Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.

Journal: BMC anesthesiology
PMID: 39300332

Abstract

BACKGROUND: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient health outcomes. However, current screening studies for patients experiencing moderate-to-severe CRBD after waking from general anesthesia are insufficient. Constructing predictive models with higher accuracy using multiple machine learning techniques for early identification of patients at risk of experiencing moderate-to-severe CRBD during general anesthesia resuscitation.

Authors

  • Suwan Dai
    Affiliated Hospital of Jiaxing University, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.
  • Yingchun Ren
    College of Data Science, Jiaxing University, Jiaxing, Zhejiang, China.
  • Lingyan Chen
    Affiliated Hospital of Jiaxing University, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.
  • Min Wu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.
  • Rong Wang
    College of Food Science and Engineering, Northwest A&F University, Yangling 712100, Shanxi, China. Electronic address: wangrong91@nwsuaf.edu.cn.
  • QingHe Zhou
    GuangZhou Women and Children's Medical Center, GuangZhou Medical University, GuangZhou, China.

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