Predicting lower extremity deep venous thrombosis in patients with aneurysmal subarachnoid hemorrhage: a machine learning study
An XGBoost-based model incorporating six routinely available variables accurately predicts LEDVT risk after aSAH and generalized to an external cohort. The web tool may help target preventive strategies for high-risk patients.
Authors
- Minfeng Tong
- Fan Jiang (ORCID: https://orcid.org/0000-0001-9466-2192)
- Pingyou He (ORCID: https://orcid.org/0000-0001-6617-1580)
- Feng Chen (ORCID: https://orcid.org/0000-0002-2699-7190)
- Wei Xu (ORCID: https://orcid.org/0000-0001-6324-5759)
- Chengwei Zhang (ORCID: https://orcid.org/0000-0002-2110-5501)
- Danfeng Yu
- Xiang Bao (ORCID: https://orcid.org/0000-0002-2233-5739)
- Chenxing Ye (ORCID: https://orcid.org/0009-0009-2080-6048)
Institutions
- Affiliated Hangzhou First People's Hospital, Westlake University, School of Medicine (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-11-24
- DOI
- https://doi.org/10.3389/fneur.2025.1659212
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Jinhua Science and Technology Bureau