肝移植术后早期移植器官功能不全的预测模型构建

黎鑫; 衣杏林; 陈妍; 邓鑫; 刘祥凤; 刘先哲; 蒋颖; 刘冠磊; 陈春梅; 邱芳; 顾健腾

doi:10.16016/j.2097-0927.202312009

您当前的位置：

首页 >

文章列表页 >

肝移植术后早期移植器官功能不全的预测模型构建

临床医学 | 更新时间：2026-01-09

- 肝移植术后早期移植器官功能不全的预测模型构建
- Construction of predictive model for early allograft dysfunction after liver transplantation
- 陆军军医大学学报 2024年46卷第7期页码：746-752
- 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：
  
  [顾健腾，E-mail: gjt1976@163.comGU Jianteng, E-mail: gjt1976@163.com
- 基金信息：
  
  重庆英才项目基金(CQYC20220303588);陆军军医大学人才基金(XZ2019-505-028)
- DOI：10.16016/j.2097-0927.202312009
  中图分类号： R181.32;R617;R657.306
- 收稿：2023-12-03，
  
  修回：2024-01-05，
  
  纸质出版：2024-04-15
- 稿件说明：
移动端阅览
黎鑫, 衣杏林, 陈妍, 等. 肝移植术后早期移植器官功能不全的预测模型构建[J]. 陆军军医大学学报, 2024,46(7):746-752.

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.
黎鑫, 衣杏林, 陈妍, 等. 肝移植术后早期移植器官功能不全的预测模型构建[J]. 陆军军医大学学报, 2024,46(7):746-752. DOI： 10.16016/j.2097-0927.202312009.

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

陈峻, 沈恬, 李建辉, 等. 中国肝癌肝移植临床实践指南(2021版)[J]. 实用器官移植电子杂志, 2022, 10(6): 481-489, 480.

CHEN J, SHEN T, LI J H, et al . Clinical practice guidelines for liver transplantation for liver cancer in China(2021 edition)[J ] . Pract J Organ Tr ansplant Electron Version, 2022, 10(6): 481-489, 480.

KWONG A J, KIM W R, LAKE J R, et al . OPTN/SRTR 2019 annual data report: liver[J ] . Am J Transplant, 2021, 21(Suppl 2): 208-315. DOI: 10.1111/ajt.16494.

OLTHOFF K M, KULIK L, SAMSTEIN B, et al . Validation of a current definition of early allograft dysfunction in liver transplant recipients and analysis of risk factors[J ] . Liver Transpl, 2010, 16(8): 943-949. DOI: 10.1002/lt.22091.

NAM K W, KWON H M, JEONG H Y, et al . Pooled cohort risk equation and subclinical cerebrovascular diseases[J ] . Euro J Neurology, 2020, 27(5): 793-799. DOI: 10.1111/ene.14155.

POSKUS T, SABONYTE-BALSAITIENE Z, JAKUBAUSKIENE L, et al . Preventing hemorrhoids during pregnancy: a multicenter, randomized clinical trial[J ] . BMC Pregnancy Childbirth, 2022, 22(1): 374. DOI: 10.1186/s12884-022-04688-x.

孟海鹏, 董汉光, 严律南. 肝移植早期供肝功能障碍的危险因素分析[J]. 中国现代普通外科进展, 2022, 25(6): 436-438, 460. DOI: 10.3969/j.issn.1009-9905.2022.06.005.

MENG H P, DONG H G, YAN L N. Risk factors of early allograft dysfunction after liver trans-plantation[J]. Chin J Curr Adv Gen Surg, 2022, 25(6): 436-438, 460. DOI: 10.3969/j.issn.1009-9905.2022.06.005.

ADAM R, AZOULAY D, CASTAING D, et al . . Liver resection as a bridge to transplantation for hepatocellular carcinoma on cirrhosis: a reasonable strategy?[J ] . Eur Surg, 2004, 36(1): 56-57. DOI: 10.1007/s10353-004-0038-y.

WIESNER R, EDWARDS E, FREEMAN R, et al . Model for end-stage liver disease(MELD) and allocation of donor livers[J ] . Gastroenterology, 2003, 124(1): 91-96. DOI: 10.1053/gast.2003.50016.

WIESNER R. MELD and PELD: application of survival models to liver allocation[J]. Liver Transplant, 2001, 7(7): 567-580. DOI: 10.1053/jlts.2001.25879.

AVOLIO A W, FRANCO A, SCHLEGEL A, et al . Development and validation of a comprehensive model to estimate early allograft failure among patients requiring early liver retransplant[J ] . JAMA Surg, 2020, 155(12): e204095. DOI: 10.1001/jamasurg.2020.4095.

IJTSMA A J C, VAN DER HILST C S, DE BOER M T, et al . The clinical relevance of the anhepatic phase during liver transplantatio n[J ] . Liver Transpl, 2009, 15(9): 1050-1055. DOI: 10.1002/lt.21791.

ESCH J S A 2nd, KNOEFEL W T, KLEIN M, et al . Portal application of autologous CD133 + bone marrow cells to the liver: a novel concept to support hepatic regeneration[J ] . Stem Cells, 2005, 23(4): 463-470. DOI: 10.1634/stemcells.2004-0283..

GU J T, SUN P, ZHAO H L, et al . Dexmedetomidine provides renoprotection against ischemia-reperfusion injury in mice[J ] . Crit Care, 2011, 15(3): R153. DOI: 10.1186/cc10283.

MA J B, CHEN Q, LI J J, et al . Dexmedetomidine-mediated prevention of renal ischemia-reperfusion injury depends in part on cholinergic anti-inflammatory mechanisms[J ] . Anesth Analg, 2020, 130(4): 1054-1062. DOI: 10.1213/ane.0000000000003820.

HUANG D B, SHEN Y N, ZHANG W, et al . A preoperative nomogram predicts prognosis of patients with hepatocellular carcinoma after liver transplantation: a multicenter retrospective study[J ] . BMC Cancer, 2021, 21(1): 280. DOI: 10.1186/s12885-021-07938-x.

GUO D D, WANG H F, LIU J, et al . Prediction of chronic kidney dise ase after orthotopic liver transplantation: development and validation of a nomogram model[J ] . BMC Nephrol, 2022, 23(1): 33. DOI: 10.1186/s12882-021-02650-1.

AGRAWAL D, SAIGAL S. Early allograft dysfunction after living donor liver transplantation—current concepts and future directions[J]. Liver Transplant, 2023, 29(8): 871-884. DOI: 10.1097/lvt.0000000000000173.

ITO T, NAINI B V, MARKOVIC D, et al . Ischemia-reperfusion injury and its relationship with early allograft dysfunction in liver transplant patients[J ] . Am J Transplant, 2021, 21(2): 614-625. DOI: 10.1111/ajt.16219.

NARAYAN R R, ABADILLA N, YANG L F, et al . Artificial intelligence for prediction of donor liver allograft steatosis and early post-transplantation graft failure[J ] . HPB, 2022, 24(5): 764-771. DOI: 10.1016/j.hpb.2021.10.004.

OHARA S, MACDONOUGH E, EGBERT L, et al . Decreasing significance of early allograft dysfunction with rising use of nonconventional donors[J ] . Medicina, 2022, 58(6): 821. DOI: 10.3390/medicina58060821.

浏览量

238

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Stanford A型主动脉夹层术后急性肾功能损伤的发生情况及其危险因素分析

胚胎着床位置对前置胎盘的预测价值及相关影响因素分析

瘦型代谢功能障碍相关脂肪性肝病风险预测模型的构建与验证

前列腺癌根治术后生化复发的危险因素分析

绝经期非肿瘤女性发生静脉血栓栓塞症的危险因素分析及预测模型构建