Prediction of hemorrhagic transformation after thrombolysis based on machine learning models combined with platelet distribution width-to-count ratio
The LR-based model incorporating PPR effectively predicts HT risk in AIS patients after IVT, providing clinicians with a rapid and accurate tool to assess thrombolytic hemorrhage risk and support treatment decision-making.
Authors
- Yuzhen Zhou (ORCID: https://orcid.org/0000-0003-3964-4359)
- Christina M. Yuan (ORCID: https://orcid.org/0000-0002-4805-6359)
- Hongyun Xu (ORCID: https://orcid.org/0000-0003-4470-9954)
- Bo Lei (ORCID: https://orcid.org/0000-0002-5497-0905)
- Xiaosheng Li (ORCID: https://orcid.org/0000-0003-3866-3988)
- Jiang Wen (ORCID: https://orcid.org/0009-0006-9372-1756)
Institutions
- Yunnan University (CN)
- Kunming Medical University (CN)
- Xuan Wu Hospital of the Capital Medical University (CN)
- First Affiliated Hospital of Kunming Medical University (CN)
- Yunnan Center for Disease Control And Prevention (CN)
- Yunnan Provincial Infectious Disease Hospital (CN)
- Pingjin Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-10-02
- DOI
- https://doi.org/10.3389/fneur.2025.1466543
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Kunming Medical University
- Yunnan Provincial Science and Technology Department
- NIH Clinical Center