• Navdeep Tangri, Lesley A Inker, Brett Hiebert, Jenna Wong, David Naimark, David Kent, and Andrew S Levey.
• Seven Oaks General Hospital, University of Manitoba, Winnipeg, Manitoba, Canada. Electronic address: ntangri@sogh.mb.ca.
• Am. J. Kidney Dis. 2017 Apr 1; 69 (4): 514-520.

BackgroundPredicting 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 DesignDevelopment 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 & ParticipantsWe studied 3,004 patients seen April 1, 2001, to December 31, 2009, in the outpatient CKD clinic of Sunnybrook Hospital in Toronto, Canada.Candidate PredictorsAge, sex, and urinary albumin-creatinine ratio at baseline. Estimated glomerular filtration rate (eGFR), serum albumin, phosphorus, calcium, and bicarbonate values as time-dependent predictors.OutcomesTreated kidney failure, defined by initiation of dialysis therapy or kidney transplantation.Analytical ApproachWe 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.ResultsWe 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%).LimitationsOur data were derived from a nephrology clinic at a single center. We were unable to include time-dependent changes in albuminuria.ConclusionsA 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.Copyright © 2016 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

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