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A novel prognostic model to predict outcome of artificial liver support system treatment.

ABSTRACT: The prognosis of Artificial liver support system (ALSS) for hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is hard to be expected, which results in multiple operations of ALSS and excessive consumption of plasma, increase in clinical cost. A total of 375 HBV-ACLF patients receiving ALSS treatment were randomly divided a train set and an independent test set. Logistic regression analysis was conducted and a decision tree was built based on 3-month survival as outcome. The ratio of total bilirubin before and after the first time of ALSS treatment was the most significant prognostic factor, we named it RPTB. Further, a decision tree based on the multivariate logistic regression model using CTP score and the RPTB was built, dividing patients into 3 main groups such as favorable prognosis group, moderate prognosis group and poor prognosis group. A clearly-presented and easily-understood decision tree was built with a good predictive value of prognosis in HBV-related ACLF patients after first-time ALSS treatment. It will help maximal the therapeutic value of ALSS treatment and may play an important role in organ allocation for liver transplantation in the future.

SUBMITTER: Shang J

PROVIDER: S-EPMC8021558 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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A novel prognostic model to predict outcome of artificial liver support system treatment.

Shang Jin J Wang Mengqiao M Wen Qin Q Ma Yuanji Y Chen Fang F Xu Yan Y Liu Chang-Hai CH Bai Lang L Tang Hong H

Scientific reports 20210405 1

The prognosis of Artificial liver support system (ALSS) for hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is hard to be expected, which results in multiple operations of ALSS and excessive consumption of plasma, increase in clinical cost. A total of 375 HBV-ACLF patients receiving ALSS treatment were randomly divided a train set and an independent test set. Logistic regression analysis was conducted and a decision tree was built based on 3-month survival as outcome. The rat ...[more]

PMID: 33820919

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Project description:Artificial liver support system (ALSS) therapy is widely used in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). We aimed to develop a predictive score to identify the subgroups who may benefit from plasma exchange (PE)-centered ALSS therapy. A total of 601 patients were retrospectively enrolled and randomly divided into a derivation cohort of 303 patients and a validation cohort of 298 patients for logistic regression analysis, respectively. Five baseline variables, including liver cirrhosis, total bilirubin, international normalized ratio of prothrombin time, infection and hepatic encephalopathy, were found independently associated with 3-month mortality. A predictive PALS model and the simplified PALS score were developed. The predicative value of PALS score (AUROC = 0.818) to 3-month prognosis was as capable as PALS model (AUROC = 0.839), R score (AUROC = 0.824) and Yue-Meng' score (AUROC = 0.810) (all p > 0.05), and superior to CART model (AUROC = 0.760) and MELD score (AUROC = 0.765) (all p < 0.05). The PALS score had significant linear correlation with 3-month mortality (R2 = 0.970, p = 0.000). PALS score of 0-2 had both sensitivity and negative predictive value of > 90% for 3-month mortality, while PALS score of 6-9 had both specificity and positive predictive value of > 90%. Patients with PALS score of 3-5 who received 3-5 sessions of ALSS therapy had much lower 3-month mortality than those who received 1-2 sessions (32.8% vs. 59.2%, p < 0.05). The more severe patients with PALS score of 6-9 could still benefit from ≥ 6 sessions of ALSS therapy compared to ≤ 2 sessions (63.6% vs. 97.0%, p < 0.05). The PALS score could predict prognosis reliably and conveniently. It could identify the subgroups who could benefit from PE-centered ALSS therapy, and suggest the reasonable sessions.Trial registration: Chinese Clinical Trial Registry, ChiCTR2000032055. Registered 19th April 2020, http://www.chictr.org.cn/showproj.aspx?proj=52471 .

Dataset Information

A novel prognostic model to predict outcome of artificial liver support system treatment.

Publications

A novel prognostic model to predict outcome of artificial liver support system treatment.

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