Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation
This simplified prediction model effectively identifies the risk of LAT in patients with paroxysmal AF, providing a valuable tool for clinical decision-making. Further studies are needed to explore AF management and risk stratification in other AF subtypes.
Authors
- Lu Jia
- Yanhong Tang (ORCID: https://orcid.org/0000-0001-7207-1965)
- Congxin Huang (ORCID: https://orcid.org/0000-0003-2817-7863)
- Qingyan Zhao (ORCID: https://orcid.org/0000-0002-9610-2452)
- Wanli Xiong
- Tao Liu (ORCID: https://orcid.org/0009-0006-6626-9813)
- Li Li (ORCID: https://orcid.org/0000-0002-2243-4614)
- Bo Yang (ORCID: https://orcid.org/0000-0001-8789-4198)
- Min Chen (ORCID: https://orcid.org/0000-0002-0093-9056)
- Shaobo Shi
- Qiqi Cao (ORCID: https://orcid.org/0000-0001-6163-3256)
- He Huang (ORCID: https://orcid.org/0000-0003-0634-5723)
Institutions
- Wuhan University (CN)
- Wuhan Ship Development & Design Institute (CN)
- Renmin Hospital of Wuhan University (CN)
Publication Details
- Journal
- BMC Cardiovascular Disorders
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1186/s12872-025-04847-w
- Citations
- 1
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Wuhan University
- Fundamental Research Funds for the Central Universities
- Renmin Hospital of Wuhan University