scholarly journals H-Y Antigen Incompatibility Not Associated with Adverse Immunologic Graft Outcomes: Deceased Donor Pair Analysis of the OPTN Database

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Douglas Scott Keith ◽  
James T. Patrie
Keyword(s):  
Acute Rejection ◽  
Kidney Transplant ◽  
Graft Survival ◽  
Graft Failure ◽  
Organ Procurement ◽  
Deceased Donor ◽  
Kidney Graft ◽  
The Difference ◽  
One Year ◽  
Deceased Donor Kidney Transplant

Background. H-Y antigen incompatibility adversely impacts bone marrow transplants however, the relevance of these antigens in kidney transplantation is uncertain. Three previous retrospective studies of kidney transplant databases have produced conflicting results.Methods. This study analyzed the Organ Procurement and Transplantation Network database between 1997 and 2009 using male deceased donor kidney transplant pairs in which the recipient genders were discordant. Death censored graft survival at six months, five, and ten years, treated acute rejection at six months and one year, and rates of graft failure by cause were the primary endpoints analyzed.Results. Death censored graft survival at six months was significantly worse for female recipients. Analysis of the causes of graft failure at six months revealed that the difference in death censored graft survival was due primarily to nonimmunologic graft failures. The adjusted and unadjusted death censored graft survivals at five and ten years were similar between the two genders as were the rates of immunologic graft failure. No difference in the rates of treated acute rejection at six months and one year was seen between the two genders.Conclusions. Male donor to female recipient discordance had no discernable effect on immunologically mediated kidney graft outcomes in the era of modern immunosuppression.

Timing of Kidney Clamping and Deceased Donor Kidney Transplant Outcomes

2021 ◽  
pp. CJN.03290321
Author(s):  
Simon Ville ◽  
Marine Lorent ◽  
Clarisse Kerleau ◽  
Anders Asberg ◽  
Christophe Legendre ◽  
...  
Keyword(s):  
Kidney Transplant ◽  
Graft Survival ◽  
Ischemia Reperfusion ◽  
Multivariable Analysis ◽  
Graft Function ◽  
Deceased Donor ◽  
Kidney Graft ◽  
Kidney Transplant Recipients ◽  
Heart Beating ◽  
Deceased Donor Kidney Transplant

BackgroundThe recognition that metabolism and immune function are regulated by an endogenous molecular clock generating circadian rhythms suggests that the magnitude of ischemia-reperfusion and subsequent inflammation on kidney transplantation, could be affected by the time of the day. MethodsAccordingly, we evaluated 5026 first kidney transplant recipients from deceased heart-beating donors. In a cause-specific multivariable analysis, we compare delayed graft function (DGF) and graft survival according to the time of kidney clamping and declamping. Participants were divided into clamping between midnight and noon (AM clamping group, 65%) or clamping between noon and midnight (PM clamping group, 35%), and similarly, AM declamping or PM declamping (25% / 75%). ResultsDGF occurred among 550 participants (27%) with AM clamping and 339 (34%) with PM clamping (adjusted OR = 0.81, 95%CI: 0.67 to 0.98, p= 0.03). No significant association of clamping time with overall death censored graft survival was observed (HR = 0.92, 95%CI: 0.77 to 1.10, p= 0.37). No significant association of declamping time with DGF or graft survival was observed. ConclusionsClamping between midnight and noon was associated with a lower incidence of DGF whilst the declamping time was not associated with kidney graft outcomes.

scholarly journals Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1810 ◽  
Author(s):  
Sameera Senanayake ◽  
Adrian Barnett ◽  
Nicholas Graves ◽  
Helen Healy ◽  
Keshwar Baboolal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kidney Transplant ◽  
Predictive Models ◽  
Graft Failure ◽  
Prediction Models ◽  
Deceased Donor ◽  
Machine Learning Techniques ◽  
Kidney Graft ◽  
Learning Techniques ◽  
Deceased Donor Kidney Transplant

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1st, 2007, to December 31st, 2017.  This included 3,758 live donor transplants and 7,365 deceased donor transplants.  Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models.  The best predictive model will be selected based on the model’s performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia.   Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques.  Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

scholarly journals Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1810
Author(s):  
Sameera Senanayake ◽  
Adrian Barnett ◽  
Nicholas Graves ◽  
Helen Healy ◽  
Keshwar Baboolal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Kidney Transplant ◽  
Predictive Models ◽  
Graft Failure ◽  
Prediction Models ◽  
Deceased Donor ◽  
Machine Learning Techniques ◽  
Kidney Graft ◽  
Learning Techniques ◽  
Deceased Donor Kidney Transplant

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017. This included 3,758 live donor transplants and 7,365 deceased donor transplants. Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models (for live donor and deceased donor transplants). The best predictive model will be selected based on the model’s performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia. Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques. Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

MO941CLINICAL OUTCOMES IN KIDNEY RE-TRANSPLANTATION

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Clara Pardinhas ◽  
Rita Leal ◽  
Francisco Caramelo ◽  
Teofilo Yan ◽  
Carolina Figueiredo ◽  
...  
Keyword(s):  
Acute Rejection ◽  
Kidney Transplant ◽  
Graft Survival ◽  
Graft Failure ◽  
Graft Function ◽  
Delayed Graft Function ◽  
First Year ◽  
Kidney Transplant Recipients ◽  
Post Transplant

Abstract Background and Aims As kidney transplants are growing in absolute numbers, so are patients with failed allografts and thus potential candidates for re-transplantation. Re-transplantation is challenging due to immunological barriers, surgical difficulties and clinical complexities but it has been proven that successful second transplantation improves life expectancy over dialysis. It is important to evaluate re-transplantation outcomes since 20% of patients on the waiting list are waiting for a second graft. Our aim was to compare major clinical outcomes such as acute rejection, graft and patient survival, between patients receiving a first or a second kidney transplant. Method We performed a retrospective study, that included 1552 patients submitted to a first (N=1443, 93%) or a second kidney transplant (N=109, 7%), between January 2008 and December 2018. Patients with more than 2 grafts or multi-organ transplant were excluded. Demographic, clinical and histocompatibility characteristics of both groups were registered from our unit database and compared. Delayed graft function was defined has the need of dialysis in the first week post-transplant. All acute rejection episodes were biopsy proven, according to Banff 2017 criteria. Follow-up time was defined at 1st June 2020 for functioning grafts or at graft failure (including death with a functioning graft). Results Recipients of a second graft were significantly younger (43 ±12 vs 50 ± 13 years old, p<0.001) and there were significantly fewer expanded-criteria donors in the second transplant group (31.5% vs 57.5%, p<0.001). The waiting time for a second graft was longer (63±50 vs 48±29 months, p=0.011). HLA mismatch was similar for both groups but PRA was significantly higher for second KT patients (21.6±25% versus 3±9%; p<0.001). All patients submitted to a second KT had thymoglobulin as induction therapy compared to 16% of the first KT group (p<0.001). We found no difference in primary dysfunction or delayed graft function between groups. Acute rejection was significantly more frequent in second kidney transplant recipients (19% vs 5%, p<0.001), being 10 acute cellular rejections, 7 were antibody mediated and 3 were borderline changes. For the majority of the patients (85%), acute rejection occurred in the first-year post-transplant. Death censored graft failure occurred in 236 (16.4%) patients with first kidney transplant and 25 (23%) patients with a second graft, p=0.08. Survival analysis showed similar graft survival for both groups (log-rank p=0.392). We found no difference in patients’ mortality at follow up for both groups. Conclusion Although second graft patients presented more episodes of biopsy proven acute rejection, especially at the first-year post-transplant, we found no differences in death censored graft survival or patients’ mortality for patients with a second kidney transplant. Second transplants should be offered to patients whenever feasible.

scholarly journals Impact of deceased donor with acute kidney injury on subsequent kidney transplant outcomes–an ANZDATA registry analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0249000
Author(s):  
Juan Pei ◽  
Yeoungjee Cho ◽  
Yong Pey See ◽  
Elaine M. Pascoe ◽  
Andrea K. Viecelli ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Kidney Transplantation ◽  
Acute Rejection ◽  
Kidney Transplant ◽  
Graft Failure ◽  
Cox Regression ◽  
Graft Function ◽  
Kidney Injury ◽  
Deceased Donor ◽  
Hazard Ratio

Background The need for kidney transplantation drives efforts to expand organ donation. The decision to accept organs from donors with acute kidney injury (AKI) can result in a clinical dilemma in the context of conflicting reports from published literature. Material and methods This observational study included all deceased donor kidney transplants performed in Australia and New Zealand between 1997 and 2017. The association of donor-AKI, defined according to KDIGO criteria, with all-cause graft failure was evaluated by multivariable Cox regression. Secondary outcomes included death-censored graft failure, death, delayed graft function (DGF) and acute rejection. Results The study included 10,101 recipients of kidneys from 5,774 deceased donors, of whom 1182 (12%) recipients received kidneys from 662 (11%) donors with AKI. There were 3,259 (32%) all-cause graft failures, which included 1,509 deaths with functioning graft. After adjustment for donor, recipient and transplant characteristics, donor AKI was not associated with all-cause graft failure (adjusted hazard ratio [HR] 1.11, 95% CI 0.99–1.26), death-censored graft failure (HR 1.09, 95% CI 0.92–1.28), death (HR 1.15, 95% CI 0.98–1.35) or graft failure when death was evaluated as a competing event (sub-distribution hazard ratio [sHR] 1.07, 95% CI 0.91–1.26). Donor AKI was not associated with acute rejection but was associated with DGF (adjusted odds ratio [OR] 2.27, 95% CI 1.92–2.68). Conclusion Donor AKI stage was not associated with any kidney transplant outcome, except DGF. Use of kidneys with AKI for transplantation appears to be justified.

scholarly journals Influence of Deceased Donor and Pretransplant Recipient Parameters on Early Overall Kidney Graft-Survival in Germany

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Carl-Ludwig Fischer-Fröhlich ◽  
Marcus Kutschmann ◽  
Johanna Feindt ◽  
Irene Schmidtmann ◽  
Günter Kirste ◽  
...  
Keyword(s):  
Risk Factors ◽  
Graft Survival ◽  
Cox Regression ◽  
Organ Procurement ◽  
Deceased Donor ◽  
Kidney Graft ◽  
Significance Level ◽  
Cardiovascular Comorbidity ◽  
Deutsche Stiftung Organtransplantation ◽  
The Impact

Background. Scarcity of grafts for kidney transplantation (KTX) caused an increased consideration of deceased donors with substantial risk factors. There is no agreement on which ones are detrimental for overall graft-survival. Therefore, we investigated in a nationwide multicentre study the impact of donor and recipient related risks known before KTX on graft-survival based on the original data used for allocation and graft acceptance.Methods. A nationwide deidentified multicenter study-database was created of data concerning kidneys donated and transplanted in Germany between 2006 and 2008 as provided by the national organ procurement organization (Deutsche Stiftung Organtransplantation) and BQS Institute. Multiple Cox regression (significance level 5%, hazard ratio [95% CI]) was conducted (n=4411, isolated KTX).Results. Risk factors associated with graft-survival were donor age (1.020 [1.013–1.027] per year), donor size (0.985 [0.977–0.993] per cm), donor’s creatinine at admission (1.002 [1.001–1.004] perµmol/L), donor treatment with catecholamine (0.757 [0.635–0.901]), and reduced graft-quality at procurement (1.549 [1.217–1.973]), as well as recipient age (1.012 [1.003–1.021] per year), actual panel reactive antibodies (1.007 [1.002–1.011] per percent), retransplantation (1.850 [1.484–2.306]), recipient’s cardiovascular comorbidity (1.436 [1.212–1.701]), and use of IL2-receptor antibodies for induction (0.741 [0.619–0.887]).Conclusion. Some donor characteristics persist to impact graft-survival (e.g., age) while the effect of others could be mitigated by elaborate donor-recipient match and care.

scholarly journals Degree of Glomerulosclerosis in Procurement Kidney Biopsies from Marginal Donor Kidneys and Their Implications in Predicting Graft Outcomes

2020 ◽  
Vol 9 (5) ◽  
pp. 1469
Author(s):  
Wisit Cheungpasitporn ◽  
Charat Thongprayoon ◽  
Pradeep K Vaitla ◽  
Api Chewcharat ◽  
Panupong Hansrivijit ◽  
...  
Keyword(s):  
Acute Rejection ◽  
Graft Survival ◽  
Graft Failure ◽  
Patient Survival ◽  
Graft Function ◽  
Deceased Donor ◽  
Delayed Graft Function ◽  
Organ Shortage ◽  
Increased Risk ◽  
Significant Difference

Background: This study aimed to assess the association between the percentage of glomerulosclerosis (GS) in procurement allograft biopsies from high-risk deceased donor and graft outcomes in kidney transplant recipients. Methods: The UNOS database was used to identify deceased-donor kidneys with a kidney donor profile index (KDPI) score > 85% from 2005 to 2014. Deceased donor kidneys were categorized based on the percentage of GS: 0–10%, 11–20%, >20% and no biopsy performed. The outcome included death-censored graft survival, patient survival, rate of delayed graft function, and 1-year acute rejection. Results: Of 22,006 kidneys, 91.2% were biopsied showing 0–10% GS (58.0%), 11–20% GS (13.5%), >20% GS (19.7%); 8.8% were not biopsied. The rate of kidney discard was 48.5%; 33.6% in 0–10% GS, 68.9% in 11–20% GS, and 77.4% in >20% GS. 49.8% of kidneys were discarded in those that were not biopsied. Death-censored graft survival at 5 years was 75.8% for 0–10% GS, 70.9% for >10% GS, and 74.8% for the no biopsy group. Among kidneys with >10% GS, there was no significant difference in death-censored graft survival between 11–20% GS and >20% GS. Recipients with >10% GS had an increased risk of graft failure (HR = 1.27, p < 0.001), compared with 0–10% GS. There was no significant difference in patient survival, acute rejection at 1-year, and delayed graft function between 0% and 10% GS and >10% GS. Conclusion: In >85% KDPI kidneys, our study suggested that discard rates increased with higher percentages of GS, and GS >10% is an independent prognostic factor for graft failure. Due to organ shortage, future studies are needed to identify strategies to use these marginal kidneys safely and improve outcomes.

scholarly journals Development and validation of a risk index to predict kidney graft survival: the Kidney Transplant Risk Index

2021 ◽  
Author(s):  
Sameera Senanayake ◽  
Sanjeewa Kularatna ◽  
Helen Healy ◽  
Nicholas Graves ◽  
Keshwar Baboolal ◽  
...  
Keyword(s):  
Decision Making ◽  
Kidney Transplant ◽  
Predictive Models ◽  
Graft Failure ◽  
Risk Index ◽  
Deceased Donor ◽  
Medical Decision ◽  
Kidney Graft ◽  
Independent Variables ◽  
Random Survival Forest

Abstract BackgroundKidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia. MethodsData included donor and recipient characteristics (n=98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model. ResultsTwo models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration). ConclusionThis index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.

MO940ACUTE REJECTION IN KIDNEY RETRANSPLANTATION: RISK FACTORS AND IMPACT IN GRAFT SURVIVAL

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Rita Leal ◽  
Clara Pardinhas ◽  
Luís Rodrigues ◽  
Maria Guedes Marques ◽  
Lidia Santos ◽  
...  
Keyword(s):  
Risk Factors ◽  
Acute Rejection ◽  
Kidney Transplant ◽  
Graft Survival ◽  
Graft Failure ◽  
Graft Loss ◽  
First Year ◽  
Post Transplant ◽  
Increased Risk

Abstract Background and Aims Kidney retransplantation confers a robust survival benefit over dialysis in selected patients and recent data has shown second graft outcomes similar to those of a first graft. However, the management of these patients is challenging, particularly due to allosensitization and an increased risk of acute rejection, which are related with poorer graft survival. The recognition of risk factors to acute rejection, dependent on the first and second graft, might help us to personalize standard care and achieve similar graft survival rates to patients with a first transplant. Our aim was to identify risk factors to second graft acute rejection, and the impact of acute rejection in graft failure. Method We performed a retrospective, longitudinal study including all patients submitted to a second kidney transplant between January 2008 and December 2019, excluding patients with more than 2 grafts or multi-organ transplant. Demographic, clinical and histocompatibility data from the donor and receptor were collected from our unit database. Delayed graft function was defined as the need of dialysis in the first week post-transplant. All acute rejection episodes were biopsy proven, according to Banff 2017 criteria. Follow-up was defined at 1st June 2020 for functioning grafts or at graft failure, with a mean time of 94±42 months. Results We included 109 patients of which 70 males (64%), mostly Caucasian (97%), with a mean age of 43±12 years at second kidney transplant. The main causes of end stage renal disease were glomerular disease (37%), undetermined cause (34%), and urological pathology (15%). First kidney transplant was performed before the year 2010 in 95 patients (87%). The median time of first graft survival was 75 months (IQR 58.5-91.4) and the main causes of first graft loss were chronic allograft nephropathy (N=62, 70.5%) and 11 patients (12.5%) presented primary disfunction due to surgical/vascular complications. During follow-up, 20 patients (18%) presented biopsy proven acute rejection: 3 patients borderline changes, 10 patients T cell mediated and 7 patients antibody mediated, the majority during the first-year post-transplant (N=17, 85%). The risk factors for second graft rejection are summarized in table 1. First year graft survival of the second transplant was 90% and survival at follow up was 72.5% (N=79). Acute rejection was an important risk factor for graft loss (OR 6.548 (95%CI[2.292 - 18.703]), p&lt;0.01). Conclusion Worst outcomes in first kidney transplant, such as acute rejection, primary dysfunction and lower graft survival were related with an increased risk of acute rejection in second graft outcomes, and consequently a higher risk of graft failure.

scholarly journals Pretransplant C3d-Fixing Donor-Specific Anti-HLA Antibodies Are Not Associated with Increased Risk for Kidney Graft Failure

2018 ◽  
Vol 29 (9) ◽  
pp. 2279-2285 ◽  
Author(s):  
Elena G. Kamburova ◽  
Bram W. Wisse ◽  
Irma Joosten ◽  
Wil A. Allebes ◽  
Arnold van der Meer ◽  
...  
Keyword(s):  
Kidney Transplant ◽  
Graft Survival ◽  
Graft Failure ◽  
Kidney Graft ◽  
Kidney Transplant Recipients ◽  
Serum Samples ◽  
Hla Antibodies ◽  
Transplant Patients ◽  
Increased Risk ◽  
Positive Serum

Background Complement-fixing antibodies against donor HLA are considered a contraindication for kidney transplant. A modification of the IgG single-antigen bead (SAB) assay allows detection of anti-HLA antibodies that bind C3d. Because early humoral graft rejection is considered to be complement mediated, this SAB-based technique may provide a valuable tool in the pretransplant risk stratification of kidney transplant recipients.Methods Previously, we established that pretransplant donor-specific anti-HLA antibodies (DSAs) are associated with increased risk for long-term graft failure in complement-dependent cytotoxicity crossmatch-negative transplants. In this study, we further characterized the DSA-positive serum samples using the C3d SAB assay.Results Among 567 pretransplant DSA-positive serum samples, 97 (17%) contained at least one C3d-fixing DSA, whereas 470 (83%) had non–C3d-fixing DSA. At 10 years after transplant, patients with C3d-fixing antibodies had a death-censored, covariate-adjusted graft survival of 60%, whereas patients with non–C3d-fixing DSA had a graft survival of 64% (hazard ratio, 1.02; 95% confidence interval, 0.70 to 1.48 for C3d-fixing DSA compared with non–C3d-fixing DSA; P=0.93). Patients without DSA had a 10-year graft survival of 78%.Conclusions The C3d-fixing ability of pretransplant DSA is not associated with increased risk for graft failure.

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