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

A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure ORIGINAL CONTRIBUTION ONLINE FIRST A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Navdeep Tangri, MD, FRCPC Context Chronic kidney disease (CKD) is common. Kidney disease severity can be classified by estimated glomerular filtration rate (GFR) and albuminuria, but more ac- Lesley A. Stevens, MD, MS, FRCPC curate information regarding risk for progression to kidney failure is required for clini- John Griffith, PhD cal decisions about testing, treatment, and referral. Hocine Tighiouart, MS Objective To develop and validate predictive models for progression of CKD. Ognjenka Djurdjev, MSc Design, Setting, and Participants Development and validation of prediction mod- David Naimark, MD, FRCPC els using demographic, clinical, and laboratory data from 2 independent Canadian co- horts of patients with CKD stages 3 to 5 (estimated GFR, 10-59 mL/min/1.73 m ) Adeera Levin, MD, FRCPC who were referred to nephrologists between April 1, 2001, and December 31, 2008. Andrew S. Levey, MD Models were developed using Cox proportional hazards regression methods and evalu- ated using C statistics and integrated discrimination improvement for discrimination, N ESTIMATED 23 MILLION calibration plots and Akaike Information Criterion for goodness of fit, and net reclas- people in the United States sification improvement (NRI) at http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA American Medical Association

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

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Publisher
American Medical Association
Copyright
Copyright 2011 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
ISSN
0098-7484
eISSN
1538-3598
DOI
10.1001/jama.2011.451
pmid
21482743
Publisher site
See Article on Publisher Site

Abstract

ORIGINAL CONTRIBUTION ONLINE FIRST A Predictive Model for Progression of Chronic Kidney Disease to Kidney Failure Navdeep Tangri, MD, FRCPC Context Chronic kidney disease (CKD) is common. Kidney disease severity can be classified by estimated glomerular filtration rate (GFR) and albuminuria, but more ac- Lesley A. Stevens, MD, MS, FRCPC curate information regarding risk for progression to kidney failure is required for clini- John Griffith, PhD cal decisions about testing, treatment, and referral. Hocine Tighiouart, MS Objective To develop and validate predictive models for progression of CKD. Ognjenka Djurdjev, MSc Design, Setting, and Participants Development and validation of prediction mod- David Naimark, MD, FRCPC els using demographic, clinical, and laboratory data from 2 independent Canadian co- horts of patients with CKD stages 3 to 5 (estimated GFR, 10-59 mL/min/1.73 m ) Adeera Levin, MD, FRCPC who were referred to nephrologists between April 1, 2001, and December 31, 2008. Andrew S. Levey, MD Models were developed using Cox proportional hazards regression methods and evalu- ated using C statistics and integrated discrimination improvement for discrimination, N ESTIMATED 23 MILLION calibration plots and Akaike Information Criterion for goodness of fit, and net reclas- people in the United States sification improvement (NRI) at

Journal

JAMAAmerican Medical Association

Published: Apr 20, 2011

References

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