ArticleBMC nephrology2024
Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies.
Article in BMC nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Time-Zero Kidney Biopsy and Deceased-Donor Transplant Outcomes: A Systematic Review and Meta-Analysis of Prognostic Value, Prediction, and Clinical Utility.Medical sciences (Basel, Switzerland) · 2026Pooled it
- Influencing factors and risk prediction nomogram model construction of hyperkalemia during kidney transplantation.Frontiers in surgery · 2025Article
- An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors.BMC medicine · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
backgroundDelayed graft function (DGF) is an important complication after kidney transplantation surgery. The present study aimed to develop and validate a nomogram for preoperative prediction of DGF on the basis of clinical and histological risk factors.
methodsThe prediction model was constructed in a development cohort comprising 492 kidney transplant recipients from May 2018 to December 2019. Data regarding donor and recipient characteristics, pre-transplantation biopsy results, and machine perfusion parameters were collected, and univariate analysis was performed. The least absolute shrinkage and selection operator regression model was used for variable selection. The prediction model was developed by multivariate logistic regression analysis and presented as a nomogram. An external validation cohort comprising 105 transplantation cases from January 2020 to April 2020 was included in the analysis.
results266 donors were included in the development cohort, 458 kidneys (93.1%) were preserved by hypothermic machine perfusion (HMP), 96 (19.51%) of 492 recipients developed DGF. Twenty-eight variables measured before transplantation surgery were included in the LASSO regression model. The nomogram consisted of 12 variables from donor characteristics, pre-transplantation biopsy results and machine perfusion parameters. Internal and external validation showed good discrimination and calibration of the nomogram, with Area Under Curve (AUC) 0.83 (95%CI, 0.78-0.88) and 0.87 (95%CI, 0.80-0.94). Decision curve analysis demonstrated that the nomogram was clinically useful.
conclusionA DGF predicting nomogram was developed that incorporated donor characteristics, pre-transplantation biopsy results, and machine perfusion parameters. This nomogram can be conveniently used for preoperative individualized prediction of DGF in kidney transplant recipients.
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