Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning.

Journal: Cardiovascular diabetology
PMID: 40346649

Abstract

BACKGROUND: Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adverse outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). However, the prognostic significance of combined SHR-GV evaluation for ASCVD mortality remains unclear. This study examines associations of SHR, GV, and their synergistic effects with mortality in patients with ASCVD across different glucose metabolic states, incorporating machine learning (ML) to identify critical risk factors influencing mortality.

Authors

  • Fuxu Wang
    Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yu Guo
    Animal Disease Control Center of Inner Mongolia, Hohhot, China.
  • Yuru Tang
    Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Shuangmei Zhao
    Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Kaige Xuan
    Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zhi Mao
    Xi'an Technological University, Xi'an, China.
  • Ruogu Lu
    Medical Innovation Research Department, Chinese PLA General Hospital, Beijing, China. ruogulu2023@163.com.
  • Rongyao Hou
    Department of Neurology, The Affiliated Hiser Hospital of Qingdao University, Qingdao, China. hrysdzc@qdu.edu.cn.
  • Xiaoyan Zhu
    Anhui Technical College of Industry and Economy, Hefei, China.

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