A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study
This model can be used to quickly screen for the risk of VTE in patients with thoracic trauma. More than 90% of unnecessary VTE tests can be avoided, which can help clinicians target interventions to high-risk groups and ensure resource optimization. Although further validation and improvement are needed, this study has considerable clinical value.
Authors
- Kaibin Liu (ORCID: https://orcid.org/0000-0002-8658-8495)
- Qian Di (ORCID: https://orcid.org/0000-0002-1584-4770)
- Dongsheng Zhang (ORCID: https://orcid.org/0000-0002-1160-1114)
- Yi Yang (ORCID: https://orcid.org/0000-0001-9044-0277)
- Zhichao Jin (ORCID: https://orcid.org/0000-0002-5592-6965)
- Yanfang Zhao (ORCID: https://orcid.org/0000-0001-9510-4686)
Institutions
- Shanghai Jiao Tong University (CN)
- First Hospital of Shijiazhuang (CN)
- Shanghai University of Medicine and Health Sciences (CN)
- Shanghai Sixth People's Hospital (CN)
Publication Details
- Journal
- World Journal of Emergency Surgery
- Published
- 2025-02-13
- DOI
- https://doi.org/10.1186/s13017-025-00583-w
- Citations
- 6
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 11.22