Predicting mortality in older adults with kidney disease: a pragmatic prediction model.

objectivesTo 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).

designRetrospective cohort study.

settingKaiser Permanente Northwest (KPNW) Health Maintenance Organization.

participantsIndividuals with severe CKD (estimated glomerular filtration rate<30 mL/min per 1.73 m2; 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.

resultsThe 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.

conclusionWhen 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.