4.2 Review

How best to estimate glomerular filtration rate? Novel filtration markers and their application

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出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/MNH.0000000000000444

关键词

estimated glomerular filtration rate; kidney function; low molecular weight proteins; metabolites; residual kidney function

资金

  1. National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (NIH) [R01DK097020]
  2. Paul Teschan Research Fund of Dialysis Clinic, Inc.

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Purpose of review Chronic kidney disease is an increasing health burden. Estimating equations using serum concentrations of creatinine and cystatin C facilitate the assessment of kidney function as reflected in estimated glomerular filtration rate (eGFR). Reduced eGFR is associated with increased risk for numerous adverse outcomes and is an important aspect in many clinical situations. However, current equations are suboptimal in some clinical settings. The review focuses on approaches to improve the estimation of GFR and aims to familiarize the reader with the underlying methodological hypotheses how new markers could contribute to improve the overall performance of estimating equations. Recent findings Low molecular weight proteins such as beta-trace-protein and beta-2-microglobulin, as well as newly discovered metabolites, show promise as new filtration markers, as they might be beneficial in populations in which creatinine or cystatin C are inaccurate. We propose that the combination of multiple novel markers, alone or in combination with creatinine, cystatin C or demographics, can potentially improve GFR estimation. For special populations such as dialysis patients, separate equations have been developed to estimate residual kidney function. Summary Current GFR estimating equations are an essential part of routine clinical practice but have limitations. The use of multiple markers combined in a single equation appears to be the most promising approach. Future research is required to validate proposed equations in diverse populations.

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