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

Abstract

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.