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3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study

BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE. METHODS:...

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Autores principales: Chen, Xiaoqiong, Wang, Tao, Ji, Zhonghua, Luo, Junyang, Lv, Weifu, Wang, Haifang, Zhao, Yujie, Duan, Chongyang, Yu, Xiangrong, Li, Qiyang, Zhang, Jiawei, Chen, Jinqiang, Zhang, Xiaoling, Huang, Mingsheng, Zhou, Shuoling, Lu, Ligong, Huang, Meiyan, Fu, Sirui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661776/
https://www.ncbi.nlm.nih.gov/pubmed/37531069
http://dx.doi.org/10.1007/s12072-023-10570-5
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author Chen, Xiaoqiong
Wang, Tao
Ji, Zhonghua
Luo, Junyang
Lv, Weifu
Wang, Haifang
Zhao, Yujie
Duan, Chongyang
Yu, Xiangrong
Li, Qiyang
Zhang, Jiawei
Chen, Jinqiang
Zhang, Xiaoling
Huang, Mingsheng
Zhou, Shuoling
Lu, Ligong
Huang, Meiyan
Fu, Sirui
author_facet Chen, Xiaoqiong
Wang, Tao
Ji, Zhonghua
Luo, Junyang
Lv, Weifu
Wang, Haifang
Zhao, Yujie
Duan, Chongyang
Yu, Xiangrong
Li, Qiyang
Zhang, Jiawei
Chen, Jinqiang
Zhang, Xiaoling
Huang, Mingsheng
Zhou, Shuoling
Lu, Ligong
Huang, Meiyan
Fu, Sirui
author_sort Chen, Xiaoqiong
collection PubMed
description BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE. METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed. RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child–Pugh score, ammonia level, or the indication for TIPS. CONCLUSION: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12072-023-10570-5.
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spelling pubmed-106617762023-08-02 3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study Chen, Xiaoqiong Wang, Tao Ji, Zhonghua Luo, Junyang Lv, Weifu Wang, Haifang Zhao, Yujie Duan, Chongyang Yu, Xiangrong Li, Qiyang Zhang, Jiawei Chen, Jinqiang Zhang, Xiaoling Huang, Mingsheng Zhou, Shuoling Lu, Ligong Huang, Meiyan Fu, Sirui Hepatol Int Original Article BACKGROUND: Overt hepatic encephalopathy (HE) should be predicted preoperatively to identify suitable candidates for transjugular intrahepatic portosystemic shunt (TIPS) instead of first-line treatment. This study aimed to construct a 3D assessment-based model to predict post-TIPS overt HE. METHODS: In this multi-center cohort study, 487 patients who underwent TIPS were subdivided into a training dataset (390 cases from three hospitals) and an external validation dataset (97 cases from another two hospitals). Candidate factors included clinical, vascular, and 2D and 3D data. Combining the least absolute shrinkage and operator method, support vector machine, and probability calibration by isotonic regression, we constructed four predictive models: clinical, 2D, 3D, and combined models. Their discrimination and calibration were compared to identify the optimal model, with subgroup analysis performed. RESULTS: The 3D model showed better discrimination than did the 2D model (training: 0.719 vs. 0.691; validation: 0.730 vs. 0.622). The model combining clinical and 3D factors outperformed the clinical and 3D models (training: 0.802 vs. 0.735 vs. 0.719; validation: 0.816 vs. 0.723 vs. 0.730; all p < 0.050). Moreover, the combined model had the best calibration. The performance of the best model was not affected by the total bilirubin level, Child–Pugh score, ammonia level, or the indication for TIPS. CONCLUSION: 3D assessment of the liver and the spleen provided additional information to predict overt HE, improving the chance of TIPS for suitable patients. 3D assessment could also be used in similar studies related to cirrhosis. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12072-023-10570-5. Springer India 2023-08-02 /pmc/articles/PMC10661776/ /pubmed/37531069 http://dx.doi.org/10.1007/s12072-023-10570-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Chen, Xiaoqiong
Wang, Tao
Ji, Zhonghua
Luo, Junyang
Lv, Weifu
Wang, Haifang
Zhao, Yujie
Duan, Chongyang
Yu, Xiangrong
Li, Qiyang
Zhang, Jiawei
Chen, Jinqiang
Zhang, Xiaoling
Huang, Mingsheng
Zhou, Shuoling
Lu, Ligong
Huang, Meiyan
Fu, Sirui
3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study
title 3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study
title_full 3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study
title_fullStr 3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study
title_full_unstemmed 3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study
title_short 3D automatic liver and spleen assessment in predicting overt hepatic encephalopathy before TIPS: a multi-center study
title_sort 3d automatic liver and spleen assessment in predicting overt hepatic encephalopathy before tips: a multi-center study
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661776/
https://www.ncbi.nlm.nih.gov/pubmed/37531069
http://dx.doi.org/10.1007/s12072-023-10570-5
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