title: Does diagnostic information contribute to predicting functional decline in long-term care?
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.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
authors.affiliation: Center for Health Quality, Outcomes and Economic Research, Bedford VAMC, Massachusetts 01730, USA. akrosen@bu.edu
authors.forename: A
authors.initials: A
authors.lastname: Rosen
authors.forename: J
authors.initials: J
authors.lastname: Wu
authors.forename: B H
authors.initials: BH
authors.lastname: Chang
authors.forename: D
authors.initials: D
authors.lastname: Berlowitz
authors.forename: A
authors.initials: A
authors.lastname: Ash
authors.forename: M
authors.initials: M
authors.lastname: Moskowitz
mesh_terms: D000203:Activities of Daily Living; D000704:Analysis of Variance; D002138:Calibration; D003362:Cost-Benefit Analysis; D016208:Databases, Factual; D003953:Diagnosis-Related Groups / Q000145:classification*; D016002:Discriminant Analysis; D006801:Humans; D016013:Likelihood Functions; D008134:Long-Term Care; D017063:Outcome Assessment, Health Care / Q000458:organization & administration*; D011237:Predictive Value of Tests; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000458:organization & administration*; D014481:United States; D014493:United States Department of Veterans Affairs