A single Bayesian estimator for iohexol clearance estimation in ICU, liver failure and renal transplant patients

Aims: Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogeneous patients. A Bayesian estimator (MAP-BE) based on a limited sampling strategy (LSS) was derived and evaluated in external patients.

Methods: Full pharmacokinetic data (7-12 samples) from 172 patients receiving iohexol for measurement of their GFR (unstable and stable ICU patients, liver failure patients and kidney transplant patients) were split into development (n = 136) and validation (n = 36) datasets. A PopPK model was developed in Monolix and was used to develop MAP-BE based on LSS. Its performance for GFR estimation was evaluated in the validation set.

Results: A two-compartment model with first-order elimination best described the data. The final model included the type of patients on volume of distribution (Vd), clearance and intercompartmental constants, serum creatinine on clearance and body weight on Vd. The best LSS included samples at 0.1-1-9 h exhibiting a relative mean prediction error (MPE) (RMSE) = -3.7% (14.3%) and better performance than the Bröchner-Mortensen formula (-3.0%/17%). Split by type of patients, the highest interindividual variability and imprecision was observed in unstable ICU patients (MPE (RMSE) = 3.7% (18.8%)) while the best performances were obtained for renal transplant patients (MPE (RMSE) = 1.0% (5.8%)). All LSS that included samples before 9 hours for the third sample were associated with an increased imprecision.

Conclusion: A single MAP-BE of iohexol based on a three-sample LSS for four heterogeneous populations was developed and allowed accurate estimation of GFR in kidney transplant patients, slightly biased in stable ICU patients and slightly imprecise in unstable ICU patients.