%0 Journal Article %J Medical Care %D 2012 %T A comparison and cross-validation of models to predict basic activity of daily living dependency in older adults. %A Daniel O. Clark %A Timothy E. Stump %A Tu, Wanzhu %A Douglas K Miller %K Accidental Falls %K Activities of Daily Living %K Age Factors %K Aged %K Aged, 80 and over %K Aging %K Body Mass Index %K Chronic disease %K Female %K Humans %K Male %K Mobility Limitation %K Models, Statistical %K Risk Assessment %K Sex Factors %X

BACKGROUND: A simple method of identifying elders at high risk for activity of daily living (ADL) dependence could facilitate essential research and implementation of cost-effective clinical care programs.

OBJECTIVE: We used a nationally representative sample of 9446 older adults free from ADL dependence in 2006 to develop simple models for predicting ADL dependence at 2008 follow-up and to compare the models to the most predictive published model. Candidate predictor variables were those of published models that could be obtained from interview or medical record data.

METHODS: Variable selection was performed using logistic regression with backward elimination in a two-third random sample (n = 6233) and validated in a one-third random sample (n = 3213). Model fit was determined using the c-statistic and evaluated vis-a-vis our replication of a published model.

RESULTS: At 2-year follow-up, 8.0% and 7.3% of initially independent persons were ADL dependent in the development and validation samples, respectively. The best fitting, simple model consisted of age and number of hospitalizations in past 2 years, plus diagnoses of diabetes, chronic lung disease, congestive heart failure, stroke, and arthritis. This model had a c-statistic of 0.74 in the validation sample. A model of just age and number of hospitalizations achieved a c-statistic of 0.71. These compared with a c-statistic of 0.79 for the published model. Sensitivity analyses demonstrated model robustness.

CONCLUSIONS: Models based on a widely available data achieve very good validity for predicting ADL dependence. Future work will assess the validity of these models using medical record data.

%B Medical Care %V 50 %P 534-9 %8 2012 Jun %G eng %N 6 %1 http://www.ncbi.nlm.nih.gov/pubmed/22581013?dopt=Abstract %R 10.1097/MLR.0b013e318245a50c