Prognosis prediction performs better in patients with non-cirrhosis hepatitis B virus-related acute-on-chronic liver failure than those with cirrhosis
Copyright © 2022 Yu, Li, Tan, Wang, Zheng, Huang, Li, Meng, Gao, Qian, Liu, Lu, Shang, Yan, Zheng, Zhang, Yin, Gu, Deng, Xiang, Zhou, Hou, Zhang, Xiong, Liu, Chen, Long, Chen, Jiang, Luo, Chen, Jiang, Zhao, Ji, Mei, Li, Li, Zheng, Zhou, Ren, Sheng and Shi..
Background: The accurate prediction of the outcome of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is impeded by population heterogeneity. The study aimed to assess the impact of underlying cirrhosis on the performance of clinical prediction models (CPMs).
Methods: Using data from two multicenter, prospective cohorts of patients with HBV-ACLF, the discrimination, calibration, and clinical benefit were assessed for CPMs predicting 28-day and 90-day outcomes in patients with cirrhosis and those without, respectively.
Results: A total of 919 patients with HBV-ACLF were identified by Chinese Group on the Study of Severe Hepatitis B (COSSH) criteria, including 675 with cirrhosis and 244 without. COSSH-ACLF IIs, COSSH-ACLFs, Chronic Liver Failure-Consortium Acute-on-Chronic Liver Failure score (CLIF-C ACLFs), Tongji Prognostic Predictor Model score (TPPMs), Model for End-Stage Liver Disease score (MELDs), and MELD-Sodium score (MELD-Nas) were all strong predictors of short-term mortality in patients with HBV-ACLF. In contrast to a high model discriminative capacity in ACLF without cirrhosis, each prognostic model represents a marked decline of C-index, net reclassification index (NRI), and integrated discrimination improvement (IDI) in predicting either 28-day or 90-day prognosis of patients with cirrhosis. The hazard analysis identified largely overlapping risk factors of poor outcomes in both subgroups, while serum bilirubin was specifically associated with short-term mortality in patients with cirrhosis and blood urea nitrogen in patients without cirrhosis. A subgroup analysis in patients with cirrhosis showed a decline of discrimination of CPMS in those with ascites or infections compared to that in those without.
Conclusion: Predicting the short-term outcome of HBV-ACLF by CPMs is optimal in patients without cirrhosis but limited in those with cirrhosis, at least partially due to the complicated ascites or infections.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Frontiers in microbiology - 13(2022) vom: 15., Seite 1013439 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Yu, Xia [VerfasserIn] |
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| Links: |
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| Themen: |
Acute-on-chronic liver failure |
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| Anmerkungen: |
Date Revised 27.12.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3389/fmicb.2022.1013439 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM350805946 |
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| 245 | 1 | 0 | |a Prognosis prediction performs better in patients with non-cirrhosis hepatitis B virus-related acute-on-chronic liver failure than those with cirrhosis |
| 264 | 1 | |c 2022 | |
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| 500 | |a Date Revised 27.12.2022 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Copyright © 2022 Yu, Li, Tan, Wang, Zheng, Huang, Li, Meng, Gao, Qian, Liu, Lu, Shang, Yan, Zheng, Zhang, Yin, Gu, Deng, Xiang, Zhou, Hou, Zhang, Xiong, Liu, Chen, Long, Chen, Jiang, Luo, Chen, Jiang, Zhao, Ji, Mei, Li, Li, Zheng, Zhou, Ren, Sheng and Shi. | ||
| 520 | |a Background: The accurate prediction of the outcome of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is impeded by population heterogeneity. The study aimed to assess the impact of underlying cirrhosis on the performance of clinical prediction models (CPMs) | ||
| 520 | |a Methods: Using data from two multicenter, prospective cohorts of patients with HBV-ACLF, the discrimination, calibration, and clinical benefit were assessed for CPMs predicting 28-day and 90-day outcomes in patients with cirrhosis and those without, respectively | ||
| 520 | |a Results: A total of 919 patients with HBV-ACLF were identified by Chinese Group on the Study of Severe Hepatitis B (COSSH) criteria, including 675 with cirrhosis and 244 without. COSSH-ACLF IIs, COSSH-ACLFs, Chronic Liver Failure-Consortium Acute-on-Chronic Liver Failure score (CLIF-C ACLFs), Tongji Prognostic Predictor Model score (TPPMs), Model for End-Stage Liver Disease score (MELDs), and MELD-Sodium score (MELD-Nas) were all strong predictors of short-term mortality in patients with HBV-ACLF. In contrast to a high model discriminative capacity in ACLF without cirrhosis, each prognostic model represents a marked decline of C-index, net reclassification index (NRI), and integrated discrimination improvement (IDI) in predicting either 28-day or 90-day prognosis of patients with cirrhosis. The hazard analysis identified largely overlapping risk factors of poor outcomes in both subgroups, while serum bilirubin was specifically associated with short-term mortality in patients with cirrhosis and blood urea nitrogen in patients without cirrhosis. A subgroup analysis in patients with cirrhosis showed a decline of discrimination of CPMS in those with ascites or infections compared to that in those without | ||
| 520 | |a Conclusion: Predicting the short-term outcome of HBV-ACLF by CPMs is optimal in patients without cirrhosis but limited in those with cirrhosis, at least partially due to the complicated ascites or infections | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a acute-on-chronic liver failure | |
| 650 | 4 | |a cirrhosis | |
| 650 | 4 | |a clinical prediction models | |
| 650 | 4 | |a hepatitis B virus | |
| 650 | 4 | |a performance | |
| 700 | 1 | |a Li, Hai |e verfasserin |4 aut | |
| 700 | 1 | |a Tan, Wenting |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xianbo |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Beiling |e verfasserin |4 aut | |
| 700 | 1 | |a Meng, Zhongji |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Yanhang |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Zhiping |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Lu, Xiaobo |e verfasserin |4 aut | |
| 700 | 1 | |a Shang, Jia |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Huadong |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Yubao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Weituo |e verfasserin |4 aut | |
| 700 | 1 | |a Yin, Shan |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Wenyi |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Guohong |e verfasserin |4 aut | |
| 700 | 1 | |a Xiang, Xiaomei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Yixin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qun |e verfasserin |4 aut | |
| 700 | 1 | |a Xiong, Shue |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Ruochan |e verfasserin |4 aut | |
| 700 | 1 | |a Long, Liyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jinjun |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Xiuhua |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Sen |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Chang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Jinming |e verfasserin |4 aut | |
| 700 | 1 | |a Ji, Liujuan |e verfasserin |4 aut | |
| 700 | 1 | |a Mei, Xue |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Tao |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Rongjiong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Xinyi |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Haotang |e verfasserin |4 aut | |
| 700 | 1 | |a Sheng, Jifang |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Yu |e verfasserin |4 aut | |
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