Assessment of the glomerular filtration rate (GFR) in kidney transplant recipients using Bayesian estimation of the iohexol clearance
| Autor: | Christelle Barbet, Matthias Büchler, Chantal Barin-Le Guellec, Isabelle Benz-de Bretagne, Joevin Besombes, Philippe Gatault, Hélène Blasco, Jean-Michel Halimi, Camille Riff |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male Mean squared error Iohexol Clinical Biochemistry Population Bayesian probability 030232 urology & nephrology Renal function 030204 cardiovascular system & hematology 03 medical and health sciences 0302 clinical medicine Pharmacokinetics Statistics medicine Humans Renal Insufficiency Chronic education Aged Mathematics education.field_of_study Bayes estimator Biochemistry (medical) Bayes Theorem General Medicine Middle Aged Kidney Transplantation Iohexol Clearance Female Algorithms Glomerular Filtration Rate medicine.drug |
| Zdroj: | Clinical Chemistry and Laboratory Medicine (CCLM). 58:577-587 |
| ISSN: | 1437-4331 1434-6621 |
| DOI: | 10.1515/cclm-2019-0904 |
| Popis: | Background Plasma iohexol clearance (CLiohexol) is a reference technique for glomerular filtration rate (GFR) determination. In routine practice, CLiohexol is calculated using one of several formulas, which have never been evaluated in kidney transplant recipients. We aimed to model iohexol pharmacokinetics in this population, evaluate the predictive performance of three simplified formulas and evaluate whether a Bayesian algorithm improves CLiohexol estimation. Methods After administration of iohexol, six blood samples were drawn from 151 patients at various time points. The dataset was split into two groups, one to develop the population pharmacokinetic (POPPK) model (n = 103) and the other (n = 48) to estimate the predictive performances of the various GFR estimation methods. GFR reference values (GFRref) in the validation dataset were obtained by non-compartmental pharmacokinetic (PK) analysis. Predictive performances of each method were evaluated in terms of bias (ME), imprecision (root mean square error [RMSE]) and number of predictions out of the ±10% or 15% error interval around the GFRref. Results A two-compartment model best fitted the data. The Bayesian estimator with samples drawn at 30, 120 and 270 min allowed accurate prediction of GFRref (ME = 0.47%, RMSE = 3.42%), as did the Brøchner-Mortensen (BM) formula (ME = − 0.0425%, RMSE = 3.40%). With both methods, none of the CL estimates were outside the ±15% interval and only 2.4% were outside the ±10% for the BM formula (and none for the Bayesian estimator). In patients with GFR ≤30 mL/min/1.73 m2, the BM formula performed very well, while the Bayesian method could not be evaluated in depth due to too small a number of patients with adequate sampling times. Conclusions GFR can be estimated with acceptable accuracy in kidney transplant patients using the BM formula, but also using a Bayesian algorithm. |
| Databáze: | OpenAIRE |
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