Development and Validation of Multiple Machine Learning Models Integrating Neutrophil‐Lymphocyte Ratio for Prediction of Hemorrhagic Transformation After Intravenous Thrombolysis in Acute Ischemic Stroke
Integrating NLR with stroke severity and neuroimaging biomarkers enhances the accuracy of HT predictions. The LR-based nomogram provided a practical tool for personalized IVT decisions, emphasizing the prognostic value of systemic inflammation in AIS management.
Authors
- Wei Zhang (ORCID: https://orcid.org/0000-0002-9446-7319)
- Runlu Cai (ORCID: https://orcid.org/0009-0003-1307-9563)
- Ying Hu (ORCID: https://orcid.org/0009-0000-3175-3388)
- Xu Yang (ORCID: https://orcid.org/0000-0003-0176-9092)
- F. Bu (ORCID: https://orcid.org/0009-0008-4857-0068)
- Xiaohong Tang (ORCID: https://orcid.org/0000-0002-2666-4548)
Institutions
- Xuzhou Medical College (CN)
- Huaian First People’s Hospital (CN)
- First Affiliated Hospital of Xi'an Jiaotong University (CN)
Publication Details
- Journal
- CNS Neuroscience & Therapeutics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1111/cns.70667
- Citations
- 2
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 2.71
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province