Exploring the prognostic impact of triglyceride-glucose index in critically ill patients with first-ever stroke: insights from traditional methods and machine learning-based mortality prediction
TyG (optimal cut-off: 9.265) was identified as an independent risk factor for ICU and hospital all-cause mortality in first-ever strokes. The ML model incorporating TyG demonstrated strong predictive performance. This emphasises the importance of insulin resistance (with TyG as a surrogate measure) in the prognostic assessment and early risk stratification of first-time stroke patients.
Authors
- Mingjuan Yang
- Ying X. Gue (ORCID: https://orcid.org/0000-0001-9994-8915)
- Garry McDowell (ORCID: https://orcid.org/0000-0002-2880-5236)
- Ruiyan Zhang (ORCID: https://orcid.org/0000-0002-6624-4660)
- Zhenkun Yang (ORCID: https://orcid.org/0000-0001-5763-7748)
- Yuanjie Li (ORCID: https://orcid.org/0000-0001-6182-2065)
- Ziyi Zhong (ORCID: https://orcid.org/0000-0003-3344-2019)
- Bi Huang
- Gregory Y. H. Lip
- Taipu Guo
- Coleen Ditchfield
- Yang Chen (ORCID: https://orcid.org/0000-0002-9772-8692)
- Yang Liu (ORCID: https://orcid.org/0000-0001-5586-623X)
Institutions
- Nanchang University (CN)
- University of Liverpool (GB)
- Tianjin Medical University General Hospital (CN)
- Liverpool Heart and Chest Hospital (GB)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Whiston Hospital (GB)
- Liverpool John Moores University (GB)
- Chongqing Medical University (CN)
- Aalborg University (DK)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12933-024-02538-y
- Citations
- 20
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 7.73