Abstract
Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.
Highlights
Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-Acute-on-chronic liver failure (ACLF))
To help clinicians identify and screen patients eligible for ALSS therapy, we developed an accurate, user-friendly, bedside prognostic model employing classification and regression tree (CART) analysis
The cohorts were similar in terms of the variables significantly influencing survival, i.e. the hepatic encephalopathy (HE) proportion; white blood cell (WBC) count; the levels of ALT, total bilirubin (TBil), INR, prothrombin time (PT), fibrinogen, D-dimer, Hb and NH3; and the scores on all systems tested
Summary
Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). To help clinicians identify and screen patients eligible for ALSS therapy, we developed an accurate, user-friendly, bedside prognostic model employing CART analysis. We compared the accuracy of our model in term of predicting 28-day mortality to that of a new Z logistic regression model (LRM-Z) and certain older prognostic models, including the model for end-stage liver disease (MELD), integrated model for end-stage liver disease (iMELD), Chronic Liver Failure Consortium acute-on-chronic liver failure (CLIF-C ACLF) score and Chinese group on the Study of Severe Hepatitis B-acute-on-chronic liver failure (COSSH-ACLF) score
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