Estimating glomerular filtration rate in a population-based study | Zendy

Anoop Shankar | Zendy; Kristine E. Lee | Zendy; Ronald Klein | Zendy; Paul Muntner | Zendy; Peter C. Brazy | Zendy; Karen J. Cruickshanks | Zendy; F. Javier Nieto | Zendy; Lorraine G. Danforth | Zendy; Carla R. Schubert | Zendy; Michael Y. Tsai | Zendy

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Estimating glomerular filtration rate in a population-based study

Author(s) -

Anoop Shankar,

Kristine E. Lee,

Ronald Klein,

Paul Muntner,

Peter C. Brazy,

Karen J. Cruickshanks,

F. Javier Nieto,

Lorraine G. Danforth,

Carla R. Schubert,

Michael Y. Tsai

Publication year - 2010

Publication title -

vascular health and risk management

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.892

H-Index - 68

eISSN - 1178-2048

pISSN - 1176-6344

DOI - 10.2147/vhrm.s11269

Subject(s) - renal function , medicine , interquartile range , cystatin c , kidney disease , population , creatinine , estimating equations , urology , generalized estimating equation , endocrinology , statistics , mathematics , environmental health , maximum likelihood

Glomerular filtration rate (GFR)-estimating equations are used to determine the prevalence of chronic kidney disease (CKD) in population-based studies. However, it has been suggested that since the commonly used GFR equations were originally developed from samples of patients with CKD, they underestimate GFR in healthy populations. Few studies have made side-by-side comparisons of the effect of various estimating equations on the prevalence estimates of CKD in a general population sample.

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