A comparison and cross-validation of models to predict basic activity of daily living dependency in older adults.

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.