To investigate the predictive significance of clinical parameters for intraoperative hemodynamic instability (HI) in patients with sustained hypertensive pheochromocytomas and paragangliomas (PPGLs) and to construct predictive models using machine learning methods, highlighting the role of AI in enhancing predictive accuracy.
Key Findings:
Sustained hypertensive patients with PPGLs have a threefold higher risk of intraoperative hypertensive emergencies and hypotensive episodes compared to normotensive patients, underscoring the need for predictive models.
Machine learning methods can effectively analyze clinical parameters to predict intraoperative HI, potentially improving patient outcomes.
Interpretation:
The study highlights the importance of developing predictive models for intraoperative HI in hypertensive PPGL patients, which could enhance surgical safety and outcomes.
Limitations:
The study is retrospective and may be subject to selection bias, which could affect the reliability of the findings.
Exclusion of patients with severe inflammation or hematologic disorders may limit generalizability.
Conclusion:
Constructing machine learning models based on clinical parameters can provide valuable insights for predicting intraoperative hemodynamic instability in hypertensive PPGL patients, potentially improving surgical management.
Kidney cancer is a growing global health problem, and both clinicians and policymakers need to prepare for a steep rise in the number of cases,” said Alexander Kutikov, MD, FACS, Chair of the Department of Urology at Fox Chase Cancer Center, and senior author of a landmark international study published in European Urology, which demonstrates that if current trends continue, kidney cancer cases could double by 2050
