scholarly journals Racial disparity in kidney transplant survival relates to late rejection and is independent of steroid withdrawal

2018 ◽  
Vol 32 (9) ◽  
pp. e13381 ◽  
Author(s):  
Angela Liu ◽  
Kenneth J. Woodside ◽  
Joshua J. Augustine ◽  
Nagaraju Sarabu
Keyword(s):  
Kidney Transplant ◽  
Racial Disparity ◽  
Steroid Withdrawal ◽  
Transplant Survival

Machine learning support for decision making in kidney transplantation: step-by-step development of a technological solution (Preprint)

2021 ◽  
Author(s):  
François-Xavier Paquette ◽  
Amir Ghassemi ◽  
Olga Bukhtiyarova ◽  
Moustapha Cisse ◽  
Natanael Gagnon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Kidney Transplantation ◽  
Kidney Transplant ◽  
Graft Survival ◽  
The United States ◽  
Technological Solution ◽  
Discriminative Ability ◽  
Potential Donor ◽  
Transplant Survival ◽  
Transplant Outcomes

BACKGROUND Kidney transplantation is the preferred treatment option for patients with end-stage renal disease. To maximize patient and graft survival, the allocation of donor organs to potential recipients requires careful consideration. OBJECTIVE To develop an innovative technological solution to enable better prediction of kidney transplant survival for each potential donor-recipient pair. METHODS We used de-identified data on past organ donors, recipients and transplant outcomes in the United States from the Scientific Registry of Transplant Recipients (SRTR). To predict transplant outcomes for potential donor-recipient pairs, we used several survival analysis models, including regression analysis (Cox Proportional Hazards), Random Survival Forests (RSF) and several artificial neural networks (DeepSurv, DeepHit, Recurrent Neural Networks (RNN)). We evaluated the performance of each model on their ability to predict the probability of graft survival after kidney transplantation from deceased donors. Three metrics were employed: the C-index, the Integrated Brier Score and the Integrated Calibration Index (ICI), along with calibration plots. RESULTS Based on the C-index metrics, the neural network-based models (DeepSurv, DeepHit, RNN) had better discriminative ability than the Cox model and RSF (0.650, 0.661, 0.659 vs 0.646 and 0.644, correspondingly). The proposed RNN model offered a compromise between the good discriminative ability and calibration and was implemented in a technological solution of TRL-4. CONCLUSIONS Our technological solution based on RNN model can effectively predict kidney transplant survival and provide support for medical professionals and candidate recipients in determining the most optimal donor-recipient pair. CLINICALTRIAL Not applicable.

Kidney Transplant Survival After BK Virus Associated Nephropathy and BK Virus Associated Nephropathy and Acute Rejection.

2014 ◽  
Vol 98 ◽  
pp. 561
Author(s):  
L. Mesa ◽  
M. Valderrama ◽  
N. Pinto ◽  
A. Arrunategui ◽  
E. Manzi ◽  
...  
Keyword(s):  
Acute Rejection ◽  
Kidney Transplant ◽  
Bk Virus ◽  
Transplant Survival

Predicting Kidney Transplant Survival Using Tree-Based Modeling

ASAIO Journal ◽  
2007 ◽  
Vol 53 (5) ◽  
pp. 592-600 ◽  
Author(s):  
Sergey Krikov ◽  
Altaf Khan ◽  
Bradley C. Baird ◽  
Lev L. Barenbaum ◽  
Alexander Leviatov ◽  
...  
Keyword(s):  
Kidney Transplant ◽  
Transplant Survival

scholarly journals Using machine learning and an ensemble of methods to predict kidney transplant survival

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0209068 ◽  
Author(s):  
Ethan Mark ◽  
David Goldsman ◽  
Brian Gurbaxani ◽  
Pinar Keskinocak ◽  
Joel Sokol
Keyword(s):  
Machine Learning ◽  
Kidney Transplant ◽  
Transplant Survival

IMPROVEMENT OF KIDNEY TRANSPLANT SURVIVAL AFTER GRAFT PRETREATMENT WITH CYCLOSPORIN A

1982 ◽  
Vol 34 (6) ◽  
pp. 356-359 ◽  
Author(s):  
J. RUCKER ◽  
L. H. TOLEDO-PEREYRA ◽  
G. H. MACKENZIE ◽  
D. A. GORDON
Keyword(s):  
Cyclosporin A ◽  
Kidney Transplant ◽  
Transplant Survival

Comparing Three Data Mining Methods to Predict Kidney Transplant Survival

2016 ◽  
Vol 24 (5) ◽  
pp. 242
Author(s):  
Leila Shahmoradi ◽  
Mostafa Langarizadeh ◽  
Gholamreza Pourmand ◽  
Ziba fard ◽  
and Borhani
Keyword(s):  
Data Mining ◽  
Kidney Transplant ◽  
Transplant Survival ◽  
Mining Methods
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