Enhancing prognostic accuracy in sepsis-induced cardiomyopathy: a machine learning approach
We established and validated a parsimonious logistic regression model with robust discrimination (AUC up to 0.88) and calibration (K-S > 0.45). The model underscores acute illness severity, aging, and hepatic dysfunction as principal determinants of short-term mortality in SIMD, offering valuable support for early risk stratification in critical care.
Authors
- Yi Wang (ORCID: https://orcid.org/0000-0002-2936-3687)
- Zhan Sun (ORCID: https://orcid.org/0000-0002-7182-8572)
- Dina Ainiwaer
- Chunbo Yang
- Xiang Li (ORCID: https://orcid.org/0000-0003-0724-0982)
- Xiangyou Yu
- Xinxin Du (ORCID: https://orcid.org/0000-0002-5128-7158)
- Huixin Cheng (ORCID: https://orcid.org/0000-0002-4319-3397)
- Ilzati Aizezi
Institutions
- Xinjiang Medical University (CN)
- Sun Yat-sen University (CN)
- Fudan University (CN)
- First Affiliated Hospital of Xinjiang Medical University (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- First Hospital of Lanzhou University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- European journal of medical research
- Published
- 2025-12-13
- DOI
- https://doi.org/10.1186/s40001-025-03354-0
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00