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April 12, 2011JAMA1,374 citations

A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure

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NTNavdeep Tangri

Key Points

  • To develop and validate predictive models for the risk of chronic kidney disease progression to kidney failure.
  • Utilized demographic, clinical, and laboratory data from two independent Canadian cohorts of CKD patients.
  • Developed models using Cox proportional hazards regression methods.
  • Evaluated models using C statistics, calibration plots, and net reclassification improvement at 1, 3, and 5 years.
  • The most accurate predictive model achieved a C statistic of 0.917 in the development cohort (95% CI, 0.901-0.933).
  • In the validation cohort, the model was more accurate than a simpler model (NRI for CKD stage 3 = 8.0%, 95% CI, 2.1%-13.9%).
  • Included variables such as age, sex, estimated GFR, albuminuria, and various serum levels.

Abstract

CONTEXT: Chronic kidney disease (CKD) is common. Kidney disease severity can be classified by estimated glomerular filtration rate (GFR) and albuminuria, but more accurate information regarding risk for progression to kidney failure is required for clinical decisions about testing, treatment, and referral. OBJECTIVE: To develop and validate predictive models for progression of CKD. DESIGN, SETTING, AND PARTICIPANTS: Development and validation of prediction models using demographic, clinical, and laboratory data from 2 independent Canadian cohorts of patients with CKD stages 3 to 5 (estimated GFR, 10-59 mL/min/1.73 m(2)) who were referred to nephrologists between April 1, 2001, and December 31, 2008. Models were developed using Cox proportional hazards regression methods and evaluated using C statistics and integrated discrimination improvement for discrimination, calibration plots and Akaike Information Criterion for goodness of fit, and net reclassification improvement (NRI) at 1, 3, and 5 years. MAIN OUTCOME MEASURE: Kidney failure, defined as need for dialysis or preemptive kidney transplantation. RESULTS: The development and validation cohorts included 3449 patients (386 with kidney failure 11%) and 4942 patients (1177 with kidney failure 24%), respectively. The most accurate model included age, sex, estimated GFR, albuminuria, serum calcium, serum phosphate, serum bicarbonate, and serum albumin (C statistic, 0.917; 95% confidence interval CI, 0.901-0.933 in the development cohort and 0.841; 95% CI, 0.825-0.857 in the validation cohort). In the validation cohort, this model was more accurate than a simpler model that included age, sex, estimated GFR, and albuminuria (integrated discrimination improvement, 3.2%; 95% CI, 2.4%-4.2%; calibration Nam and D'Agostino χ(2) statistic, 19 vs 32; and reclassification for CKD stage 3 NRI, 8.0%; 95% CI, 2.1%-13.9% and for CKD stage 4 NRI, 4.1%; 95% CI, -0.5% to 8.8%). CONCLUSION: A model using routinely obtained laboratory tests can accurately predict progression to kidney failure in patients with CKD stages 3 to 5.

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Cite This Study

Navdeep Tangri (2011) studied this question.

synapsesocial.com/papers/6a09c3424db7968590518660https://doi.org/10.1001/jama.2011.451
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