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DOI 10.1038/s41598-019-53029-0

Медицинская библиотека · Scientific reports · 2019

Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis.

Huang Kaizhou, Ji Feiyang, Xie Zhongyang, Wu Daxian, Xu Xiaowei, Gao Hainv, Ouyang Xiaoxi, Xiao Lanlan, Zhou Menghao, Zhu Danhua, Li Lanjuan

СтатьяПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

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.

Для цитирования: Huang Kaizhou, Ji Feiyang, Xie Zhongyang, Wu Daxian, Xu Xiaowei, Gao Hainv, Ouyang Xiaoxi, Xiao Lanlan, Zhou Menghao, Zhu Danhua, Li Lanjuan. Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis.. Scientific reports. 2019. DOI: 10.1038/s41598-019-53029-0.

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