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Development and validation of a model to predict 5-year risk of death without ESRD among older adults with CKD
Nisha Bansal1, Ronit Katz2, Ian H De Boer2
1Kidney Research Institute, Division of Nephrology, University of Washington, Seattle, Washington; nbansal@nephrology.washington.edu.
Insights
A new prediction tool for elderly patients with chronic kidney disease (CKD) estimates 5-year mortality risk using nine clinical factors. This simple model aids clinical decisions and patient counseling for CKD mortality.
Area of Science:
- Nephrology
- Geriatrics
- Epidemiology
Background:
- Chronic kidney disease (CKD) is a significant risk factor for mortality, especially in elderly populations.
- Accurate mortality prediction tools are crucial for guiding clinical management in older adults with CKD.
Purpose of the Study:
- To develop and validate a prediction equation for 5-year mortality risk in elderly individuals with CKD.
- To identify key clinical variables that effectively predict mortality in this demographic.
Main Methods:
- A prediction equation was developed using Cox regression in the Cardiovascular Health Study cohort (n=828).
- Predictor variables included demographics, physical measures, comorbidities, medications, and kidney function (eGFR, urine albumin-to-creatinine ratio).
- The model was externally validated in the Health, Aging, and Body Composition Study cohort (n=789).
Main Results:
- The final model incorporated age, sex, race, eGFR, urine albumin-to-creatinine ratio, smoking, diabetes, heart failure, and stroke.
- The model achieved a c-statistic of 0.72 in the development cohort and 0.69 in the validation cohort.
- A point-based system derived from the model predicted 5-year mortality ranging from 3.8% to 83.6%.
Conclusions:
- A simple, nine-variable prediction tool can effectively estimate 5-year mortality risk in elderly patients with CKD.
- This tool can assist clinicians in patient counseling and decision-making regarding CKD management.
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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