Dependence of vancomycin clearance on renal function via regression and bootstrap methods

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Frequently, the estimation of vancomycin on the basis of renal function is too rough because the unknown parameters of a regression function between the vancomycin clearance (Cl) and the creatinine clearance(ClCR) are based on small sample sizes.


In this study we aim to compare linear and nonlinear regression, spline interpolation and nonlinear kernel estimation for defining the relationship between measured Cl and ClCR.


We used data from published papers and appropriate numerical methods. The variability and accuracy of the estimated regression functions were determined from bootstrap methods and kernel density estimators. Tests to prove the usually assumed linearity of the regression were carried out and the influence of patient age and weight on Cl was determined.


A linear relationship reported by several authors earlier has been determined as ClVAN = 0·763ClCR + 2·715, (ml/min) (Cl = 0·011ClCR + 0·055, (ml/min/kg)).


Nonparametric regression analysis shows that a nonlinear approximately parabolic function could fit the relationship between Cl andClCR in the present case somewhat better than a linear function.

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