Prediction of postoperative stroke in patients experienced coronary artery bypass grafting surgery: a machine learning approach
Mechanical ventilation time, preoperative creatinine value, preoperative renal insufficiency, diabetes, the use of an intra-aortic balloon pump (IABP), age, Cardiopulmonary bypass time, Aortic cross-clamp time, Chronic Obstructive Pulmonary Disease (COPD) history, preoperative arrhythmia and Renal artery stenosis in the preoperative and intraoperative period was associated with significant postoperative stroke risk, and these factors can be identified and modeled to assist in implementing proactive measures to protect the brain in high-risk patients after surgery.
Authors
- Qiannan Guo (ORCID: https://orcid.org/0000-0003-3787-2657)
- Zhengfeng Fan (ORCID: https://orcid.org/0000-0003-0772-4434)
- FAYUAN LIU
- Chen Wang (ORCID: https://orcid.org/0000-0003-2322-5758)
- Kan Wang (ORCID: https://orcid.org/0000-0002-2072-0414)
- Yixuan Wang (ORCID: https://orcid.org/0000-0002-0014-4050)
- Shiqi Chen (ORCID: https://orcid.org/0000-0003-0821-986X)
- Jiawei Shi (ORCID: https://orcid.org/0000-0002-0643-7671)
- Nianguo Dong (ORCID: https://orcid.org/0000-0003-1558-4523)
- Lizhao Yan (ORCID: https://orcid.org/0000-0002-0015-4937)
Institutions
- Union Hospital (HK)
- Huazhong University of Science and Technology (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2024-12-13
- DOI
- https://doi.org/10.3389/fcvm.2024.1448740
- Citations
- 4
- Primary Topic
- Cerebrovascular and Carotid Artery Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.68