Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients - Takeaways - MDSpire

Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients

  • By

  • Houming Zhao

  • Lu Tang

  • Zhuoran Li

  • Xintao Li

  • Tongyu Jia

  • Jin Luo

  • Yujie Dong

  • Shangwei Li

  • Xin Ma

  • Peng Zhang

  • September 15, 2025

  • 0 min

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  • 1

    Pheochromocytomas and paragangliomas are endocrine tumors that can cause severe systemic effects due to excessive catecholamine secretion.

  • 2

    Sustained hypertensive patients with PPGLs face a threefold higher risk of intraoperative hemodynamic instability compared to normotensive patients.

  • 3

    Machine learning models can analyze clinical parameters to predict intraoperative hemodynamic instability in hypertensive PPGL patients.

  • 4

    The study included 197 patients with sustained hypertension who underwent surgery for PPGLs, following strict inclusion and exclusion criteria.

  • 5

    Clinical data collected included demographic information, laboratory results, and intraoperative hemodynamic status to optimize predictive modeling.

Original Source(s)

Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients

  • by Houming Zhao, Lu Tang, Zhuoran Li, Xintao Li, Tongyu Jia, Jin Luo, Yujie Dong, Shangwei Li, Xin Ma, Peng Zhang

  • September 15, 2025

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