A multi-center big-data approach for precise PICC-RVT prognosis and identification of major risk factors in clinical practice
Our method excels in predicting early PICC-RVT risks, especially in asymptomatic patients. The findings in this paper offers insights into controllable PICC risk factors that could benefit vast patients and reduce disease burden through stratification and early intervention.
Authors
- Jiangbo Pu (ORCID: https://orcid.org/0000-0002-3704-5937)
- Xin Zhang (ORCID: https://orcid.org/0000-0002-0767-9541)
- Laiyou Wang
- Hong Sun (ORCID: https://orcid.org/0000-0003-1467-6143)
- Qiong Liu (ORCID: https://orcid.org/0000-0002-7714-4382)
- Bowen Zhang (ORCID: https://orcid.org/0000-0003-0432-745X)
- Ting Li (ORCID: https://orcid.org/0000-0001-9756-1442)
- Yue Li (ORCID: https://orcid.org/0000-0002-4331-7396)
- Jia Li (ORCID: https://orcid.org/0000-0002-4524-2607)
- Hengjie Su (ORCID: https://orcid.org/0009-0007-8176-9549)
- Xinxin Yan (ORCID: https://orcid.org/0009-0000-2171-0106)
- Biao Sun
Institutions
- Tianjin University (CN)
- Shanxi Medical University (CN)
- Tiangong University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- First Hospital of Shanxi Medical University (CN)
- Guangdong Academy of Medical Sciences (CN)
- Guangdong Provincial People's Hospital (CN)
- Macquarie University (AU)
Publication Details
- Journal
- Heliyon
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e39178
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chinese Academy of Medical Sciences