American Journal of Kidney Diseases

A Dynamic Predictive Model for Progression of CKD

Published:September 29, 2016DOI:


      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.


      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.


      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%).


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


      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

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to American Journal of Kidney Diseases
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Zhang Q.L.
        • Rothenbacher D.
        Prevalence of chronic kidney disease in population-based studies: systematic review.
        BMC Public Health. 2008; 8: 117
        • Levey A.S.
        • Coresh J.
        Chronic kidney disease.
        Lancet. 2012; 379: 165-180
        • Keith D.S.
        • Nichols G.A.
        • Gullion C.M.
        • Brown J.B.
        • Smith D.H.
        Longitudinal follow-up and outcomes among a population with chronic kidney disease in a large managed care organization.
        Arch Intern Med. 2004; 164: 659-663
        • Taal M.W.
        • Brenner B.M.
        Predicting initiation and progression of chronic kidney disease: developing renal risk scores.
        Kidney Int. 2006; 70: 1694-1705
        • O'Hare A.M.
        • Bertenthal D.
        • Walter L.C.
        • et al.
        When to refer patients with chronic kidney disease for vascular access surgery: should age be a consideration?.
        Kidney Int. 2007; 71: 555-561
        • Levey A.S.
        • de Jong P.E.
        • Coresh J.
        • et al.
        The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report.
        Kidney Int. 2011; 80: 17-28
        • Matsushita K.
        • van der Velde M.
        • Astor B.C.
        • et al.
        Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis.
        Lancet. 2010; 375: 2073-2081
        • Tangri N.
        • Stevens L.A.
        • Griffith J.
        • et al.
        A predictive model for progression of chronic kidney disease to kidney failure.
        JAMA. 2011; 305: 1553-1559
        • Tangri N.
        • Grams M.E.
        • Levey A.S.
        • et al.
        Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis.
        JAMA. 2016; 315: 164-174
        • Hosmer D.W.
        • Lemeshow S.
        • May S.
        Applied Survival Analysis: Regression Modeling of Time-to-Event Data. 2nd ed. John Wiley & Sons Inc, New York, NY2011
        • Steyerberg E.
        Clinical Prediction Models: A Practical Approach to Development, Validation and Updating.
        Springer, New York, NY2009
        • D'Agostino R.B.
        • Nam B.H.
        Evaluation of the Performance of Survival Analysis Models: Discrimination and Calibration Measures.
        Elsevier, Amsterdam, The Netherlands2004: 1-25
        • Pencina M.J.
        • D'Agostino Sr., R.B.
        • D'Agostino Jr., R.B.
        • Vasan R.S.
        Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.
        Stat Med. 2008; 27: 207-212
        • Pencina M.J.
        • D'Agostino R.B.
        Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.
        Stat Med. 2004; 23: 2109-2123
        • Steyerberg E.W.
        • Vickers A.J.
        • Cook N.R.
        • et al.
        Assessing the performance of prediction models: a framework for traditional and novel measures.
        Epidemiology. 2010; 21: 128-138
        • Matsushita K.
        • Selvin E.
        • Bash L.D.
        • Franceschini N.
        • Astor B.C.
        • Coresh J.
        Change in estimated GFR associates with coronary heart disease and mortality.
        J Am Soc Nephrol. 2009; 20: 2617-2624
        • Turin T.C.
        • Coresh J.
        • Tonelli M.
        • et al.
        Short-term change in kidney function and risk of end-stage renal disease.
        Nephrol Dial Transplant. 2012; 27: 3835-3843
        • Turin T.C.
        • Coresh J.
        • Tonelli M.
        • et al.
        One-year change in kidney function is associated with an increased mortality risk.
        Am J Nephrol. 2012; 36: 41-49
        • Turin T.C.
        • Hemmelgarn B.R.
        Change in kidney function over time and risk for adverse outcomes: is an increasing estimated GFR harmful?.
        Clin J Am Soc Nephrol. 2011; 6: 1805-1806
        • Reich H.N.
        • Gladman D.D.
        • Urowitz M.B.
        • et al.
        Persistent proteinuria and dyslipidemia increase the risk of progressive chronic kidney disease in lupus erythematosus.
        Kidney Int. 2011; 79: 914-920
        • Reich H.N.
        • Troyanov S.
        • Scholey J.W.
        • Cattran D.C.
        Remission of proteinuria improves prognosis in IgA nephropathy.
        J Am Soc Nephrol. 2007; 18: 3177-3183
        • Pavkov M.E.
        • Knowler W.C.
        • Hanson R.L.
        • Bennett P.H.
        • Nelson R.G.
        Predictive power of sequential measures of albuminuria for progression to ESRD or death in Pima Indians with type 2 diabetes.
        Am J Kidney Dis. 2008; 51: 759-766
        • Inker L.A.
        • Levey A.S.
        • Pandya K.
        • et al.
        Early change in proteinuria as a surrogate end point for kidney disease progression: an individual patient meta-analysis.
        Am J Kidney Dis. 2014; 64: 74-85
        • Li L.
        • Astor B.C.
        • Lewis J.
        • et al.
        Longitudinal progression trajectory of GFR among patients with CKD.
        Am J Kidney Dis. 2012; 59: 504-512
        • Komenda P.
        • Rigatto C.
        • Tangri N.
        Estimated glomerular filtration rate and albuminuria: diagnosis, staging, and prognosis.
        Curr Opin Nephrol Hypertens. 2014; 23: 251-257
        • Tangri N.
        • Inker L.A.
        • Tighiouart H.
        • et al.
        Filtration markers may have prognostic value independent of glomerular filtration rate.
        J Am Soc Nephrol. 2012; 23: 351-359
        • Feldman H.I.
        • Appel L.J.
        • Chertow G.M.
        • et al.
        The Chronic Renal Insufficiency Cohort (CRIC) Study: design and methods.
        J Am Soc Nephrol. 2003; 14: S148-S153

      Linked Article

      • From Static to Dynamic Risk Prediction: Time Is Everything
        American Journal of Kidney DiseasesVol. 69Issue 4
        • Preview
          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.
        • Full-Text
        • PDF