Machine learning-based prediction of 1-year mortality using nutritional and inflammatory factors for type A acute aortic dissection with malperfusion
Nutritional and inflammatory factors significantly contribute to the 1-year mortality risk of ATAAD patients with malperfusion. Machine learning models that incorporate these factors, particularly random forest and XGBoost, can effectively stratify patient risk and support clinical decision-making. These findings underscore the importance of a comprehensive approach to risk assessment that includes metabolic and inflammatory markers to enhance patient outcomes and guide personalized interventions.
Authors
- Bo Zhang (ORCID: https://orcid.org/0009-0006-9400-8135)
- Hang Xing (ORCID: https://orcid.org/0000-0003-3917-8343)
- Yanda Zhang
- David Marimekala
- Yi Song (ORCID: https://orcid.org/0000-0001-7915-6961)
- Long Wang (ORCID: https://orcid.org/0009-0009-8788-9116)
- Jing Yuan (ORCID: https://orcid.org/0009-0003-3463-2030)
- Ting Wang (ORCID: https://orcid.org/0009-0003-3537-7676)
Institutions
- Nantong University (CN)
- Brown University (US)
- Rhode Island Hospital (US)
- Zhengzhou University (CN)
- Affiliated Hospital of Nantong University (CN)
- Henan Cancer Hospital (CN)
- Henan Provincial Chest Hospital (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-09-29
- DOI
- https://doi.org/10.3389/fcvm.2025.1539267
- Primary Topic
- Aortic Disease and Treatment Approaches
- Type
- article
- Field-Weighted Citation Impact
- 0.00