Evidence map›Paper›PMID 37531069›Full record

ArticleHepatology international2023

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

Xiaoqiong Chen, Tao Wang, Zhonghua Ji, Junyang Luo, Weifu Lv, Haifang Wang, Yujie Zhao, Chongyang Duan, Xiangrong Yu, Qiyang Li and 8 more

Abstract readMulticenter Study
In one paragraph

Article in Hepatology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. [Research advances on hepatic encephalopathy following transjugular intrahepatic portosystemic shunt].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Xiaoqiong Chen *Zhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China.
Tao Wang *School of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China.
Zhonghua Ji *Department of Anesthesia, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Junyang Luo *Department of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Weifu LvInterventional Radiology Department, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Haifang WangDepartment of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Yujie ZhaoZhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China.
Chongyang DuanDepartment of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China.
Xiangrong YuDepartment of Radiology, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), Zhuhai, China.
Qiyang LiDepartment of Radiology, Shenzhen People's Hospital, Shenzhen, China.
Jiawei ZhangSchool of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China.
Jinqiang ChenZhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China.
Xiaoling ZhangSchool of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China.
Mingsheng HuangDepartment of Interventional Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Shuoling ZhouSchool of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China.
Ligong LuZhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China. llg0902@sina.com.
Meiyan HuangSchool of Biomedical Engineering, Southern Medical University, No. 1023-1063 Shatai Road, Guangzhou, 510515, Guangdong, China. huangmeiyan10@sina.com.
Sirui FuZhuhai Interventional Medical Centre, Zhuhai Hospital Affiliated with Jinan University (Zhuhai People's Hospital), No. 79 Kangning Road, Zhuhai, 519000, Guangdong Province, China. bicia870428@sina.com.

Funding

Guangdong Basic and Applied Basic Research Foundation 2021A1515012011Guangdong Basic and Applied Basic Research Foundation 2021A1515110252National Natural Science Foundation of China 82230067National Natural Science Foundation of China 82272069Nurture Programme of Zhuhai People's Hospital 2020XSYC-09Science and Technology Planning Project of Guangzhou 201904010417Science and Technology Planning Project of Guangzhou 202201020570
6 · The paper itself

Abstract

backgroundOvert 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.

methodsIn 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.

resultsThe 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.

conclusion3D 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.

Indexed as

Hepatic EncephalopathyPortasystemic Shunt, Transjugular IntrahepaticCohort StudiesHumansLiver CirrhosisRetrospective StudiesSpleenTreatment Outcome2D factors2D model3D assessment3D factors3D modelAppletOptimal modelOvert HEPredictionTransjugular intrahepatic portosystemic shunt

Identifiers

PMID37531069
PMCPMC10661776

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.