Rapid and Accurate Estimation of Creatinine Clearance in the Muscle-Wasted Elderly by Computed Tomography

Abstract

To develop a new method for accurately predicting creatinine clearance (Ccr) in the debilitated elderly without urine collections, the total muscle volume of both thighs (MV thigh), as a new predictive parameter, was calculated using computed tomography in 68 debilitated patients aged 61–92 years. A regression line between 24-hour urinary creatinine excretion and MV thigh was divided by serum creatinine (Scr), and then the following formula was derived: Ccr(ml/min) = 9 × [MV thigh (cm³) –– 100]/[1,000 × Scr(mg/dl)] Predicted Ccr by the new method more closely correlated with measured Ccr (r = 0.862) than did predicted Ccr by Cockcroft and Gault’s method (the C-G method) (r = 0.727). Statistical analysis of prediction error (= predicted Ccr-measured Ccr) suggested that the new method could provide more accurate and less biased estimations of Ccr than did the C-G method, even in patients with renal insufficiency. By using computed tomography, we have proposed a more accurate method for predicting Ccr of the debilitated elderly than the C-G method, although the new method has inherent disadvantages of radiation exposure and high cost.

The relationship between estimated glomerular filtration rate, demographic and anthropometric variables is mediated by muscle mass in non-diabetic patients with chronic kidney disease

“…Since Cr is the balance of creatinine released from skeletal muscle and is removed by the kidneys, it is possible that the variables currently included in GFR prediction equations, such as age, gender, height, weight and ethnicity, are employed because they are important determinants of muscle mass [8] as well as independent predictors of kidney function. Although previous studies have used body composition to estimate kidney function [9][10][11][12][13][14], none of them have investigated the mediating relationship between the commonly used demographic/anthropometric predictor variables with Cr and GFR, which requires simultaneously investigating relationships between: (i) demographic/anthropometric variables and muscle mass; (ii) demographic/anthropometric variables and GFR; and (iii) muscle mass and GFR, using hierarchial multiple regression analyses. Thus, the first aim of this study was to test the hypothesis that muscle mass mediates a significant proportion of the variance in GFR explained by age, gender, height and weight.…”