Assessment of the glomerular filtration rate (GFR) in kidney transplant recipients using Bayesian estimation of the iohexol clearance

The EKFC equation outperforms the CKD-EPI and CKiD equations for GFR estimation in adolescent and young adult kidney transplant patients

AIM

This study evaluated the bias and accuracy of the CKD-EPI/CKiD and EKFC equations compared with the reference exogenous tracer-based assessment of glomerular filtration rate (GFR) in adult and pediatric patients according to their renal transplant status.

METHODS

We assessed the bias and P30 accuracy of the CKD-EPI/CKiD and EKFC equations compared with iohexol-based GFR measurement.

RESULTS

In the overall population (n = 59), the median age was 29 years (IQR, 16.0-46.0) and the median measured GFR was 73.9 mL/min/1.73m2 (IQR, 57.3-84.6). Among non-kidney transplant patients, the median was 77.7 mL/min/1.73m2 (IQR, 59.3-86.5), while among kidney transplant patients, it was 60.5 mL/min/1.73m2 (IQR, 54.2-66.8). The bias associated with the EKFC and CKD-EPI/CKiD equations was significantly higher among kidney transplant patients than among non-kidney transplant patients, with a difference between medians (Hodges-Lehmann) of +10.4 mL/min/1.73m2 (95% CI, 2.2-18.9; p = .02) for the EKFC and +12.1 mL/min/1.73m2 (95% CI, 4.2-21.4; p = .006) for the CKD-EPI/CKiD equations. In multivariable analysis, kidney transplant status emerged as an independent factor associated with a bias of >3.4 mL/min/1.73m2 (odds ratio, 7.7; 95% CI, 1.4-43.3; p = .02) for the EKFC equation and a bias of >13.4 mL/min/1.73m2 (odds ratio, 15.0; 95% CI, 2.6-85.7; p = .002) for the CKD-EPI/CKiD equations.

CONCLUSION

In our study, which included adolescent and young adult kidney transplant patients, both the CKD-EPI/CKiD and EKFC equations tended to overestimate the measured glomerular filtration rate, with the EKFC equation exhibiting less bias. Renal transplant status significantly influenced the degree of estimation bias.

Glomerular filtration rate in children and young adults with haemato-oncological disease and infection is best described by three-compartment iohexol model

BACKGROUND

Children with cancer and infection may develop glomerular hyperfiltration. With the aim to determine the prevalence of glomerular hyperfiltration in children and young adults with haemato-oncological disease and infection, we developed population pharmacokinetic model of iohexol. We further aimed to assess the accuracy of estimated glomerular filtration rate (eGFR) equations and single- or two-point measured GFR (mGFR) formulas compared with GFR based on iohexol clearance from our population pharmacokinetic model (iGFR).

PROCEDURE

Hospitalised patients (0.5-25 years) with haemato-oncological disease and infection were included if their eGFR was ≥80 ml/min/1.73 m² at the screening visit. Iohexol plasma concentrations were described by population pharmacokinetic model. Bias, precision and accuracy of 23 eGFR equations and 18 mGFR formulas were calculated.

RESULTS

Total of 32 iohexol administrations were performed in 28 patients. Median (range) eGFR was 136 ml/min/1.73 m2 (74-234) and age 15.1 years (0.8-26.0). Three-compartment model with allometric scaling of central, one peripheral compartment and clearance (with power 0.75) to weight fitted the best. Median (range) iGFR was 103 ml/min/1.73 m² (68-140). All except one eGFR equation overestimated GFR. Lund-Malmö revised eGFR equation performed the best, followed by Gao equation. Of single- or two-point mGFR formulas, 15 overestimated iGFR. Modified Jacobsson formula at 5.5 hours performed the best, followed by Fleming formula at 3 hours.

CONCLUSIONS

In children and young adults with haemato-oncological disease and infection, renal function is best described by iohexol clearance from three-compartment pharmacokinetic model, while eGFR equations and single- and two-point mGFR formulas overestimate iGFR.

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

AIM

Iohexol clearance has been proposed to estimate the glomerular filtration rate (GFR). A population pharmacokinetics (popPK) model was developed from heterogenous 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 a 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 performances for GFR estimation were 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 0.1-1-9h exhibiting a relative MPE(RMSE) = -3.7%(14.3%) and better performances 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 9h for the third sample were associated with an increased imprecision.

CONCLUSION

A single MAP-BE of iohexol based on a 3-sample-LSS for 4 heterogeneous population 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.

Advancement of pharmacokinetic models of iohexol in patients aged 70 years or older with impaired kidney function

Plasma clearance of iohexol is a pivotal metric to quantify glomerular filtration rate (GFR), but the optimal timing and frequency of plasma sampling remain to be assessed. In this study, we evaluated the impact of a Bayesian estimation procedure on iohexol clearance estimates, and we identified an optimal sampling strategy based on data in individuals aged 70+. Assuming a varying number of random effects, we re-estimated previously developed population pharmacokinetic two- and three-compartment models in a model development group comprising 546 patients with iohexol concentration data up to 300 min post injection. Model performance and optimal sampling times were assessed in an evaluation group comprising 104 patients with reduced GFR and concentration data up to 1440 min post injection. Two- and three-compartment models with random effects for all parameters overestimated clearance values (bias 5.07 and 4.40 mL/min, respectively) and underpredicted 24-h concentrations (bias − 14.5 and − 12.0 µg/ml, respectively). Clearance estimates improved distinctly when limiting random effects of the three-compartment model to clearance and central volume of distribution. Two blood samples, one early and one 300 min post injection, were sufficient to estimate iohexol clearance. A simplified three-compartment model is optimal to estimate iohexol clearance in elderly patients with reduced GFR.

Model-Based Estimation of Iohexol Plasma Clearance for Pragmatic Renal Function Determination in the Renal Transplantation Setting

BACKGROUND

Iohexol plasma clearance-based glomerular filtration rate (GFR) determination provides an accurate method for renal function evaluation. This technique is increasingly advocated for clinical situations that dictate highly accurate renal function assessment, as an alternative to conventional serum creatinine-based methods with limited accuracy or poor feasibility. In the renal transplantation setting, this particularly applies to living renal transplant donor eligibility screening, renal transplant function monitoring and research purposes. The dependency of current iohexol GFR estimation techniques on extensive sampling, however, has limited its clinical application. We developed a population pharmacokinetic model and limited sampling schedules, implemented in the online InsightRX precision dosing platform, to facilitate pragmatic iohexol GFR assessment.

METHODS

Iohexol concentrations (n = 587) drawn 5 min to 4 h after administration were available from 67 renal transplant recipients and 41 living renal transplant donor candidates with measured iohexol GFRs of 27-117 mL/min/1.73 m². These were split into a model development (n = 72) cohort and an internal validation (n = 36) cohort. External validation was performed with 1040 iohexol concentrations from 268 renal transplant recipients drawn between 5 min and 4 h after administration, and extended iohexol curves up to 24 h from 11 random patients with impaired renal function. Limited sampling schedules based on one to four blood draws within 4 h after iohexol administration were evaluated in terms of bias and imprecision, using the mean relative prediction error and mean absolute relative prediction error. The total deviation index and percentage of limited sampling schedule-based GFR predictions within ± 10% of those of the full model (P₁₀) were assessed to aid interpretation.

RESULTS

Iohexol pharmacokinetics was best described with a two-compartmental first-order elimination model, allometrically scaled to fat-free mass, with patient type as a covariate on clearance and the central distribution volume. Model validity was confirmed during the internal and external validation. Various limited sampling schedules based on three to four blood draws within 4 h showed excellent predictive performance (mean relative prediction error < ± 0.5%, mean absolute relative prediction error < 3.5%, total deviation index < 5.5%, P₁₀ > 97%). The best limited sampling schedules based on three to four blood draws within 3 h showed reduced predictive performance (mean relative prediction error < ± 0.75%, mean absolute relative prediction error < 5.5%, total deviation index < 9.5%, P₁₀ ≥ 85%), but may be considered for their enhanced clinical feasibility when deemed justified.

CONCLUSIONS

Our online pharmacometric tool provides an accurate, pragmatic, and ready-to-use technique for measured GFR-based renal function evaluation for clinical situations where conventional methods lack accuracy or show limited feasibility. Additional adaptation and validation of our model and limited sampling schedules for renal transplant recipients with GFRs below 30 mL/min is warranted before considering this technique in these patients.

A Machine Learning Approach to Estimate the Glomerular Filtration Rate in Intensive Care Unit Patients Based on Plasma Iohexol Concentrations and Covariates

OBJECTIVE

This work aims to evaluate whether a machine learning approach is appropriate to estimate the glomerular filtration rate in intensive care unit patients based on sparse iohexol pharmacokinetic data and a limited number of predictors.

METHODS

Eighty-six unstable patients received 3250 mg of iohexol intravenously and had nine blood samples collected 5, 30, 60, 180, 360, 540, 720, 1080, and 1440 min thereafter. Data splitting was performed to obtain a training (75%) and a test set (25%). To estimate the glomerular filtration rate, 37 candidate potential predictors were considered and the best machine learning approach among multivariate-adaptive regression spline and extreme gradient boosting (Xgboost) was selected based on the root-mean-square error. The approach associated with the best results in a ten-fold cross-validation experiment was then used to select the best limited combination of predictors in the training set, which was finally evaluated in the test set.

RESULTS

The Xgboost approach yielded the best performance in the training set. The best combination of covariates comprised iohexol concentrations at times 180 and 720 min; the relative deviation from these theoretical times; the difference between these two concentrations; the Simplified Acute Physiology Score II; serum creatinine; and the fluid balance. It resulted in a root-mean-square error of 6.2 mL/min and an r² of 0.866 in the test set. Interestingly, the eight patients in the test set with a glomerular filtration rate < 30 mL/min were all predicted accordingly.

CONCLUSIONS

Xgboost provided accurate glomerular filtration rate estimation in intensive care unit patients based on two timed blood concentrations after iohexol intravenous administration and three additional predictors.