Enhancing early mortality prediction for sepsis-associated acute respiratory distress syndrome patients via optimized machine learning algorithm: development and multiple databases' validation of the SAFE-Mo.

Journal: International journal of surgery (London, England)
Published Date: Jun 20, 2025

Abstract

BACKGROUND: Acute respiratory distress syndrome (ARDS) is associated with high mortality, with sepsis accounts for 31-34% of cases. Given the global burden of sepsis (508 cases per 100,000 person-years) and its association with 20% of all global deaths, early mortality prediction in patients with sepsis-associated ARDS is critical. This study developed and validated the Sepsis-associated ARDS Fatality Evaluation Model (SAFE-Mo), a machine learning model designed to predict early mortality in sepsis-associated ARDS patients, enabling earlier identification of high-risk individuals.

Authors

  • Luofeng Jiang
    Department of Burn Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
  • Chuting Yu
    Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, 200433, China; National Clinical Research Center for Digestive Diseases (Shanghai), Shanghai, 200433, China.
  • Chaoran Xie
    Department of Burn Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
  • Yongjun Zheng
    Department of Burn Surgery, The First Affiliated Hospital of Naval Medical University, Shanghai, China.
  • Zhaofan Xia
    Department of Burns, Changhai Hospital, Second Military Medical University, Shanghai, China.

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