Risk stratification of decompensated cirrhosis patients by Chronic Liver Failure Consortium scores: Classification and regression tree analysis.
OBJECTIVE
Decompensated cirrhosis patients have greatly variable prognosis. The aim of the study was to carry out a risk stratification for those patients by Chronic Liver Failure (CLIF) Consortium scores.


METHODS
The performance of CLIF Consortium acute-on-chronic liver failure scores (CLIF-C ACLFs) and CLIF Consortium Acute Decompensation scores (CLIF-C ADs) were validated in 209 patients with ACLF and 1245 patients without ACLF at admission from the Ningbo Cohort. A classification and regression tree (CRT) analysis by CLIF-C ACLFs/CLIF-C ADs was carried out to stratify death risk among patients.


RESULTS
The CLIF-C ACLFs and CLIF-C ADs showed higher predictive accuracy than Model for End-stage Liver Disease (MELD) scores, MELD plus serum sodium (MELD-Na) scores, and Child-Turcotte-Pugh classification (CP) at main time points (28, 90, 180, and 365 days), determined by area under the receiver-operating characteristic curve and concordance index in ACLF and no-ACLF patients at admission. The CRT analysis categorized ACLF patients into two groups (advanced and early ACLF), and no-ACLF patients into three groups (high-, medium-, and low-risk AD) according to risk of death. However, early ACLF and high-risk AD patients had comparable mortality at the main time points. The CRT model had a higher area under the receiver-operating characteristic curve than MELDs, MELD-Nas, and CPs in predicting prognosis in all patients.


CONCLUSIONS
The CLIF-C ACLF and CLIF-C AD are better prognostic scores than MELD, MELD-Na, and CP in predicting mortality of ACLF and no-ACLF patients. A combined use of CLIF- Sequential Organ Failure Assessment, CLIF-C ACLFs, and CLIF-C ADs could identify cirrhosis patients at high death risk and assist clinical decisions for management.
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yu
Y
Shi
Division of Hepatobiliary and Pancreatic Surgery, Key Laboratory of Combined Multi-organ Transplantation, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Zheyue
Z
Shu
Department of Epidemiology and Health Statistics, Zhejiang University School of Public Health, Hangzhou, China.
Wenjie
W
Sun
Department of Infectious Diseases, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Ningbo, China.
Qiao
Q
Yang
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ye
Y
Yu
Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China.
Gang
G
Yang
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Disease, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wei
W
Wu
Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China.
Si
S
Chen
Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China.
Wei
W
Huang
Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China.
Tingting
T
Wang
Department of Hepatology, Ningbo Multiple Organ Injury Research Center, Ningbo No.2 Hospital, School of Medicine, Ningbo University, Ningbo, China.
Huadong
H
Yan
CLIF Consortium score prognosis; classification and regression tree analysis; decompensated cirrhosis; risk stratification