Machine learning models and restricted cubic spline were employed to analyze and predict postoperative ischemic stroke in type A aortic dissection patients
The Random Forest model model demonstrates the best predictive capacity for postoperative ischemic stroke in TAAD patients, offering clinicians a tool for early postoperative risk stratification and personalized therapeutic optimization.
Authors
- Wenjian Ma (ORCID: https://orcid.org/0000-0002-2883-7297)
- Siji Chen (ORCID: https://orcid.org/0000-0003-1984-9023)
- Yi Hu (ORCID: https://orcid.org/0000-0001-9319-5692)
- Qianxian Li
- Shuanglei Zhao
- Yang Zhao (ORCID: https://orcid.org/0000-0003-4203-2003)
- Ming Gong (ORCID: https://orcid.org/0009-0009-5665-6600)
Institutions
- Shenyang Medical College (CN)
- Capital Medical University (CN)
- Beijing Anzhen Hospital (CN)
Publication Details
- Journal
- BMC Cardiovascular Disorders
- Published
- 2025-12-10
- DOI
- https://doi.org/10.1186/s12872-025-05375-3
- Primary Topic
- Aortic Disease and Treatment Approaches
- Type
- article
- Field-Weighted Citation Impact
- 0.00