Ensemble learning prediction model for intraoperative hemodynamic instability in patients with pheochromocytoma.

ABSTRACT:

Background

Accurately predicting intraoperative hemodynamic instability (HI) in patients with pheochromocytoma is essential for improving prognosis; however, clinically applicable large-sample, high-precision predictive models remain limited. This study develops and validates an ensemble learning (EL) model to predict HI risk.

Methods

This cohort study included a derivation cohort (n = 353) and an external validation cohort (n = 51), from January 2011 to February 2023. General clinical and intraoperative hemodynamic data were collected. Ensemble feature selection was used to identify key predictors. 5-fold cross-validation was repeated 1000 times to develop the EL model. Shapley Additive Explanations was used to analyze feature contributions, and the model was implemented as

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12740857 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature