Alpha-fetoprotein and APRI as predictive markers for patients with Type C hepatitis B-related acute-on-chronic liver...

Type C hepatitis B-related acute-on-chronic liver failure (HBV-ACLF), which is based on decompensated cirrhosis, has different laboratory tests, precipitating events, organ failure and clinical outcomes. The predictors of prognosis for type C HBV-ACLF patients are different from those for other subgroups. This study aimed to construct a novel, short-term prognostic score that applied serological indicators of hepatic regeneration and noninvasive assessment of liver fibrosis to predict outcomes in patients with type C HBV-ACLF.

Patients with type C HBV-ACLF were observed for 90 days. Demographic information, clinical examination, and laboratory test results of the enrolled patients were collected. Univariate and multivariate logistic regression were performed to identify independent prognostic factors and develop a novel prognostic scoring system. A receiver operating characteristic (ROC) curve was used to analyse the performance of the model.

A total of 224 patients with type C HBV-ACLF were finally included. The overall survival rate within 90 days was 47.77%. Age, total bilirubin (TBil), international normalized ratio (INR), alpha-fetoprotein (AFP), white blood cell (WBC), serum sodium (Na), and aspartate aminotransferase/platelet ratio index (APRI) were found to be independent prognostic factors. According to the results of the logistic regression analysis, a new prognostic model (named the A3Twin score) was established. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.851 [95% CI (0.801-0.901)], the sensitivity was 78.8%, and the specificity was 71.8%, which were significantly higher than those of the MELD, IMELD, MELD-Na, TACIA and COSSH-ACLF II scores (all P < 0.001). Patients with lower A3Twin scores (<-9.07) survived longer.

A new prognostic scoring system for patients with type C HBV-ACLF based on seven routine indices was established in our study and can accurately predict short-term mortality and might be used to guide clinical management.