Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischemic Stroke
We developed and validated the first interpretable machine learning model for CIR prediction after EVT, surpassing traditional methods. Our CIR risk prediction platform enables early intervention and personalized treatment. The number of EVT attempts has emerged as a key determinant, underscoring the need for optimized procedural timing to improve outcomes.
Authors
- Suya Li (ORCID: https://orcid.org/0000-0002-1388-1308)
- Yibin Fang
- Shifei Ye
- Xiaolong Hu (ORCID: https://orcid.org/0000-0003-3450-3733)
- Wei Cao (ORCID: https://orcid.org/0000-0001-9615-3754)
- Dayong Qi
- Yue Chen (ORCID: https://orcid.org/0000-0002-4403-9930)
- Peng Li (ORCID: https://orcid.org/0009-0007-1018-4543)
- Meng Du (ORCID: https://orcid.org/0000-0003-2434-3772)
- Tianheng Zheng
Institutions
- Tongji University (CN)
- Shanghai Tenth People's Hospital (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
Publication Details
- Journal
- Therapeutics and Clinical Risk Management
- Published
- 2025-05-01
- DOI
- https://doi.org/10.2147/tcrm.s520362
- Citations
- 3
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 4.06
Funders
- National Natural Science Foundation of China