Development of an explainable prediction model for portal vein system thrombosis post-splenectomy in patients with cirrhosis.

Journal: BMJ health & care informatics
PMID: 40216454

Abstract

BACKGROUND: Portal vein system thrombosis (PVST) is a common and potentially life-threatening complication following splenectomy plus pericardial devascularisation (SPDV) in patients with cirrhosis and portal hypertension. Early prediction of PVST is critical for timely intervention. This study aimed to develop a machine learning-based prediction model for PVST occurrence within 3 months after splenectomy.

Authors

  • Dou Qu
    Institute for Precision Medicine, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shaanxi, China.
  • Duwei Dai
    Institute of Medical Artificial Intelligence, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
  • Guodong Li
    Department of Interventional Radiology, Fudan University Shanghai Cancer Center, Xuhui District, Shanghai, China.
  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.
  • Caixia Dong
    Institute of Medical Artificial Intelligence, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
  • Junxia Zhao
    Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China.
  • Lingbo An
    Hepatobiliary, splenic and pancreatic surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Xiaojie Song
    Shaanxi Provincial Clinical Medical Research Center for Liver and Spleen Diseases, Xi'an, China.
  • Jiazhen Zhu
    Hepatobiliary, splenic and pancreatic surgery, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
  • Zong Fang Li
    Institute for Precision Medicine, Xi'an Jiaotong University Second Affiliated Hospital, Xi'an, Shaanxi, China lizongfang@xjtu.edu.cn.

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