A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure, April 20, 2011, Tangri et al. 305 (15): 1553 — JAMA

ONLINE FIRST

JAMA. 2011;305(15):1553-1559. Published online April 11, 2011. doi: 10.1001/jama.2011.451

A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure
Navdeep Tangri, MD, FRCPC; Lesley A. Stevens, MD, MS, FRCPC; John Griffith, PhD; Hocine Tighiouart, MS; Ognjenka Djurdjev, MSc; David Naimark, MD, FRCPC; Adeera Levin, MD, FRCPC; Andrew S. Levey, MD

[+] Author Affiliations

Author Affiliations: Department of Medicine (Drs Tangri, Stevens, and Levey) and Biostatistics Research Center, Tufts Clinical and Translational Science Institute (Dr Griffith and Mr Tighiouart), Tufts Medical Center, Boston, Massachusetts; Department of Medicine, University of British Columbia, and British Columbia Provincial Renal Agency, Vancouver, British Columbia, Canada (Dr Levin and Ms Djurdjev); and Department of Medicine, Sunnybrook Hospital, University of Toronto, Toronto, Ontario, Canada (Dr Naimark).


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 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.


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 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%]).


Conclusion
A model using routinely obtained laboratory tests can accurately predict progression to kidney failure in patients with CKD stages 3 to 5.




KEYWORDS: ALBUMINURIA, CREATININE, DISEASE PROGRESSION, GLOMERULAR FILTRATION RATE, KIDNEY DISEASES, KIDNEY FAILURE, CHRONIC, LABORATORY TECHNIQUES AND PROCEDURES, MODELS, STATISTICAL, PROGNOSIS, RISK ASSESSMENT.



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A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure, April 20, 2011, Tangri et al. 305 (15): 1553 — JAMA