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Chinese Journal of Transplantation(Electronic Edition) ›› 2025, Vol. 19 ›› Issue (03): 138-144. doi: 10.3877/cma.j.issn.1674-3903.2025.03.004

• Original Article • Previous Articles    

Establishment of a risk prediction model for lower extremity deep vein thrombosis after liver transplantation

Xue Guan, Ying Yu(), Jing Li, Ying Liu, Yajuan Cui, Minghe Liu   

  1. Department of Hepatobiliary and Pancreatic Surgery Ⅰ, General Surgery Center, the First Hospital of Jilin University, Changchun 130000, China
  • Received:2024-09-25 Online:2025-06-25 Published:2025-09-06
  • Contact: Ying Yu

Abstract:

Objective

To investigate the risk factors of lower extremity deep vein thrombosis(LEDVT) in liver transplant recipients, and to establish and validate a risk prediction model for LEDVT in recipients after liver transplantation.

Methods

A total of 336 recipients who underwent allogeneic orthotopic liver transplantation at the First Hospital of Jilin University from January 2020 to October 2023 were selected as the research subjects. The sample.split function was used to randomly divide the recipients into the modeling group (n=235) and the validation group (n=101) at a ratio of 7∶3. Through literature review, group discussion and clinical knowledge, the predictors of LEDVT in recipients after liver transplantation were determined. The group t test or Mann-Whitney U test was used for comparison of measurement data between groups. Comparison of counting data between groups was performed using the chi-square test or the Fisher exact probability method. Those predictors with P<0.05 in the univariate analysis were included in multivariate Logistic regression analysis to clarify the independent risk factors of LEDVT in recipients after liver transplantation.The nomogram was drawn using R (version 4.3.2) software, and a web-based calculator of the postoperative LEDVT risk prediction model for liver transplant recipients was developed on the shinyapps.io, and the area under the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow goodness-of-fit test and clinical decision curve were used to evaluate the discrimination, accuracy and clinical benefit of the LEDVT risk prediction model in liver transplant recipients. A P<0.05 was considered statistically significant.

Results

Among the 235 recipients in the modeling group, there were 49 cases in the LEDVT group and 186 cases in the non LEDVT group, with a LEDVT incidence rate of 20.8%(49/235). There were statistically significant differences in age, preoperative hepatic encephalopathy, daily living ability level, coagulation factor response time, postoperative AST, ALT, Na+ level, Ca2+ level, prothrombin time (PT), and international standardized ratio between the LEDVT group and non LEDVT group (Z=-3.552, -2.808, -2.567, -2.161, -2.297, -1.986, -3.815 and -2.395, χ2=13.822 and 36.213, all P<0.05).The results of multivariate Logistic regression analysis showed that the age (OR=1.048, 95%CI: 1.002-1.096), preoperative presence of hepatic encephalopathy (OR=2.484, 95%CI: 1.041-5.930), preoperative daily living ability (moderate dependence) (OR=5.266, 95%CI: 1.685-16.458), preoperative daily living ability (severe dependence) (OR=8.342, 95%CI: 1.748-39.802), postoperative Na+ level (OR=1.105, 95%CI: 1.001-1.220), and postoperative PT (OR=0.827, 95%CI: 0.737-0.928) were independent risk factors for postoperative LEDVT in liver transplant recipients (all P<0.05).The area under the ROC curve of the LEDVT risk prediction model of the modeling group and validation group were 0.811 (95%CI: 0.745-0.876) and 0.736 (95%CI: 0.615-0.856), respectively, the Hosmer Lemeshow test result showed χ2=5.166 and 10.378, all P<0.05. Good clinical benefits were shown both in the modeling group and validation group.

Conclusion

The risk prediction model established in this study has a good prediction effect and can provide a reference basis for clinical medical staff to evaluate the risk of postoperative LEDVT in liver transplant recipients.

Key words: Liver transplantation, Lower extremity deep vein thrombosis, Risk factor, Risk prediction model

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