scholarly journals Kidney Transplant Outcomes of Patients With Multiple Myeloma

Author(s):  
Cihan Heybeli ◽  
Andrew J. Bentall ◽  
Mariam Priya Alexander ◽  
Hatem Amer ◽  
Francis K. Buadi ◽  
...  
Author(s):  
Kaitlyn Dykes ◽  
Sameer Desale ◽  
Javaid Basit ◽  
Krystsina Miatlovich ◽  
Craig Kessler

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Juhan Lee ◽  
Eun Jin Kim ◽  
Jae Geun Lee ◽  
Beom Seok Kim ◽  
Kyu Ha Huh ◽  
...  

AbstractSerum bilirubin, a potent endogenous antioxidant, has been associated with decreased risks of cardiovascular disease, diabetes, and kidney disease. However, the effects of serum bilirubin on kidney transplant outcomes remain undetermined. We analyzed 1628 patients who underwent kidney transplantations between 2003 and 2017. Patients were grouped into sex-specific quartiles according to mean serum bilirubin levels, 3–12 months post-transplantation. Median bilirubin levels were 0.66 mg/dL in males and 0.60 mg/dL in females. The intra-individual variability of serum bilirubin levels was low (9%). Serum bilirubin levels were inversely associated with graft loss, death-censored graft failure, and all-cause mortality, independent of renal function, donor status, and transplant characteristics. Multivariable analysis revealed that the lowest serum bilirubin quartile was associated with increased risk of graft loss (HR 2.64, 95% CI 1.67–4.18, P < 0.001), death-censored graft failure (HR 2.97, 95% CI 1.63–5.42, P < 0.001), and all-cause mortality (HR 2.07, 95% CI 1.01–4.22, P = 0.046). Patients with lower serum bilirubin were also at greater risk of rejection and exhibited consistently lower glomerular filtration rates than those with higher serum bilirubin. Serum bilirubin levels were significantly associated with transplantation outcomes, suggesting that bilirubin could represent a therapeutic target for improving long-term transplant outcomes.


2008 ◽  
Vol 85 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Jesse D. Schold ◽  
Titte R. Srinivas ◽  
Richard J. Howard ◽  
Ian R. Jamieson ◽  
Herwig-Ulf Meier-Kriesche

2010 ◽  
Vol 90 (10) ◽  
pp. 1079-1084 ◽  
Author(s):  
Neeraj Singh ◽  
Arjang Djamali ◽  
David Lorentzen ◽  
John D. Pirsch ◽  
Glen Leverson ◽  
...  

Nephron ◽  
2018 ◽  
Vol 139 (4) ◽  
pp. 332-333
Author(s):  
Gioacchino Li Cavoli ◽  
Barbara Oliva ◽  
Flavia Caputo

2021 ◽  
Author(s):  
Swati Choudhry ◽  
Susan W. Denfield ◽  
Vikas R. Dharnidharka ◽  
Yunfei Wang ◽  
Hari P. Tunuguntla ◽  
...  

2021 ◽  
Author(s):  
François-Xavier Paquette ◽  
Amir Ghassemi ◽  
Olga Bukhtiyarova ◽  
Moustapha Cisse ◽  
Natanael Gagnon ◽  
...  

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.


Sign in / Sign up

Export Citation Format

Share Document