Construction of predictive model for early allograft dysfunction after liver transplantation

Xin LI; Xinglin YI; Yan CHEN; Xin DENG; Xiangfeng LIU; Xianzhe LIU; Ying JIANG; Guanlei LIU; Chunmei CHEN; Fang QIU; Jianteng GU

doi:10.16016/j.2097-0927.202312009

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Construction of predictive model for early allograft dysfunction after liver transplantation

Clinical Medicine | 更新时间：2026-01-09

- Construction of predictive model for early allograft dysfunction after liver transplantation
- Journal of Army Medical University Vol. 46, Issue 7, Pages: 746-752(2024)
- 作者机构：
  
  1.400038 重庆, 陆军军医大学(第三军医大学)第一附属医院麻醉科
  2.400038 重庆, 陆军军医大学(第三军医大学)第一附属医院呼吸科
- 作者简介：
- 基金信息：
  
  the Chongqing Talent Program(CQYC20220303588);the Excellent Talents Foundation of Army Medical University(XZ2019-505-028)
- DOI：10.16016/j.2097-0927.202312009
  CLC： R181.32;R617;R657.306
- Received：03 December 2023，
  
  Revised：2024-01-05，
  
  Published：15 April 2024
- 稿件说明：
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Xin LI, Xinglin YI, Yan CHEN, et al. Construction of predictive model for early allograft dysfunction after liver transplantation[J]. Journal of Army Medical University, 2024, 46(7): 746-752.
DOI：

Xin LI, Xinglin YI, Yan CHEN, et al. Construction of predictive model for early allograft dysfunction after liver transplantation[J]. Journal of Army Medical University, 2024, 46(7): 746-752. DOI： 10.16016/j.2097-0927.202312009.

摘要

目的

分析肝移植术后早期移植物功能不全(early allograft dysfunction

EAD)的相关因素并构建预测模型。

方法

收集2008年12月至2021年12月于我院麻醉科手术室实施肝移植手术的375例患者

其中术后发生EAD的患者90例

未发生EAD的患者266例

比较2组患者30项基线资料。按照7:3的比例进行分组后

在训练集中依次采用单因素和多因素Logistic回归分析评价EAD的相关因素并构建列线图

采用受试者工作特征曲线(receiver operating characteristic

ROC)、临床决策曲线分析(decision curve analysis

DCA)、灵敏度、特异度、阳性预测值、阴性预测值、Kappa值等指标评估模型表现。

结果

肝移植术后EAD发生率为24%。多因素Logistic回归分析显示

术前肿瘤复发史(

=3.15

95%

: 1.28~7.77

=0.013)、手术时间(

=1.22

95%

: 1.04~1.42

=0.015)与术后发生EAD有关

根据约登指数鉴定的0.519为截点划分预测结局后

训练集和验证集中模型表现尚可

临床决策曲线提示模型有较好的临床适用性。

结论

术后EAD的危险因素为术前肿瘤复发史、手术时间

建立的模型能较好地预测患者的预后。

Abstract

Objective

To analyze the factors related to early allograft dysfunction (EAD) after liver transplantation and to construct a predictive model.

Methods

A total of 375 patients who underwent liver transplantation in our hospital from December 2008 to December 2021 were collected

including 90 patients with EAD and 266 patients without EAD.Thirty items of baseline data for the 2 groups were compared and analyzed.After grouping in a ratio of 7:3

univariate and multivariate logistic regression analyses were used in the training set to evaluate the factors related to EAD and construct a nomogram.Receiver operating characteristic (ROC) curve

decision curve analysis (DCA)

sensitivity

specificity

positive predictive value

negative predictive value

Kappa value and other indicators were used to evaluate the model performance.

Results

The incidence of EAD after liver transplantation was 24%.Multivariate logistic regression analysis showed that preoperative tumor recurrence history (

=3.15

95%

: 1.28~7.77

=0.013) and operation time (

=1.22

95%

: 1.04~1.42

=0.015) were related to the occurrence of EAD after surgery.After predicting the outcome according to the cut-off point of 0.519 identified by the Youden index

the model performance in the both training set and validation set was acceptable.DCA suggested the model has good clinical applicability.

Conclusion

The risk factors for EAD after liver transplantation are preoperative tumor recurrence history and operation time

and the established model has predictive effect on prognosis.

关键词

Keywords

references

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