Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model.

Journal: BMC pulmonary medicine
PMID: 35941641

Abstract

BACKGROUND: Noninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs).

Authors

  • Huan Wang
    Key Laboratory of Adaptation and Evolution of Plateau Biota (AEPB), Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Qinghai, P. R. China.
  • Qin-Yu Zhao
    College of Engineering and Computer Science, Australian National University, Canberra, ACT, Australia.
  • Jing-Chao Luo
    Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Kai Liu
    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China.
  • Shen-Ji Yu
    Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Jie-Fei Ma
    Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Ming-Hao Luo
    Shanghai Medical College, Fudan University, Shanghai, China.
  • Guang-Wei Hao
    Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Ying Su
    College of Marine Life Science, Ocean University of China, Qingdao, China.
  • Yi-Jie Zhang
    Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.
  • Guo-Wei Tu
    Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. tu.guowei@zs-hospital.sh.cn.
  • Zhe Luo
    Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai, China. luo.zhe@zs-hospital.sh.cn.

Keywords

External Resources

Popular Topics
Recent Journals