Predictive value of different scoring models for short-term prognosis of patients with aute-on-chronic liver failure
WU Huan, WU Long, ZHU Juan-juan, ZHANG Quan, SHEN Xiao-xu
2022, 27(2):
152-159.
Asbtract
(
287 )
PDF (730KB)
(
220
)
References |
Related Articles |
Metrics
Objective To investigate the independent risk factors of prognosis in patients with acute-on-chronic liver failure (ACLF), and compare the predictive value of different assessment models for short-term prognosis. Methods A total of 246 patients with ACLF admitted to our hospital from July 2018 to September 2020 were enrolled, and divided into survival group and death group according to the treatment end-point and the clinical outcome 3 months after discharge. SPSS and Yibei statistical software were used to compare the clinical data of the patients in the two groups. Receiver operator characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the predictive efficiency of each scoring model on the short-term prognosis. Results There were significant differences of age, white blood cell (WBC), neutrophils (NEU), neutrophils/nymphocytes (NLR), hemoglobin (Hgb), platelets (PLT), alpha-fetoprotein (AFP), total bilirubin (TBil), direct bilirubin (DBil), indirect bilirubin (IBil), creatinine (CR), natrium (Na), prothrombin time (PT), international normalized ratio (INR), prothrombin activity (PTA), lactic acid (LA) and ammonia (AMM) between the 2 groups. The incidence rates of ACLF-related complications, including lung infection and hepatic encephalopathy (HE), hepatorenal syndrome (HS), upper gastrointestinal bleeding, ascites, spontaneous bacterial peritonitis (SBP), in death group were significantly higher than those in survival group. Multivariate logistic regression analysis showed that age, NLR, IBIL, HE, and ascites were independent risk factors affecting short-term prognosis in patients with ACL. The mortality rates of early, middle and end stages of liver failure were 11.54%, 32.89%, and 77.12%, respectively, which were significantly different between the two groups (P<0.001). The average value of different scoring models in the different stages from high to low were end stage of liver failure, middle stage of liver failure, and early stage of liver failure. Except for the Logstic Regression Model (LRM), the differences among the different liver failure stage groups compared by the other 9 scoring models were statistically significant. The values of Child-Turcotte-Pugh score (CTP), end-stage liver disease score (MELD), MELD-Na score (MELD-Na), MESO score (MESO), integrated MELD score (iMELD), Maddrey discriminant function (MDF), Asia Pacific Association for the Study of Liver Diseases ACLF Research Alliance-ACLF (AARC), Age-Bilirubin-INR-Creatinine (ABIC) and Albumin-bilirubin score (ALBI) in survival group were statistically different from those in death group. Divide the different scoring models into 2-3 intervals according to the scores. Fisher exact test was used when the sample size was less than 5. Pearson chi-square test was used when the sample size in the group was greater than 5. The results showed that all the scoring models had significant differences in the comparison of different score intervals between groups (P<0.001). The AUC values of the 10 scoring models were between 0.5 and 0.8, suggesting a certain predictive value for the prognosis of patients with ACLF. Among them, the AUC value of the ARRC scoring model was the largest (AUC=0.765), followed by CTP, ABIC, iMELD, MDF and the AUC values of the above scoring model were all > 0.7. The AUC value of the LRM scoring model was the smallest (AUC=0.586), indicating it had the worst predictive value. Conclusion In addition to the LRM scoring model, the remaining scoring models CTP, ARRC, MELD, MELD-Na, iMELD, MESO, ABIC, MDF, ALBI can better predict the short-term prognosis of ACLF patients, and the ARRC scoring model has the highest predictive value.
