Feon W Cheng1, Xiang Gao1, Le Bao2, Diane C Mitchell1, Craig Wood3, Martin J Sliwinski4, Helen Smiciklas-Wright1, Christopher D Still4, David D K Rolston5, Gordon L Jensen6. 1. Department of Nutritional Sciences, Pennsylvania State University, University Park, Pennsylvania, USA. 2. Department of Statistics, Pennsylvania State University, University Park, Pennsylvania, USA. 3. Obesity Institute, Geisinger Health System, Danville, Pennsylvania, USA. 4. Department of Human Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA. 5. Department of Internal Medicine, Geisinger Health System, Danville, Pennsylvania, USA. 6. University of Vermont College of Medicine, Burlington, Vermont, USA.
Abstract
OBJECTIVE: To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. METHODS: The conditional inference tree analysis, a data mining approach, was used to construct a risk stratification algorithm for developing functional limitation based on BMI and other potential risk factors for disability in 1,951 older adults without functional limitations at baseline (baseline age 73.1 ± 4.2 y). We also analyzed the data with multivariate stepwise logistic regression and compared the two approaches (e.g., cross-validation). Over a mean of 9.2 ± 1.7 years of follow-up, 221 individuals developed functional limitation. RESULTS: Higher BMI, age, and comorbidity were consistently identified as significant risk factors for functional decline using both methods. Based on these factors, individuals were stratified into four risk groups via the conditional inference tree analysis. Compared to the low-risk group, all other groups had a significantly higher risk of developing functional limitation. The odds ratio comparing two extreme categories was 9.09 (95% confidence interval: 4.68, 17.6). CONCLUSIONS: Higher BMI, age, and comorbid disease were consistently identified as significant risk factors for functional decline among older individuals across all approaches and analyses.
OBJECTIVE: To examine the risk factors of developing functional decline and make probabilistic predictions by using a tree-based method that allows higher order polynomials and interactions of the risk factors. METHODS: The conditional inference tree analysis, a data mining approach, was used to construct a risk stratification algorithm for developing functional limitation based on BMI and other potential risk factors for disability in 1,951 older adults without functional limitations at baseline (baseline age 73.1 ± 4.2 y). We also analyzed the data with multivariate stepwise logistic regression and compared the two approaches (e.g., cross-validation). Over a mean of 9.2 ± 1.7 years of follow-up, 221 individuals developed functional limitation. RESULTS: Higher BMI, age, and comorbidity were consistently identified as significant risk factors for functional decline using both methods. Based on these factors, individuals were stratified into four risk groups via the conditional inference tree analysis. Compared to the low-risk group, all other groups had a significantly higher risk of developing functional limitation. The odds ratio comparing two extreme categories was 9.09 (95% confidence interval: 4.68, 17.6). CONCLUSIONS: Higher BMI, age, and comorbid disease were consistently identified as significant risk factors for functional decline among older individuals across all approaches and analyses.