Discrepancies between transcutaneous and estimated glomerular filtration rates in rats with chronic kidney disease.

Accurate assessment of the glomerular filtration rate (GFR) is crucial for researching kidney disease in rats. Although validation of methods that assess GFR is crucial, large-scale comparisons between different methods are lacking. Both transcutaneous GFR (tGFR) and a newly developed estimated GFR (eGFR) equation by our group provide a low-invasive approach enabling repeated measurements. The tGFR is a single bolus method using FITC-labeled sinistrin to measure GFR based on half-life of the transcutaneous signal, whilst the eGFR is based on urinary sinistrin clearance. Here, we retrospectively compared tGFR, using both 1- and 3- compartment models (tGFR_1c and tGFR_3c, respectively) to the eGFR in a historic cohort of 43 healthy male rats and 84 male rats with various models of chronic kidney disease. The eGFR was on average considerably lower than tGFR-1c and tGFR-3c (mean differences 855 and 216 µL/min, respectively) and only 20 and 47% of measurements were within 30% of each other, respectively. The relative difference between eGFR and tGFR was highest in rats with the lowest GFR. Possible explanations for the divergence are problems inherent to tGFR, such as technical issues with signal measurement, description of the signal kinetics, and translation of half-life to tGFR, which depends on distribution volume. The unknown impact of isoflurane anesthesia used in determining mGFR remains a limiting factor. Thus, our study shows that there is a severe disagreement between GFR measured by tGFR and eGFR, stressing the need for more rigorous validation of the tGFR and possible adjustments to the underlying technique.

Optimizing carboplatin dosing by an improved prediction of carboplatin clearance using a CT-enhanced estimate of renal function.

AIMS: Carboplatin is generally dosed based on a modified Calvert formula, in which the Cockcroft-Gault-based creatinine clearance (CRCL) is used as proxy for the glomerular filtration rate (GFR). The Cockcroft-Gault formula (CG) overpredicts CRCL in patients with an aberrant body composition. The CT-enhanced estimate of RenAl FuncTion (CRAFT) was developed to compensate for this overprediction. We aimed to evaluate whether carboplatin clearance is better predicted by CRCL based on the CRAFT compared to the CG. METHODS: Data of four previously conducted trials was used. The CRAFT was divided by serum creatinine to derive CRCL. The difference between CRAFT- and CG-based CRCL was assessed by population pharmacokinetic modelling. Furthermore, the difference in calculated carboplatin dose was assessed in a heterogeneous dataset. RESULTS: In total, 108 patients were included in the analysis. Addition of the CRAFT- and CG-based CRCL as covariate on carboplatin clearance led, respectively, to an improved model fit with a 26-point drop in objective function value and a worsened model fit with an increase of 8 points. In 19 subjects with serum creatinine <50 µmol/L, the calculated carboplatin dose was 233 mg higher using the CG. CONCLUSIONS: Carboplatin clearance is better predicted by CRAFT vs. CG-based CRCL. In subjects with low serum creatinine, the calculated carboplatin dose using CG exceeds the dose using CRAFT, which might explain the need for dose capping when using the CG. Therefore, the CRAFT might be an alternative for dose capping while still dosing accurately.

Deep learning body-composition analysis of clinically acquired CT-scans estimates creatinine excretion with high accuracy in patients and healthy individuals.

Assessment of daily creatinine production and excretion plays a crucial role in the estimation of renal function. Creatinine excretion is estimated by creatinine excretion equations and implicitly in eGFR equations like MDRD and CKD-EPI. These equations are however unreliable in patients with aberrant body composition. In this study we developed and validated equations estimating creatinine production using deep learning body-composition analysis of clinically acquired CT-scans. We retrospectively included patients in our center that received any CT-scan including the abdomen and had a 24-h urine collection within 2 weeks of the scan (n = 636). To validate the equations in healthy individuals, we included a kidney donor dataset (n = 287). We used a deep learning algorithm to segment muscle and fat at the 3rd lumbar vertebra, calculate surface areas and extract radiomics parameters. Two equations for CT-based estimate of RenAl FuncTion (CRAFT 1 including CT parameters, age, weight, and stature and CRAFT 2 excluding weight and stature) were developed and compared to the Cockcroft-Gault and the Ix equations. CRAFT1 and CRAFT 2 were both unbiased (MPE = 0.18 and 0.16 mmol/day, respectively) and accurate (RMSE = 2.68 and 2.78 mmol/day, respectively) in the patient dataset and were more accurate than the Ix (RMSE = 3.46 mmol/day) and Cockcroft-Gault equation (RMSE = 3.52 mmol/day). In healthy kidney donors, CRAFT 1 and CRAFT 2 remained unbiased (MPE = - 0.71 and - 0.73 mmol/day respectively) and accurate (RMSE = 1.86 and 1.97 mmol/day, respectively). Deep learning-based extraction of body-composition parameters from abdominal CT-scans can be used to reliably estimate creatinine production in both patients as well as healthy individuals. The presented algorithm can improve the estimation of renal function in patients who have recently had a CT scan. The proposed methods provide an improved estimation of renal function that is fully automatic and can be readily implemented in routine clinical practice.

Deep learning-based classification of kidney transplant pathology: a retrospective, multicentre, proof-of-concept study.

BACKGROUND: Histopathological assessment of transplant biopsies is currently the standard method to diagnose allograft rejection and can help guide patient management, but it is one of the most challenging areas of pathology, requiring considerable expertise, time, and effort. We aimed to analyse the utility of deep learning to preclassify histology of kidney allograft biopsies into three main broad categories (ie, normal, rejection, and other diseases) as a potential biopsy triage system focusing on transplant rejection. METHODS: We performed a retrospective, multicentre, proof-of-concept study using 5844 digital whole slide images of kidney allograft biopsies from 1948 patients. Kidney allograft biopsy samples were identified by a database search in the Departments of Pathology of the Amsterdam UMC, Amsterdam, Netherlands (1130 patients) and the University Medical Center Utrecht, Utrecht, Netherlands (717 patients). 101 consecutive kidney transplant biopsies were identified in the archive of the Institute of Pathology, RWTH Aachen University Hospital, Aachen, Germany. Convolutional neural networks (CNNs) were trained to classify allograft biopsies as normal, rejection, or other diseases. Three times cross-validation (1847 patients) and deployment on an external real-world cohort (101 patients) were used for validation. Area under the receiver operating characteristic curve (AUROC) was used as the main performance metric (the primary endpoint to assess CNN performance). FINDINGS: Serial CNNs, first classifying kidney allograft biopsies as normal (AUROC 0·87 [ten times bootstrapped CI 0·85-0·88]) and disease (0·87 [0·86-0·88]), followed by a second CNN classifying biopsies classified as disease into rejection (0·75 [0·73-0·76]) and other diseases (0·75 [0·72-0·77]), showed similar AUROC in cross-validation and deployment on independent real-world data (first CNN normal AUROC 0·83 [0·80-0·85], disease 0·83 [0·73-0·91]; second CNN rejection 0·61 [0·51-0·70], other diseases 0·61 [0·50-0·74]). A single CNN classifying biopsies as normal, rejection, or other diseases showed similar performance in cross-validation (normal AUROC 0·80 [0·73-0·84], rejection 0·76 [0·66-0·80], other diseases 0·50 [0·36-0·57]) and generalised well for normal and rejection classes in the real-world data. Visualisation techniques highlighted rejection-relevant areas of biopsies in the tubulointerstitium. INTERPRETATION: This study showed that deep learning-based classification of transplant biopsies could support pathological diagnostics of kidney allograft rejection. FUNDING: European Research Council; German Research Foundation; German Federal Ministries of Education and Research, Health, and Economic Affairs and Energy; Dutch Kidney Foundation; Human(e) AI Research Priority Area of the University of Amsterdam; and Max-Eder Programme of German Cancer Aid.

A systematic review and meta-analysis of COVID-19 in kidney transplant recipients: Lessons to be learned.

Kidney transplant recipients (KTR) may be at increased risk of adverse COVID-19 outcomes, due to prevalent comorbidities and immunosuppressed status. Given the global differences in COVID-19 policies and treatments, a robust assessment of all evidence is necessary to evaluate the clinical course of COVID-19 in KTR. Studies on mortality and acute kidney injury (AKI) in KTR in the World Health Organization COVID-19 database were systematically reviewed. We selected studies published between March 2020 and January 18th 2021, including at least five KTR with COVID-19. Random-effects meta-analyses were performed to calculate overall proportions, including 95% confidence intervals (95% CI). Subgroup analyses were performed on time of submission, geographical region, sex, age, time after transplantation, comorbidities, and treatments. We included 74 studies with 5559 KTR with COVID-19 (64.0% males, mean age 58.2 years, mean 73 months after transplantation) in total. The risk of mortality, 23% (95% CI: 21%-27%), and AKI, 50% (95% CI: 44%-56%), is high among KTR with COVID-19, regardless of sex, age and comorbidities, underlining the call to accelerate vaccination programs for KTR. Given the suboptimal reporting across the identified studies, we urge researchers to consistently report anthropometrics, kidney function at baseline and discharge, (changes in) immunosuppressive therapy, AKI, and renal outcome among KTR.

Simplified Iohexol-Based Method for Measurement of Glomerular Filtration Rate in Goats and Pigs.

The preclinical evaluation of novel therapies for chronic kidney disease requires a simple method for the assessment of kidney function in a uremic large animal model. An intravenous bolus of iohexol was administered to goats (13 measurements in n = 3 goats) and pigs (23 measurements in n = 5 pigs) before and after induction of kidney failure, followed by frequent blood sampling up to 1440 min. Plasma clearance (CL) was estimated by a nonlinear mixed-effects model (CLNLME) and by a one-compartmental pharmacokinetic disposition model using iohexol plasma concentrations during the terminal elimination phase (CL1CMT). A simple method (CLSM) for the calculation of plasma clearance was developed based on the most appropriate relationship between CLNLME and CL1CMT. CLSM and CLNLME showed good agreement (CLNLME/CLSM ratio: 1.00 ± 0.07; bias: 0.03 ± 1.64 mL/min; precision CLSM and CLNLME: 80.9% and 80.7%, respectively; the percentage of CLSM estimates falling within ±30% (P30) or ±10% (P10) of CLNLME: 53% and 12%, respectively). For mGFRNLME vs. mGFRSM, bias was -0.25 ± 2.24 and precision was 49.2% and 53.6%, respectively, P30 and P10 for mGFR based on CLSM were 71% and 24%, respectively. A simple method for measurement of GFR in healthy and uremic goats and pigs was successfully developed, which eliminates the need for continuous infusion of an exogenous marker, urine collection and frequent blood sampling.

A plasma creatinine- and urea-based equation to estimate glomerular filtration rate in rats.

Monitoring renal function is a vital part of kidney research involving rats. The laborious measurement of glomerular filtration rate (GFR) with administration of exogenous filtration markers does not easily allow serial measurements. Using an in-house database of inulin clearances, we developed and validated a plasma creatinine- and plasma urea-based equation to estimate GFR in a large cohort of male rats [development cohort n = 325, R2 = 0.816, percentage of predictions that fell within 30% of the true value (P30) = 76%] that had high accuracy in the validation cohort (n = 116 rats, R2 = 0.935, P30 = 79%). The equation was less accurate in rats with nonsteady-state creatinine, in which the equation should therefore not be used. In conclusion, applying this equation facilitates easy and repeatable estimates of GFR in rats.NEW & NOTEWORTHY This is the first equation, that we know of, which estimates glomerular filtration rate in rats based on a single measurement of body weight, plasma creatinine, and plasma urea.

Validation of multiparametric MRI by histopathology after nephrectomy: a case study.

OBJECTIVES: Renal multiparametric MRI (mpMRI) is a promising tool to monitor renal allograft health to enable timely treatment of chronic allograft nephropathy. This study aims to validate mpMRI by whole-kidney histology following transplantectomy. MATERIALS AND METHODS: A patient with kidney transplant failure underwent mpMRI prior to transplantectomy. The mpMRI included blood oxygenation level-dependent (BOLD) MRI, T1 and T2 mapping, diffusion-weighted imaging (DWI), 2D phase contrast (2DPC) and arterial spin labeling (ASL). Parenchymal mpMRI measures were compared to normative values obtained in 19 healthy controls. Differences were expressed in standard deviations (SD) of normative values. The mpMRI measures were compared qualitatively to histology. RESULTS: The mpMRI showed a heterogeneous parenchyma consistent with extensive interstitial hemorrhage on histology. A global increase in T1 (+ 3.0 SD) and restricted diffusivity (- 3.6 SD) were consistent with inflammation and fibrosis. Decreased T2 (- 1.8 SD) indicated fibrosis or hemorrhage. ASL showed diminished cortical perfusion (- 2.9 SD) with patent proximal arteries. 2DPC revealed a 69% decrease in renal perfusion. Histological evaluation showed a dense inflammatory infiltrate and fibrotic changes, consistent with mpMRI results. Most interlobular arteries were obliterated while proximal arteries were patent, consistent with ASL findings. DISCUSSION: mpMRI findings correlated well with histology both globally as well as locally.

Early Estimation of Renal Function After Transplantation to Enable Appropriate Dosing of Critical Drugs: Retrospective Analysis of 103 Patients in a Single Center.

BACKGROUND: Immediately after renal transplantation (RTX), estimation of renal function (eGFR) is important for drug dosing and the detection of potential complications. Conventional formulas cannot be used since the serum creatinine concentration is not at steady-state. In this study, we evaluated different dynamic renal function formulas (DRFFs) to estimate eGFR immediately after RTX. METHODS: We retrospectively included 154 RTX patients, of whom 45 had delayed graft function (DGF) and required dialysis, and 6 had unstable graft function without the need for dialysis; 103 patients had early, and thereafter stable, graft function (EGF). DRFFs were evaluated to calculate eGFR 1 day after transplantation (T1) using a new dynamic creatinine clearance calculation (D3C), two previously published formulas (Jelliffe, and the kinetic eGFR [KeGFR]), and a naive predictor (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] at T1). The estimated DRFF-based renal functions at T1 were compared with the CKD-EPI after stabilization of renal function 3 days after transplantation (eGFR-T3), which was considered the underlying renal function immediately after RTX. RESULTS: The D3C showed low bias (mean prediction error [MPE] - 4.5 ml/min/1.73 m2) and performed well on other outcome measures (R2 = 0.82, root mean squared error [RMSE] = 11.8 ml/min/1.73 m2, percentage of predictions within 30% of the reference value [p30%] = 76%). In addition, the D3C outperformed the KeGFR (MPE 20.5 ml/min/1.73 m2, R2 = 0.79, RMSE = 26.9 ml/min/1.73 m2, p30% = 29%), Jelliffe (MPE - 13.3 ml/min/1.73 m2, R2 = 0.76, RMSE = 19.1 ml/min/1.73 m2, p30% = 53%), and the naive predictor (bias - 24.8 ml/min/1.73 m2, R2 = 0.60, RMSE = 30.2 ml/min/1.73 m2, p30% = 21%). CONCLUSIONS: The newly developed D3C enables reliable assessment of renal function immediately after RTX, provides crucial information for drug dosing, and might also advance the detection of functional decline, potentially improving treatment and renal outcome.

Decreased native renal T1 up to one week after gadobutrol administration in healthy volunteers.

BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used in MRI, despite safety concerns regarding deposition in brain and other organs. In animal studies gadolinium was detected for weeks after administration in the kidneys, but this has not yet been demonstrated in humans. PURPOSE: To find evidence for the prolonged presence of gadobutrol in the kidneys in healthy volunteers. STUDY TYPE: Combined retrospective and prospective analysis of a repeatability study. POPULATION: Twenty-three healthy volunteers with normal renal function (12 women, age range 40-76 years), of whom 21 were used for analysis. FIELD STRENGTH/SEQUENCE: Inversion recovery-based T1 map at 3T. ASSESSMENT: T1 maps were obtained twice with a median interval of 7 (range: 4-16) days. The T1 difference (ΔT1 ) between both scans was compared between the gadolinium group (n = 16, 0.05 mmol/kg gadobutrol administered after T1 mapping during both scan sessions) and the control group (n = 5, no gadobutrol). T1 maps were analyzed separately for cortex and medulla. STATISTICAL TESTS: Mann-Whitney U-tests to detect differences in ΔT1 between groups and linear regression to relate time between scans and estimated glomerular filtration rate (eGFR) to ΔT1 . RESULTS: ΔT1 differed significantly between the gadolinium and control group: median ΔT1 cortex -98 vs. 7 msec (P < 0.001) and medulla -68 msec vs. 19 msec (P = 0.001), respectively. The bias corresponds to renal gadobutrol concentrations of 8 nmol/g tissue (cortex) and 4 nmol/g tissue (medulla), ie, ~2.4 µmol for both kidneys (0.05% of original dose). ΔT1 correlated in the gadolinium group with duration between acquisitions for both cortex (regression coefficient (ß) 16.5 msec/day, R2 0.50, P < 0.001) and medulla (ß 11.5 msec/day, R2 0.32, P < 0.001). Medullary ΔT1 correlated with eGFR (ß 1.13 msec/(ml/min) R2 0.25, P = 0.008). DATA CONCLUSION: We found evidence of delayed renal gadobutrol excretion after a single contrast agent administration in subjects with normal renal function. Even within this healthy population, elimination delay increased with decreasing kidney function. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;52:622-631.

Histological characteristics of Acute Tubular Injury during Delayed Graft Function predict renal function after renal transplantation.

Acute Tubular Injury (ATI) is the leading cause of Delayed Graft Function (DGF) after renal transplantation (RTX). Biopsies taken 1 week after RTX often show extensive tubular damage, which in most cases resolves due to the high regenerative capacity of the kidney. Not much is known about the relation between histological parameters of renal damage and regeneration immediately after RTX and renal outcome in patients with DGF. We retrospectively evaluated 94 patients with DGF due to ATI only. Biopsies were scored for morphological characteristics of renal damage (edema, casts, vacuolization, and dilatation) by three independent blinded observers. The regenerative potential was quantified by tubular cells expressing markers of proliferation (Ki67) and dedifferentiation (CD133). Parameters were related to renal function after recovery (CKD-EPI 3, 6, and 12 months posttransplantation). Quantification of morphological characteristics was reproducible among observers (Kendall's W ≥ 0.56). In a linear mixed model, edema and casts significantly associated with eGFR within the first year independently of clinical characteristics. Combined with donor age, edema and casts outperformed the Nyberg score, a well-validated clinical score to predict eGFR within the first year after transplantation (R2  = 0.29 vs. R2  = 0.14). Although the number of Ki67+ cells correlated to the extent of acute damage, neither CD133 nor Ki67 correlated with renal functional recovery. In conclusion, the morphological characteristics of ATI immediately after RTX correlate with graft function after DGF. Despite the crucial role of regeneration in recovery after ATI, we did not find a correlation between dedifferentiation marker CD133 or proliferation marker Ki67 and renal recovery after DGF.