Predicting Mortality in Older Adults with Kidney Disease: A Pragmatic Prediction Model.

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Predicting Mortality in Older Adults with Kidney Disease: A Pragmatic Prediction Model.

J Am Geriatr Soc. 2015 Mar 4;

Authors: Weiss JW, Platt RW, Thorp ML, Yang X, Smith DH, Petrik A, Eckstrom E, Morris C, O'Hare AM, Johnson ES

OBJECTIVES: To develop mortality risk prediction models for older adults with chronic kidney disease (CKD) that include comorbidities and measures of health status and use not associated with particular comorbid conditions (nondisease-specific measures).
DESIGN: Retrospective cohort study.
SETTING: Kaiser Permanente Northwest (KPNW) Health Maintenance Organization.
PARTICIPANTS: Individuals with severe CKD (estimated glomerular filtration rate <30 mL/min per 1.73 m(2) ; N = 4,054; n = 1,915 aged 65-79, n = 2,139 aged ≥80) who received care at KPNW between 2000 and 2008.
MEASUREMENTS: Cox proportional hazards analysis was used to examine the association between selected participant characteristics and all-cause mortality and to generate age group-specific risk prediction models. Predicted and observed risks were evaluated according to quintile. Predictors from the Cox models were translated into a points-based system. Internal validation was used to provide best estimates of how these models might perform in an external population.
RESULTS: The risk prediction models used 16 characteristics to identify participants with the highest risk of mortality at 2 years for adults aged 65 to 79 and 80 and older. Predicted and observed risks agreed within 5% for each quintile; a 4 to 5 times difference in 2-year predicted mortality risk was observed between the highest and lowest quintiles. The c-statistics for each model (0.68-0.69) indicated effective discrimination without evidence of significant overfit (slope shrinkage 0.06-0.09). Models for each age group performed similarly for mortality prediction at 6 months and 2 years in terms of discrimination and calibration.
CONCLUSION: When validated, these risk prediction models may be helpful in supporting discussions about prognosis and treatment decisions sensitive to prognosis in older adults with CKD in real-world clinical settings.

PMID: 25739329 [PubMed - as supplied by publisher]

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