[1]武 倩,李 英,马燕粉,等.丙型肝炎肝硬化患者进展为肝细胞肝癌预测模型建立及验证[J].现代检验医学杂志,2024,39(05):6-11.[doi:10.3969/j.issn.1671-7414.2024.05.002]
　WU Qian,LI Ying,MA Yanfen,et al.Establishment and Validation of Prediction Model for Hepatocellular Carcinoma Progression in Patients with Hepatitis C Cirrhosis[J].Journal of Modern Laboratory Medicine,2024,39(05):6-11.[doi:10.3969/j.issn.1671-7414.2024.05.002]
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丙型肝炎肝硬化患者进展为肝细胞肝癌预测模型建立及验证()
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《现代检验医学杂志》[ISSN:/CN:]

卷:
第39卷
期数:
2024年05期
页码:
6-11
栏目:
论著
出版日期:
2024-09-15

文章信息/Info

Title:
Establishment and Validation of Prediction Model for Hepatocellular Carcinoma Progression in Patients with Hepatitis C Cirrhosis
文章编号:
1671-7414（2024）05-006-06
作者:
武 倩1; 李 英2; 马燕粉1; 仝晓宁1; 张 宁1; 何晓璇1; 王晓琴1
（1. 西安交通大学第一附属医院检验科，西安 710061；2. 陕西省康复医院检验科，西安 710065）
Author(s):
WU Qian1;  LI Ying2;  MA Yanfen1;  TONG Xiaoning1;  ZHANG Ning1;  HE Xiaoxuan1;  WANG Xiaoqin1
（1.Department of Laboratory Medicine, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China; 2.Department of Laboratory Medicine, Shaanxi KangFu Hospital, Xi’an 710065, China）
关键词:
丙型肝炎; 肝硬化; 肝细胞癌; 预测模型
分类号:
R735.7；R730.43
DOI:
10.3969/j.issn.1671-7414.2024.05.002
文献标志码:
A
摘要:
目的 利用实验室常用检测指标筛选丙型肝炎肝硬化患者进展为肝细胞癌（hepatocellular carcinoma，HCC）的影响因素，并利用这些指标建立预测模型并验证。方法 收集2020 年6 月~2023 年5 月在西安交通大学第一附属医院住院治疗的丙型肝炎肝硬化患者231 例和丙型肝炎HCC 患者179 例作为训练集，2023 年6 月~2024 年2 月住院治疗的丙型肝炎肝硬化患者105 例和丙型肝炎HCC 患者86 例作为验证集。比较训练集两组研究对象的实验室常规检测指标，应用Logistic 回归分析筛选肝细胞癌发生的独立预测因素。采用受试者工作特征（receiver operating characteristic，ROC）曲线构建模型及验证。结果 训练集中HCC 组年龄、男性比例、ALT，AST，AFP，WBC，NEU，MO，PLT，MPV，PDW，Fbg，NLR 及PLR 水平均高于肝硬化组（H=-9.07~-2.19），而INR 及LMR 水平均低于肝硬化组（H=-4.49，-2.65），差异具有统计学意义（均P<0.05）；TP，eGFR，LY 及AST/ALT 值在两组患者间差异无统计学意义（H=-1.46~-0.15，均P>0.05）。多因素Logistic 回归分析显示，年龄（OR= 1.048，95% CI: 1.023~1.074）、AST（OR=1.010，95% CI：1.002~1.019）、NEU（OR=1.186，95% CI：1.018~1.382）、Fbg（OR=2.245，95% CI：1.639~3.076）是肝癌患者的独立危险因素（均P<0.05）；性别（OR=0.524，95%CI：0.314~0.874）是肝癌患者的保护因素（均P<0.05），用此5 个独立危险因素构建HCC 列线图预测模型，训练集AUC（95%CI）为 0.813（0.771~0.854），验证集AUC（95%CI）为0.712（0.639~0.784），Hosmer-Lemeshow 检测显示训练集P=0.650，验证集P=0.310 模型的拟合度较好。结论 基于年龄、性别、AST，NEU，Fbg 建立的HCC 预测模型具有良好的预测效能和临床应用价值。
Abstract:
Objective To screen the influencing factors of hepatitis C cirrhosis patients progressing to hepatocellular carcinoma (HCC) using commonly used laboratory testing indicators, establish a prediction model using these indicators and validate them. Methods A total of 231 patients with hepatitis C cirrhosis and 179 patients with hepatitis C HCC hospitalized at the First Affiliated Hospital of Xi’an Jiaotong University between June 2020 and May 2023 were enrolled as the training set, and 105 patients with hepatitis C cirrhosis and 86 patients with hepatitis C HCC hospitalized between June 2023 and February 2024 were enrolled as the validation set. The routine laboratory test indexes of the study subjects in the two groups within the training set were compared, and logistic regression analysis was applied to screen the independent predictors of hepatocellular carcinoma occurrence. Receiver operating characteristic (ROC) curve was used to construct the curve model and validate the model. Results The age, male ratio, ALT, AST, AFP, WBC, NEU, MO, PLT, MPV, PDW, Fbg, NLR and PLR levels of the HCC group were higher than those of the cirrhosis group in the training set (H=-9.07~-2.19), while the levels of INR and LMR were lower than those of the cirrhosis group(H=-4.49,-2.65), and the differences were significant (all P<0.05). The differences in TP, eGFR, LY and AST/ALT values between the two groups of patients were not significant (H=-1.46~-0.15, all P>0.05). Multifactorial Logistic regression analysis showed that age (OR= 1.048, 95% CI:1.023~1.074), AST (OR=1.010, 95% CI:1.002~1.019), NEU (OR=1.186, 95% CI:1.018~1.382) and Fbg (OR=2.245, 95% CI:1.639~3.076) were independent risk factors for hepatocellular carcinoma patients (all P<0.05), while gender (OR=0.524, 95% CI：0.314~0.874) was a protective factor for hepatocellular carcinoma patients (P<0.05). Using these five independent risk factors to construct the HCC column-line graph prediction model, the AUC for the training set and the validation set AUC（95% CI） were 0.813 (0.771~ 0.854) and 0.712 (0.639~0.784), respectively, and the Hosmer-Lemeshow test showed a good fit of the model with P=0.650 for the training set and P=0.310 for the validation set. Conclusion The prediction model of HCC based on age, gender, AST, NEU and Fbg can have good predictive efficacy and clinical application value.

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备注/Memo

备注/Memo:
基金项目：陕西省重点研发项目 (2022SF-243)。
作者简介：武倩（1987-），女 ，硕士，主管技师，主要从事临床血液检验，E-mail:710641296@qq.com。
通讯作者：王晓琴（1971-），女，博士，主任技师，硕士研究生导师，研究方向：肿瘤免疫与分子生物学。
更新日期/Last Update: 2024-09-15