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Volume 42 Issue 2
Feb.  2026
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Article Navigation >  Journal of Clinical Hepatology  > 2026  >  42(2): 380-386

Risk factors for acute kidney injury after liver transplantation and establishment of a predictive model

DOI: 10.12449/JCH260218
  • 1.

    Academy of Medical Sciences，Shanxi Medical University，Taiyuan 030001，China

  • 2.

    Department of Hepatobiliary Surgery and Liver Transplant Center，The First Hospital of Shanxi Medical University，Taiyuan 030001，China

Research funding:

National Natural Science Foundation of China (82470693);

Shanxi Provincial Key Laboratory Project (202204010931008);

Shanxi Central Guidance Local Science and Technology Development Fund Project (YDZJSX2021B012);

Shanxi Key Research and Development Program (202302130501013)

More Information
  • Corresponding author: XU Jun， junxuty@163.com （ORCID： 0000-0003-3755-9660）
  • Received Date: 2025-08-22
  • Accepted Date: 2025-12-18
  • Published Date: 2026-02-25
    Abstract
  •   Objective  To investigate the risk factors for acute kidney injury （AKI） after liver transplantation， and to establish and validate a risk prediction model， and to provide a basis for early identification of high-risk patients and intervention in clinical practice.  Methods  A single-center retrospective study was conducted， and clinical data were collected from 162 patients who received liver transplantation in Liver Transplantation Center of The First Hospital of Shanxi Medical University from March 2020 to June 2025. The patients were divided into AKI group with 69 patients and non-AKI group with 93 patients according to the diagnostic criteria for AKI established by the Kidney Disease： Improving Global Outcomes organization and the presence or absence of AKI within 7 days after surgery. The independent-samples t test was used for comparison of normally distributed continuous data between groups， while the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. The univariate differential analysis was used to obtain the factors associated with AKI after liver transplantation， and the multivariate logistic regression analysis was used to identify the independent risk factors and establish a nomogram model； the Bootstrap method with 1 000 repeated samples was used to perform internal validation of the model. The dataset was randomly divided into a training set and a validation set at a ratio of 7∶3， and the receiver operating characteristic （ROC） curve， the calibration curve， and decision curve analysis （DCA） were used to assess the discriminatory ability， calibration， and clinical applicability of the predictive model.  Results  Body mass index （BMI） （odds ratio ［OR］=1.281， 95% confidence interval ［CI］： 1.037　—　1.582， P=0.022）， serum creatinine （OR=1.097， 95%CI： 1.020　—　1.181， P=0.013）， intraoperative blood loss （OR=1.005， 95%CI： 1.002　—　1.009， P=0.004）， and cold ischemia time （OR=0.984， 95%CI： 0.976　—　0.991， P<0.001） were independent risk factors for the development of AKI after liver transplantation. The nomogram prediction model established based on the above factors had an area under the ROC curve （AUC） of 0.964 （95%CI： 0.931　—　0.997）， with an optimal cutoff value of 0.319， a sensitivity of 0.971， and a specificity of 0.903. In the training set （n=113）， the nomogram had an AUC of 0.969 （95% CI： 0.933　—　0.971）， while in the validation set （n=49）， the nomogram had an AUC of 0.941 （95%CI： 0.855　—　0.944）. The calibration curve showed good consistency between the predicted incidence rate and the actual incidence rate， and DCA showed that it had good net clinical benefit.  Conclusion  BMI， serum creatinine， cold ischemia time， and intraoperative blood loss are independent risk factors for the development of AKI after liver transplantation， and the nomogram prediction model established based on these factors performs well and has a good value in predicting the development of AKI after liver transplantation.

     

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