The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.

Journal: Frontiers in medicine
Published Date: May 16, 2025

Abstract

BACKGROUND: Acute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), with a mortality rate ranging from 15 to 20%. Accurate stratification of acute gastrointestinal bleeding into acute variceal gastrointestinal bleeding (AVGIB) and acute non-variceal gastrointestinal bleeding (ANGIB) subtypes is clinically essential as distinct entities require markedly different therapeutic approaches and even divergent prognostic implications. AUGIB characterized by hemorrhagic shock, hypotension, multiple organ dysfunction (MODS), and even circulatory failure is life-threatening. Machine learning (ML) prediction model can be an effective tool for mortality prediction, enabling the timely identification of high-risk patients and improving outcomes.

Authors

  • Zhou Liu
    Department of Radiology, National Cancer Center/Cancer Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China.
  • Guijun Jiang
    Department of Intensive Care Unit, Renmin Hospital of Wuhan University, Wuhan, China.
  • Liang Zhang
  • Palpasa Shrestha
    Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Yugang Hu
    Department of Ultrasound, Renmin Hospital of Wuhan University, Wuhan, China.
  • Yi Zhu
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Guang Li
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
  • Yuanguo Xiong
    Department of Pharmacy, Renmin Hospital of Wuhan University, Wuhan, China.
  • Liying Zhan
    Department of Intensive Care Unit, Renmin Hospital of Wuhan University, Wuhan, China.

Keywords

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