ArticleOncotarget2018
Evaluation of predictive models for delayed graft function of deceased kidney transplantation.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Combining Clinical Parameters and Acute Tubular Injury Grading Is Superior in Predicting the Prognosis of Deceased-Donor Kidney Transplantation: A 7-Year Observational Study.Frontiers in immunology · 2022Observational
- A prediction model of delayed graft function in deceased donor for renal transplant: a multi-center study from China.Renal failure · 2021Observational
- Risk factors and outcomes of prolonged recovery from delayed graft function after deceased kidney transplantation.Renal failure · 2020Article
- Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning.Scientific reports · 2020Article
- The value of cystatin C and urinary and serum neutrophil gelatinase-associated lipocalin during the perioperative period of renal transplantation.Translational andrology and urology · 2019Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to evaluate the predictive power of five available delayed graft function (DGF)-prediction models for kidney transplants in the Chinese population.
resultsAmong the five models, the Irish 2010 model scored the best in performance for the Chinese population. Irish 2010 model had an area under the receiver operating characteristic (ROC) curve of 0.737. Hosmer-Lemeshow goodness-of-fit test showed that the Irish 2010 model had a strong correlation between the calculated DGF risk and the observed DGF incidence ( MATERIALS AND
methodsA total of 711 renal transplant cases using deceased donors from China Donation after Citizen's Death Program at our center between February 2007 and August 2016 were included in the analysis using the five predictive models (Irish 2010, Irish 2003, Chaphal 2014, Zaza 2015, Jeldres 2009).
conclusionsIrish 2010 model has the best predictive power for DGF risk in Chinese population among the five models. However, it may not be suitable for allograft recipients whose donor aged ≤ 5-year-old.
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