Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission
MG severity is the primary determinant of ICU admission, with shorter disease duration increasing the risk, possibly due to greater susceptibility to exacerbations. The XGBoost model exhibited excellent performance and accuracy, effectively identifying critical clinical factors for predicting ICU admission risk in MG patients. This novel, personalized approach to risk stratification elucidates crucial risk factors and has the potential to enhance clinical decision-making, optimize resource allocation, and ultimately improve patient outcomes.
Authors
- Chen‐Chih Chung (ORCID: https://orcid.org/0000-0001-6743-6667)
- Hsu‐Ling Yeh
- Chao-Yang Kuo (ORCID: https://orcid.org/0000-0001-8867-5683)
- Hou‐Chang Chiu
- Emily Chia‐Yu Su (ORCID: https://orcid.org/0000-0003-4801-5159)
- Jiann‐Horng Yeh (ORCID: https://orcid.org/0009-0006-1877-3644)
Institutions
- Fu Jen Catholic University (TW)
- National Yang Ming Chiao Tung University (TW)
- Kaohsiung Medical University (TW)
- National Taipei University of Nursing and Health Science (TW)
- Taipei Medical University Hospital (TW)
- Taipei Medical University-Shuang Ho Hospital (TW)
- Memorial Hospital (US)
- Taipei Medical University (TW)
Publication Details
- Journal
- Heliyon
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e41084
- Citations
- 8
- Primary Topic
- Myasthenia Gravis and Thymoma
- Type
- article
- Field-Weighted Citation Impact
- 2.95
Funders
- Taipei Medical University
- National Science and Technology Council
- Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare