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  • Title: New sampling strategy using a Bayesian approach to assess iohexol clearance in kidney transplant recipients.
    Author: Benz-de Bretagne I, Le Guellec C, Halimi JM, Gatault P, Barbet C, Alnajjar A, Büchler M, Lebranchu Y, Andres CR, Vourcʼh P, Blasco H.
    Journal: Ther Drug Monit; 2012 Jun; 34(3):289-97. PubMed ID: 22585184.
    Abstract:
    BACKGROUND: Glomerular filtration rate (GFR) measurement is a major issue in kidney transplant recipients for clinicians. GFR can be determined by estimating the plasma clearance of iohexol, a nonradiolabeled compound. For practical and convenient application for patients and caregivers, it is important that a minimal number of samples are drawn. The aim of this study was to develop and validate a Bayesian model with fewer samples for reliable prediction of GFR in kidney transplant recipients. METHODS: Iohexol plasma concentration-time curves from 95 patients were divided into an index (n = 63) and a validation set (n = 32). Samples (n = 4-6 per patient) were obtained during the elimination phase, that is, between 120 and 270 minutes. Individual reference values of iohexol clearance (CL(iohexol)) were calculated from k (elimination slope) and V (volume of distribution from intercept). Individual CL(iohexol) values were then introduced into the Bröchner-Mortensen equation to obtain the GFR (reference value). A population pharmacokinetic model was developed from the index set and validated using standard methods. For the validation set, we tested various combinations of 1, 2, or 3 sampling time to estimate CL(iohexol). According to the different combinations tested, a maximum a posteriori Bayesian estimation of CL(iohexol) was obtained from population parameters. Individual estimates of GFR were compared with individual reference values through analysis of bias and precision. A capability analysis allowed us to determine the best sampling strategy for Bayesian estimation. RESULTS: A 1-compartment model best described our data. Covariate analysis showed that uremia, serum creatinine, and age were significantly associated with k(e), and weight with V. The strategy, including samples drawn at 120 and 270 minutes, allowed accurate prediction of GFR (mean bias: -3.71%, mean imprecision: 7.77%). With this strategy, about 20% of individual predictions were outside the bounds of acceptance set at ± 10%, and about 6% if the bounds of acceptance were set at ± 15%. CONCLUSIONS: This Bayesian approach can help to reduce the number of samples required to calculate GFR using Bröchner-Mortensen formula with good accuracy.
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