Analysis of risk factors for sepsis-related liver injury and construction of a prediction model
In conclusion, this study developed a highly accurate predictive model for SRLI using clinically accessible serum indicators, which could aid in early detection and intervention, potentially reducing mortality rates.
Authors
- Xin Nie (ORCID: https://orcid.org/0000-0003-2759-2950)
- He Zhang
- Yong He (ORCID: https://orcid.org/0000-0003-1877-316X)
- Binwu Ying (ORCID: https://orcid.org/0000-0001-8077-3908)
- He He (ORCID: https://orcid.org/0000-0002-1513-3987)
- Ying Liu (ORCID: https://orcid.org/0009-0004-1136-9134)
- Wan He (ORCID: https://orcid.org/0000-0002-4619-5013)
- Fei Ding (ORCID: https://orcid.org/0000-0003-2340-5644)
- Chi Wang (ORCID: https://orcid.org/0009-0009-5842-1274)
Institutions
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Zhongshan Hospital of Xiamen University (CN)
- Shanghai Clinical Research Center (CN)
Publication Details
- Journal
- Frontiers in Public Health
- Published
- 2024-12-06
- DOI
- https://doi.org/10.3389/fpubh.2024.1475292
- Citations
- 10
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.87