Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients
In patients with hypertensive PPGLs, inflammatory, coagulation, and other clinical parameters are correlated with a high risk of intraoperative HI. ML models have a good predictive ability for HI in patients with sustained hypertensive PPGLs.
Authors
- Peng Zhang (ORCID: https://orcid.org/0000-0003-3706-3833)
- Xin Ma (ORCID: https://orcid.org/0000-0003-2340-991X)
- Jin Jun Luo (ORCID: https://orcid.org/0000-0001-9883-0763)
- Zhuoran Li (ORCID: https://orcid.org/0000-0001-6192-4301)
- Lu Tang (ORCID: https://orcid.org/0000-0002-9679-5834)
- Yujie Dong (ORCID: https://orcid.org/0000-0002-2000-0484)
- Shangwei Li (ORCID: https://orcid.org/0000-0002-8400-4281)
- Xintao Li (ORCID: https://orcid.org/0000-0002-5589-6452)
- Tongyu Jia
- Houming Zhao
Institutions
- Nankai University (CN)
- Chinese PLA General Hospital (CN)
- United States Air Force (US)
- Air Force General Hospital PLA (CN)
Publication Details
- Journal
- World Journal of Urology
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1007/s00345-025-05890-0
- Citations
- 3
- Primary Topic
- Adrenal and Paraganglionic Tumors
- Type
- article
- Field-Weighted Citation Impact
- 6.02