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中华消化病与影像杂志(电子版) ›› 2025, Vol. 15 ›› Issue (02) : 144 -148. doi: 10.3877/cma.j.issn.2095-2015.2025.02.009

论著

HBV 相关慢加急性肝衰竭患者诱发肝性脑病风险列线图模型构建与验证应用
方海丽1, 王俊1,(), 栾琳1   
  1. 1. 266000 山东省,青岛市第八人民医院感染性疾病科
  • 收稿日期:2024-09-14 出版日期:2025-04-01
  • 通信作者: 王俊

Construction and validation of nomogram model for risk of hepatic encephalopathy induced by HBV-related patients with chronic and acute liver failure

Haili Fang1, Jun Wang1,(), Lin Luan1   

  1. 1. Department of Infectious Diseases,Qingdao Eighth People's Hospital,Qingdao 266000,China
  • Received:2024-09-14 Published:2025-04-01
  • Corresponding author: Jun Wang
引用本文:

方海丽, 王俊, 栾琳. HBV 相关慢加急性肝衰竭患者诱发肝性脑病风险列线图模型构建与验证应用[J/OL]. 中华消化病与影像杂志(电子版), 2025, 15(02): 144-148.

Haili Fang, Jun Wang, Lin Luan. Construction and validation of nomogram model for risk of hepatic encephalopathy induced by HBV-related patients with chronic and acute liver failure[J/OL]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2025, 15(02): 144-148.

目的

构建HBV 相关慢加急性肝衰竭(ACLF)患者诱发肝性脑病风险列线图模型,并验证其临床应用效能。

方法

选取青岛市第八人民医院2020 年1 月至2024 年1 月收治的464 例HBV-ACLF 患者,按照7∶3 比例分别纳入训练集(n=325)、验证集(n=139)。按照训练集患者肝性脑病发生情况,将其分别纳入发生组、未发生组,对比2 组患者临床资料,使用Logistic 多因素模型归纳影响HBV-ACLF 患者发生肝性脑病的风险因素,并基于风险因素建立Nomogram 列线图预测模型。将预测模型应用于验证集,绘制受试者工作特征曲线(ROC)分析预测模型的曲线下面积(AUC)和预测效能。

结果

训练集325 例患者中,72 例发生肝性脑病,发生率为22.15%;验证集139 例患者中,29 例发生肝性脑病,发生率为20.86%。Logistic 多因素回归分析示,终末期肝病模型(MELD)评分≥25 分、发生腹膜炎、国际标准化比值≥2.54、活化部分凝血酶原时间≥48.10 s、病情处于中期-晚期均为影响HBV-ACLF 患者诱发肝性脑病的独立风险因素(P<0.05)。DCA 曲线示,列线图模型在训练集、验证集均表现出良好的临床净收益。以总分≥117 分为截断值,列线图模型预测训练集、验证集发生肝性脑病的AUC 分别为0.957、0.899,灵敏度分别为95.83%、89.66%,特异性分别为95.65%、90.00%。Delong 检验结果示,列线图模型肝性脑病的预测价值较ACLF 评分(Z=2.594)、MELD 评分(Z=3.113)更高(P<0.05)。

结论

HBV-ACLF 患者肝性脑病发生风险较高,且与病情、症状、凝血功能等因素密切相关,基于上述因素建立的列线图模型能够为患者肝性脑病风险评估提供可靠参考。

Objective

To establish a nomogram model of the risk of hepatic encephalopathy induced by hepatitis B virus (HBV)-related chronic and acute liver failure (HBV-ACLF) and verify its clinical application efficiency.

Methods

A total of 464 patients with HBV-ACLF admitted to Qingdao Eighth People's Hospital from January 2020 to January 2024 were enrolled in the training set (n=325) and the verification set (n=139) according to the ratio of 7∶3. According to the occurrence of hepatic encephalopathy in the training set,the patients were divided into the occurrence group and the non-occurrence group,and the clinical data of the two groups were compared. Logistic multivariate model was used to summarize the risk factors affecting the occurrence of hepatic encephalopathy in HBV-ACLF patients,and nomogram prediction model was established based on the risk factors. The prediction model was applied to the verification set,and the receiver operating characteristic curve (ROC) was plotted to analyze the area under curve (AUC) and prediction efficiency of the prediction model.

Results

Among the 325 patients in the training set,72 patients developed hepatic encephalopathy,the incidence rate was 22.15%. Among the 139 patients in the verification set,29 cases developed hepatic encephalopathy,with an incidence rate of 20.86%. Logistic multivariate regression analysis showed that model of end-stage liver disease (MELD) score≥25,peritonitis,international normalized ratio≥2.54,activated partial thromboplastin time≥48.10 s,and illness in the middle to late stage were all independent risk factors for HBV-ACLF patients to induce hepatic encephalopathy (P<0.05). DCA curve showed that the nomogram model showed good clinical net benefit in both training set and verification set. Taking the total score≥117 as the cut-off value,the AUC of the nomogram model in predicting the occurrence of hepatic encephalopathy in the training set and the verification set was 0.957 and 0.899,respectively,with sensitivity of 95.83% and 89.66%,specificity of 95.65% and 90.00%,respectively. Delong test showed that the predictive value of nomogram model was higher than ACLF score (Z=2.594) and MELD score (Z=3.113) (P<0.05).

Conclusion

The risk of hepatic encephalopathy in HBV-ACLF patients is high,and it is closely related to the condition,symptoms,coagulation function and other factors. The nomogram model based on the above factors can provide reliable reference for the risk assessment of hepatic encephalopathy in patients.

表1 2 组患者临床资料比较
表2 训练集发生组与未发生组临床资料比较
表3 影响HBV-ACLF 患者诱发肝性脑病的多因素回归分析结果
图1 HBV-ACLF 患者诱发肝性脑病的列线图模型
图2 HBV-ACLF 患者诱发肝性脑病的列线图模型的DCA 曲线
图3 HBV-ACLF 患者诱发肝性脑病的列线图模型的ROC 曲线
表4 HBV-ACLF 患者诱发肝性脑病的列线图模型的效能(%)
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