Prognostic value of glycaemic variability for mortality in critically ill atrial fibrillation patients and mortality prediction model using machine learning
GV is a significant predictor of ICU short-term, mid-term, and long-term all-cause mortality in patients with AF (the potential risk stratification threshold is 20.0%). ML models incorporating GV demonstrated high efficiency in predicting short-term mortality and GV was ranked anterior in importance. These findings underscore the potential of GV as a valuable biomarker in guiding clinical decisions and improving patient outcomes in this high-risk population.
Authors
- Ying X. Gue (ORCID: https://orcid.org/0000-0001-9994-8915)
- Garry McDowell (ORCID: https://orcid.org/0000-0002-2880-5236)
- Yang Chen
- Feifan Wang (ORCID: https://orcid.org/0000-0003-0771-3153)
- Tao Chen (ORCID: https://orcid.org/0000-0003-3758-4743)
- Ziyi Zhong (ORCID: https://orcid.org/0000-0003-3344-2019)
- Bi Huang
- Gregory Y. H. Lip
- Zhengkun Yang
- Yang Liu (ORCID: https://orcid.org/0000-0002-1053-8205)
Institutions
- Nanchang University (CN)
- University of Liverpool (GB)
- Tianjin Medical University General Hospital (CN)
- Liverpool Heart and Chest Hospital (GB)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Liverpool John Moores University (GB)
- Zhejiang University (CN)
- Chongqing Medical University (CN)
- Aalborg University (DK)
- Aalto University (FI)
Publication Details
- Journal
- Cardiovascular Diabetology
- Published
- 2024-11-26
- DOI
- https://doi.org/10.1186/s12933-024-02521-7
- Citations
- 27
- Primary Topic
- Hyperglycemia and glycemic control in critically ill and hospitalized patients
- Type
- article
- Field-Weighted Citation Impact
- 9.66