Enhanced Mortality Risk Prediction in Critically Ill COVID-19 Patients Using Stress Hyperglycemia Ratio and Machine Learning: A Multicenter Retrospective Analysis - Takeaways - MDSpire

Enhanced Mortality Risk Prediction in Critically Ill COVID-19 Patients Using Stress Hyperglycemia Ratio and Machine Learning: A Multicenter Retrospective Analysis

  • By

  • Jiaxing Du

  • Keze Ma

  • Zhiwei Ye

  • Juanli Song

  • Sujun Chen

  • Jie Yu

  • Bing Liu

  • Zixuan Jiang

  • Fen Zhang

  • January 16, 2026

  • 0 min

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  • 1

    The study investigates the prognostic value of the stress hyperglycemia ratio (SHR) in critically ill COVID-19 patients.

  • 2

    SHR accounts for baseline glycemic control, offering a more accurate assessment of stress-induced hyperglycemia than traditional blood glucose measurements.

  • 3

    Machine learning techniques are employed to enhance mortality risk prediction in critically ill COVID-19 patients using SHR data.

  • 4

    The analysis utilized a comprehensive dataset from Northwestern Medicine's ICU database, including over 4,000 COVID-19 patients.

  • 5

    Findings aim to optimize glycemic management and guide individualized treatment strategies for critically ill COVID-19 patients.

Original Source(s)

Enhanced Mortality Risk Prediction in Critically Ill COVID-19 Patients Using Stress Hyperglycemia Ratio and Machine Learning: A Multicenter Retrospective Analysis

  • by Jiaxing Du, Keze Ma, Zhiwei Ye, Juanli Song, Sujun Chen, Jie Yu, Bing Liu, Zixuan Jiang, Fen Zhang

  • January 16, 2026

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