Machine learning-based nomogram for mortality risk stratification in cirrhotic patients with sepsis: a single-center predictive model
The nomogram model developed using Lasso regression appears to demonstrate promising predictive potential for in-hospital mortality in patients with liver cirrhosis complicated by sepsis. This tool may offer valuable support for clinical decision-making and could potentially aid in guiding early interventions for patients identified as higher risk.
Authors
- Yun Sun (ORCID: https://orcid.org/0000-0002-1770-3146)
- Xing-Cheng Zhang
- Bowen Li (ORCID: https://orcid.org/0000-0002-7166-2672)
- Junping Wei (ORCID: https://orcid.org/0000-0001-9092-727X)
- Zhonghua Lu (ORCID: https://orcid.org/0000-0002-1107-545X)
- Xin Lei (ORCID: https://orcid.org/0009-0000-1763-3507)
- Nan Shan (ORCID: https://orcid.org/0009-0003-0968-2175)
Institutions
- Fuyang Normal University (CN)
- Anhui Medical University (CN)
- Fuyang City People's Hospital (CN)
- Bozhou People's Hospital (CN)
- The People's Hospital of LinXia (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
- Second Affiliated Hospital of Anhui Medical University (CN)
- Fuyang Second People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-10-21
- DOI
- https://doi.org/10.3389/fmed.2025.1684527
- Primary Topic
- Liver Disease and Transplantation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Anhui Medical University