Daniel O Clark1, Timothy E Stump, Wanzhu Tu, Douglas K Miller. 1. Indiana University Center for Aging Research, Regenstrief Institute Inc, Indiana University School of Medicine, Indianapolis, IN 46202, USA. daniclar@iupui
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.
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.
Authors: Steven R Counsell; Christopher M Callahan; Amna B Buttar; Daniel O Clark; Kathryn I Frank Journal: J Am Geriatr Soc Date: 2006-07 Impact factor: 5.562
Authors: Steven R Counsell; Christopher M Callahan; Wanzhu Tu; Timothy E Stump; Gregory W Arling Journal: J Am Geriatr Soc Date: 2009-08 Impact factor: 5.562