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American Journal of Kidney Diseases

A Dynamic Predictive Model for Progression of CKD

Published:September 29, 2016DOI:https://doi.org/10.1053/j.ajkd.2016.07.030

      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.

      Index Words

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      Linked Article

      • From Static to Dynamic Risk Prediction: Time Is Everything
        American Journal of Kidney DiseasesVol. 69Issue 4
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          As patients with chronic kidney disease are followed up longitudinally, can accumulating information that becomes available over time be used to improve prediction of the risk for end-stage kidney disease? In this issue of AJKD, Dr Tangri and coauthors1 address this question by comparing the performance of static risk prediction based on demographic, clinical, and laboratory covariates available at a single baseline time point with the performance that could in principle be achieved by a time-updated approach if it were possible to incorporate future information for changes in the covariates after baseline.
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