A Dynamic Predictive Model for Progression of CKD

Abstract

Background: Predicting the progression of chronic kidney disease (CKD) is vital for clinical decision making and patient-provider communication. We previously developed an accurate static prediction model that used single-timepoint measurements of demographic and laboratory variables.

Study design: Development of a dynamic predictive model using demographic, clinical, and time-dependent laboratory data from a cohort of patients with CKD stages 3 to 5.

Setting & participants: We studied 3,004 patients seen April 1, 2001, to December 31, 2009, in the outpatient CKD clinic of Sunnybrook Hospital in Toronto, Canada.

Candidate predictors: Age, sex, and urinary albumin-creatinine ratio at baseline. Estimated glomerular filtration rate (eGFR), serum albumin, phosphorus, calcium, and bicarbonate values as time-dependent predictors.

Outcomes: Treated kidney failure, defined by initiation of dialysis therapy or kidney transplantation.

Analytical approach: We describe a dynamic (latest-available-measurement) prediction model using time-dependent laboratory values as predictors of outcome. Our static model included all 8 candidate predictors. The latest-available-measurement model includes age and the latter 5 variables as time-dependent predictors. We used Cox proportional hazards models for time to kidney failure and compared discrimination, calibration, model fit, and net reclassification for the models.

Results: We studied 3,004 patients, who had 344 kidney failure events over a median follow-up of 3 years and an average of 5 clinic visits. eGFR was more strongly associated with kidney failure in the latest-available-measurement model versus the baseline visit static model (HR, 0.44 vs 0.65). The association of calcium level was unchanged, but male sex and phosphorus, albumin, and bicarbonate levels were no longer significant. Discrimination and goodness of fit showed incremental improvement with inclusion of time-dependent covariates (integrated discrimination improvement, 0.73%; 95% CI, 0.56%-0.90%).

Limitations: Our data were derived from a nephrology clinic at a single center. We were unable to include time-dependent changes in albuminuria.

Conclusions: A latest-available-measurement predictive model with eGFR as a time-dependent predictor can incrementally improve risk prediction for kidney failure over a static model with only a single eGFR.

Keywords: Kidney Failure Risk Equation (KFRE); Risk prediction; albuminuria; chronic kidney disease (CKD); disease progression; disease trajectory; eGFR slope; estimated glomerular filtration rate (eGFR); kidney failure; predictive model; renal replacement therapy (RRT); time-dependent predictor.