Construction and evaluation of a nomogram risk prediction model for post-hepatectomy liver failure in hepatocellular carcinoma patients with hepatitis B infection at low viral load

HAN Yan; LI Yujie; YI Bin

doi:10.16016/j.2097-0927.202501065

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Construction and evaluation of a nomogram risk prediction model for post-hepatectomy liver failure in hepatocellular carcinoma patients with hepatitis B infection at low viral load

Clinical Medicine | 更新时间：2025-03-28

- Construction and evaluation of a nomogram risk prediction model for post-hepatectomy liver failure in hepatocellular carcinoma patients with hepatitis B infection at low viral load
- Journal of Army Medical University Vol. 47, Issue 6, Pages: 561-570(2025)
- 作者机构：
  
  陆军军医大学（第三军医大学）第一附属医院麻醉科，重庆
- 作者简介：
  
  YI Bin， E-mail： yibin1974@tmmu.edu.cn
- 基金信息：
  
  the General Program of National Natural Science Foundation of China(82070630;82470655)
- DOI：10.16016/j.2097-0927.202501065
  CLC： R181.32;R575.3;R657.307
- Received：23 January 2025，
  
  Revised：03 March 2025，
  
  Published：30 March 2025
- 稿件说明：
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韩嫣，李雨捷，易斌.低病毒载量乙肝肝癌人群肝切除术后肝衰发生风险预测模型的建立与分析［J］.陆军军医大学学报，2025，47（06）：561-570.DOI：10.16016/j.2097-0927.202501065.

‍HAN Y，LI Y J，YI B.Construction and evaluation of a nomogram risk prediction model for post-hepatectomy liver failure in hepatocellular carcinoma patients with hepatitis B infection at low viral load［J］.J Army Med Univ，2025，47（06）：561-570.DOI：10.16016/j.2097-0927.202501065.
韩嫣，李雨捷，易斌.低病毒载量乙肝肝癌人群肝切除术后肝衰发生风险预测模型的建立与分析［J］.陆军军医大学学报，2025，47（06）：561-570.DOI：10.16016/j.2097-0927.202501065. DOI：

‍HAN Y，LI Y J，YI B.Construction and evaluation of a nomogram risk prediction model for post-hepatectomy liver failure in hepatocellular carcinoma patients with hepatitis B infection at low viral load［J］.J Army Med Univ，2025，47（06）：561-570.DOI：10.16016/j.2097-0927.202501065. DOI：

摘要

目的

探讨低病毒载量乙肝肝癌人群发生肝切除术后肝衰（post-hepatectomy liver failure，PHLF）的影响因素，并构建风险预测模型。

方法

选择2015年1月1日至2023年3月1日在陆军军医大学第一附属医院麻醉科首次接受肝切除术的403例低病毒载量乙肝肝癌患者作为研究对象，按照7∶3比例采用简单随机抽样法分为训练集和验证集，通过Lasso回归和多因素Logistic回归分析筛选PHLF发生的影响因素并建立列线图预测模型。通过多种指标对模型的性能进行评估，包括受试者工作特征曲线下面积（area under the receiver operating characteristic curve， AUC）、校准曲线、决策曲线分析和临床影响曲线分析。

结果

本研究确定抗病毒治疗史、饮酒史、乙肝表面抗原值及国际标准化比值为低病毒载量乙肝肝癌人群发生PHLF的独立影响因素。基于上述指标建立的预测模型在训练集和验证集中表现出卓越的区分能力，AUC值分别为0.744（95%

：0.671~0.818）和0.737（95%

：0.599~0.876）。校准曲线显示出模型的高准确性（训练集：

=0.995；验证集：

=0.701），决策曲线分析和临床影响曲线分析均表明模型提供了更大的临床益处。

结论

本研究建立的预测模型能够有效评估低病毒载量乙肝肝癌人群发生PHLF的风险，具有良好的预测性能，对及时识别高危人群具有一定的指导意义。

Abstract

Objective‍　‍To investigate the influencing factors for post-hepatectomy liver failure (PHLF) in hepatocellular carcinoma (HCC) patients with HBV infection at low viral load

and then construct a risk prediction model. Methods‍　‍A total of 403 HCC patients who underwent initial hepatectomy in the First Affiliated Hospital of Army Medical University between January 1

2015 and March 1

2023 were recruited

and randomly assigned into a training set and a verification set in a ratio of 7:3. Lasso regression and multivariate logistic regression analyses were applied to screen the risk factors for occurrence of PHLF

and based on these identified factors

a nomogram prediction model was constructed. Receiver operating characteristic (ROC) curve analysis (area under the curve

AUC)

calibration curve analysis

decision curve analysis

and clinical impact curve analysis were preformed to assess the predictive efficacy of the model. Results‍

　‍History of anti-viral therapy

history of drinking

logHBsAg

and international normalized ratio (INR) were independent influencing factors for the occurrence of PHLF in HCC patients with HBV infection at low viral load. The model established based on these indicators demonstrated excellent discriminative capabilities in both the training and validation sets

with an AUC value of 0.744 and 0.737

respectively. Calibration curve analysis indicated our model of high accuracy (training:

=0.995; validation:

=0.701)

and decision curve analysis and clinical impact curve analysis displayed that our model provided greater clinical benefit. Conclusion‍　‍Our model can effectively evaluate the risk of PHLF in HCC patients with HBV infection at low viral load

and shows good predictive performance

which has certain guiding significance for timely identification of high-risk populations.

关键词

Keywords

references

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