Nisha Bansal1, Ronit Katz2, Ian H De Boer2, Carmen A Peralta3, Linda F Fried4, David S Siscovick5, Dena E Rifkin6, Calvin Hirsch7, Steven R Cummings8, Tamara B Harris9, Stephen B Kritchevsky10, Mark J Sarnak11, Michael G Shlipak3, Joachim H Ix5. 1. Kidney Research Institute, Division of Nephrology, University of Washington, Seattle, Washington; nbansal@nephrology.washington.edu. 2. Kidney Research Institute, Division of Nephrology, University of Washington, Seattle, Washington; 3. Division of Nephrology, University of California, San Francisco, California; 4. Division of Nephrology, University of Pittsburgh, Pittsburgh, Pennsylvania; 5. New York Academy of Medicine New York, New York; 6. Division of Nephrology, University of California, San Diego, California; 7. Department of Medicine, University of California, Davis, Sacramento, California; 8. San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, California; 9. National Institute on Aging; 10. Sticht Center on Aging and Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina; and. 11. Division of Nephrology, Tufts Medical Center, Boston, Massachusetts.
Abstract
BACKGROUND AND OBJECTIVES: CKD is associated with mortality. Accurate prediction tools for mortality may guide clinical decision-making, particularly among elderly persons with CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A prediction equation was developed for 5-year risk of mortality among participants with CKD in the Cardiovascular Health Study. Sixteen candidate predictor variables were explored, which included demographics, physical examination measures, comorbidity, medication use, and kidney function measures (eGFR calculated from serum creatinine and the CKD Epidemiology Collaboration equation and the urine albumin-to-creatinine ratio). Models were developed using Cox regression and evaluated using c statistics. A final parsimonious model was externally validated in an independent cohort of community-living elders with CKD in the Health, Aging, and Body Composition Study. RESULTS: The development cohort included 828 participants who had a mean age of 80 (±5.6) years and an eGFR of 47 (±11) ml/min per 1.73 m(2), and median albumin-to-creatinine ratio of 13 (interquartile range 6-51) mg/g. The validation cohort included 789 participants who had a mean age of 74 (±2.8) years and an eGFR of 50 (±9) ml/min per 1.73 m(2), and median albumin-to-creatinine ratio of 13 (interquartile range 6-42) mg/g. The final model for 5-year mortality risk included age, sex, race, eGFR, urine albumin-to-creatinine ratio, smoking, diabetes mellitus, and history of heart failure and stroke (c statistic=0.72; 95% confidence interval, 0.68 to 0.74). When a point-based system was assigned for each of nine variables in the equation, the estimated risk of death within 5 years ranged from 3.8% among participants with the lowest scores to 83.6% among participants with nine points. The model performed fair in external validation (c statistic=0.69; 95% confidence interval, 0.64 to 0.74). CONCLUSIONS: A simple prediction tool using nine readily available clinical variables can assist in predicting 5-year mortality risk in elderly patients with CKD, which may be useful in counseling patients and guiding clinical decision making.
BACKGROUND AND OBJECTIVES: CKD is associated with mortality. Accurate prediction tools for mortality may guide clinical decision-making, particularly among elderly persons with CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A prediction equation was developed for 5-year risk of mortality among participants with CKD in the Cardiovascular Health Study. Sixteen candidate predictor variables were explored, which included demographics, physical examination measures, comorbidity, medication use, and kidney function measures (eGFR calculated from serum creatinine and the CKD Epidemiology Collaboration equation and the urine albumin-to-creatinine ratio). Models were developed using Cox regression and evaluated using c statistics. A final parsimonious model was externally validated in an independent cohort of community-living elders with CKD in the Health, Aging, and Body Composition Study. RESULTS: The development cohort included 828 participants who had a mean age of 80 (±5.6) years and an eGFR of 47 (±11) ml/min per 1.73 m(2), and median albumin-to-creatinine ratio of 13 (interquartile range 6-51) mg/g. The validation cohort included 789 participants who had a mean age of 74 (±2.8) years and an eGFR of 50 (±9) ml/min per 1.73 m(2), and median albumin-to-creatinine ratio of 13 (interquartile range 6-42) mg/g. The final model for 5-year mortality risk included age, sex, race, eGFR, urine albumin-to-creatinine ratio, smoking, diabetes mellitus, and history of heart failure and stroke (c statistic=0.72; 95% confidence interval, 0.68 to 0.74). When a point-based system was assigned for each of nine variables in the equation, the estimated risk of death within 5 years ranged from 3.8% among participants with the lowest scores to 83.6% among participants with nine points. The model performed fair in external validation (c statistic=0.69; 95% confidence interval, 0.64 to 0.74). CONCLUSIONS: A simple prediction tool using nine readily available clinical variables can assist in predicting 5-year mortality risk in elderly patients with CKD, which may be useful in counseling patients and guiding clinical decision making.
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