ArticleChinese medical journal2018
Predictive Score Model for Delayed Graft Function Based on Hypothermic Machine Perfusion Variables in Kidney Transplantation.
Article in Chinese medical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
- Development and validation of a multidimensional machine learning model for predicting delayed graft function in deceased donor kidney transplantation.International urology and nephrology · 2026Article
- Development and validation of a novel nomogram model for predicting delayed graft function in deceased donor kidney transplantation based on pre-transplant biopsies.BMC nephrology · 2024Article
- Renal Function at Discharge Among Kidney Recipients Experiencing Delayed Graft Function and Its Associations With Long-term Outcomes.Transplantation direct · 2022Article
- The role of hypothermic machine perfusion in selecting renal grafts with advanced histological score.Artificial organs · 2022Article
- [Value of prediction models for prognosis prediction of colorectal cancer: an analysis based on TCPA database].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Optimizing organs for transplantation; advancements in perfusion and preservation methods.Transplantation reviews (Orlando, Fla.) · 2020Review
- Organ Restoration With Normothermic Machine Perfusion and Immune Reaction.Frontiers in immunology · 2020Review
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9 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHypothermic machine perfusion (HMP) is being used more often in cardiac death kidney transplantation; however, the significance of assessing organ quality and predicting delayed graft function (DGF) by HMP parameters is still controversial. Therefore, we used a readily available HMP variable to design a scoring model that can identify the highest risk of DGF and provide the guidance and advice for organ allocation and DCD kidney assessment.
methodsFrom September 1, 2012 to August 31, 2016, 366 qualified kidneys were randomly assigned to the development and validation cohorts in a 2:1 distribution. The HMP variables of the development cohort served as candidate univariate predictors for DGF. The independent predictors of DGF were identified by multivariate logistic regression analysis with a P < 0.05. According to the odds ratios (ORs) value, each HMP variable was assigned a weighted integer, and the sum of the integers indicated the total risk score for each kidney. The validation cohort was used to verify the accuracy and reliability of the scoring model.
resultsHMP duration (OR = 1.165, 95% confidence interval [CI]: 1.008-1.360, P = 0.043), resistance (OR = 2.190, 95% CI: 1.032-10.20, P < 0.001), and flow rate (OR = 0.931, 95% CI: 0.894-0.967, P = 0.011) were the independent predictors of identified DGF. The HMP predictive score ranged from 0 to 14, and there was a clear increase in the incidence of DGF, from the low predictive score group to the very high predictive score group. We formed four increasingly serious risk categories (scores 0-3, 4-7, 8-11, and 12-14) according to the frequency associated with the different risk scores of DGF. The HMP predictive score indicates good discriminative power with a c-statistic of 0.706 in the validation cohort, and it had significantly better prediction value for DGF compared to both terminal flow (P = 0.012) and resistance (P = 0.006).
conclusionThe HMP predictive score is a good noninvasive tool for assessing the quality of DCD kidneys, and it is potentially useful for physicians in making optimal decisions about the organs donated.
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