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

ABSTRACT:

Purpose

To create machine learning (ML) models based on inflammatory markers and coagulation parameters for predicting intraoperative hemodynamic Instability (HI) in sustained hypertensive patients with pheochromocytomas and paragangliomas (PPGLs).

Methods

197 sustained hypertensive PPGLs patients who underwent laparoscopic or robotic-assisted surgeries were included. Univariate and multivariate logistic regression (LR) analyses were conducted to identify the independent risk factors for HI. Various ML methods were employed to construct predictive models, including random forest (RF) and support vector machine (SVM). The receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration curve, and Hosmer-Lemeshow test were employed to assess the perfo

SUBMITTER: Zhao H 

PROVIDER: S-EPMC12436509 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature