Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

Journal: Antimicrobial resistance and infection control
Published Date: Jul 6, 2024

Abstract

BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay.

Authors

  • Yun Li
    School of Public Health, University of Michigan, Ann Arbor, MI, USA.
  • Yuan Cao
    Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Min Wang
    National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, Wenzhou University, Wenzhou 325035, China.
  • Lu Wang
    Department of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, China.
  • Yiqi Wu
  • Yuan Fang
    Department of Neurology, Dongyang People's Hospital, Affiliated to Wenzhou Medical University, Dongyang, China.
  • Yan Zhao
    Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.
  • Xiaoli Liu
    Neurology Department, Zhejiang Hospital, Zhejiang 310013, China.
  • Hong Liang
    Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123, USA hliang@tamu.edu.
  • Mengmeng Yang
    The First Medical Centre, Chinese PLA General Hospital, Beijing, China.
  • Rui Yuan
    Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.
  • Feihu Zhou
    Department of Critical Care Medicine, The First Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Zhengbo Zhang
    Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, 100853, Beijing, China. Electronic address: zhengbozhang@126.com.
  • Hongjun Kang
    Department of Critical Care Medicine, the First Medical Centre, Chinese PLA General Hospital, Beijing, China.

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