Identifying septic shock subgroups to tailor fluid strategies through multi-omics integration
Open accessAbstract
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. Fluid management in septic shock lacks personalized approaches, which are critical for improving patient outcomes. Here, the authors show that a proteomic signature can help identify patients who may benefit from a restrictive fluid strategy, potentially reducing hospital mortality rates.
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Machine learning has been used to stratify critically ill patients based on their response to specific therapeutic interventions, potentially improving clinical outcomes.
“For example, machine learning has been used to stratify critically ill patients based on their response to specific therapeutic interventions, potentially improving clinical outcomes [37].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [37]
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Self-citation
- Authors
- Zhang, Z., Chen, L., Sun, B., Ruan, Z., Pan, P., Zhang, W., Jiang, X., Zheng, S., Cheng, S., Xian, L., Wang, B., Yang, J., Zhang, B., Xu, P., Zhong, Z., Cheng, L., Ni, H., the Chinese Multi-omics Advances In Sepsis (CMAISE) Consortium, & Hong, Y.
- Affiliations
- Shaoxing University (CN)
- Zhejiang University (CN)
- Binzhou Medical University (CN)
- Wenzhou Medical University (CN)
- Chinese PLA General Hospital (CN)
- Hainan Medical University (CN)
- Sir Run Run Shaw Hospital (CN)
- Corresponding author
- Zhang, Zhongheng
- ORCID
- Venue
- Nature Communications
- Published
- 2024-10-19
- Volume
- 15
- Issue
- 1
- Pages
- 9028-9028
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11489719
- OpenAlex
- W4403527108
- Cited by
- 37
- References
- 63
- Citations by year
- 2026: 12 · 2025: 23 · 2024: 2
- FWCI
- 11.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (82272180)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Life Sciences › Immunology and Microbiology › Immunology › Inflammation biomarkers and pathways
- Keywords
- septic shock, fluid management, restrictive fluid strategy, patient subgroups, transcriptomics, proteomic signatures, multi-omics integration, in-hospital mortality, personalized treatment
- MeSH terms
- Multiomics, Aged, Female, Fluid Therapy, Humans, Male, Middle Aged, Shock, Septic, Hospital Mortality, Proteomics, Transcriptome
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