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.

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. MethodsWe 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

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 ( CD 133). 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 ( R 2 = 0.29 vs. R 2 = 0.14). Although the number of Ki67+ cells correlated to the extent of acute damage, neither CD 133 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 CD 133 or proliferation marker Ki67 and renal recovery after DGF.

Monitoring renal function is a vital part of kidney research involving rats. The laborious measurement of 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 fall within 30% of the true value (p30)=76%) which had high accuracy in the validation cohort (n=116 rats, R2=0.935, p30=79%). The equation was less accurate in rats with non-steady-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.

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.