scholarly journals Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index

2021 ◽  
Vol 21 (1) ◽  
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 Background Kidney 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. Methods Data 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. Results Two 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). Conclusion This index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.

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.

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.

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 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 4101 Creating a Culturally Sensitive Report Card for African American (AA) Kidney Transplant Candidates

2020 ◽  
Vol 4 (s1) ◽  
pp. 54-54
Author(s):  
Warren McKinney ◽  
Marilyn J. Bruin ◽  
Sauman Chu ◽  
Bertram L. Kasiske ◽  
Ajay K. Israni
Keyword(s):  
Decision Making ◽  
Focus Groups ◽  
Kidney Transplant ◽  
Culturally Sensitive ◽  
Report Card ◽  
Medical Decision ◽  
Transplant Recipients ◽  
Transplant Program ◽  
Study Population ◽  
Transplant Candidates

OBJECTIVES/GOALS: AA are over-represented on the waitlist for kidney transplant and are often unaware of how waitlist acceptance practices differ across transplant programs and influence access to transplant. We will develop a culturally sensitive transplant program report card to communicate these variations. METHODS/STUDY POPULATION: Scientific Registry of Transplant Recipients (SRTR) data will be used to identity clinical factors strongly associated with AA access to transplant. Interviews and focus groups with AA kidney transplant candidates and their families will collect feedback on the SRTR report card and inform the development of the culturally sensitive report card. Additional focus groups will evaluate its effect on knowledge and medical decision making. We will collaborate with the stakeholders, including AA transplant candidates and their families, transplant programs, SRTR, and providers, to identify strategies to disseminate the report card in the AA community RESULTS/ANTICIPATED RESULTS: To date, no investigation has systematically collected feedback on the SRTR transplant program report card from AA candidates to ensure that the tool is accessible and effective in the AA community. We hypothesize that a culturally sensitive report card will improve AA candidates’ knowledge of program factors that impact access to transplant and enable informed decisions about where they pursue a transplant evaluation. The results of this study have the potential to change how AA patients are counselled while seeking transplantation. DISCUSSION/SIGNIFICANCE OF IMPACT: A culturally sensitive report card can reach more AA patients and enable more informed decision making by providing education about differences in transplant programs that may impact their access to transplant. In the future, we will design a trial to evaluate the prototype.

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 Fatal Granulomatous Amoebic Encephalitis Caused by Acanthamoeba in a Patient With Kidney Transplant: A Case Report

2015 ◽  
Vol 2 (3) ◽  
Author(s):  
Ahmad Salameh ◽  
Nancy Bello ◽  
Jennifer Becker ◽  
Tirdad Zangeneh
Keyword(s):  
Case Report ◽  
Combination Therapy ◽  
Kidney Transplant ◽  
Deceased Donor ◽  
Kidney Graft ◽  
Donor Kidney ◽  
Fatal Infection ◽  
Deceased Donor Kidney ◽  
Granulomatous Amoebic Encephalitis

Abstract Granulomatous amoebic encephalitis (GAE) due to Acanthamoeba is almost a uniformly fatal infection in immune-compromised hosts despite multidrug combination therapy. We report a case of GAE in a female who received a deceased donor kidney graft. She was treated with a combination of miltefosine, pentamidine, sulfadiazine, fluconazole, flucytosine, and azithromycin.

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