Wu Jian, Shi Cuifen, Sheng Xinyu, Xu Yanping, Zhang Jinrong, Zhao Xinguo, Yu Jiong, Shi Xinhui, Li Gongqi, Cao Hongcui, Li Lanjuan
State Key Laboratory for the Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Department of Laboratory Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
J Clin Transl Hepatol. 2021 Dec 28;9(6):828-837. doi: 10.14218/JCTH.2020.00117. Epub 2021 May 6.
Timely and effective assessment scoring systems for predicting the mortality of patients with hepatitis E virus-related acute liver failure (HEV-ALF) are urgently needed. The present study aimed to establish an effective nomogram for predicting the mortality of HEV-ALF patients.
The nomogram was based on a cross-sectional set of 404 HEV-ALF patients who were identified and enrolled from a cohort of 650 patients with liver failure. To compare the performance with that of the model for end-stage liver disease (MELD) scoring and CLIF-Consortium-acute-on-chronic liver failure score (CLIF-C-ACLFs) models, we assessed the predictive accuracy of the nomogram using the concordance index (C-index), and its discriminative ability using time-dependent receiver operating characteristics (td-ROC) analysis, respectively.
Multivariate logistic regression analysis of the development set carried out to predict mortality revealed that γ-glutamyl transpeptidase, albumin, total bilirubin, urea nitrogen, creatinine, international normalized ratio, and neutrophil-to-lymphocyte ratio were independent factors, all of which were incorporated into the new nomogram to predict the mortality of HEV-ALF patients. The area under the curve of this nomogram for mortality prediction was 0.671 (95% confidence interval: 0.602-0.740), which was higher than that of the MELD and CLIF-C-ACLFs models. Moreover, the td-ROC and decision curves analysis showed that both discriminative ability and threshold probabilities of the nomogram were superior to those of the MELD and CLIF-C-ACLFs models. A similar trend was observed in the validation set.
The novel nomogram is an accurate and efficient mortality prediction method for HEV-ALF patients.
迫切需要用于预测戊型肝炎病毒相关急性肝衰竭(HEV-ALF)患者死亡率的及时有效的评估评分系统。本研究旨在建立一种有效的列线图来预测HEV-ALF患者的死亡率。
该列线图基于从650例肝衰竭患者队列中识别并纳入的404例HEV-ALF患者的横断面数据集。为了将其性能与终末期肝病模型(MELD)评分和CLIF-慢性肝衰竭联盟急性-on-慢性肝衰竭评分(CLIF-C-ACLFs)模型进行比较,我们分别使用一致性指数(C-index)评估列线图的预测准确性,并使用时间依赖性受试者工作特征(td-ROC)分析评估其判别能力。
对用于预测死亡率的开发集进行多因素逻辑回归分析显示,γ-谷氨酰转肽酶、白蛋白、总胆红素、尿素氮、肌酐、国际标准化比值和中性粒细胞与淋巴细胞比值是独立因素,所有这些因素都被纳入新的列线图以预测HEV-ALF患者的死亡率。该列线图用于死亡率预测的曲线下面积为0.671(95%置信区间:0.602-0.740),高于MELD和CLIF-C-ACLFs模型。此外,td-ROC和决策曲线分析表明,列线图的判别能力和阈值概率均优于MELD和CLIF-C-ACLFs模型。在验证集中观察到类似趋势。
新型列线图是一种准确有效的HEV-ALF患者死亡率预测方法。