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Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration

Author

Listed:
  • Zhongheng Zhang

    (Zhejiang University School of Medicine
    Shaoxing University)

  • Lin Chen

    (Zhejiang University School of Medicine)

  • Bin Sun

    (Binzhou Medical University Hospital)

  • Zhanwei Ruan

    (Wenzhou Medical University)

  • Pan Pan

    (Chinese PLA General Hospital)

  • Weimin Zhang

    (Dongyang)

  • Xuandong Jiang

    (Dongyang)

  • Shaojiang Zheng

    (The First Affiliated Hospital of Hainan Medical University
    Hainan Medical University)

  • Shaowen Cheng

    (The First Affiliated Hospital of Hainan Medical University)

  • Lina Xian

    (The First Affiliated Hospital of Hainan Medical University)

  • Bingshu Wang

    (The Second Affiliated Hospital of Hainan Medical University)

  • Jie Yang

    (Zhejiang University School of Medicine)

  • Bo Zhang

    (Zhejiang University School of Medicine)

  • Ping Xu

    (Zigong Fourth People’s Hospital)

  • Zhitao Zhong

    (Zigong Fourth People’s Hospital)

  • Lingxia Cheng

    (Zigong Fourth People’s Hospital)

  • Hongying Ni

    (Zhejiang University School of Medicine)

  • Yucai Hong

    (Zhejiang University School of Medicine)

Abstract

Fluid management remains a critical challenge in the treatment of septic shock, with individualized approaches lacking. This study aims to develop a statistical model based on transcriptomics to identify subgroups of septic shock patients with varied responses to fluid strategy. The study encompasses 494 septic shock patients. A benefit score is derived from the transcriptome space, with higher values indicating greater benefits from restrictive fluid strategy. Adherence to the recommended strategy is associated with a hazard ratio of 0.82 (95% confidence interval: 0.64–0.92). When applied to the baseline hospital mortality rate of 16%, adherence to the recommended fluid strategy could potentially lower this rate to 13%. A proteomic signature comprising six proteins is developed to predict the benefit score, yielding an area under the curve of 0.802 (95% confidence interval: 0.752–0.846) in classifying patients who may benefit from a restrictive strategy. In this work, we develop a proteomic signature with potential utility in guiding fluid strategy for septic shock patients.

Suggested Citation

  • Zhongheng Zhang & Lin Chen & Bin Sun & Zhanwei Ruan & Pan Pan & Weimin Zhang & Xuandong Jiang & Shaojiang Zheng & Shaowen Cheng & Lina Xian & Bingshu Wang & Jie Yang & Bo Zhang & Ping Xu & Zhitao Zhon, 2024. "Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53239-9
    DOI: 10.1038/s41467-024-53239-9
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    References listed on IDEAS

    as
    1. Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
    2. Timothy E. Sweeney & Thanneer M. Perumal & Ricardo Henao & Marshall Nichols & Judith A. Howrylak & Augustine M. Choi & Jesús F. Bermejo-Martin & Raquel Almansa & Eduardo Tamayo & Emma E. Davenport & K, 2018. "A community approach to mortality prediction in sepsis via gene expression analysis," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
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