Rule, Andrew D.

ABSTRACT Background One of the challenges in living kidney donor screening is to estimate remaining kidney function after donation. Here we developed a new model to predict post-donation measured glomerular filtration rate (mGFR) from pre-donation serum creatinine, age and sex. Methods In the prospective development cohort (TransplantLines, n = 511), several prediction models were constructed and tested for accuracy, precision and predictive capacity for short- and long-term post-donation 125I-iothalamate mGFR. The model with optimal performance was further tested in specific high-risk subgroups (pre-donation eGFR <90 mL/min/1.73 m2, a declining 5-year post-donation mGFR slope or age >65 years) and validated in internal (n = 509) and external (Mayo Clinic, n = 1087) cohorts. Results In the development cohort, pre-donation estimated GFR (eGFR) was 86 ± 14 mL/min/1.73 m2 and post-donation mGFR was 64 ± 11 mL/min/1.73 m2. Donors with a pre-donation eGFR ≥90 mL/min/1.73 m2 (present in 43%) had a mean post-donation mGFR of 69 ± 10 mL/min/1.73 m2 and 5% of these donors reached an mGFR <55 mL/min/1.73 m2. A model using pre-donation serum creatinine, age and sex performed optimally, predicting mGFR with good accuracy (mean bias 2.56 mL/min/1.73 m2, R2 = 0.29, root mean square error = 11.61) and precision [bias interquartile range (IQR) 14 mL/min/1.73 m2] in the external validation cohort. This model also performed well in donors with pre-donation eGFR <90 mL/min/1.73 m2 [bias 0.35 mL/min/1.73 m2 (IQR 10)], in donors with a negative post-donation mGFR slope [bias 4.75 mL/min/1.73 m2 (IQR 13)] and in donors >65 years of age [bias 0.003 mL/min/1.73 m2 (IQR 9)]. Conclusions We developed a novel post-donation mGFR prediction model based on pre-donation serum creatinine, age and sex.

Background: Acute kidney injury (AKI) survivors are at heightened risk for poor short- and long-term health outcomes. Even among those who recover after an AKI episode, the risk for chronic kidney disease is 4- to 6-fold higher than in patients without AKI, underscoring the importance of identifying methods to improve AKI survivorship. Objective: The purpose of this report was to describe the development and feasibility of a novel multidisciplinary approach to caring for AKI survivors at care transitions (ACT). Design: Observational process improvement initiative. Setting: Single academic medical center in the United States. Patients: The studied population was adults with stage 3 AKI not discharging on dialysis who were established with a primary care provider (PCP) at our institution. Methods: An electronic health record tool was developed prior to implementation to identify AKI survivors. The ACT program encompassed engaging patients in the hospital, delivering education by nephrology-trained nurses before discharge, completing recommended laboratory testing after discharge, and conducting structured kidney-focused follow-up with a pharmacist and a PCP within 7 to 14 days after discharge. Patients could be referred for nephrology evaluation at the discretion of the PCP. Results: Preliminary data demonstrated that most AKI survivors of interest could be identified, educated, and followed up with this model. This strategy appeared feasible, scalable, and maximized the unique expertise of each member of the multidisciplinary team. Limitations: Small sample size, future assessment of process, clinical, and patient-reported outcomes needed. Conclusions: The multidisciplinary ACT workflow supported by clinical decision support was feasible and addressed gaps in existing care transition models. Team-based care delivery in primary care appears to be a mechanism to extend the capacity for kidney health monitoring for AKI survivors.

Background and objectives Urinary stone disease has been associated with inflammation, but the specific cell interactions that mediate events remain poorly defined. This study compared calcification and inflammatory cell patterns in kidney tissue from radical nephrectomy specimens of patients without and with a history of urinary stone disease. Design, setting, participants, & measurements Nontumor parenchyma of biobanked radical nephrectomy specimens from age- and sex-matched stone formers ( n =44) and nonstone formers ( n =82) were compared. Calcification was detected by Yasue staining and inflammatory cell populations by immunohistochemistry for CD68 (proinflammatory M1 macrophages), CD163 and CD206 (anti-inflammatory M2 macrophages), CD3 (T lymphocytes), and tryptase (mast cells). Calcifications and inflammatory cells were quantified in cortex and medulla using Image-Pro analysis software. Results Calcification in the medulla of stone formers was higher than in nonstone formers ( P <0.001). M1 macrophages in the cortex and medulla of stone formers were greater than in nonstone formers ( P <0.001), and greater in stone former medulla than stone former cortex ( P =0.02). There were no differences in age, sex, body mass index, tumor characteristics (size, stage, or thrombus), vascular disease status, or eGFR between the groups. M2 macrophages, T lymphocytes, and mast cells did not differ by stone former status. There was a correlation between M1 macrophages and calcification in the medulla of stone formers (rho=0.48; P =0.001) and between M2 macrophages and calcification in the medulla of nonstone formers (rho=0.35; P =0.001). T lymphocytes were correlated with calcification in the cortex of both nonstone formers (rho=0.27; P =0.01) and stone formers (rho=0.42; P =0.004), whereas mast cells and calcification were correlated only in the cortex of stone formers (rho=0.35; P =0.02). Conclusions Higher medullary calcification stimulated accumulation of proinflammatory rather than anti-inflammatory macrophages in stone formers.

ABSTRACTSince the first living donor kidney transplantation about six decades ago, significant progress has been made in terms of extending allograft survival. However, to date, only a small number of studies have compared the functional changes of the donated kidney to that of the remaining kidney. Although relatively small, the study by Gonzalez Rinne et al. demonstrated the adaptive capacity of the transplanted kidney in 30 donor–recipient pairs. The glomerular filtration rate (GFR) in both donors and recipients was obtained 12 months after transplantation and the authors identified three scenarios: (i) where donors had a higher GFR than recipients; (ii) where donors had a lower GFR than recipients; and (iii) where donors had a similar GFR to recipients. The mechanisms mediating GFR adaptability after kidney transplantation seem to be associated with body surface area (including sex differences in body surface area). Microstructural analysis of human and animal models of renal physiology provides some clues to the physiological adaptation of the transplanted organ. The nephron number from endowment and age-related loss and the adaptive ability for compensatory glomerular hyperfiltration likely play a major role.

Background In kidney transplantation, a contrast CT scan is obtained in the donor candidate to detect subclinical pathology in the kidney. Recent work from the Aging Kidney Anatomy study has characterized kidney, cortex, and medulla volumes using a manual image-processing tool. However, this technique is time consuming and impractical for clinical care, and thus, these measurements are not obtained during donor evaluations. This study proposes a fully automated segmentation approach for measuring kidney, cortex, and medulla volumes. Methods A total of 1930 contrast-enhanced CT exams with reference standard manual segmentations from one institution were used to develop the algorithm. A convolutional neural network model was trained ( n =1238) and validated ( n =306), and then evaluated in a hold-out test set of reference standard segmentations ( n =386). After the initial evaluation, the algorithm was further tested on datasets originating from two external sites ( n =1226). Results The automated model was found to perform on par with manual segmentation, with errors similar to interobserver variability with manual segmentation. Compared with the reference standard, the automated approach achieved a Dice similarity metric of 0.94 (right cortex), 0.90 (right medulla), 0.94 (left cortex), and 0.90 (left medulla) in the test set. Similar performance was observed when the algorithm was applied on the two external datasets. Conclusions A fully automated approach for measuring cortex and medullary volumes in CT images of the kidneys has been established. This method may prove useful for a wide range of clinical applications.

AimThe Cockcroft‐Gault (CG) creatinine‐based equation is still used to estimate glomerular filtration rate (eGFR) for drug dosage adjustment. Incorrect eGFR may lead to hazardous over‐ or underdosing.MethodsIn a cross‐sectional analysis, CG was validated against measured GFR (mGFR) in 14 804 participants and compared with the Modification‐of‐Diet‐in‐Renal‐Diseases (MDRD), Chronic‐Kidney‐Disease‐Epidemiology (CKD‐EPI), Lund‐Malmö‐Revised (LMR) and European‐Kidney‐Function‐Consortium (EKFC) equations. Validation focused on bias, imprecision and accuracy (percentage of estimates within ±30% of mGFR, P30), overall and stratified for mGFR, age and body mass index at mGFR <60 mL/min, as well as classification in mGFR stages.ResultsThe CG equation performed worse than the other equations, overall and in mGFR, age and BMI subgroups in terms of bias (systematic overestimation), imprecision and accuracy except for patients ≥65 years where bias and P30 were similar to MDRD and CKD‐EPI, but worse than LMR and EKFC. In subjects with mGFR <60 mL/min and at BMI 18.5‐25 kg/m2, all equations performed similarly, and for BMI < 18.5 kg/m2 CG and LMR had the best results though all equations had poor P30‐accuracy. At BMI ≥ 25 kg/m2 the bias of the CG increased with increasing BMI (+17.2 mL/min at BMI ≥ 40 kg/m2). The four more recent equations also classified mGFR stages better than CG.ConclusionsThe CG equation showed poor ability to estimate GFR overall and in analyses stratified for mGFR, age and BMI. CG was inferior to correctly classify the patients in the mGFR staging compared to more recent creatinine‐based equations.