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

Study Questions:

What are the predictors for progression of chronic kidney disease (CKD)?

Methods:

The study investigators used demographic, clinical, and laboratory data from two independent Canadian groups of patients with stages 3-5 CKD (estimated glomerular filtration rate [eGFR], 10-59 ml/min/1.73 m2) who were referred to nephrologists between April 2001 and December 2008. The study cohort (or development and validation groups) included 3,449 patients (11% with kidney failure [n = 386]) and 4,942 patients (24% with kidney failure [n = 1,177]), respectively. The primary outcome measured was kidney failure, defined as need for dialysis or pre-emptive kidney transplantation. The investigators developed models using Cox proportional hazard regression methods and evaluated them 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.

Results:

The investigators found that most accurate model included gender, age, eGFR, 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 gender, age, eGFR, 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%]).

Conclusions:

The authors concluded that a model using routinely obtained laboratory tests can accurately predict progression to kidney failure in patients with CKD stages 3-5.

Perspective:

This is an important study that suggests routine laboratory tests are useful in predicting progression of CKD. Further studies combining these routine laboratory tests with novel biomarkers [such as serum cystatine C, neutrophil gelatinase-associated lipocalin (NGAL) and urinary KIM-1, IL-18, NGAL, and N-acetyl-β-(D)glucosaminidase (NAG)] improve the ability to predict progression of chronic CKD, particularly in the context of cardiorenal syndrome (see Table).

Clinical Topics: Dyslipidemia, Heart Failure and Cardiomyopathies, Lipid Metabolism, Acute Heart Failure, Chronic Heart Failure, Heart Failure and Cardiac Biomarkers

Keywords: Lipocalins, Renal Dialysis, Kidney Transplantation, Biological Markers, Cardio-Renal Syndrome, Canada, Hexosaminidases, Glomerular Filtration Rate, Renal Insufficiency, Chronic


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