.edu
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.
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