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

TLDR

Chronic kidney disease is common and its severity is usually classified by estimated glomerular filtration rate and albuminuria, yet more accurate risk information for progression to kidney failure is needed for clinical decision‑making. The study aims to develop and validate predictive models for progression of chronic kidney disease to kidney failure. The authors used demographic, clinical, and laboratory data from two independent Canadian CKD cohorts and applied Cox proportional hazards regression to build and evaluate predictive models. The final model, incorporating age, sex, eGFR, albuminuria, serum calcium, phosphate, bicarbonate, and albumin, achieved C statistics of 0.917 in the development cohort and 0.841 in validation, outperforming a simpler model that used only age, sex, eGFR, and albuminuria.

Abstract

<h3>Context</h3>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.<h3>Objective</h3>To develop and validate predictive models for progression of CKD.<h3>Design, Setting, and Participants</h3>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 m²) 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.<h3>Main Outcome Measure</h3>Kidney failure, defined as need for dialysis or preemptive kidney transplantation.<h3>Results</h3>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 χ² 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%]).<h3>Conclusion</h3>A model using routinely obtained laboratory tests can accurately predict progression to kidney failure in patients with CKD stages 3 to 5.