Development and application of an early prediction model for risk of bloodstream infection based on real-world study.

Journal: BMC medical informatics and decision making
PMID: 40369550

Abstract

BACKGROUND: Bloodstream Infection (BSI) is a severe systemic infectious disease that can lead to sepsis and Multiple Organ Dysfunction Syndrome (MODS), resulting in high mortality rates and posing a major public health burden globally. Early identification of BSI is crucial for effective intervention, reducing mortality, and improving patient outcomes. However, existing diagnostic methods are flawed by low specificity, long detection times and high demands on testing platforms. The development of artificial intelligence provides a new approach for early disease identification. This study aims to explore the optimal combination of routine laboratory data and clinical monitoring indicators, and to utilize machine learning algorithms to construct an early, rapid, and universally applicable BSI risk prediction model, to assist in the early diagnosis of BSI in clinical practice.

Authors

  • Xiefei Hu
    Department of Clinical Laboratory, Chongqing Emergency Medical Center, School of Medicine, Chongqing University Central Hospital, Chongqing University, Chongqing, China.
  • Shenshen Zhi
    Department of Clinical Laboratory, Chongqing Emergency Medical Center, School of Medicine, Chongqing University Central Hospital, Chongqing University, Chongqing, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Yuming Cheng
    Beckman Coulter Commercial Enterprise (China) Co., Ltd, Shanghai, China.
  • Haiping Fan
    School of Medicine, ChongQing University, Chongqing, China.
  • Haorong Li
    Chongqing University of Posts and Telecommunications, Chongqing, China.
  • Zihao Meng
    Chongqing University of Posts and Telecommunications, Chongqing, China.
  • Jiaxin Xie
    School of Medicine, ChongQing University, Chongqing, China.
  • Shu Tang
    Chongqing University of Posts and Telecommunications, Chongqing, China. tangshu@cqupt.edu.cn.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.

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