Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.

Journal: BMC medical informatics and decision making
PMID: 39994766

Abstract

BACKGROUND: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical treatment challenges. Particularly, since the outbreak of COVID-19, there has been a gradual increase in SAB patients, with a growing proportion of (Methicillin-resistant Staphylococcus aureus) MRSA infections. Therefore, we have constructed and validated a pediction model for recurrent SAB using machine learning. This model aids physicians in promptly assessing the condition and intervening proactively.

Authors

  • Yuan Li
    NHC Key Lab of Hormones and Development and Tianjin Key Lab of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Institute of Endocrinology, Tianjin, China.
  • Shuang Song
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Liying Zhu
    Department of Infectious Disease, Nanjing First Hospital, Nanjing Medical University, Nan jing, 210006, China.
  • Xiaorun Zhang
    Department of Infectious Disease, Nanjing First Hospital, Nanjing Medical University, Nan jing, 210006, China.
  • Yijiao Mou
    Department of Infectious Disease, Nanjing First Hospital, Nanjing Medical University, Nan jing, 210006, China.
  • Maoxing Lei
    Department of Infectious Disease, Nanjing First Hospital, Nanjing Medical University, Nan jing, 210006, China.
  • Wenjing Wang
    School of Economics, Tianjin University of Commerce, Tianjin, 300134, China. Electronic address: maggiewwj@163.com.
  • Zhen Tao
    Department of Infectious Disease, Nanjing First Hospital, Nanjing Medical University, Nan jing, 210006, China. tz1010@126.com.

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