Suppr超能文献

预测乙型肝炎病毒相关慢加急性肝衰竭患者肝移植的生存获益:一项观察性队列研究

Predicting the survival benefit of liver transplantation in HBV-related acute-on-chronic liver failure: an observational cohort study.

作者信息

Li Peng, Liang Xi, Luo Jinjin, Li Jiaqi, Xin Jiaojiao, Jiang Jing, Shi Dongyan, Lu Yingyan, Hassan Hozeifa Mohamed, Zhou Qian, Hao Shaorui, Zhang Huafen, Wu Tianzhou, Li Tan, Yao Heng, Ren Keke, Guo Beibei, Zhou Xingping, Chen Jiaxian, He Lulu, Yang Hui, Hu Wen, Ma Shiwen, Li Bingqi, You Shaoli, Xin Shaojie, Chen Yu, Li Jun

机构信息

State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Rd., Hangzhou 310003, China.

Precision Medicine Center, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China.

出版信息

Lancet Reg Health West Pac. 2022 Nov 10;32:100638. doi: 10.1016/j.lanwpc.2022.100638. eCollection 2023 Mar.

Abstract

BACKGROUND

Liver transplantation (LT) is an effective therapy for acute-on-chronic liver failure (ACLF) but is limited by organ shortages. We aimed to identify an appropriate score for predicting the survival benefit of LT in HBV-related ACLF patients.

METHODS

Hospitalized patients with acute deterioration of HBV-related chronic liver disease (n = 4577) from the Chinese Group on the Study of Severe Hepatitis B (COSSH) open cohort were enrolled to evaluate the performance of five commonly used scores for predicting the prognosis and transplant survival benefit. The survival benefit rate was calculated to reflect the extended rate of the expected lifetime with vs. without LT.

FINDINGS

In total, 368 HBV-ACLF patients received LT. They showed significantly higher 1-year survival than those on the waitlist in both the entire HBV-ACLF cohort (77.2%/52.3%, p < 0.001) and the propensity score matching cohort (77.2%/27.6%, p < 0.001). The area under the receiver operating characteristic curve (AUROC) showed that the COSSH-ACLF II score performed best (AUROC 0.849) at identifying the 1-year risk of death on the waitlist and best (AUROC 0.864) at predicting 1-year outcome post-LT (COSSH-ACLFs/CLIF-C ACLFs/MELDs/MELD-Nas: AUROC 0.835/0.825/0.796/0.781; all p < 0.05). The C-indexes confirmed the high predictive value of COSSH-ACLF IIs. Survival benefit rate analyses showed that patients with COSSH-ACLF IIs 7-10 had a higher 1-year survival benefit rate from LT (39.2%-64.3%) than those with score <7 or >10. These results were prospectively validated.

INTERPRETATION

COSSH-ACLF IIs identified the risk of death on the waitlist and accurately predicted post-LT mortality and survival benefit for HBV-ACLF. Patients with COSSH-ACLF IIs 7-10 derived a higher net survival benefit from LT.

FUNDING

This study was supported by the National Natural Science Foundation of China (No. 81830073, No. 81771196) and the National Special Support Program for High-Level Personnel Recruitment (Ten-thousand Talents Program).

摘要

背景

肝移植(LT)是治疗慢加急性肝衰竭(ACLF)的有效方法,但受器官短缺限制。我们旨在确定一个合适的评分系统,以预测LT对乙肝相关ACLF患者的生存获益。

方法

从中国重型乙型肝炎研究组(COSSH)开放队列中纳入4577例乙肝相关慢性肝病急性恶化的住院患者,评估5种常用评分系统预测预后和移植生存获益的性能。计算生存获益率以反映LT与未进行LT情况下预期寿命的延长率。

结果

共有368例乙肝相关ACLF患者接受了LT。在整个乙肝相关ACLF队列(77.2%/52.3%,p<0.001)和倾向评分匹配队列(77.2%/27.6%,p<0.001)中,接受LT患者的1年生存率均显著高于等待名单上的患者。受试者工作特征曲线下面积(AUROC)显示,COSSH-ACLF II评分在识别等待名单上1年死亡风险方面表现最佳(AUROC 0.849),在预测LT后1年结局方面也表现最佳(AUROC 0.864)(COSSH-ACLFs/CLIF-C ACLFs/MELDs/MELD-Nas:AUROC 0.835/0.825/0.796/0.781;均p<0.05)。C指数证实了COSSH-ACLF II的高预测价值。生存获益率分析显示,COSSH-ACLF II评分为7-10分的患者LT后的1年生存获益率(39.2%-64.3%)高于评分<7或>10分的患者。这些结果得到了前瞻性验证。

解读

COSSH-ACLF II可识别等待名单上的死亡风险,并准确预测乙肝相关ACLF患者LT后的死亡率和生存获益。COSSH-ACLF II评分为7-10分的患者从LT中获得的净生存获益更高。

资助

本研究得到了国家自然科学基金(81830073号、81771196号)和国家高层次人才特殊支持计划(万人计划)的支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/456e/9923183/10c8830d1453/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验