Early prediction of sepsis associated encephalopathy in elderly ICU patients using machine learning models: a retrospective study based on the MIMIC-IV database.

Journal: Frontiers in cellular and infection microbiology
PMID: 40313459

Abstract

BACKGROUND: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological disorders and the absence of specific biomarkers, early clinical diagnosis remains challenging. This study aimed to develop a predictive model for SAE in elderly ICU patients.

Authors

  • Yupeng Han
    Department of Anesthesiology, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian.
  • Xiyuan Xie
    Department of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
  • Jiapeng Qiu
    Department of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
  • Yijie Tang
    Department of Anesthesiology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
  • Zhiwei Song
    Department of Infection Diseases, Xianju County People's Hospital, Taizhou, Zhejiang, China.
  • Wangyu Li
    Department of Anesthesiology, China-Japan Union Hospital, Jilin University, 126th Xiantai Avenue, Changchun, 130021, Jilin, People's Republic of China.
  • Xiaodan Wu
    Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China.

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