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Published on: May 10, 2013
How to Assess the Glomerular Filtration Rate, and Which Method is Deemed Most Reliable?
Fabrizio Cristiano1, Cosima Posari1, Benito d'Angelo1
1UOSD Nefrologia e Dialisi Ospedale di Ortona, Asl 2 Lanciano Vasto Chieti.
Insights
Chronic kidney disease (CKD) is a growing global health issue. Early diagnosis using estimated glomerular filtration rate (eGFR) is crucial for timely treatment and managing CKD prevalence.
Area of Science:
- Nephrology
- Clinical Chemistry
- Public Health
Background:
- Global prevalence of chronic kidney disease (CKD) is increasing, leading to significant morbidity, mortality, and economic burden.
- CKD often progresses undetected due to reliance on laboratory measures like estimated glomerular filtration rate (eGFR) and urine analysis.
- Creatinine-based equations are foundational for estimating glomerular filtration rate (GFR), but require standardization and face accuracy challenges in specific populations.
Purpose of the Study:
- To review the role of estimated glomerular filtration rate (eGFR) in the diagnosis and management of chronic kidney disease (CKD).
- To discuss the development and limitations of creatinine-based equations for GFR estimation.
- To highlight the clinical and public health utility of eGFR in addressing the rising burden of CKD.
Main Methods:
- Review of renal physiology principles, specifically creatinine clearance (CrCl) and GFR estimation.
- Analysis of various creatinine-based equations for calculating CrCl and eGFR.
- Discussion of standardization requirements for creatinine assays and accuracy considerations in diverse patient subgroups.
- Exploration of advanced mathematical models for enhanced CrCl estimation in challenging clinical scenarios.
Main Results:
- Creatinine-based equations, traceable via isotope dilution mass spectrometry, provide immediate eGFR measurements in adults.
- Standardization of creatinine assays is essential to minimize inter-laboratory variability.
- Refined mathematical models improve CrCl estimation, particularly in elderly, debilitated, or comorbid patients.
Conclusions:
- Estimated glomerular filtration rate (eGFR) is a vital clinical tool for the early detection and treatment of chronic kidney disease (CKD).
- Standardized creatinine measurements and advanced equations enhance the accuracy of GFR estimation.
- eGFR serves as a critical public health metric for monitoring and managing the global prevalence of CKD.
Abstract:
The prevalence of chronic kidney disease (CKD) continues to rise globally, paralleled by an increase in associated morbidity and mortality, as well as significant implications for patient quality of life and national economies. Chronic kidney disease often progresses unrecognized by patients and physicians, despite diagnosis relying on two simple laboratory measures: estimated glomerular filtration rate (eGFR) and urine analysis. GFR measurement has been grounded in renal physiology, specifically the concept of clearance, with creatinine identified as a suitable endogenous marker for estimating creatinine clearance (CrCl). On this foundation, various equations have been developed to calculate CrCl or estimated GFR (eGFR) using four variables that incorporate creatinine and certain demographic information, such as sex and age. However, creatinine measurement requires standardization to minimize assay variability across laboratories. Moreover, the accuracy of these equations remains contentious in certain patient subgroups. For these reasons, additional mathematical models have been devised to enhance CrCl estimation, for example, when urine collection is impractical, in elderly or debilitated patients, and in individuals with trauma, diabetes, or obesity. Presently, eGFR in adults can be immediately measured and reported using creatinine-based equations traceable through isotope dilution mass spectrometry. In conclusion, leveraging insights from renal physiology, eGFR can be employed clinically for early diagnosis and treatment of CKD, as well as a public health tool to estimate its prevalence.
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