scholarly journals Tubular Cell Dropout in Preimplantation Deceased Donor Biopsies as a Predictor of Delayed Graft Function

2021 ◽  
Vol 7 (7) ◽  
pp. e716
Author(s):  
Zachary M. Avigan ◽  
Nikhil Singh ◽  
Judith A. Kliegel ◽  
Marlene Weiss ◽  
Gilbert W. Moeckel ◽  
...  
Keyword(s):  
Graft Function ◽  
Deceased Donor ◽  
Delayed Graft Function ◽  
Tubular Cell

scholarly journals Endogenous intronic antisense long non-coding RNA, MGAT3-AS1, and kidney transplantation

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Subagini Nagarajah ◽  
Shengqiang Xia ◽  
Marianne Rasmussen ◽  
Martin Tepel
Keyword(s):  
Kidney Transplantation ◽  
Predictive Value ◽  
Graft Function ◽  
Deceased Donor ◽  
Delayed Graft Function ◽  
Transplant Recipients ◽  
Donor Kidney ◽  
Non Coding Rna ◽  
Deceased Donor Kidney ◽  
Long Non Coding Rna

Abstract β-1,4-mannosylglycoprotein 4-β-N-acetylglucosaminyltransferase (MGAT3) is a key molecule for the innate immune system. We tested the hypothesis that intronic antisense long non-coding RNA, MGAT3-AS1, can predict delayed allograft function after kidney transplantation. We prospectively assessed kidney function and MGAT3-AS1 in 129 incident deceased donor kidney transplant recipients before and after transplantation. MGAT3-AS1 levels were measured in mononuclear cells using qRT-PCR. Delayed graft function was defined by at least one dialysis session within 7 days of transplantation. Delayed graft function occurred in 22 out of 129 transplant recipients (17%). Median MGAT3-AS1 after transplantation was significantly lower in patients with delayed graft function compared to patients with immediate graft function (6.5 × 10−6, IQR 3.0 × 10−6 to 8.4 × 10−6; vs. 8.3 × 10−6, IQR 5.0 × 10−6 to 12.8 × 10−6; p < 0.05). The median preoperative MGAT3-AS1 was significantly lower in kidney recipients with delayed graft function (5.1 × 10−6, IQR, 2.4 × 10−6 to 6.8 × 10−6) compared to recipients with immediate graft function (8.9 × 10−6, IQR, 6.8 × 10−6 to 13.4 × 10−6; p < 0.05). Receiver-operator characteristics showed that preoperative MGAT3-AS1 predicted delayed graft function (area under curve, 0.83; 95% CI, 0.65 to 1.00; p < 0.01). We observed a positive predictive value of 0.57, and a negative predictive value of 0.95. Long non-coding RNA, MGAT3-AS1, indicates short-term outcome in patients with deceased donor kidney transplantation.

Gene Expression Patterns in Deceased Donor Kidneys Developing Delayed Graft Function After Kidney Transplantation

2008 ◽  
Vol 85 (4) ◽  
pp. 626-635 ◽  
Author(s):  
Valeria R. Mas ◽  
Kellie J. Archer ◽  
Kenneth Yanek ◽  
Catherine I. Dumur ◽  
Maria I. Capparuccini ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Transplantation ◽  
Expression Patterns ◽  
Graft Function ◽  
Deceased Donor ◽  
Delayed Graft Function ◽  
Gene Expression Patterns

scholarly journals Three-Year Outcomes of a Randomized, Double-Blind, Placebo-Controlled Study Assessing Safety and Efficacy of C1 Esterase Inhibitor for Prevention of Delayed Graft Function in Deceased Donor Kidney Transplant Recipients

2019 ◽  
Vol 15 (1) ◽  
pp. 109-116 ◽  
Author(s):  
Edmund Huang ◽  
Ashley Vo ◽  
Jua Choi ◽  
Noriko Ammerman ◽  
Kathlyn Lim ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Graft Failure ◽  
Ischemia Reperfusion Injury ◽  
Ischemia Reperfusion ◽  
Controlled Trial ◽  
Graft Function ◽  
Deceased Donor ◽  
Delayed Graft Function ◽  
C1 Esterase Inhibitor ◽  
Esterase Inhibitor

Background and objectivesDelayed graft function is related to ischemia-reperfusion injury and may be complement dependent. We previously reported from a randomized, placebo-controlled trial that treatment with C1 esterase inhibitor was associated with a shorter duration of delayed graft function and higher eGFR at 1 year. Here, we report longer-term outcomes from this trial.Design, setting, participants, & measurementsThis is a post hoc analysis of a phase 1/2, randomized, controlled trial enrolling 70 recipients of deceased donor kidney transplants at risk for delayed graft function (NCT02134314). Subjects were randomized to receive C1 esterase inhibitor 50 U/kg (n=35) or placebo (n=35) intraoperatively and at 24 hours. The cumulative incidence functions method was used to compare graft failure and death over 3.5 years. eGFR slopes were compared using a linear mixed effects model.ResultsThree deaths occurred among C1 esterase inhibitor–treated patients compared with none receiving placebo. Seven graft failures developed in the placebo group compared with one among C1 esterase inhibitor–treated recipients; the cumulative incidence of graft failure was lower over 3.5 years among C1 esterase inhibitor–treated recipients compared with placebo (P=0.03). Although no difference in eGFR slopes was observed between groups (P for group-time interaction =0.12), eGFR declined in placebo-treated recipients (−4 ml/min per 1.73 m2 per year; 95% confidence interval, −8 to −0.1) but was stable in C1 esterase inhibitor–treated patients (eGFR slope: 0.5 ml/min per 1.73 m2 per year; 95% confidence interval, −4 to 5). At 3.5 years, eGFR was 56 ml/min per 1.73 m2 (95% confidence interval, 42 to 70) in the C1 esterase inhibitor group versus 35 ml/min per 1.73 m2 (95% confidence interval, 21 to 48) in the placebo group, with an estimated mean eGFR difference of 21 ml/min per 1.73 m2 (95% confidence interval, 2 to 41 ml/min per 1.73 m2).ConclusionsTreatment of patients at risk for ischemia-reperfusion injury and delayed graft function with C1 esterase inhibitor was associated with a lower incidence of graft failure.

scholarly journals Artificial Intelligence—A Tool for Risk Assessment of Delayed-Graft Function in Kidney Transplant

2021 ◽  
Vol 10 (22) ◽  
pp. 5244
Author(s):  
Andrzej Konieczny ◽  
Jakub Stojanowski ◽  
Klaudia Rydzyńska ◽  
Mariusz Kusztal ◽  
Magdalena Krajewska
Keyword(s):  
Machine Learning ◽  
Risk Assessment ◽  
Kidney Transplant ◽  
Graft Function ◽  
Deceased Donor ◽  
Delayed Graft Function ◽  
Clinical Tool ◽  
Kidney Donor ◽  
Common Input ◽  
Discriminant Ability

Delayed-graft function (DGF) might be responsible for shorter graft survival. Therefore, a clinical tool predicting its occurrence is vital for the risk assessment of transplant outcomes. In a single-center study, we conducted data mining and machine learning experiments, resulting in DGF predictive models based on random forest classifiers (RF) and an artificial neural network called multi-layer perceptron (MLP). All designed models had four common input parameters, determining the best accuracy and discriminant ability: donor’s eGFR, recipient’s BMI, donor’s BMI, and recipient–donor weight difference. RF and MLP designs, using these parameters, achieved an accuracy of 84.38% and an area under curve (AUC) 0.84. The model additionally implementing a donor’s age, gender, and Kidney Donor Profile Index (KDPI) accomplished an accuracy of 93.75% and an AUC of 0.91. The other configuration with the estimated post-transplant survival (EPTS) and the kidney donor risk profile (KDRI) achieved an accuracy of 93.75% and an AUC of 0.92. Using machine learning, we were able to assess the risk of DGF in recipients after kidney transplant from a deceased donor. Our solution is scalable and can be improved during subsequent transplants. Based on the new data, the models can achieve better outcomes.

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