肝脏 ›› 2026, Vol. 31 ›› Issue (7): 948-953.

• 肝纤维化及肝硬化 • 上一篇    下一篇

失代偿期酒精性肝炎肝硬化患者再代偿的预测模型构建及效能评估

许丹青, 撒采芬, 牟春燕, 李文彦, 木唤, 刘立, 张映媛   

  1. 650200 昆明 昆明市第三人民医院,云南省传染性疾病临床医学中心
  • 收稿日期:2025-10-24 出版日期:2026-07-31 发布日期:2026-08-21
  • 通讯作者: 刘立,Email:liuli197210@163.com;张映媛,Email:1017536500@qq.com
  • 基金资助:
    昆明市科技计划项目(2025-NS-028);昆明市卫生科研项目(2025-03-08-002)

The construction and evaluation of a predictive model for recompensation in decompensated patients with alcoholic hepatitis related-cirrhosis

Xu Danqing, Sa Caifen, Mou Chunyan, Li Wenyan, Mu Huan, Liu Li, Zhang Yingyuan   

  1. The Third People′s Hospital of Kunming, the Clinical Medical Center for Infectious Diseases of Yunnan Province, Kunming 650200,China
  • Received:2025-10-24 Online:2026-07-31 Published:2026-08-21
  • Contact: Liu Li,Email:liuli197210@163.com;Zhang Yingyuan,Email:1017536500@qq.com

摘要: 目的 研究失代偿期酒精性肝炎肝硬化患者再代偿发生的影响因素,建立预测模型,评估模型的效能。方法 收集2016年1月1日—2022年12月31日就诊于昆明市第三人民医院并被诊断为失代偿期酒精性肝炎肝硬化的患者91例。至少1年之内无门静脉高压相关并发症纳入再代偿组(n=25),未再代偿者为持续失代偿组(n=66)。收集相关临床资料,对可能影响再代偿发生的因素进行单因素及多因素Cox比例风险回归模型分析,使用rms程序包建立列线图风险预测模型;Hosmer-Iemeshow检验评估模型的拟合度,绘制受试者工作特征(ROC)曲线并计算曲线下面积(AUC)。使用Calibtation Curves程序包绘制校准曲线,使用rmda程序包绘制临床决策曲线及临床影响曲线,并对风险预测模型进行评价。结果 单因素Cox回归分析结果显示,再代偿组人类免疫缺陷病毒感染史、腹水分级、AST、ALT、IL-6、门静脉流速、脾静脉宽度与持续失代偿组相比,差异均有统计学意义(P均<0.05)。多因素回归分析结果显示:腹水分级(无腹水HR=14.930,P=0.010,少量腹水HR=10.665,P=0.023,中大量腹水,P=0.034)、ALT(HR=1.012,P=0.006)、IL-6(HR=0.975,P=0.012)是失代偿期酒精性肝炎肝硬化患者再代偿的独立影响因素。用以上3个影响因素构建列线图模型,Hosmer-Lemesho检验显示,该模型具有较好的拟合度(χ2=11.592,P=0.170),该模型的AUC为0.752,灵敏度为57.6%,特异度为84.0%,校准曲线显示该模型对预测再代偿的发生具有较好的一致性。临床决策曲线和临床影响曲线显示在一定阈值范围内该模型具有较好的临床实用性。结论 腹水量较少、ALT水平较高的失代偿期酒精性肝炎肝硬化患者更容易出现再代偿,IL-6水平高的患者不易出现再代偿。

关键词: 酒精性肝炎, 肝硬化失代偿期, 再代偿, 影响因素, 预测模型

Abstract: Objective To study on the influencing factors of recompensation in patients with decompensated alcoholic liver cirrhosis, and to establish and evaluate a predictive model. Methods A total of 91 patients in the decompensated stage of liver cirrhosis who were admitted to the Third People′s Hospital of Kunming were included in this study. By reviewing the patients′ data, those without portal hypertension-related complications for at least one year were classified into the recompensation group (n=25), while the others were classified into the persistent decompensation group (n=66). The clinical data of all patients were collected. Univariate and multivariate Cox proportional hazards regression models were used to analyze the factors that might affect recompensation. The "rms" package was used to establish a nomogram risk prediction model. The Hosmer-Lemeshow test was used to evaluate the model′s goodness of fit, and the receiver operating characteristic curve (ROC curve) was drawn to calculate the area under the curve (AUC). The "Calibration Curves" package was used to draw calibration curves, and the "rmda" package was used to draw clinical decision curves and clinical impact curves to evaluate the risk prediction model. Results The results of univariate Cox regression analysis showed that there were statistically significant differences between the re-compensation group and the persistent decompensation group in terms of medical history, abdominal fluid classification, AST, ALT, IL-6, portal vein flow velocity, and splenic vein width (all P values < 0.05). The results of multivariate regression analysis showed that abdominal fluid classification (no abdominal fluid HR=14.930, P=0.010, small amount of abdominal fluid HR=10.665, P=0.023, moderate to large amount of abdominal fluid, P=0.034), ALT (HR=1.012, P=0.006), and IL-6 (HR=0.975, P=0.012) were independent influencing factors for re-compensation in patients with alcoholic hepatitis and liver cirrhosis in the decompensated stage. A nomogram model was constructed using these three influencing factors, and the Hosmer-Lemesho test showed that the model had good fit (χ2=11.592, P=0.170). The area under the AUC of the model was 0.752, with a sensitivity of 57.6% and a specificity of 84.0%. The calibration curve showed that the model had good consistency in predicting the occurrence of re-compensation. The clinical decision curve and clinical impact curve showed that the model had good clinical practicability within a certain threshold range. Conclusion Patients in the decompensated stage of alcoholic hepatitis related cirrhosis with less ascites volume and higher ALT level are more likely to achieve re-compensation, while patients with high IL-6 level are less likely to achieve re-compensation.

Key words: Alcoholic hepatitis, Decompensated stage of liver cirrhosis, Recompensation, Influencing factors, Prediction models