Kirsten L Johansen1,2,3, Cynthia Delgado1,2, George A Kaysen4,5,6, Glenn M Chertow7, Janet Chiang8, Lorien S Dalrymple9, Mark R Segal3, Barbara A Grimes3. 1. Division of Nephrology, University of California, San Francisco. 2. Nephrology Section, San Francisco Veterans Affairs Medical Center, California. 3. Department of Epidemiology and Biostatistics, University of California, San Francisco. 4. Department of Biochemistry and Molecular Medicine. 5. Department of Medicine, University of California, Davis. 6. Division of Nephrology, University of California, Davis. 7. Division of Nephrology, School of Medicine, Stanford University, California. 8. Division of Endocrinology, University of California, San Francisco. 9. Fresenius Medical Care North America, Waltham, Massachusetts.
Abstract
BACKGROUND: Understanding how components of frailty change over time and how they can be modeled as time-dependent predictors of mortality could lead to better risk prediction in the dialysis population. METHODS: We measured frailty at baseline, 12 months, and 24 months among 727 patients receiving hemodialysis in Northern California and Atlanta. We examined the likelihood of meeting frailty components (weight loss, exhaustion, low physical activity, weak grip strength, and slow gait speed) as a function of time in logistic regression analysis and association of frailty components with mortality in time-updated multivariable Cox models. RESULTS: Physical activity and gait speed declined, exhaustion and grip strength did not change, and the odds of meeting the weight loss criterion declined with time. All five components were associated with higher mortality in multivariable analyses, but gait speed was the strongest individual predictor. All frailty components except physical inactivity were independently associated with mortality when all five components were included in the same model. The number of frailty components met was associated with mortality in a gradient that ranged from a hazard ratio of 2.73 for one component to 10.07 for five components met; the model including all five components was the best model based on Akaike information criterion. CONCLUSIONS: Measurement of all frailty components was necessary for optimal mortality prediction, and the number of components met was strongly associated with mortality in this cohort.
BACKGROUND: Understanding how components of frailty change over time and how they can be modeled as time-dependent predictors of mortality could lead to better risk prediction in the dialysis population. METHODS: We measured frailty at baseline, 12 months, and 24 months among 727 patients receiving hemodialysis in Northern California and Atlanta. We examined the likelihood of meeting frailty components (weight loss, exhaustion, low physical activity, weak grip strength, and slow gait speed) as a function of time in logistic regression analysis and association of frailty components with mortality in time-updated multivariable Cox models. RESULTS: Physical activity and gait speed declined, exhaustion and grip strength did not change, and the odds of meeting the weight loss criterion declined with time. All five components were associated with higher mortality in multivariable analyses, but gait speed was the strongest individual predictor. All frailty components except physical inactivity were independently associated with mortality when all five components were included in the same model. The number of frailty components met was associated with mortality in a gradient that ranged from a hazard ratio of 2.73 for one component to 10.07 for five components met; the model including all five components was the best model based on Akaike information criterion. CONCLUSIONS: Measurement of all frailty components was necessary for optimal mortality prediction, and the number of components met was strongly associated with mortality in this cohort.
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