Nomogram for predicting myocardial injury in pediatric patients undergoing living donor liver transplantation for biliary atresia

The nomogram developed in this study effectively predicts myocardial injury in pediatric LDLT patients, showing good accuracy and potential for clinical application.

Authors

Institutions

Publication Details

Journal
World Journal of Gastrointestinal Surgery
Published
2025-03-29
DOI
https://doi.org/10.4240/wjgs.v17.i4.103263
Primary Topic
Organ Transplantation Techniques and Outcomes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Nomogram for predicting myocardial injury in pediatric patients undergoing living donor liver transplantation for biliary atresia

Xinyuan Gong, Weihua Liu, Jingyi Xue, Yongle Jing et al.
World Journal of Gastrointestinal Surgery
Organ Transplantation Techniques and Outcomes
article

Nomogram for predicting myocardial injury in pediatric patients undergoing living donor liver transplantation for biliary atresia

Xinyuan Gong, Weihua Liu, Jingyi Xue, Yongle Jing, Yiqi Weng, Wenli Yu, Lei Jiang, Xiaoyu Huang, Lu Che, Tianying Li, Yuli Wu
article en

Abstract

The nomogram developed in this study effectively predicts myocardial injury in pediatric LDLT patients, showing good accuracy and potential for clinical application.

World Journal of Gastrointestinal SurgeryVol. 17(4)
Nankai University (CN), Tianjin First Center Hospital (CN), Tianjin Medical University (CN)
Good health and well-being
Openalex Percentile: Top 90%
Organ Transplantation Techniques and Outcomes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.