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Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study

BACKGROUND/PURPOSE: Overt hepatic encephalopathy (HE) risk should be preoperatively predicted to identify patients suitable for curative transjugular intrahepatic portosystemic shunt (TIPS) instead of palliative treatments. METHODS: A total of 185 patients who underwent TIPS procedure were randomise...

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Autores principales: Yang, Yang, Fu, Sirui, Cao, Bin, Hao, Kenan, Li, Yong, Huang, Jianwen, Shi, Wenfeng, Duan, Chongyang, Bai, Xiao, Tang, Kai, Yang, Shirui, He, Xiaofeng, Lu, Ligong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286937/
https://www.ncbi.nlm.nih.gov/pubmed/33977364
http://dx.doi.org/10.1007/s12072-021-10188-5
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author Yang, Yang
Fu, Sirui
Cao, Bin
Hao, Kenan
Li, Yong
Huang, Jianwen
Shi, Wenfeng
Duan, Chongyang
Bai, Xiao
Tang, Kai
Yang, Shirui
He, Xiaofeng
Lu, Ligong
author_facet Yang, Yang
Fu, Sirui
Cao, Bin
Hao, Kenan
Li, Yong
Huang, Jianwen
Shi, Wenfeng
Duan, Chongyang
Bai, Xiao
Tang, Kai
Yang, Shirui
He, Xiaofeng
Lu, Ligong
author_sort Yang, Yang
collection PubMed
description BACKGROUND/PURPOSE: Overt hepatic encephalopathy (HE) risk should be preoperatively predicted to identify patients suitable for curative transjugular intrahepatic portosystemic shunt (TIPS) instead of palliative treatments. METHODS: A total of 185 patients who underwent TIPS procedure were randomised (130 in the training dataset and 55 in the validation dataset). Clinical factors and imaging characteristics were assessed. Three different models were established by logistic regression analyses based on clinical factors (Model(C)), imaging characteristics (Model(I)), and a combination of both (Model(CI)). Their discrimination, calibration, and decision curves were compared, to identify the best model. Subgroup analysis was performed for the best model. RESULTS: Model(CI), which contained two clinical factors and two imaging characteristics, was identified as the best model. The areas under the curve of Model(C), Model(I), and Model(CI) were 0.870, 0.963, and 0.978 for the training dataset and 0.831, 0.971, and 0.969 for the validation dataset. The combined model outperformed the clinical and imaging models in terms of calibration and decision curves. The performance of Model(CI) was not influenced by total bilirubin, Child–Pugh stages, model of end-stage liver disease score, or ammonia. The subgroup with a risk score ≥ 0.88 exhibited a higher proportion of overt HE (training dataset: 13.3% vs. 97.4%, p < 0.001; validation dataset: 0.0% vs. 87.5%, p < 0.001). CONCLUSION: Our combination model can successfully predict the risk of overt HE post-TIPS. For the low-risk subgroup, TIPS can be performed safely; however, for the high-risk subgroup, it should be considered more carefully. GRAPHIC ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12072-021-10188-5.
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spelling pubmed-82869372021-07-20 Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study Yang, Yang Fu, Sirui Cao, Bin Hao, Kenan Li, Yong Huang, Jianwen Shi, Wenfeng Duan, Chongyang Bai, Xiao Tang, Kai Yang, Shirui He, Xiaofeng Lu, Ligong Hepatol Int Original Article BACKGROUND/PURPOSE: Overt hepatic encephalopathy (HE) risk should be preoperatively predicted to identify patients suitable for curative transjugular intrahepatic portosystemic shunt (TIPS) instead of palliative treatments. METHODS: A total of 185 patients who underwent TIPS procedure were randomised (130 in the training dataset and 55 in the validation dataset). Clinical factors and imaging characteristics were assessed. Three different models were established by logistic regression analyses based on clinical factors (Model(C)), imaging characteristics (Model(I)), and a combination of both (Model(CI)). Their discrimination, calibration, and decision curves were compared, to identify the best model. Subgroup analysis was performed for the best model. RESULTS: Model(CI), which contained two clinical factors and two imaging characteristics, was identified as the best model. The areas under the curve of Model(C), Model(I), and Model(CI) were 0.870, 0.963, and 0.978 for the training dataset and 0.831, 0.971, and 0.969 for the validation dataset. The combined model outperformed the clinical and imaging models in terms of calibration and decision curves. The performance of Model(CI) was not influenced by total bilirubin, Child–Pugh stages, model of end-stage liver disease score, or ammonia. The subgroup with a risk score ≥ 0.88 exhibited a higher proportion of overt HE (training dataset: 13.3% vs. 97.4%, p < 0.001; validation dataset: 0.0% vs. 87.5%, p < 0.001). CONCLUSION: Our combination model can successfully predict the risk of overt HE post-TIPS. For the low-risk subgroup, TIPS can be performed safely; however, for the high-risk subgroup, it should be considered more carefully. GRAPHIC ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12072-021-10188-5. Springer India 2021-05-11 /pmc/articles/PMC8286937/ /pubmed/33977364 http://dx.doi.org/10.1007/s12072-021-10188-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Yang, Yang
Fu, Sirui
Cao, Bin
Hao, Kenan
Li, Yong
Huang, Jianwen
Shi, Wenfeng
Duan, Chongyang
Bai, Xiao
Tang, Kai
Yang, Shirui
He, Xiaofeng
Lu, Ligong
Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study
title Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study
title_full Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study
title_fullStr Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study
title_full_unstemmed Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study
title_short Prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study
title_sort prediction of overt hepatic encephalopathy after transjugular intrahepatic portosystemic shunt treatment: a cohort study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286937/
https://www.ncbi.nlm.nih.gov/pubmed/33977364
http://dx.doi.org/10.1007/s12072-021-10188-5
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