Risk Prediction Models for Patients With Chronic Kidney Disease

Abstract

Patients with chronic kidney disease (CKD) are at increased risk for kidney failure, cardiovascular events, and all-cause mortality. Accurate models are needed to predict the individual risk for these outcomes. To systematically review risk prediction models for kidney failure, cardiovascular events, and death in patients with CKD. MEDLINE search of English-language articles published from 1966 to November 2012. Cohort studies that examined adults with any stage of CKD who were not receiving dialysis and had not had a transplant; had at least 1 year of follow-up; and reported on a model that predicted the risk for kidney failure, cardiovascular events, or all-cause mortality. Reviewers extracted data on study design, population characteristics, modeling methods, metrics of model performance, risk of bias, and clinical usefulness. Thirteen studies describing 23 models were found. Eight studies (11 models) involved kidney failure, 5 studies (6 models) involved all-cause mortality, and 3 studies (6 models) involved cardiovascular events. Measures of estimated glomerular filtration rate or serum creatinine level were included in 10 studies (17 models), and measures of proteinuria were included in 9 studies (15 models). Only 2 studies (4 models) met the criteria for clinical usefulness, of which 1 study (3 models) presented reclassification indices with clinically useful risk categories. A validated risk-of-bias tool and comparisons of the performance of different models in the same validation population were lacking. Accurate, externally validated models for predicting risk for kidney failure in patients with CKD are available and ready for clinical testing. Further development of models for cardiovascular events and all-cause mortality is needed. None.