Dataset Information

Associations of deceased donor kidney injury with kidney discard and function after transplantation.

ABSTRACT: Deceased donor kidneys with acute kidney injury (AKI) are often discarded due to fear of poor outcomes. We performed a multicenter study to determine associations of AKI (increasing admission-to-terminal serum creatinine by AKI Network stages) with kidney discard, delayed graft function (DGF) and 6-month estimated glomerular filtration rate (eGFR). In 1632 donors, kidney discard risk increased for AKI stages 1, 2 and 3 (compared to no AKI) with adjusted relative risks of 1.28 (1.08-1.52), 1.82 (1.45-2.30) and 2.74 (2.0-3.75), respectively. Adjusted relative risk for DGF also increased by donor AKI stage: 1.27 (1.09-1.49), 1.70 (1.37-2.12) and 2.25 (1.74-2.91), respectively. Six-month eGFR, however, was similar across AKI categories but was lower for recipients with DGF (48 [interquartile range: 31-61] vs. 58 [45-75] ml/min/1.73m(2) for no DGF, p?

SUBMITTER: Hall IE

PROVIDER: S-EPMC4563988 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Associations of deceased donor kidney injury with kidney discard and function after transplantation.

Hall I E IE Schröppel B B Doshi M D MD Ficek J J Weng F L FL Hasz R D RD Thiessen-Philbrook H H Reese P P PP Parikh C R CR

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 20150311 6

Deceased donor kidneys with acute kidney injury (AKI) are often discarded due to fear of poor outcomes. We performed a multicenter study to determine associations of AKI (increasing admission-to-terminal serum creatinine by AKI Network stages) with kidney discard, delayed graft function (DGF) and 6-month estimated glomerular filtration rate (eGFR). In 1632 donors, kidney discard risk increased for AKI stages 1, 2 and 3 (compared to no AKI) with adjusted relative risks of 1.28 (1.08-1.52), 1.82 ( ...[more]

PMID: 25762442

Similar Datasets

Project description:Several recipient biomarkers are reported to predict graft dysfunction, but these are not useful in decision making for the acceptance or allocation of deceased donor kidneys; thus, it is necessary to develop donor biomarkers predictive of graft dysfunction. To address this issue, we prospectively enrolled 94 deceased donors and their 109 recipients who underwent transplantation between 2010 and 2013 at 4 Korean transplantation centers. We investigated the predictive values of donor urinary neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and L-type fatty acid binding protein (L-FABP) for reduced graft function (RGF). We also developed a prediction model of RGF using these donor biomarkers. RGF was defined as delayed or slow graft function. Multiple logistic regression analysis was used to generate a prediction model, which was internally validated using a bootstrapping method. Multiple linear regression analysis was used to assess the association of biomarkers with 1-year graft function. Notably, donor urinary NGAL levels were associated with donor AKI (P = 0.014), and donor urinary NGAL and L-FABP were predictive for RGF, with area under the receiver-operating characteristic curves (AUROC) of 0.758 and 0.704 for NGAL and L-FABP, respectively. The best-fit model including donor urinary NGAL, L-FABP, and serum creatinine conveyed a better predictive value for RGF than donor serum creatinine alone (P = 0.02). In addition, we generated a scoring method to predict RGF based on donor urinary NGAL, L-FABP, and serum creatinine levels. Diagnostic performance of the RGF prediction score (AUROC 0.808) was significantly better than that of the DGF calculator (AUROC 0.627) and the kidney donor profile index (AUROC 0.606). Donor urinary L-FABP levels were also predictive of 1-year graft function (P = 0.005). Collectively, these findings suggest donor urinary NGAL and L-FABP to be useful biomarkers for RGF, and support the use of a new scoring system based on donor biomarkers to facilitate decision-making in acceptance and allocation of deceased donor kidneys and contribute to maximal organ utilization.

Project description:BackgroundEarly prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making.MethodsData of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms.Results430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model.ConclusionsOur GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT.

Dataset Information

Associations of deceased donor kidney injury with kidney discard and function after transplantation.

Publications

Associations of deceased donor kidney injury with kidney discard and function after transplantation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets