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 patien...

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Autores principales:	Jin Shang, Mengqiao Wang, Qin Wen, Yuanji Ma, Fang Chen, Yan Xu, Chang-Hai Liu, Lang Bai, Hong Tang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1e83c8c5f3be4e799470db2322c9a869
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spelling	oai:doaj.org-article:1e83c8c5f3be4e799470db2322c9a8692021-12-02T14:21:11ZA novel prognostic model to predict outcome of artificial liver support system treatment10.1038/s41598-021-87055-82045-2322https://doaj.org/article/1e83c8c5f3be4e799470db2322c9a8692021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87055-8https://doaj.org/toc/2045-2322Abstract 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.Jin ShangMengqiao WangQin WenYuanji MaFang ChenYan XuChang-Hai LiuLang BaiHong TangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jin Shang Mengqiao Wang Qin Wen Yuanji Ma Fang Chen Yan Xu Chang-Hai Liu Lang Bai Hong Tang A novel prognostic model to predict outcome of artificial liver support system treatment
description	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.
format	article
author	Jin Shang Mengqiao Wang Qin Wen Yuanji Ma Fang Chen Yan Xu Chang-Hai Liu Lang Bai Hong Tang
author_facet	Jin Shang Mengqiao Wang Qin Wen Yuanji Ma Fang Chen Yan Xu Chang-Hai Liu Lang Bai Hong Tang
author_sort	Jin Shang
title	A novel prognostic model to predict outcome of artificial liver support system treatment
title_short	A novel prognostic model to predict outcome of artificial liver support system treatment
title_full	A novel prognostic model to predict outcome of artificial liver support system treatment
title_fullStr	A novel prognostic model to predict outcome of artificial liver support system treatment
title_full_unstemmed	A novel prognostic model to predict outcome of artificial liver support system treatment
title_sort	novel prognostic model to predict outcome of artificial liver support system treatment
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1e83c8c5f3be4e799470db2322c9a869
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A novel prognostic model to predict outcome of artificial liver support system treatment

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