Literature DB >> 19167732

Nomograms for predicting graft function and survival in living donor kidney transplantation based on the UNOS Registry.

H Y Tiong1, D A Goldfarb, M W Kattan, J M Alster, L Thuita, C Yu, A Wee, E D Poggio.   

Abstract

PURPOSE: We developed nomograms that predict transplant renal function at 1 year (Modification of Diet in Renal Disease equation [estimated glomerular filtration rate]) and 5-year graft survival after living donor kidney transplantation.
MATERIALS AND METHODS: Data for living donor renal transplants were obtained from the United Network for Organ Sharing registry for 2000 to 2003. Nomograms were designed using linear or Cox regression models to predict 1-year estimated glomerular filtration rate and 5-year graft survival based on pretransplant information including demographic factors, immunosuppressive therapy, immunological factors and organ procurement technique. A third nomogram was constructed to predict 5-year graft survival using additional information available by 6 months after transplantation. These data included delayed graft function, any treated rejection episodes and the 6-month estimated glomerular filtration rate. The nomograms were internally validated using 10-fold cross-validation.
RESULTS: The renal function nomogram had an r-square value of 0.13. It worked best when predicting estimated glomerular filtration rate values between 50 and 70 ml per minute per 1.73 m(2). The 5-year graft survival nomograms had a concordance index of 0.71 for the pretransplant nomogram and 0.78 for the 6-month posttransplant nomogram. Calibration was adequate for all nomograms.
CONCLUSIONS: Nomograms based on data from the United Network for Organ Sharing registry have been validated to predict the 1-year estimated glomerular filtration rate and 5-year graft survival. These nomograms may facilitate individualized patient care in living donor kidney transplantation.

Entities:  

Mesh:

Year:  2009        PMID: 19167732     DOI: 10.1016/j.juro.2008.10.164

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  12 in total

1.  Predictive Score for Posttransplantation Outcomes.

Authors:  Miklos Z Molnar; Danh V Nguyen; Yanjun Chen; Vanessa Ravel; Elani Streja; Mahesh Krishnan; Csaba P Kovesdy; Rajnish Mehrotra; Kamyar Kalantar-Zadeh
Journal:  Transplantation       Date:  2017-06       Impact factor: 4.939

2.  Development and validation of a new prediction model for graft function using preoperative marginal factors in living-donor kidney transplantation.

Authors:  Yuta Matsukuma; Kosuke Masutani; Shigeru Tanaka; Akihiro Tsuchimoto; Toshiaki Nakano; Yasuhiro Okabe; Yoichi Kakuta; Masayoshi Okumi; Kazuhiko Tsuruya; Masafumi Nakamura; Takanari Kitazono; Kazunari Tanabe
Journal:  Clin Exp Nephrol       Date:  2019-08-23       Impact factor: 2.801

3.  A new clinical prediction tool for 5-year kidney transplant outcome.

Authors:  Colin R Lenihan; Joseph B Lockridge; Jane C Tan
Journal:  Am J Kidney Dis       Date:  2014-04       Impact factor: 8.860

4.  Influence of Donor's Renal Function on the Outcome of Living Kidney Transplantation: 10-Year Follow-up.

Authors:  Hyun Cheol Jeong; Seong Ho Lee; Dae Yul Yang; Sung Yong Kim; Hayoung Kim; Sam Uel Lee; Jeong Won Kim; Won Ki Lee
Journal:  Korean J Urol       Date:  2012-02-20

5.  Predicting urine output after kidney transplantation: development and internal validation of a nomogram for clinical use.

Authors:  Aderivaldo Cabral Dias; João Ricardo Alves; Pedro Rincon Cintra da Cruz; Viviane Brandão Bandeira de Mello Santana; Cassio Luis Zanettini Riccetto
Journal:  Int Braz J Urol       Date:  2019 May-Jun       Impact factor: 1.541

6.  Adding propensity scores to pure prediction models fails to improve predictive performance.

Authors:  Amy S Nowacki; Brian J Wells; Changhong Yu; Michael W Kattan
Journal:  PeerJ       Date:  2013-08-01       Impact factor: 2.984

7.  Chronic graft loss and death in patients with post-transplant malignancy in living kidney transplantation: a competing risk analysis.

Authors:  Mahmoud Salesi; Zohreh Rostami; Abbas Rahimi Foroushani; Ali Reza Mehrazmay; Jamile Mohammadi; Behzad Einollahi; Saeed Asgharian; Mohammad Reza Eshraghian
Journal:  Nephrourol Mon       Date:  2014-03-10

8.  Risk Factors of Graft Survival After Diagnosis of Post-kidney Transplant Malignancy: Using Cox Proportional Hazard Model.

Authors:  Abbas Rahimi Foroushani; Mahmoud Salesi; Zohreh Rostami; Ali Reza Mehrazmay; Jamile Mohammadi; Behzad Einollahi; Mohammad Reza Eshraghian
Journal:  Iran Red Crescent Med J       Date:  2015-11-14       Impact factor: 0.611

9.  Predicting donor, recipient and graft survival in living donor kidney transplantation to inform pretransplant counselling: the donor and recipient linked iPREDICTLIVING tool - a retrospective study.

Authors:  Maria C Haller; Christine Wallisch; Geir Mjøen; Hallvard Holdaas; Daniela Dunkler; Georg Heinze; Rainer Oberbauer
Journal:  Transpl Int       Date:  2020-02-24       Impact factor: 3.782

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

Authors:  Sameera Senanayake; Adrian Barnett; Nicholas Graves; Helen Healy; Keshwar Baboolal; Sanjeewa Kularatna
Journal:  F1000Res       Date:  2019-10-29
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.