临床肝胆病杂志2026，Vol.42Issue(1)：151-159,9.DOI:10.12449/JCH260118
慢加急性肝衰竭患者90天死亡的危险因素分析及预测模型构建
Risk factors for 90-day mortality in patients with acute-on-chronic liver failure and establishment of a predictive model
摘要
Abstract
Objective To investigate the independent predictive factors for 90-day mortality in patients with acute-on-chronic liver failure(ACLF),to establish a risk predictive model,and to assess its predictive efficacy in comparison with MELD,MELD-Na,MELD 3.0,and COSSH-ACLF Ⅱ.Methods A retrospective analysis was performed for the clinical data of 394 patients with ACLF who were admitted to The Affiliated Hospital of Inner Mongolia Medical University and Hohhot Second Hospital from July 2018 to July 2024,and general information and laboratory markers on admission were collected from all patients.The independent-samples t test or the Mann-Whitney U test was used for comparison of quantitative data between two groups,and the chi-square test or the adjusted chi-square test was used for comparison of qualitative data between two groups.The LASSO regression analysis was used to identify related variables,and the multivariate logistic regression analysis was used to establish a predictive model and generate a nomogram.The receiver operating characteristic(ROC)curve,the area under the ROC curve(AUC),calibration curve,and clinical decision curve were used to assess the performance of the model.Results A total of 394 patients with ACLF were included in this study,with 136 patients in the training set,58 in the internal validation set,and 200 in the external validation set.The cohort had a mean age of 52.9±11.7 years,among whom male patients accounted for 72.84%(287/394),the patients with HBV infection accounted for 22.33%(88/394),the patients with alcohol-related causes accounted for 45.94%(181/394),and the patients with other causes(including drug-induced and autoimmune diseases)accounted for 31.73%(125/394).The overall 90-day mortality rate was 27.41%(108/394).The multivariate logistic regression analysis showed that diabetes(odds ratio[OR]=5.831,95%confidence interval[CI]:1.587—21.424,P=0.008),cystatin C(Cys-C)(OR=2.984,95%CI:1.501—5.933,P=0.002),and spontaneous peritonitis(SBP)(OR=5.692,95%CI:2.150—15.071,P<0.001)were independent risk factors,and a nomogram was generated based on these factors.This model had an AUC of 0.836 in the training set,0.881 in the internal validation set,and 0.878 in the external validation set,showing a good discriminatory ability.The calibration curve showed a good degree of fitting,with a relatively high net clinical benefit.The subgroup analysis based on etiology showed that the model had an AUC of 0.850 in the patients with HBV infection,0.858 in the patients with alcohol-induced ACLF,and 0.908 in the patients with other etiologies,indicating that the model had a good discriminatory ability across the populations with different etiologies.Compared with traditional scores,the model(AUC=0.836)had a significantly better predictive value than MELD(AUC=0.619,Z=3.197,P=0.001),MELD-Na(AUC=0.651,Z=2.998,P=0.003),MELD 3.0(AUC=0.601,Z=3.682,P<0.001),and COSSH-ACLF Ⅱ(AUC=0.719,Z=2.396,P=0.017)alone.Conclusion Diabetes,SBP,and Cys-C are independent risk factors for 90-day mortality in patients with ACLF.Compared with MELD,MELD-Na,MELD 3.0,and COSSH-ACLF Ⅱ scores,this model has a higher predictive value for 90-day prognosis in patients with ACLF and is suitable for patients with ACLF caused by various etiologies.关键词
慢加急性肝功能衰竭/预后/危险因素/列线图Key words
Acute-on-Chronic Liver Failure/Prognosis/Risk Factors/Nomogram引用本文复制引用
孙静,王婷济,段志娇,张丽,李艳梅..慢加急性肝衰竭患者90天死亡的危险因素分析及预测模型构建[J].临床肝胆病杂志,2026,42(1):151-159,9.基金项目
内蒙古自治区自然科学基金(2025ZD009) （2025ZD009）
内蒙古自治区自然科学基金(2023QN08061) （2023QN08061）
内蒙古医科大学青年基金(YKD2023QN014) （YKD2023QN014）
2022年"草原英才"工程高层次培养人才项目 Natural Science Foundation of Inner Mongolia Autonomous Region(2025ZD009) （2025ZD009）
Natural Science Foundation of Inner Mongolia Autonomous Region(2023QN08061) （2023QN08061）
Youth Foundation of Inner Mongolia Medical University(YKD2023QN014) （YKD2023QN014）
High-level Talent Training Program of the"Grassland Elite"Project(2022) （2022）
