高代谢风险慢性乙型肝炎患者发生肝硬化的影响因素分析及预测模型构建

邹玉萍; 姚莉; 邹军; 李立威; 蔡福庆; 黄杰安

doi:10.12449/JCH250616

高代谢风险慢性乙型肝炎患者发生肝硬化的影响因素分析及预测模型构建

DOI: 10.12449/JCH250616

广西医科大学第二附属医院消化内科，南宁 530007

基金项目:

广西卫生健康委员会自筹经费科研课题 (Z-A20220664)

伦理学声明：本研究方案于2022年8月18日经由广西医科大学第二附属医院伦理委员会审批，批号：2022KY（0532）。

利益冲突声明：本文不存在任何利益冲突。

作者贡献声明：邹玉萍负责设计论文框架，分析资料，起草论文；姚莉负责分析解释过程及修改文章；李立威负责统计学分析，绘制图表；邹军、蔡福庆、黄杰安参与论文修改。

详细信息

通信作者:
黄杰安， hjagxmu@163.com （ORCID： 0000-0003-0431-1888）

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出版历程
- 收稿日期: 2024-09-28
- 录用日期: 2024-12-02
- 出版日期: 2025-06-25

Risk factors for liver cirrhosis in chronic hepatitis B patients with high metabolic risks and establishment of a predictive model

Department of Gastroenterology，The Second Affiliated Hospital of Guangxi Medical University，Nanning 530007，China

Research funding:

The Self-raised Foundation of Guangxi Zhuang Autonomous Region, China (Z-A20220664)

More Information

Corresponding author: HUANG Jiean， hjagxmu@163.com （ORCID： 0000-0003-0431-1888）

摘要

摘要: 目的探讨高代谢风险慢性乙型肝炎（CHB）患者发生肝硬化的主要危险因素，并构建无创预测模型，评估其与FIB-4、APRI、GPR及Forns指数模型的诊断效能差异。方法选取2017年9月1日—2022年10月31日于广西医科大学第二附属医院诊治的527例高代谢风险CHB患者，并按照7∶3比例随机分为建模组（n=368）和验证组（n=159）。在建模组中通过LASSO回归、多因素Logistic回归分析筛选出独立影响因素，并建立列线图模型，采用受试者操作特征曲线（ROC曲线）、校准曲线和决策曲线在建模组和验证组中对列线图预测模型进行验证以判断其区分度、校准度和临床实用性。通过Delong检验比较列线图预测模型与其他模型ROC曲线下面积（AUC）的差异。结果多因素Logistic回归分析显示，前白蛋白（OR=0.993，95%CI：0.988～0.999，P=0.019）、凝血酶时间（OR=1.182，95%CI：1.006～1.385，P=0.047）、log₁₀TBil（OR=1.710，95%CI：1.239～2.419，P=0.001）、log₁₀AFP（OR=1.327，95%CI：1.052～1.683，P=0.018）是高代谢风险CHB患者发生肝硬化的独立影响因素。依据多因素分析结果，构建风险预测模型列线图，其AUC为0.837（95%CI：0.788～0.888），特异度为73.5%，敏感度为84.7%，诊断效能高于FIB-4（0.739）、APRI（0.802）、GPR（0.800）、Forns指数（0.709）（Z值分别为2.815、2.271、1.989、2.722，P值分别为0.005、0.017、0.045、0.006）。结论基于前白蛋白、凝血酶时间、log₁₀TBil、log₁₀AFP建立的列线图模型具有一定的临床应用价值。
- 乙型肝炎，慢性 /
- 高代谢风险 /
- 肝硬化 /
- 影响因素分析 /
- 列线图
Abstract: Objective To investigate the main risk factors for liver cirrhosis in chronic hepatitis B （CHB） patients with high metabolic risk， to establish a noninvasive predictive model， and to compare the diagnostic efficiency of this model and other models including fibrosis-4 （FIB-4）， aspartate aminotransferase-to-platelet ratio index （APRI）， gamma-glutamyl transpeptidase-to-platelet ratio （GPR）， and Forns index. Methods A total of 527 CHB patients with high metabolic risks who were admitted to The Second Affiliated Hospital of Guangxi Medical University from September 1， 2017 to October 31， 2022 were enrolled as subjects， and they were randomly divided into modeling group with 368 patients and validation group with 159 patients at a ratio of 7∶3. The LASSO regression analysis and the multivariate Logistic regression analysis were performed for the modeling group to identify independent risk factors， and a nomogram model was established. The receiver operating characteristic （ROC） curve， the calibration curve， and the decision curve analysis were used to validate the nomogram prediction model in the modeling group and the validation group and assess its discriminatory ability， calibration， and clinical practicability. The Delong test was used to compare the area under the ROC curve （AUC） of the nomogram prediction model and other models. Results The multivariate Logistic regression analysis showed that prealbumin （odds ratio ［OR］ = 0.993， 95% confidence interval ［CI］： 0.988 — 0.999， P= 0.019）， thrombin time （OR=1.182， 95% CI： 1.006 — 1.385， P=0.047）， log₁₀ total bilirubin （TBil）（OR=1.710， 95%CI： 1.239 — 2.419， P=0.001）， and log₁₀ alpha-fetoprotein （AFP）（OR=1.327， 95%CI： 1.052 — 1.683， P=0.018） were independent influencing factors for liver cirrhosis in CHB patients with high metabolic risks. A nomogram model for risk prediction was established based on the multivariate analysis， which had an AUC of 0.837 （95%CI： 0.788 — 0.888）， a specificity of 73.5%， and a sensitivity of 84.7%， as well as a significantly higher diagnostic efficiency than the models of FIB-4 （0.739）， APRI （0.802）， GPR （0.800）， and Forns index （0.709）（Z=2.815， 2.271， 1.989， and 2.722， P=0.005， 0.017， 0.045， and 0.006）. Conclusion The nomogram model established based on prealbumin， thrombin time， log₁₀ TBil， and log₁₀ AFP has a certain clinical application value.
- Hepatitis B， Chronic /
- High Metabolic Risk /
- Liver Cirrhosis /
- Root Cause Analysis /
- Nomograms

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注： a，log（λ）与LASSO回归系数的关系；b，惩罚项的交叉验证。

图 1 高代谢风险CHB患者发生肝硬化的LASSO回归分析

Figure 1. LASSO regression analysis of significant liver cirrhosis in patients with high metabolic risk chronic hepatitis B

下载: 全尺寸图片幻灯片

图 2 高代谢风险CHB患者发生肝硬化的列线图模型

Figure 2. A nomographic model of cirrhosis in patients with high metabolic

下载: 全尺寸图片幻灯片

注： a，建模组；b，验证组。

图 3 列线图模型预测高代谢风险CHB患者发生肝硬化的ROC曲线

Figure 3. A nomogram model predicts the ROC curve of cirrhosis in patients with high metabolic risk CHB

下载: 全尺寸图片幻灯片

注： a，建模组；b，验证组。

图 4 列线图模型在建模组和验证组中的决策曲线

Figure 4. Decision curve analysis of the nomogram model in modeling and verifying the population

下载: 全尺寸图片幻灯片

注： a，建模组；b，验证组。

图 5 列线图模型在建模组和验证组中的校准曲线

Figure 5. Calibration curves of the nomogram model in the modeling and validation cohorts

下载: 全尺寸图片幻灯片

图 6 列线图模型与各评分系统预测高代谢风险CHB患者发生肝硬化的ROC曲线分析

Figure 6. ROC curve analysis of the nomogram model and scoring systems predicting cirrhosis in patients with high metabolic risk CHB

下载: 全尺寸图片幻灯片

表 1 建模组和验证组基线特征

Table 1. Modeling cohort and validating cohort baseline characteristics

项目	建模组（n=368）	验证组（n=159）	P值
年龄（岁）	46.96±12.51	46.42±13.05	0.550
性别［例（%）］			0.155
女	85（23.10）	46（28.93）
男	283（76.90）	113（71.07）
身体质量指数（kg/m²）	23.83±3.65	23.87±4.04	0.975
腹围（cm）	65.30±12.72	64.26±12.32	0.304
高血压［例（%）］	105（28.53）	35（22.01）	0.120
糖尿病［例（%）］	90（24.46）	42（26.42）	0.634
LSM［例（%）］			0.145
<7.3 kPa	124（33.70）	54（33.96）
7.3～9.6 kPa	81（22.01）	30（18.87）
9.7～12.3 kPa	57（15.49）	25（15.72）
12.4～16.9 kPa	55（14.95）	17（10.69）
≥17.0 kPa	51（13.86）	33（20.75）
脂肪衰减（db/m）	215.8±33.4	214.6±34.2	0.590
PLT（×10⁹/L）	208.38±68.55	213.43±77.26	0.996
Alb（g/dL）	40.80±6.11	41.08±5.47	0.451
PA（mg/L）	195.49±75.21	189.34±80.07	0.378
TBil（μmol/L）	11.20（7.80～18.06）	11.70（8.40～22.35）	0.281
DBil（μmol/L）	4.40（3.00～7.50）	4.50（3.30～10.20）	0.239
IBil（μmol/L）	6.50（4.28～9.53）	6.90（4.60～10.95）	0.221
AST（U/L）	35.6±8.1	33.2±7.9	0.202
ALT（U/L）	38.2±9.4	39.6±6.5	0.323
空腹血糖（mmol/L）	6.50±3.05	6.38±2.98	0.390
餐后2 h血糖（mmol/L）	9.89±4.73	10.15±4.66	0.419
TG（mg/dL）	1.28（0.94～1.92）	1.29（0.90～1.89）	0.749
HDL（mg/dL）	0.98±0.38	0.95±0.37	0.618
糖化血红蛋白（mmol/L）	7.12±2.50	7.21±2.60	0.955
总胆固醇（mg/dL）	4.15±1.30	4.03±1.09	0.535
TT（s）	14.51±1.90	14.73±2.02	0.134
AFP（μg/L）	2.61（1.82～4.39）	2.64（1.73～4.82）	0.754
HBsAg（IU/mL）	204.5±87.0	187.2±95.9	0.056
抗-HBs（mIU/mL）	1.04（1.00～1.62）	1.02（1.00～1.65）	0.850
HBeAg（PEIU/mL）	1.01（1.00～1.04）	1.01（1.00～1.13）	0.262
抗-HBe（PEIU/mL）	3.9±1.7	3.7±1.8	0.126
抗-HBc（PEIU/mL）	7.7±1.5	7.7±1.5	0.319
HBV DNA（log₁₀IU/mL）	3.70±1.97	3.60±1.94	0.768
注：PA，前白蛋白；TT，凝血酶时间。

下载: 导出CSV

表 2 预测变量多因素Logistic回归分析

Table 2. Multivariate Logistic regression analysis of predictive variables

项目	β值	SE	Wald	OR（95%CI）	P值
截距	-4.953	1.525	-3.249	0.007（0.000～0.134）	0.001
PA	-0.007	0.003	-2.343	0.993（0.988～0.999）	0.019
TT	0.159	0.080	1.986	1.182（1.006～1.385）	0.047
log₁₀TBil	0.537	0.171	3.147	1.710（1.239～2.419）	0.001
log₁₀AFP	0.283	0.120	2.375	1.327（1.052～1.683）	0.018

下载: 导出CSV

表 3 列线图模型与各评分系统预测高代谢风险CHB患者发生肝硬化的ROC曲线分析

Table 3. Analysis of the ROC curve for the prediction of cirrhosis in patients with high metabolic risk CHB by the nomogram model and various scoring systems

项目	AUC	95%CI	敏感度（%）	特异度（%）	cut-off值	PPV	NPV
列线图模型	0.837	0.788～0.888	84.7	73.5	0.113	0.379	0.962
FIB-4	0.739	0.682～0.810	66.1	74.8	0.134	0.333	0.920
APRI	0.802	0.742～0.849	78.0	73.8	0.130	0.362	0.946
GPR	0.800	0.751～0.843	86.4	66.0	0.122	0.327	0.962
Forns指数	0.709	0.664～0.799	57.6	87.1	0.234	0.459	0.915
注：PPV，阳性预测值；NPV，阴性预测值。

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