肝硬化伴自发性细菌性腹膜炎的诊断模型研究及验证

向守书; 谈军涛; 文元久; 谭超; 龚军; 赵文龙

doi:10.16016/j.1000-5404.202104100

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肝硬化伴自发性细菌性腹膜炎的诊断模型研究及验证

临床医学 | 更新时间：2026-01-09

- 肝硬化伴自发性细菌性腹膜炎的诊断模型研究及验证
- Establishment and verification of a diagnostic model of liver cirrhosis with spontaneous bacterial peritonitis
- 第三军医大学学报 2021年43卷第20期页码：2226-2234
- 作者机构：
  
  1.400016 重庆，重庆医科大学医学信息学院
  2.400038 重庆，陆军军医大学(第三军医大学)全科医学系
- 作者简介：
  
  [ 赵文龙，E-mail：cqzhaowl@163.com ZHA Wenlong, E-mail: cqzhaowl@163.com
- 基金信息：
  
  重庆医科大学智慧医学项目(重庆医科大学智慧医学项目)
- DOI：10.16016/j.1000-5404.202104100
  中图分类号： R195.4;R572.2;R575.2
- 收稿：2021-04-16，
  
  修回：2021-07-05，
  
  纸质出版：2021-10-30
- 稿件说明：
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向守书, 谈军涛, 文元久, 等. 肝硬化伴自发性细菌性腹膜炎的诊断模型研究及验证[J]. 第三军医大学学报, 2021,43(20):2226-2234.

Shoushu XIANG, Juntao TAN, Yuanjiu WEN, et al. Establishment and verification of a diagnostic model of liver cirrhosis with spontaneous bacterial peritonitis[J]. Journal of Third Military Medical University, 2021, 43(20): 2226-2234.
向守书, 谈军涛, 文元久, 等. 肝硬化伴自发性细菌性腹膜炎的诊断模型研究及验证[J]. 第三军医大学学报, 2021,43(20):2226-2234. DOI： 10.16016/j.1000-5404.202104100.

Shoushu XIANG, Juntao TAN, Yuanjiu WEN, et al. Establishment and verification of a diagnostic model of liver cirrhosis with spontaneous bacterial peritonitis[J]. Journal of Third Military Medical University, 2021, 43(20): 2226-2234. DOI： 10.16016/j.1000-5404.202104100.

摘要

目的

筛选肝硬化伴自发性细菌性腹膜炎(spontaneous bacterial peritonitis，SBP) 的影响因素，建立肝硬化伴SBP的诊断模型。

方法

收集重庆A、B两家三甲医院2015年7月至2019年12月肝硬化患者7 461例，根据患者住院期间是否发生SBP分为SBP组(1 173例)和非SBP组(6 288例)。随机抽取A院70%(3 776例)患者作为训练集，剩余30%(1 619例)作为内部验证集，B院患者(2 066例)作为外部验证集。通过单因素分析和Logistic回归分析筛选变量，建立Logistic回归、随机森林、决策树、XGBoost和优化Logistic回归模型。在内部验证和外部验证中，比较并验证模型对肝硬化伴SBP的诊断价值。

结果

机器学习算法均提示指标失代偿(

=5.354，95%

: 3.770-7.803)，淋巴细胞百分比(

=0.951，95%

: 0.939-0.962)、总胆红素(

=1.003，95%

: 1.002-1.004)、C反应蛋白是否异常(

=1.626，95%

: 1.310-2.017)、国际标准化比值(

=1.346，95%

: 1.091-1.681)、前白蛋白(

=0.990，95%

: 0.987-0.993)、终末期肝病模型评分(model for end stage liver disease，MELD) (

=1.038，95%

: 1.015-1.063)为显著性因素，优化Logistic内部验证的AUC为0.860，灵敏度0.872，特异度0.719；外部验证的AUC为0.818；灵敏度为0.662，特异度为0.812。经过delong test检测优化Logistic回归模型和其余模型的AUC差异无统计学意义。

结论

基于机器学习算法建立的肝硬化合并SBP风险预测模型具有较高的诊断价值，其中优化Logistic回归模型的诊断效能在内部验证和外部验证中均表现良好，可为临床诊断SBP提供参考。

Abstract

Objective

To screen the influencing factors of liver cirrhosis with spontaneous bacterial peritonitis (SBP) so as to establish and verify a diagnostic model of cirrhosis with SBP.

Methods

A total of 7 461 patients with liver cirrhosis were collected from 2 Grade-A hospitals in Chongqing (Hospital A and B) between July 2015 and December 2019. According to the occurrence of SBP during hospitalization

they were divided into SBP (

=1 173) and non-SBP (

=6 288) groups. A total of 3 776 patients (70%) from Hospital A were randomly selected as the training set

while the remaining 1 619 patients (30%) and the 2 066 patients from hospital B were subjected as the internal and external validation sets respectively. Univariate and logistic analyses were used to screen variables

and logistic regression

random forest (RF)

decision tree (DT) and XGBoost models were subsequently established using the training set. Then

the optimized logistic regression model was built on the basis of the above 4 models. Finally

the 5 models were applied in the internal and external verification sets for eval

uating and comparing the diagnostic value of different models for cirrhosis with SBP.

Results

The machine learning algorithm suggested that the 7 common influencing factors with significance in all models were as follows: decompensation stage (

=5.354

95%

: 3.770-7.803)

lymphocyte percentage (

=0.951

95%

: 0.939-0.962)

total bilirubin (

=1.003

95%

: 1.002-1.004)

abnormal C-reactive protein (

=1.626

95%

: 1.310-2.017)

international normalized ratio (

=1.346

95%

: 1.091-1.681)

prealbumin (

=0.990

95%

: 0.987-0.993) and model for end stage liver disease (MELD) score (

=1.038

95%

: 1.015-1.063). The AUC of the internal verification of the optimized logistic model was 0.860

with a sensitivity of 0.872 and a specificity of 0.719; while the corresponding values of the external verification were 0.818

0.662 and 0.812

respectively. Delong test showed that there was no statistical difference in AUC between the optimized logistic regression model and the other models with favorable performance.

Conclusion

The risk prediction models established by machine learning algorithm for liver cirrhosis complicated with SBP have high diagnostic value. Among them

the optimized logistic regression model has performed well in both internal and external verification

and is able to provide reference for clinical diagnosis of SBP.

关键词

Keywords

references

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