Does Diagnostic Information Contribute to Predicting Functional Decline in Long-Term Care

Bibliographic Data

ID9105039
AuthorsAmy K Rosen (0000-0002-7539-7749, Boston University, corresponding author), Amy Rosen, Jeanne Wu (corresponding author), Bei-Hung Chang, Bei‐Hung Chang (0000-0002-7164-9945, Boston University, corresponding author), Dan R Berlowitz (0000-0002-8783-5611, Boston University, corresponding author), Dan Berlowitz, Arlene S Ash (0000-0002-8448-0253), Arlene Ash, Mark A Moskowitz, Mark Moskowitz
Year2000
Volume38
Issue6
Pages647-659
Publication date2000-06-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-200006000-00006
PMID10843312
OpenAlexW2034916535
LanguageEN
Citations received3
References cited41

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

Cohort · Cohort study · Decile · Diagnosis code · Econometrics · Environmental health · Long-term care · Population · Regression · Regression analysis · Retrospective cohort study · Statistics · Term (time) · Frailty in Older Adults · Geriatric Care and Nursing Homes · Internal Medicine · Mathematics · Medicine · Sepsis Diagnosis and Treatment

Tools

Open DOI