A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure
What are the predictors for progression of chronic kidney disease (CKD)?
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.
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%]).
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.
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).
Keywords: Lipocalins, Renal Dialysis, Kidney Transplantation, Biological Markers, Cardio-Renal Syndrome, Canada, Hexosaminidases, Glomerular Filtration Rate, Renal Insufficiency, Chronic
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