|Title||A comparison and cross-validation of models to predict basic activity of daily living dependency in older adults.|
|Publication Type||Journal Article|
|Year of Publication||2012|
|Authors||Clark, DO, Stump, TE, Tu, W, Miller, DK|
|Date Published||2012 Jun|
|Keywords||Accidental Falls, Activities of Daily Living, Age Factors, Aged, Aged, 80 and over, Aging, Body Mass Index, Chronic disease, Female, Humans, Male, Mobility Limitation, Models, Statistical, Risk Assessment, Sex Factors|
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.
|User Guide Notes|
|Alternate Journal||Med Care|
|PubMed Central ID||PMC3351695|
|Grant List||P30 AG024967-07 / AG / NIA NIH HHS / United States |
P30 AG024967 / AG / NIA NIH HHS / United States
R01 AG031222 / AG / NIA NIH HHS / United States
R01 AG031222-02 / AG / NIA NIH HHS / United States
R01AG031222 / AG / NIA NIH HHS / United States