Assessing Kidney Function — Measured and Estimated Glomerular Filtration Rate

Johns Hopkins Medicine, Baltimore, Maryland, United States
New England Journal of Medicine (Impact Factor: 55.87). 07/2006; 354(23):2473-83. DOI: 10.1056/NEJMra054415
Source: PubMed


In the coming years, estimates of the glomerular filtration rate (GFR) may replace the measurement of serum creatinine as the primary tool for the assessment of kidney function. Indeed, many clinical laboratories already report estimated GFR values whenever serum creatinine is measured. This review considers current methods of measuring GFR and GFR-estimating equations and their strengths and weaknesses as applied to chronic kidney disease.

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    • "Their findings showed that age and anemia were the strongest factors associated with CKD in their population [13] . There have been several studies on the GFR variations among different populations of CKD patients [9, 10,141516. In this regard, intelligent and machine learning methods have been increasingly used in the context of health and disease forecasting. "
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    ABSTRACT: Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. Methods. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m 2 of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Results. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current G F R ( t ) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Conclusions. Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.
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    • "Based on serum creatinine, Cockcroft-Gault formula and modification of diet in renal disease (MDRD) equations are of limited value in cirrhotic patients; they overestimate the GFR as well [7]. Serum cystatin C (CysC) has been proposed as a novel biomarker of the renal function [8]. Several studies have reported its value in different sets of patients [9] [10] [11] [12] [13] [14] [15] [16]. "

    Full-text · Dataset · Jan 2016
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    • "All Rights Reserved. nephrology [14] [15] "

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